text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
false |
# Dataset Card for VilaSum
## 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
- **Paper:**[Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
- **Point of Contact:** [Ona de Gibert Bonet](mailto:ona.degibert@bsc.es)
### Dataset Summary
VilaSum is a summarization dataset for evaluation. It is extracted from a newswire corpus crawled from the Catalan news portal [VilaWeb](https://www.vilaweb.cat/). The corpus consists of 13,843 instances that are composed by the headline and the body.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 35.04.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
```
{
'summary': 'Un vídeo corrobora les agressions a dues animalistes en un correbou del Mas de Barberans',
'text': 'Noves imatges, a les quals ha tingut accés l'ACN, certifiquen les agressions i la destrucció del material d'enregistrament que han denunciat dues activistes d'AnimaNaturalis en la celebració d'un acte de bous a la plaça al Mas de Barberans (Montsià). En el vídeo es veu com unes quantes persones s'abalancen sobre les noies que reben estirades i cops mentre els intenten prendre les càmeres. Membres de la comissió taurina intervenen per aturar els presumptes agressors però es pot escoltar com part del públic victoreja la situació. Els Mossos d'Esquadra presentaran aquest dilluns al migdia l'atestat dels fets al Jutjat d'Amposta. Dissabte ja es van detenir quatre persones que van quedar en llibertat a l'espera de ser cridats pel jutge. Es tracta de tres homes i una dona de Sant Carles de la Ràpita, tots ells membres de la mateixa família.'
}
```
### Data Fields
- `summary` (str): Summary of the piece of news
- `text` (str): The text of the piece of news
### Data Splits
Due to the reduced size of the dataset, we use it only for evaluation as a test set.
- test: 13,843 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
We obtained each headline and its corresponding body of each news piece on [VilaWeb](https://www.vilaweb.cat/) and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences.
#### Who are the source language producers?
The news portal [VilaWeb](https://www.vilaweb.cat/).
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by MT4All CEF project and the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
```bibtex
@misc{degibert2022sequencetosequence,
title={Sequence-to-Sequence Resources for Catalan},
author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
year={2022},
eprint={2202.06871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A]
|
false |
# ViquiQuAD, An extractive QA dataset for Catalan, from the Wikipedia
## 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://zenodo.org/record/4562345#.YK41aqGxWUk
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [Carlos Rodríguez-Penagos](mailto:carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](mailto:carme.armentano@bsc.es)
### Dataset Summary
ViquiQuAD, An extractive QA dataset for Catalan, from the Wikipedia.
This dataset contains 3111 contexts extracted from a set of 597 high quality original (no translations) articles in the Catalan Wikipedia "[Viquipèdia](https://ca.wikipedia.org/wiki/Portada)", and 1 to 5 questions with their answer for each fragment.
Viquipedia articles are used under [CC-by-sa](https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence.
This dataset can be used to fine-tune and evaluate extractive-QA and Language Models.
### Supported Tasks and Leaderboards
Extractive-QA, Language Model
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
```
{
'id': 'P_66_C_391_Q1',
'title': 'Xavier Miserachs i Ribalta',
'context': "En aquesta època es va consolidar el concepte modern del reportatge fotogràfic, diferenciat del fotoperiodisme[n. 2] i de la fotografia documental,[n. 3] pel que fa a l'abast i el concepte. El reportatge fotogràfic implica més la idea de relat: un treball que vol més dedicació de temps, un esforç d'interpretació d'una situació i que culmina en un conjunt d'imatges. Això implica, d'una banda, la reivindicació del fotògraf per opinar, fet que li atorgarà estatus d'autor; l'autor proposa, doncs, una interpretació pròpia de la realitat. D'altra banda, el consens que s'estableix entre la majoria de fotògrafs és que el vehicle natural de la imatge fotogràfica és la pàgina impresa. Això suposà que revistes com Life, Paris-Match, Stern o Época assolissin la màxima esplendor en aquest període.",
'question': 'De què es diferenciava el reportatge fotogràfic?',
'answers': [{
'text': 'del fotoperiodisme[n. 2] i de la fotografia documental',
'answer_start': 92
}]
}
```
### Data Fields
Follows [Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets.
- `id` (str): Unique ID assigned to the question.
- `title` (str): Title of the Wikipedia article.
- `context` (str): Wikipedia section text.
- `question` (str): Question.
- `answers` (list): List of answers to the question, each containing:
- `text` (str): Span text answering to the question.
- `answer_start` Starting offset of the span text answering to the question.
### Data Splits
- train: 11259 examples
- developement: 1493 examples
- test: 1428 examples
## Dataset Creation
### Curation Rationale
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Source Data
- [Catalan Wikipedia](https://ca.wikipedia.org)
#### Initial Data Collection and Normalization
The source data are scraped articles from the [Catalan wikipedia](https://ca.wikipedia.org) site.
From a set of high quality, non-translation, articles in the Catalan Wikipedia, 597 were randomly chosen, and from them 3111, 5-8 sentence contexts were extracted. We commissioned creation of between 1 and 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). In total, 15153 pairs of a question and an extracted fragment that contains the answer were created.
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
#### Who are the source language producers?
Volunteers who collaborate with Catalan Wikipedia.
### Annotations
#### Annotation process
We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)).
#### Who are the annotators?
Annotation was commissioned to an specialized company that hired a team of native language speakers.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
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.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4562344)
### Contributions
[N/A] |
false |
# ECDC : An overview of the European Union's highly multilingual parallel corpora
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [No Warranty](#no-warranty)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction
- **Repository:** https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction
- **Paper:** https://dl.acm.org/doi/10.1007/s10579-014-9277-0
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. The data gets distributed via the [web pages of the EC's Joint Research Centre (JRC)](https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction).
### Supported Tasks and Leaderboards
`translation`: The dataset can be used to train a model for translation.
### Languages
In our case, the corpora consists of a pair of source and target sentences for all 22 different languages from the European Union (EU).
**List of languages :** `English (en)`, `Swedish (sv)`, `Polish (pl)`, `Hungarian (hu)`,`Lithuanian (lt)`, `Latvian (lv)`, `German (de)`, `Finnish (fi)`, `Slovak (sk)`,`Slovenian (sl)`, `French (fr)`, ,`Czech (cs)`,`Danish (da)`, `Italian (it)`,`Maltese (mt)`,`Dutch (nl)`,`Portuguese (pt)`,`Romanian (ro)`, `Spanish (es)`,`Estonian (et)`, `Bulgarian (bg)`,`Greek (el)`, `Irish (ga)`, `Icelandic (is)` and `Norwegian (no)`.
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/ECDC", "en-it", split='train', download_mode='force_redownload')
print(dataset)
print(dataset[0])
```
## Dataset Structure
### Data Instances
```plain
key,lang,source_text,target_text
doc_0,en-bg,Vaccination against hepatitis C is not yet available.,Засега няма ваксина срещу хепатит С.
doc_1355,en-bg,Varicella infection,Инфекция с варицела
doc_2349,en-bg,"If you have any questions about the processing of your e-mail and related personal data, do not hesitate to include them in your message.","Ако имате въпроси относно обработката на вашия адрес на електронна поща и свързаните лични данни, не се колебайте да ги включите в съобщението си."
doc_192,en-bg,Transmission can be reduced especially by improving hygiene in food production handling.,Предаването на инфекцията може да бъде ограничено особено чрез подобряване на хигиената при манипулациите в хранителната индустрия.
```
### Data Fields
**key** : The document identifier `String`.
**lang** : The pair of source and target language of type `String`.
**source_text** : The source text of type `String`.
**target_text** : The target text of type `String`.
### Data Splits
|lang | key |
|-----|-----|
|en-bg|2567 |
|en-cs|2562 |
|en-da|2577 |
|en-de|2560 |
|en-el|2530 |
|en-es|2564 |
|en-et|2581 |
|en-fi|2617 |
|en-fr|2561 |
|en-ga|1356 |
|en-hu|2571 |
|en-is|2511 |
|en-it|2534 |
|en-lt|2545 |
|en-lv|2542 |
|en-mt|2539 |
|en-nl|2510 |
|en-no|2537 |
|en-pl|2546 |
|en-pt|2531 |
|en-ro|2555 |
|en-sk|2525 |
|en-sl|2545 |
|en-sv|2527 |
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction).
### Source Data
<!-- #### Initial Data Collection and Normalization
ddd -->
#### Who are the source language producers?
Every data of this corpora as been uploaded by on [JRC](https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction).
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Considerations for Using the Data
### Other Known Limitations
The nature of the task introduce a variability in the quality of the target translations.
## Additional Information
### Dataset Curators
__Hugging Face ECDC__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus)
__An overview of the European Union's highly multilingual parallel corpora__: Steinberger Ralf, Mohamed Ebrahim, Alexandros Poulis, Manuel Carrasco-Benitez, Patrick Schlüter, Marek Przybyszewski & Signe Gilbro.
### Licensing Information
By downloading or using the ECDC-Translation Memory, you are bound by the [ECDC-TM usage conditions (PDF)](https://wt-public.emm4u.eu/Resources/ECDC-TM/2012_10_Terms-of-Use_ECDC-TM.pdf).
### No Warranty
Each Work is provided ‘as is’ without, to the full extent permitted by law, representations,
warranties, obligations and liabilities of any kind, either express or implied, including, but
not limited to, any implied warranty of merchantability, integration, satisfactory quality and
fitness for a particular purpose.
Except in the cases of wilful misconduct or damages directly caused to natural persons, the
Owner will not be liable for any incidental, consequential, direct or indirect damages,
including, but not limited to, the loss of data, lost profits or any other financial loss arising
from the use of, or inability to use, the Work even if the Owner has been notified of the
possibility of such loss, damages, claims or costs, or for any claim by any third party. The
Owner may be liable under national statutory product liability laws as far as such laws apply
to the Work.
### Citation Information
Please cite the following paper when using this dataset.
```latex
@article{10.1007/s10579-014-9277-0,
author = {Steinberger, Ralf and Ebrahim, Mohamed and Poulis, Alexandros and Carrasco-Benitez, Manuel and Schl\"{u}ter, Patrick and Przybyszewski, Marek and Gilbro, Signe},
title = {An Overview of the European Union's Highly Multilingual Parallel Corpora},
year = {2014},
issue_date = {December 2014},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
volume = {48},
number = {4},
issn = {1574-020X},
url = {https://doi.org/10.1007/s10579-014-9277-0},
doi = {10.1007/s10579-014-9277-0},
abstract = {Starting in 2006, the European Commission's Joint Research Centre and other European Union organisations have made available a number of large-scale highly-multilingual parallel language resources. In this article, we give a comparative overview of these resources and we explain the specific nature of each of them. This article provides answers to a number of question, including: What are these linguistic resources? What is the difference between them? Why were they originally created and why was the data released publicly? What can they be used for and what are the limitations of their usability? What are the text types, subject domains and languages covered? How to avoid overlapping document sets? How do they compare regarding the formatting and the translation alignment? What are their usage conditions? What other types of multilingual linguistic resources does the EU have? This article thus aims to clarify what the similarities and differences between the various resources are and what they can be used for. It will also serve as a reference publication for those resources, for which a more detailed description has been lacking so far (EAC-TM, ECDC-TM and DGT-Acquis).},
journal = {Lang. Resour. Eval.},
month = {dec},
pages = {679–707},
numpages = {29},
keywords = {DCEP, EAC-TM, EuroVoc, JRC EuroVoc Indexer JEX, Parallel corpora, DGT-TM, Eur-Lex, Highly multilingual, Linguistic resources, DGT-Acquis, European Union, ECDC-TM, JRC-Acquis, Translation memory}
}
```
|
false |
# EMEA-V3 : European parallel translation corpus from the European Medicines Agency
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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://opus.nlpl.eu/EMEA.php
- **Repository:** https://github.com/qanastek/EMEA-V3/
- **Paper:** https://aclanthology.org/L12-1246/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
`EMEA-V3` is a parallel corpus for neural machine translation collected and aligned by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) during the [OPUS project](https://opus.nlpl.eu/).
### Supported Tasks and Leaderboards
`translation`: The dataset can be used to train a model for translation.
### Languages
In our case, the corpora consists of a pair of source and target sentences for all 22 different languages from the European Union (EU).
**List of languages :** `Bulgarian (bg)`,`Czech (cs)`,`Danish (da)`,`German (de)`,`Greek (el)`,`English (en)`,`Spanish (es)`,`Estonian (et)`,`Finnish (fi)`,`French (fr)`,`Hungarian (hu)`,`Italian (it)`,`Lithuanian (lt)`,`Latvian (lv)`,`Maltese (mt)`,`Dutch (nl)`,`Polish (pl)`,`Portuguese (pt)`,`Romanian (ro)`,`Slovak (sk)`,`Slovenian (sl)`,`Swedish (sv)`.
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/EMEA-V3", split='train', download_mode='force_redownload')
print(dataset)
print(dataset[0])
```
## Dataset Structure
### Data Instances
```plain
lang,source_text,target_text
bg-cs,EMEA/ H/ C/ 471,EMEA/ H/ C/ 471
bg-cs,ABILIFY,ABILIFY
bg-cs,Какво представлява Abilify?,Co je Abilify?
bg-cs,"Abilify е лекарство, съдържащо активното вещество арипипразол.","Abilify je léčivý přípravek, který obsahuje účinnou látku aripiprazol."
bg-cs,"Предлага се под формата на таблетки от 5 mg, 10 mg, 15 mg и 30 mg, като диспергиращи се таблетки (таблетки, които се разтварят в устата) от 10 mg, 15 mg и 30 mg, като перорален разтвор (1 mg/ ml) и като инжекционен разтвор (7, 5 mg/ ml).","Je dostupný ve formě tablet s obsahem 5 mg, 10 mg, 15 mg a 30 mg, ve formě tablet dispergovatelných v ústech (tablet, které se rozpustí v ústech) s obsahem 10 mg, 15 mg a 30 mg, jako perorální roztok (1 mg/ ml) nebo jako injekční roztok (7, 5 mg/ ml)."
bg-cs,За какво се използва Abilify?,Na co se přípravek Abilify používá?
```
### Data Fields
**lang** : The pair of source and target language of type `String`.
**source_text** : The source text of type `String`.
**target_text** : The target text of type `String`.
### Data Splits
| | bg | cs | da | de | el | en | es | et | fi | fr | hu | it | lt | lv | mt | nl | pl | pt | ro | sk | sl | sv |
|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| **bg** | 0 | 342378 | 349675 | 348061 | 355696 | 333066 | 349936 | 336142 | 341732 | 358045 | 352763 | 351669 | 348679 | 342721 | 351097 | 353942 | 355005 | 347925 | 351099 | 345572 | 346954 | 342927 |
| **cs** | 342378 | 0 | 354824 | 353397 | 364609 | 335716 | 356506 | 340309 | 349040 | 363614 | 358353 | 357578 | 353232 | 347807 | 334353 | 355192 | 358357 | 351244 | 330447 | 346835 | 348411 | 346894 |
| **da** | 349675 | 354824 | 0 | 387202 | 397654 | 360186 | 387329 | 347391 | 379830 | 396294 | 367091 | 388495 | 360572 | 353801 | 342263 | 388250 | 368779 | 382576 | 340508 | 356890 | 357694 | 373510 |
| **de** | 348061 | 353397 | 387202 | 0 | 390281 | 364005 | 386335 | 346166 | 378626 | 393468 | 366828 | 381396 | 360907 | 353151 | 340294 | 377770 | 367080 | 381365 | 337562 | 355805 | 358700 | 376925 |
| **el** | 355696 | 364609 | 397654 | 390281 | 0 | 372824 | 393051 | 354874 | 384889 | 403248 | 373706 | 391389 | 368576 | 360047 | 348221 | 396284 | 372486 | 387170 | 342655 | 364959 | 363778 | 384569 |
| **en** | 333066 | 335716 | 360186 | 364005 | 372824 | 0 | 366769 | 333667 | 357177 | 373152 | 349176 | 361089 | 339899 | 336306 | 324695 | 360418 | 348450 | 361393 | 321233 | 338649 | 338195 | 352587 |
| **es** | 349936 | 356506 | 387329 | 386335 | 393051 | 366769 | 0 | 348454 | 378158 | 394253 | 368203 | 378076 | 360645 | 354126 | 340297 | 381188 | 367091 | 376443 | 337302 | 358745 | 357961 | 379462 |
| **et** | 336142 | 340309 | 347391 | 346166 | 354874 | 333667 | 348454 | 0 | 341694 | 358012 | 352099 | 351747 | 345417 | 339042 | 337302 | 350911 | 354329 | 345856 | 325992 | 343950 | 342787 | 340761 |
| **fi** | 341732 | 349040 | 379830 | 378626 | 384889 | 357177 | 378158 | 341694 | 0 | 387478 | 358869 | 379862 | 352968 | 346820 | 334275 | 379729 | 358760 | 374737 | 331135 | 348559 | 348680 | 368528 |
| **fr** | 358045 | 363614 | 396294 | 393468 | 403248 | 373152 | 394253 | 358012 | 387478 | 0 | 373625 | 385869 | 368817 | 361137 | 347699 | 388607 | 372387 | 388658 | 344139 | 363249 | 366474 | 383274 |
| **hu** | 352763 | 358353 | 367091 | 366828 | 373706 | 349176 | 368203 | 352099 | 358869 | 373625 | 0 | 367937 | 361015 | 354872 | 343831 | 368387 | 369040 | 361652 | 340410 | 357466 | 361157 | 356426 |
| **it** | 351669 | 357578 | 388495 | 381396 | 391389 | 361089 | 378076 | 351747 | 379862 | 385869 | 367937 | 0 | 360783 | 356001 | 341552 | 384018 | 365159 | 378841 | 337354 | 357562 | 358969 | 377635 |
| **lt** | 348679 | 353232 | 360572 | 360907 | 368576 | 339899 | 360645 | 345417 | 352968 | 368817 | 361015 | 360783 | 0 | 350576 | 337339 | 362096 | 361497 | 357070 | 335581 | 351639 | 350916 | 349636 |
| **lv** | 342721 | 347807 | 353801 | 353151 | 360047 | 336306 | 354126 | 339042 | 346820 | 361137 | 354872 | 356001 | 350576 | 0 | 336157 | 355791 | 358607 | 349590 | 329581 | 348689 | 346862 | 345016 |
| **mt** | 351097 | 334353 | 342263 | 340294 | 348221 | 324695 | 340297 | 337302 | 334275 | 347699 | 343831 | 341552 | 337339 | 336157 | 0 | 341111 | 344764 | 335553 | 338137 | 335930 | 334491 | 335353 |
| **nl** | 353942 | 355192 | 388250 | 377770 | 396284 | 360418 | 381188 | 350911 | 379729 | 388607 | 368387 | 384018 | 362096 | 355791 | 341111 | 0 | 369694 | 383913 | 339047 | 359126 | 360054 | 379771 |
| **pl** | 355005 | 358357 | 368779 | 367080 | 372486 | 348450 | 367091 | 354329 | 358760 | 372387 | 369040 | 365159 | 361497 | 358607 | 344764 | 369694 | 0 | 357426 | 335243 | 352527 | 355534 | 353214 |
| **pt** | 347925 | 351244 | 382576 | 381365 | 387170 | 361393 | 376443 | 345856 | 374737 | 388658 | 361652 | 378841 | 357070 | 349590 | 335553 | 383913 | 357426 | 0 | 333365 | 354784 | 352673 | 373392 |
| **ro** | 351099 | 330447 | 340508 | 337562 | 342655 | 321233 | 337302 | 325992 | 331135 | 344139 | 340410 | 337354 | 335581 | 329581 | 338137 | 339047 | 335243 | 333365 | 0 | 332373 | 330329 | 331268 |
| **sk** | 345572 | 346835 | 356890 | 355805 | 364959 | 338649 | 358745 | 343950 | 348559 | 363249 | 357466 | 357562 | 351639 | 348689 | 335930 | 359126 | 352527 | 354784 | 332373 | 0 | 348396 | 346855 |
| **sl** | 346954 | 348411 | 357694 | 358700 | 363778 | 338195 | 357961 | 342787 | 348680 | 366474 | 361157 | 358969 | 350916 | 346862 | 334491 | 360054 | 355534 | 352673 | 330329 | 348396 | 0 | 347727 |
| **sv** | 342927 | 346894 | 373510 | 376925 | 384569 | 352587 | 379462 | 340761 | 368528 | 383274 | 356426 | 377635 | 349636 | 345016 | 335353 | 379771 | 353214 | 373392 | 331268 | 346855 | 347727 | 0 |
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://opus.nlpl.eu/EMEA.php).
### Source Data
<!-- #### Initial Data Collection and Normalization
ddd -->
#### Who are the source language producers?
Every data of this corpora as been uploaded by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) on [Opus](https://opus.nlpl.eu/EMEA.php).
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Considerations for Using the Data
### Other Known Limitations
The nature of the task introduce a variability in the quality of the target translations.
## Additional Information
### Dataset Curators
__Hugging Face EMEA-V3__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus)
__OPUS : Parallel Data, Tools and Interfaces in OPUS__: [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se).
<!-- ### Licensing Information
ddd -->
### Citation Information
Please cite the following paper when using this dataset.
```latex
@inproceedings{tiedemann-2012-parallel,
title = Parallel Data, Tools and Interfaces in OPUS,
author = {
Tiedemann, Jorg
},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)",
month = may,
year = 2012,
address = Istanbul, Turkey,
publisher = European Language Resources Association (ELRA),
url = http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf,
pages = 2214--2218,
abstract = This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.,
}
```
|
false |
# WMT-16-PubMed : European parallel translation corpus from the European Medicines Agency
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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://www.statmt.org/wmt16/biomedical-translation-task.html
- **Repository:** https://github.com/biomedical-translation-corpora/corpora
- **Paper:** https://aclanthology.org/W16-2301/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
`WMT-16-PubMed` is a parallel corpus for neural machine translation collected and aligned for ACL 2016 during the [WMT'16 Shared Task: Biomedical Translation Task](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Supported Tasks and Leaderboards
`translation`: The dataset can be used to train a model for translation.
### Languages
The corpora consists of a pair of source and target sentences for all 4 different languages :
**List of languages :** `English (en)`,`Spanish (es)`,`French (fr)`,`Portuguese (pt)`.
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/WMT-16-PubMed", split='train', download_mode='force_redownload')
print(dataset)
print(dataset[0])
```
## Dataset Structure
### Data Instances
```plain
lang doc_id workshop publisher source_text target_text
0 en-fr 26839447 WMT'16 Biomedical Translation Task - PubMed pubmed Global Health: Where Do Physiotherapy and Reha... La place des cheveux et des poils dans les rit...
1 en-fr 26837117 WMT'16 Biomedical Translation Task - PubMed pubmed Carabin Les Carabins
2 en-fr 26837116 WMT'16 Biomedical Translation Task - PubMed pubmed In Process Citation Le laboratoire d'Anatomie, Biomécanique et Org...
3 en-fr 26837115 WMT'16 Biomedical Translation Task - PubMed pubmed Comment on the misappropriation of bibliograph... Du détournement des références bibliographique...
4 en-fr 26837114 WMT'16 Biomedical Translation Task - PubMed pubmed Anti-aging medicine, a science-based, essentia... La médecine anti-âge, une médecine scientifiqu...
... ... ... ... ... ... ...
973972 en-pt 20274330 WMT'16 Biomedical Translation Task - PubMed pubmed Myocardial infarction, diagnosis and treatment Infarto do miocárdio; diagnóstico e tratamento
973973 en-pt 20274329 WMT'16 Biomedical Translation Task - PubMed pubmed The health areas politics A política dos campos de saúde
973974 en-pt 20274328 WMT'16 Biomedical Translation Task - PubMed pubmed The role in tissue edema and liquid exchanges ... O papel dos tecidos nos edemas e nas trocas lí...
973975 en-pt 20274327 WMT'16 Biomedical Translation Task - PubMed pubmed About suppuration of the wound after thoracopl... Sôbre as supurações da ferida operatória após ...
973976 en-pt 20274326 WMT'16 Biomedical Translation Task - PubMed pubmed Experimental study of liver lesions in the tre... Estudo experimental das lesões hepáticas no tr...
```
### Data Fields
**lang** : The pair of source and target language of type `String`.
**source_text** : The source text of type `String`.
**target_text** : The target text of type `String`.
### Data Splits
`en-es` : 285,584
`en-fr` : 614,093
`en-pt` : 74,300
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Source Data
<!-- #### Initial Data Collection and Normalization
ddd -->
#### Who are the source language producers?
The shared task as been organized by :
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Considerations for Using the Data
### Other Known Limitations
The nature of the task introduce a variability in the quality of the target translations.
## Additional Information
### Dataset Curators
__Hugging Face WMT-16-PubMed__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus)
__WMT'16 Shared Task: Biomedical Translation Task__:
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
<!-- ### Licensing Information
ddd -->
### Citation Information
Please cite the following paper when using this dataset.
```latex
@inproceedings{bojar-etal-2016-findings,
title = Findings of the 2016 Conference on Machine Translation,
author = {
Bojar, Ondrej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Neveol, Aurelie and
Neves, Mariana and
Popel, Martin and
Post, Matt and
Rubino, Raphael and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin and
Zampieri, Marcos,
},
booktitle = Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers,
month = aug,
year = 2016,
address = Berlin, Germany,
publisher = Association for Computational Linguistics,
url = https://aclanthology.org/W16-2301,
doi = 10.18653/v1/W16-2301,
pages = 131--198,
}
```
|
false |
# Dataset Card for QReCC: Question Rewriting in Conversational 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 Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- [**Repository:**](https://github.com/apple/ml-qrecc)
- [**Paper:**](https://arxiv.org/pdf/2010.04898.pdf)
- [**Leaderboard:**](https://www.tira.io/task/scai-qrecc/dataset/scai-qrecc21-test-dataset-2021-07-20)
### Dataset Summary
QReCC (Question Rewriting in Conversational Context) is an end-to-end open-domain question answering dataset comprising of 14K conversations with 81K question-answer pairs. The goal of this dataset is to provide a challenging benchmark for end-to-end conversational question answering that includes the individual subtasks of question rewriting, passage retrieval and reading comprehension.
The task in QReCC is to find answers to conversational questions within a collection of 10M web pages split into 54M passages. Answers to questions in the same conversation may be distributed across several web pages.
The passage collection should be downloaded from [**Zenodo**](https://zenodo.org/record/5115890#.YaeD7C8RppR) (passages.zip)
### Supported Tasks and Leaderboards
`question-answering`
### Languages
English
## Dataset Structure
### Data Instances
An example from the data set looks as follows:
```
{
"Context": [
"What are the pros and cons of electric cars?",
"Some pros are: They're easier on the environment. Electricity is cheaper than gasoline. Maintenance is less frequent and less expensive. They're very quiet. You'll get tax credits. They can shorten your commute time. Some cons are: Most EVs have pretty short ranges. Recharging can take a while."
],
"Question": "Tell me more about Tesla",
"Rewrite": "Tell me more about Tesla the car company.",
"Answer": "Tesla Inc. is an American automotive and energy company based in Palo Alto, California. The company specializes in electric car manufacturing and, through its SolarCity subsidiary, solar panel manufacturing.",
"Answer_URL": "https://en.wikipedia.org/wiki/Tesla,_Inc.",
"Conversation_no": 74,
"Turn_no": 2,
"Conversation_source": "trec"
}
```
### Data Splits
- train: 63501
- test: 16451
## Dataset Creation
### Source Data
- QuAC
- TREC CAsT
- Natural Questions
## Additional Information
### Licensing Information
[CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
### Citation Information
```
@inproceedings{ qrecc,
title={Open-Domain Question Answering Goes Conversational via Question Rewriting},
author={Anantha, Raviteja and Vakulenko, Svitlana and Tu, Zhucheng and Longpre, Shayne and Pulman, Stephen and Chappidi, Srinivas},
booktitle={ NAACL },
year={2021}
}
``` |
true |
# Dataset Card for hate-multi
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
## Dataset Description
### Dataset Summary
This dataset contains a collection of text labeled as offensive (class 1) or not (class 0).
## Dataset Creation
The dataset was creating by aggregating multiple publicly available datasets.
### Source Data
The following datasets were used:
* https://huggingface.co/datasets/hate_speech_offensive - Tweet text cleaned by lower casing, removing mentions and urls. Dropped instanced labeled as 'hate speech'
* https://sites.google.com/site/offensevalsharedtask/olid - Tweet text cleaned by lower casing, removing mentions and urls. Used 'subtask_a' column for labeling.
|
true |
# Dataset Card for "imdb-javanese"
## 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 Sample Size](#data-instances-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)
## Dataset Description
- **Homepage:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Repository:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Point of Contact:** [Wilson Wongso](https://github.com/w11wo)
- **Size of downloaded dataset files:** 17.0 MB
- **Size of the generated dataset:** 47.5 MB
- **Total amount of disk used:** 64.5 MB
### Dataset Summary
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the [original IMDB Dataset](https://huggingface.co/datasets/imdb) to Javanese using the multi-lingual MarianMT Transformer model from [`Helsinki-NLP/opus-mt-en-mul`](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
An example of `javanese_imdb_train.csv` looks as follows.
| label | text |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | "Drama romantik sing digawé karo direktur Martin Ritt kuwi ora dingertèni, nanging ana momen-momen sing marahi karisma lintang Jane Fonda lan Robert De Niro (kelompok sing luar biasa). Dhèwèké dadi randha sing ora isa mlaku, iso anu anyar lan anyar-inventor-- kowé isa nganggep isiné. Adapsi novel Pat Barker ""Union Street"" (yak titel sing apik!) arep dinggo-back-back it on bland, lan pendidikan film kuwi gampang, nanging isih nyenengké; a rosy-hued-inventor-fantasi. Ora ana sing ngganggu gambar sing sejati ding kok iso dinggo nggawe gambar sing paling nyeneng." |
| 0 | "Pengalaman wong lanang sing nduwé perasaan sing ora lumrah kanggo babi. Mulai nganggo tuladha sing luar biasa yaiku komedia. Wong orkestra termel digawé dadi wong gila, sing kasar merga nyanyian nyanyi. Sayangé, kuwi tetep absurd wektu WHOLE tanpa ceramah umum sing mung digawé. Malah, sing ana ing jaman kuwi kudu ditinggalké. Diyalog kryptik sing nggawé Shakespeare marah gampang kanggo kelas telu. Pak teknis kuwi luwih apik timbang kowe mikir nganggo cinematografi sing apik sing jenengé Vilmos Zsmond. Masa depan bintang Saly Kirkland lan Frederic Forrest isa ndelok." |
### Data Fields
- `text`: The movie review translated into Javanese.
- `label`: The sentiment exhibited in the review, either `1` (positive) or `0` (negative).
### Data Splits Sample Size
| train | unsupervised | test |
| ----: | -----------: | ----: |
| 25000 | 50000 | 25000 |
## 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
If you use this dataset in your research, please cite:
```
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
```
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
|
false |
# Dataset Card for annotated_reference_strings
## 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.github.com/kylase](https://www.github.com/kylase)
- **Repository:** [https://www.github.com/kylase](https://www.github.com/kylase)
- **Point of Contact:** [Yuan Chuan Kee](https://www.github.com/kylase)
### Dataset Summary
The `annotated_reference_strings` dataset comprises millions of the annotated reference strings, i.e. each token of the strings have an associated label such as author, title, year, etc.
These strings are synthesized using citation processor on millions of citations obtained from various sources, spanning different scientific domains.
### Supported Tasks
This dataset can be used for structure prediction.
### Languages
The dataset is composed of reference strings that are in English.
## Dataset Structure
### Data Instances
```json
{
"source": "pubmed",
"lang": "en",
"entry_type": "article",
"doi_prefix": "pubmed19n0001",
"csl_style": "annual-reviews",
"content": "<citation-number>8.</citation-number> <author>Mohr W.</author> <year>1977.</year> <title>[Morphology of bone tumors. 2. Morphology of benign bone tumors].</title> <container-title>Aktuelle Probleme in Chirurgie und Orthopadie.</container-title> <volume>5:</volume> <page>29–42</page>"
}
```
#### Important Note
1. Each citation is rendered to _at most_ **17** CSL styles. Therefore, there will be near duplicates.
2. All characters (including punctuations) of a segment (**a segment consists of 1 or more token**) are enclosed by tag(s).
1. Only tokens that act as "conjunctions" are not enclosed in tags. These tokens will be labelled as `other`.
3. There will be instances which a segment can be enclosed by more than one tag e.g. `<issued><year>2021</year></issued>`. This depends on how the styles' author(s).
### Data Fields
- `source`: Describe the source of the citation. `{pubmed, jstor, crossref}`
- `lang`: Describe the language of the citation. `{en}`
- `entry_type`: Describe the BibTeX entry type. `{article, book, inbook, misc, techreport, phdthesis, incollection, inproceedings}`
- `doi_prefix`: For JSTOR and CrossRef, it is the prefix of the DOI. For PubMed, it is the directory (e.g. `pubmed19nXXXX` where `XXXX` is 4 digits) of which the citation is generated from.
- `csl_style`: The CSL style which the citation is rendered as.
- `content`: The rendered citation of a specific style with each segment enclosed by tags named after the CSL variables
### Data Splits
Data splits are not available yet.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The citations that are used to generate these reference strings are obtained from 3 main sources:
- [PubMed](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) (2019 Baseline)
- CrossRef via [Open Academic Graph v2](https://www.microsoft.com/en-us/research/project/open-academic-graph/)
- JSTOR Sample Datasets (not available online as of publication date)
If the citation is not in BibTeX format, [bibutils](https://sourceforge.net/p/bibutils/home/Bibutils/) is used to convert it to BibTeX.
#### Who are the source language producers?
The manner which the citations are rendered as reference strings are based on rules/specifications dictated by the publisher.
[Citation Style Language](https://citationstyles.org/) (CSL) is an established standard which such specifications are prescribed.
Thousands of citation styles are available.
### Annotations
#### Annotation process
The annotation process involves 2 main interventions:
1. Modification of the styles' CSL specification to inject the CSL variable names as part of the render process
2. Sanitization of the rendered strings using regular expressions to ensure all tokens and characters are enclosed in the tags
#### Who are the annotators?
The original CSL specification are available on [GitHub](https://github.com/citation-style-language/styles).
The modification of the styles and the sanitization process are done by the author of this work.
## Additional Information
### Licensing Information
This dataset is licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
This dataset is a product of a Master Project done in the National University of Singapore.
If you are using it, please cite the following:
```bibtex
@techreport{kee2021,
author = {Yuan Chuan Kee},
title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
institution = {National University of Singapore},
year = {2021}
}
```
### Contributions
Thanks to [@kylase](https://github.com/kylase) for adding this dataset.
|
false |
# Corpus tesis de la SCJN
En su primera versión contiene textos correspondientes a las tesis de la décima y undécima época publicadas al mes de febrero del 2022 por la SCJN.
## Dataset Structure
### Data Instances
Un ejemplo de 'train' se ve de la siguiente forma:
```
{
'id': '3',
'text': 'a la luz de las disposiciones del sistema de derechos humanos, los principios tanto de buena fe como de protección de las apariencias constituyen un límite tendente a evitar el dolo para el disfuncional ejercicio de los actos procesales, al cumplir con la función de colmar las inevitables lagunas legales, en tanto que la norma sólo previene abusos comunes, prohibiéndolos en forma enunciativa, porque de considerarlos limitativamente, muchas conductas o declaraciones contrarias a otras precedentes y, por tanto, indebidas, escaparían de la regulación. ambos principios sirven para analizar el caso en el que, en una primera ejecutoria de amparo promovido contra el auto de vinculación a proceso, se declaró irregularmente llevada a cabo una diligencia de reconocimiento de una persona por una fotografía (imputado), al inobservarse las formas procesales, por lo que en cumplimiento con la sentencia, se dictó auto de no vinculación a proceso y, en atención al deber de investigar conforme a los parámetros convencionales, la autoridad practicó una posterior diligencia, esta vez conforme a las disposiciones adjetivas que la rigen; sin embargo, si el defensor se retiró sin firmarla, aduciendo que lo haría posteriormente, sin que así se hubiera logrado, no obstante las gestiones tendientes a ello por la autoridad investigadora, quien pormenorizadamente las detalló en una certificación. actuación que debe ser sometida en cada caso al escrutinio constitucional, considerando que no puede alegar la nulidad quien ha incurrido conscientemente a su producción, porque buscaría aprovecharse de su personal dolo, al provocar daños por medio del uso desviado de medios legales inicialmente legítimos, si se les considera aisladamente. ahora bien, ponderado el caso concreto, se advierte que no obstante alegar en favor de su defenso el propio dolo, se produjeron las consecuencias inherentes a la diligencia en los términos establecidos en la norma, pues incluso consta que intervino activamente en la diligencia; lo que conduce a estimar infundado el agravio expuesto en el sentido de que debe negársele validez, al tender a beneficiar al quejoso del dolo del defensor expresado en retirarse sin firmar, indicando que regresaría a hacerlo, sin que hubiera actuado conforme a esa manifestación precedente, pretendiendo que, de prosperar la falta de formalidad en la segunda diligencia, la cual ahora le es atribuible, afectaría la expectativa creada en otros sujetos de derecho, en la especie, las víctimas, incluso, el exceso en el ejercicio de la acción constitucional alentaría la práctica viciosa de actos cuyos frutos serían aprovechables por quienes los realizan y, por otra parte, tanto las autoridades investigadoras como los tribunales se harían en alguna forma partícipes de ese proceder irregular, si consideraran permitido ese comportamiento sólo porque la ley omitió prohibirlo, incumpliendo las primeras con el deber de investigar la verdad conforme a los parámetros convencionales y, los segundos, al otorgarles credibilidad.'
}
```
### Data Fields
Los campos son los mismos para todos los splits.
- `id`: a `string` feature.
- `text`: a `string` features.
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|scjn_corpus_tesis|27913|0|0|
## Dataset Creation
### Annotations
### Dataset Curators
Ana Gabriela Palomeque Ortiz, from SCJN - Unidad General de Administración del Conocimiento Jurídico.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Other Known Limitations
La información contenida en este dataset es para efectos demostrativos y no representa una fuente oficial de la Suprema Corte de Justicia de la Nación.
## License
<br/>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/deed.es">Attribution-ShareAlike 4.0 International License</a>.
|
false |
This is an automatically translated version of [vblagoje/lfqa](https://huggingface.co/datasets/vblagoje/lfqa), a dataset used for long form question answering training. The model used for translating the dataset is [marianMT english-spanish](https://huggingface.co/Helsinki-NLP/opus-mt-en-es). |
false |
# Dataset Card for MultiWOZ 2.1
- **Repository:** https://github.com/budzianowski/multiwoz
- **Paper:** https://aclanthology.org/2020.lrec-1.53
- **Leaderboard:** https://github.com/budzianowski/multiwoz
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('multiwoz21')
ontology = load_ontology('multiwoz21')
database = load_database('multiwoz21')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
MultiWOZ 2.1 fixed the noise in state annotations and dialogue utterances. It also includes user dialogue acts from ConvLab (Lee et al., 2019) as well as multiple slot descriptions per dialogue state slot.
- **How to get the transformed data from original data:**
- Download [MultiWOZ_2.1.zip](https://github.com/budzianowski/multiwoz/blob/master/data/MultiWOZ_2.1.zip).
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- Create a new ontology in the unified format, taking slot descriptions from MultiWOZ 2.2.
- Correct some grammar errors in the text, mainly following `tokenization.md` in MultiWOZ_2.1.
- Normalize slot name and value. See `normalize_domain_slot_value` function in `preprocess.py`.
- Correct some non-categorical slots' values and provide character level span annotation.
- Concatenate multiple values in user goal & state using `|`.
- Add `booked` information in system turns from original belief states.
- Remove `Booking` domain and remap all booking relevant dialog acts to unify the annotation of booking action in different domains, see `booking_remapper.py`.
- **Annotations:**
- user goal, dialogue acts, state.
### Supported Tasks and Leaderboards
NLU, DST, Policy, NLG, E2E, User simulator
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 8438 | 113556 | 13.46 | 13.23 | 2.8 | 98.84 | 99.48 | 86.39 | 98.22 |
| validation | 1000 | 14748 | 14.75 | 13.5 | 2.98 | 98.84 | 99.46 | 86.59 | 98.17 |
| test | 1000 | 14744 | 14.74 | 13.5 | 2.93 | 99.21 | 99.32 | 85.83 | 98.58 |
| all | 10438 | 143048 | 13.7 | 13.28 | 2.83 | 98.88 | 99.47 | 86.35 | 98.25 |
8 domains: ['attraction', 'hotel', 'taxi', 'restaurant', 'train', 'police', 'hospital', 'general']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@inproceedings{eric-etal-2020-multiwoz,
title = "{M}ulti{WOZ} 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines",
author = "Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.53",
pages = "422--428",
ISBN = "979-10-95546-34-4",
}
```
### Licensing Information
Apache License, Version 2.0 |
true | # AutoTrain Dataset for project: sentiment_analysis_project
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project sentiment_analysis_project.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Realizing that I don`t have school today... or tomorrow... or for the next few months. I really nee[...]",
"target": 1
},
{
"text": "Good morning tweeps. Busy this a.m. but not in a working way",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=3, names=['negative', 'neutral', 'positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 16180 |
| valid | 4047 |
|
false |
# Dataset Card for french-open-fiscal-texts
## 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://echanges.dila.gouv.fr/OPENDATA/JADE/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court "Conseil d'Etat".
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
fr-FR
## Dataset Structure
### Data Instances
```json
{
"file": "CETATEXT000007584427.xml",
"title": "Cour administrative d'appel de Marseille, 3�me chambre - formation � 3, du 21 octobre 2004, 00MA01080, in�dit au recueil Lebon",
"summary": "",
"content": "Vu la requête, enregistrée le 22 mai 2000, présentée pour M. Roger X, par Me Luherne, élisant domicile ...), et les mémoires complémentaires en date des 28 octobre 2002, 22 mars 2004 et 16 septembre 2004 ; M. X demande à la Cour :\n\n\n \n 11/ d'annuler le jugement n° 951520 en date du 16 mars 2000 par lequel le Tribunal administratif de Montpellier a rejeté sa requête tendant à la réduction des cotisations supplémentaires à l'impôt sur le revenu et des pénalités dont elles ont été assorties, auxquelles il a été assujetti au titre des années 1990, 1991 et 1992 ;\n\n\n \n 22/ de prononcer la réduction desdites cotisations ;\n\n\n \n 3°/ de condamner de l'Etat à lui verser une somme de 32.278 francs soit 4.920,75 euros"
}
```
### Data Fields
`file`: identifier on the JADE OPENDATA file
`title`: Name of the law case
`summary`: Summary provided by JADE (may be missing)
`content`: Text content of the case law
### Data Splits
train
test
## Dataset Creation
### Curation Rationale
This dataset is an attempt to gather multiple tax related french text law.
The first intent it to build model to summarize law cases
### Source Data
#### Initial Data Collection and Normalization
Collected from the https://echanges.dila.gouv.fr/OPENDATA/
- Filtering xml files containing "Code général des impôts" (tax related)
- Extracting content, summary, identifier, title
#### Who are the source language producers?
DILA
### 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 |
# Dataset Card for pl-corpus
## 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:** [UlyssesNER-Br homepage](https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor)
- **Repository:** [UlyssesNER-Br repository](https://github.com/Convenio-Camara-dos-Deputados/ulyssesner-br-propor)
- **Paper:** [UlyssesNER-Br: A corpus of brazilian legislative documents for named entity recognition. In: Computational Processing of the Portuguese Language](https://link.springer.com/chapter/10.1007/978-3-030-98305-5_1)
- **Point of Contact:** [Hidelberg O. Albuquerque](mailto:hidelberg.albuquerque@ufrpe.br)
### Dataset Summary
PL-corpus is part of the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines The presented corpus consists of 150 public bills from Brazilian Chamber of Deputies, manually annotated. Its contains semantic categories and types.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Brazilian Portuguese.
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@InProceedings{ALBUQUERQUE2022,
author="Albuquerque, Hidelberg O.
and Costa, Rosimeire
and Silvestre, Gabriel
and Souza, Ellen
and da Silva, N{\'a}dia F. F.
and Vit{\'o}rio, Douglas
and Moriyama, Gyovana
and Martins, Lucas
and Soezima, Luiza
and Nunes, Augusto
and Siqueira, Felipe
and Tarrega, Jo{\~a}o P.
and Beinotti, Joao V.
and Dias, Marcio
and Silva, Matheus
and Gardini, Miguel
and Silva, Vinicius
and de Carvalho, Andr{\'e} C. P. L. F.
and Oliveira, Adriano L. I.",
title="{UlyssesNER-Br}: A Corpus of Brazilian Legislative Documents for Named Entity Recognition",
booktitle="Computational Processing of the Portuguese Language",
year="2022",
pages="3--14",
}
``` |
false |
# Dataset Card for librispeech_asr
## 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:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
- **Repository:** [Needs More Information]
- **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-other)
- **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com)
### Dataset Summary
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
### 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/sota/speech-recognition-on-librispeech-test-clean and ranks models based on their WER.
### Languages
The audio is in English. There are two configurations: `clean` and `other`.
The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
a different dataset, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
## 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
The size of the corpus makes it impractical, or at least inconvenient
for some users, to distribute it as a single large archive. Thus the
training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
A simple automatic
procedure was used to select the audio in the first two sets to be, on
average, of higher recording quality and with accents closer to US
English. An acoustic model was trained on WSJ’s si-84 data subset
and was used to recognize the audio in the corpus, using a bigram
LM estimated on the text of the respective books. We computed the
Word Error Rate (WER) of this automatic transcript relative to our
reference transcripts obtained from the book texts.
The speakers in the corpus were ranked according to the WER of
the WSJ model’s transcripts, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
respectively accounting for 100h and 360h of the training data.
For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
| | Train.500 | Train.360 | Train.100 | Valid | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| clean | - | 104014 | 28539 | 2703 | 2620|
| other | 148688 | - | - | 2864 | 2939 |
## 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
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Andrés Pitta: andres.pitta@un.org**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
false |
# Dataset Card for "twitter-pos"
## 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://gate.ac.uk/wiki/twitter-postagger.html](https://gate.ac.uk/wiki/twitter-postagger.html)
- **Repository:** [https://github.com/GateNLP/gateplugin-Twitter](https://github.com/GateNLP/gateplugin-Twitter)
- **Paper:** [https://aclanthology.org/R13-1026/](https://aclanthology.org/R13-1026/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 51.96 MiB
- **Size of the generated dataset:** 251.22 KiB
- **Total amount of disk used:** 52.05 MB
### Dataset Summary
Part-of-speech information is basic NLP task. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.
This dataset contains two datasets for English PoS tagging for tweets:
* Ritter, with train/dev/test
* Foster, with dev/test
Splits defined in the Derczynski paper, but the data is from Ritter and Foster.
* Ritter: [https://aclanthology.org/D11-1141.pdf](https://aclanthology.org/D11-1141.pdf),
* Foster: [https://www.aaai.org/ocs/index.php/ws/aaaiw11/paper/download/3912/4191](https://www.aaai.org/ocs/index.php/ws/aaaiw11/paper/download/3912/4191)
### Supported Tasks and Leaderboards
* [Part of speech tagging on Ritter](https://paperswithcode.com/sota/part-of-speech-tagging-on-ritter)
### Languages
English, non-region-specific. `bcp47:en`
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{'id': '0', 'tokens': ['Antick', 'Musings', 'post', ':', 'Book-A-Day', '2010', '#', '243', '(', '10/4', ')', '--', 'Gray', 'Horses', 'by', 'Hope', 'Larson', 'http://bit.ly/as8fvc'], 'pos_tags': [23, 23, 22, 9, 23, 12, 22, 12, 5, 12, 6, 9, 23, 23, 16, 23, 23, 51]}
```
### Data Fields
The data fields are the same among all splits.
#### twitter-pos
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
```
### Data Splits
| name |tokens|sentences|
|---------|----:|---------:|
|ritter train|10652|551|
|ritter dev |2242|118|
|ritter test |2291|118|
|foster dev |2998|270|
|foster test |2841|250|
## 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
### Citation Information
```
@inproceedings{ritter2011named,
title={Named entity recognition in tweets: an experimental study},
author={Ritter, Alan and Clark, Sam and Etzioni, Oren and others},
booktitle={Proceedings of the 2011 conference on empirical methods in natural language processing},
pages={1524--1534},
year={2011}
}
@inproceedings{foster2011hardtoparse,
title={\# hardtoparse: POS Tagging and Parsing the Twitterverse},
author={Foster, Jennifer and Cetinoglu, Ozlem and Wagner, Joachim and Le Roux, Joseph and Hogan, Stephen and Nivre, Joakim and Hogan, Deirdre and Van Genabith, Josef},
booktitle={Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence},
year={2011}
}
@inproceedings{derczynski2013twitter,
title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data},
author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina},
booktitle={Proceedings of the international conference recent advances in natural language processing ranlp 2013},
pages={198--206},
year={2013}
}
```
### Contributions
Author uploaded ([@leondz](https://github.com/leondz)) |
false | # Dataset Card for Million Headlines
## 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:** [Kaggle dataset](https://www.kaggle.com/datasets/therohk/million-headlines)
- **Point of Contact:** Rohit Kulkarni)
### Dataset Summary
This contains data of news headlines published over a period of eighteen years. Sourced from the reputable Australian news source ABC (Australian Broadcasting Corporation)
## Dataset Structure
### Data Instances
For each instance, there is a integer for the data, a string for news headline.
### Data Fields
- `publish date`: a integer that represents the data
- `headline`: a string for the news headline
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people that were in the headlines.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset represents one news service in Australia and should not be considered representative of all news or headlines.
### Discussion of Biases
News headlines may contain biases and should not be considered neutral.
### Licensing Information
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). |
false |
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
false |
# Dataset Card for PROTEINS
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://academic.oup.com/bioinformatics/article/21/suppl_1/i47/202991)**
- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip):**:
- **Paper:**: Protein function prediction via graph kernels (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-proteins)
### Dataset Summary
The `PROTEINS` dataset is a medium molecular property prediction dataset.
### Supported Tasks and Leaderboards
`PROTEINS` should be used for molecular property prediction (aiming to predict whether molecules are enzymes or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 1113 |
| average #nodes | 39.06 |
| average #edges | 72.82 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by TUDataset.
This information can be found back using
```python
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root='', name = 'PROTEINS')
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license, please open an issue if you have info about it.
### Citation Information
```
@article{10.1093/bioinformatics/bti1007,
author = {Borgwardt, Karsten M. and Ong, Cheng Soon and Schönauer, Stefan and Vishwanathan, S. V. N. and Smola, Alex J. and Kriegel, Hans-Peter},
title = "{Protein function prediction via graph kernels}",
journal = {Bioinformatics},
volume = {21},
number = {suppl_1},
pages = {i47-i56},
year = {2005},
month = {06},
abstract = "{Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.Availability: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.Contact:borgwardt@dbs.ifi.lmu.de}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/bti1007},
url = {https://doi.org/10.1093/bioinformatics/bti1007},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/21/suppl\_1/i47/524364/bti1007.pdf},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
false |
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/opus_books_en_pt", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 1404
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'en': "There was nothing so very remarkable in that; nor did Alice think it so very much out of the way to hear the Rabbit say to itself, 'Oh dear!",
'pt': 'Não havia nada de tão extraordinário nisso; nem Alice achou assim tão fora do normal ouvir o Coelho dizer para si mesmo: —"Oh, céus!'}}
``` |
false |
Port of the credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `default.payment.next.month`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/credit-card")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['default.payment.next.month'])
y = df['default.payment.next.month'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['default.payment.next.month'])
y_test = df['default.payment.next.month'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
``` |
true | # GoEmotions Spanish
## A Spanish translation (using [EasyNMT](https://github.com/UKPLab/EasyNMT)) of the [GoEmotions](https://huggingface.co/datasets/sst2) dataset.
#### For more information check the official [Model Card](https://huggingface.co/datasets/go_emotions) |
false |
## Dataset Description
A small subset in each dataset of `pile-v2`(~1000 samples) of [pile-v2]() dataset, each has 1,000 random samples from the original dataset. The dataset has 255MB of text (code and english).
## Languages
The dataset contains technical text on programming languages and natural language with the following subsets,
- Bible
- TED2020
- PileOfLaw
- StackExchange
- GithubIssues
- Opensubtitles
- USPTO
- S2ORC
- DevDocs
- CodePileReddit2022
- USENET
- GNOME
- ASFPublicMail
- PileV2Reddit2020
- CodePilePosts
- Discourse
- Tanzil
- arXiv
- UbuntuIRC
- PubMed
- CodePileReddit2020
- CodePileReddit2021
- GlobalVoices
- FreeLaw_Options
- PileV2Posts
## Dataset Structure
```python
from datasets import load_dataset
load_dataset("CarperAI/pile-v2-small")
```
### How to use it
You can either load the whole dataset like above, or load a specific subset such as arxiv by specifying the folder directory:
```python
load_dataset("CarperAI/pile-v2-small", data_dir="data/arxiv")
```
|
true |
# Germeval Task 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback
In the connected, modern world, customer feedback is a valuable source for insights on the quality of products or services. This feedback allows other customers to benefit from the experiences of others and enables businesses to react on requests, complaints or recommendations. However, the more people use a product or service, the more feedback is generated, which results in the major challenge of analyzing huge amounts of feedback in an efficient, but still meaningful way.
Thus, we propose a shared task on automatically analyzing customer reviews about “Deutsche Bahn” - the german public train operator with about two billion passengers each year.
Example:
> “RT @XXX: Da hört jemand in der Bahn so laut ‘700 Main Street’ durch seine Kopfhörer, dass ich mithören kann. :( :( :(“
As shown in the example, insights from reviews can be derived on different granularities. The review contains a general evaluation of the travel (The customer disliked the travel). Furthermore, the review evaluates a dedicated aspect of the train travel (“laut” → customer did not like the noise level).
Consequently, we frame the task as aspect-based sentiment analysis with four sub tasks:
## Data format
```
ID <tab> Text <tab> Relevance <tab> Sentiment <tab> Aspect:Polarity (whitespace separated)
```
## Links
- http://ltdata1.informatik.uni-hamburg.de/germeval2017/
- https://sites.google.com/view/germeval2017-absa/
## How to cite
```bibtex
@inproceedings{germevaltask2017,
title = {{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}},
author = {Michael Wojatzki and Eugen Ruppert and Sarah Holschneider and Torsten Zesch and Chris Biemann},
year = {2017},
booktitle = {Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback},
address={Berlin, Germany},
pages={1--12}
}
``` |
false |
# Dataset Card for OLM December 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from a December 2022 Wikipedia snapshot. |
true |
This dataset is processed version of Social Chemistry 101(SChem) dataset including text and the annotation disagreement labels. <br>
Paper: Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information <br>
Authors: Ruyuan Wan, Jaehyung Kim, Dongyeop Kang <br>
Github repo: https://github.com/minnesotanlp/Quantifying-Annotation-Disagreement <br>
Source Data: [Social Chemistry 101(Forbes et al. 2020)](https://github.com/mbforbes/social-chemistry-101) <br> |
false |
# Dataset Card for `nyt`
The `nyt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/nyt#nyt).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,864,661
This dataset is used by: [`nyt_trec-core-2017`](https://huggingface.co/datasets/irds/nyt_trec-core-2017), [`nyt_wksup`](https://huggingface.co/datasets/irds/nyt_wksup), [`nyt_wksup_train`](https://huggingface.co/datasets/irds/nyt_wksup_train), [`nyt_wksup_valid`](https://huggingface.co/datasets/irds/nyt_wksup_valid)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/nyt', 'docs')
for record in docs:
record # {'doc_id': ..., 'headline': ..., 'body': ..., 'source_xml': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Sandhaus2008Nyt,
title={The new york times annotated corpus},
author={Sandhaus, Evan},
journal={Linguistic Data Consortium, Philadelphia},
volume={6},
number={12},
pages={e26752},
year={2008}
}
```
|
false |
# Dataset Card for NeuCLIR1
## Dataset Description
- **Website:** https://neuclir.github.io/
- **Repository:** https://github.com/NeuCLIR/download-collection
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection designed to be similar to HC4 and a large portion of documents from HC4 are ported to this collection.
The documents are Web pages from Common Crawl in Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 2.2M |
| `rus` (Russian) | 4.6M |
| `zho` (Chinese) | 3.2M |
### Data Fields
- `id`: unique identifier for this document
- `cc_file`: source file from connon crawl
- `time`: extracted date/time from article
- `title`: title extracted from article
- `text`: extracted article body
- `url`: source URL
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/neuclir1')
dataset['fas'] # Persian documents
dataset['rus'] # Russian documents
dataset['zho'] # Chinese documents
```
|
true |
# Dataset Card for HumSet
## 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)
## Dataset Description
- **Homepage:** [http://blog.thedeep.io/humset/](http://blog.thedeep.io/humset/)
- **Repository:** [https://github.com/the-deep/humset](https://github.com/the-deep/humset)
- **Paper:** [EMNLP Findings 2022](https://aclanthology.org/2022.findings-emnlp.321)
- **Leaderboard:**
- **Point of Contact:**[the DEEP NLP team](mailto:nlp@thedeep.io)
### Dataset Summary
HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.
### Supported Tasks and Leaderboards
This dataset is intended for multi-label classification
### Languages
This dataset is in English, French and Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- **entry_id**: unique identification number for a given entry. (string)
- **lead_id**: unique identification number for the document to which the corrisponding entry belongs. (string)
- **project_id** unique identification number for the project to which the corrisponding entry belongs. (string)
- **sectors**, **pillars_1d**, **pillars_2d**, **subpillars_1d**, **subpillars_2d**: labels assigned to the corresponding entry. Since this is a multi-label dataset (each entry may have several annotations belonging to the same category), they are reported as arrays of strings. See the paper for a detailed description of these categories. (list)
- **lang**: language. (str)
- **n_tokens**: number of tokens (tokenized using NLTK v3.7 library). (int64)
- **project_title**: the name of the project where the corresponding annotation was created. (str)
- **created_at**: date and time of creation of the annotation in stardard ISO 8601 format. (str)
- **document**: document URL source of the excerpt. (str)
- **excerpt**: excerpt text. (str)
### Data Splits
The dataset includes a set of train/validation/test splits, with 117435, 16039 and 15147 examples respectively.
## Dataset Creation
The collection originated from a multi-organizational platform called <em>the Data Entry and Exploration Platform (DEEP)</em> developed and maintained by Data Friendly Space (DFS). The platform facilitates classifying primarily qualitative information with respect to analysis frameworks and allows for collaborative classification and annotation of secondary data.
### Curation Rationale
[More Information Needed]
### Source Data
Documents are selected from different sources, ranging from official reports by humanitarian organizations to international and national media articles. See the paper for more informations.
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
#### Annotation process
HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three
languages of English, French, and Spanish, originally taken from publicly-available resources. For
each document, analysts have identified informative snippets (entries, or excerpt in the imported dataset) with respect to common <em>humanitarian frameworks</em> and assigned one or many classes to each entry.
#### 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
NLP team at [Data Friendly Space](https://datafriendlyspace.org/)
### Licensing Information
The GitHub repository which houses this dataset has an Apache License 2.0.
### Citation Information
```
@inproceedings{fekih-etal-2022-humset,
title = "{H}um{S}et: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response",
author = "Fekih, Selim and
Tamagnone, Nicolo{'} and
Minixhofer, Benjamin and
Shrestha, Ranjan and
Contla, Ximena and
Oglethorpe, Ewan and
Rekabsaz, Navid",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.321",
pages = "4379--4389",
}
```
|
false | # Dataset Card for 1000 Website Screenshots with Metadata
## Dataset Description
- **Homepage:** [silatus.com](https://silatus.com/datasets)
- **Point of Contact:** [datasets@silatus.com](mailto:datasets@silatus.com)
### Dataset Summary
Silatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: [https://silatus.com/datasets](https://silatus.com/datasets)
This dataset includes:
**High-res screenshots**
- 1024x1024px
- Loaded Javascript
- Loaded Images
**Text metadata**
- Site title
- Navbar content
- Full page text data
- Page description
**Visual metadata**
- Content (images, videos, inputs, buttons) absolute & relative positions
- Color profile
- Base font |
false |
<div align="center">
<img width="640" alt="keremberke/pcb-defect-segmentation" src="https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']
```
### Number of Images
```json
{'valid': 25, 'train': 128, 'test': 36}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/pcb-defect-segmentation", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8](https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8?ref=roboflow2huggingface)
### Citation
```
@misc{ defects-2q87r_dataset,
title = { Defects Dataset },
type = { Open Source Dataset },
author = { Diplom },
howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } },
url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-27 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 189 images.
Defect are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
false | # range3/wikipedia-ja-20230101
This dataset consists of a parquet file from the wikipedia dataset with only Japanese data extracted. It is generated by the following python code.
このデータセットは、wikipediaデータセットの日本語データのみを抽出したparquetファイルで構成されます。以下のpythonコードによって生成しています。
```py
import datasets
dss = datasets.load_dataset(
"wikipedia",
language="ja",
date="20230101",
beam_runner="DirectRunner",
)
for split,ds in dss.items():
ds.to_parquet(f"wikipedia-ja-20230101/{split}.parquet")
```
|
true | ERROR: type should be string, got "\nhttps://huggingface.co/spaces/huggingface/datasets-tagging\n\n\n# Dataset Card for Swiss Doc2doc Information Retrieval\n\n## Table of Contents\n- [Table of Contents](#table-of-contents)\n- [Dataset Description](#dataset-description)\n - [Dataset Summary](#dataset-summary)\n - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)\n - [Languages](#languages)\n- [Dataset Structure](#dataset-structure)\n - [Data Instances](#data-instances)\n - [Data Fields](#data-fields)\n - [Data Splits](#data-splits)\n- [Dataset Creation](#dataset-creation)\n - [Curation Rationale](#curation-rationale)\n - [Source Data](#source-data)\n - [Annotations](#annotations)\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\n- [Considerations for Using the Data](#considerations-for-using-the-data)\n - [Social Impact of Dataset](#social-impact-of-dataset)\n - [Discussion of Biases](#discussion-of-biases)\n - [Other Known Limitations](#other-known-limitations)\n- [Additional Information](#additional-information)\n - [Dataset Curators](#dataset-curators)\n - [Licensing Information](#licensing-information)\n - [Citation Information](#citation-information)\n - [Contributions](#contributions)\n\n## Dataset Description\n\n- **Homepage:**\n- **Repository:**\n- **Paper:**\n- **Leaderboard:**\n- **Point of Contact:**\n\n### Dataset Summary\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.\n\n### Supported Tasks and Leaderboards\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (https://huggingface.co/datasets/rcds/swiss_legislation) and Swiss Leading desicions (https://huggingface.co/datasets/rcds/swiss_leading_decisions).\n\n### Languages\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n## Dataset Structure\n\n### Data Instances\n\n```\n{\n \"decision_id\": \"000127ef-17d2-4ded-8621-c0c962c18fd5\",\n \"language\": de,\n \"year\": 2018,\n \"chamber\": \"CH_BGer_008\",\n \"region\": \"Federation\",\n \"origin_chamber\": 47,\n \"origin_court\": 8,\n \"origin_canton\": 151,\n \"law_area\": \"social_law\",\n \"law_sub_area\": ,\n \"laws\": \"['75488867-c001-4eb9-93b9-04264ea91f55', 'e6b06567-1236-4210-adb3-e11c26e497d5', '04bf6369-99cb-41fa-8aff-413679bc8c18', ...],\n \"cited_rulings\": \"['fe8a76b3-8b0f-4f27-a277-2d887140e7ab', '16fef75e-e8d5-4a51-8230-a9ca3676c8a9', '6d21b282-3b23-41dd-9350-6ba5386df9b1', '302fd9f3-e78a-4a9f-9f8d-cde51fcbdfe7']\",\n \"facts\": \"Sachverhalt: A. A._, geboren 1954, war ab November 2002 als Pflegehilfe im Altersheim C._ angestellt. Am 23. Dezember 2002 meldete sie sich erstmals unter Hinweis auf Depressionen ...\",\n \"considerations\": \"Erwägungen: 1. 1.1. Die Beschwerde kann wegen Rechtsverletzung gemäss Art. 95 und Art. 96 BGG erhoben werden. Das Bundesgericht wendet das ...\",\n \"rulings\": \"Demnach erkennt das Bundesgericht: 1. Die Beschwerde wird abgewiesen. 2. Die Gerichtskosten von Fr. 800.- werden der Beschwerdeführerin ...\",\n}\n```\n\n### Data Fields\n\n```\ndecision_id: (str) a unique identifier of the for the document\nlanguage: (str) one of (de, fr, it)\nyear: (int) the publication year\nchamber: (str) the chamber of the case\nregion: (str) the region of the case\norigin_chamber: (str) the chamber of the origin case\norigin_court: (str) the court of the origin case\norigin_canton: (str) the canton of the origin case\nlaw_area: (str) the law area of the case\nlaw_sub_area:(str) the law sub area of the case\nlaws: (str) a list of law ids\ncited rulings: (str) a list of cited rulings ids\nfacts: (str) the facts of the case\nconsiderations: (str) the considerations of the case\nrulings: (str) the rulings of the case\n```\n\n### Data Splits\n\nThe dataset was split date-stratisfied\n- Train: 2002-2015\n- Validation: 2016-2017\n- Test: 2018-2022\n\n| Language | Subset | Number of Documents (Training/Validation/Test) | \n|------------|------------|------------------------------------------------| \n| German | **de** | 86'832 (59'170 / 19'002 / 8'660) |\n| French | **fr** | 46'203 (30'513 / 10'816 / 4'874) |\n| Italian | **it** | 8'306 (5'673 / 1'855 / 778) |\n\n\n## Dataset Creation\n\n### Curation Rationale\n\nThe dataset was created by Stern et al. (2023).\n\n### Source Data\n\n#### Initial Data Collection and Normalization\n\nThe original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n#### Who are the source language producers?\n\nThe original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n### Annotations\n\n#### Annotation process\n\nThe decisions have been annotated with the citation ids using html tags and parsers.\nFor more details on laws (rcds/swiss_legislation) and rulings (rcds/swiss_rulings).\n\n#### Who are the annotators?\n\nStern annotated the citations.\nMetadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).\n\n### Personal and Sensitive Information\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.\n\n## Considerations for Using the Data\n\n### Social Impact of Dataset\n\n[More Information Needed]\n\n### Discussion of Biases\n\n[More Information Needed]\n\n### Other Known Limitations\n\n[More Information Needed]\n\n## Additional Information\n\n### Dataset Curators\n\n[More Information Needed]\n\n### Licensing Information\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)\n© Swiss Federal Supreme Court, 2002-2022\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf\n\n### Citation Information\n\n*Visu, Ronja, Joel*\n*Title: Blabliblablu*\n*Name of conference*\n```\ncit\n```\n\n### Contributions\n\nThanks to [@Stern5497](https://github.com/stern5497) for adding this dataset." |
false | # SAMSum
- Source: https://huggingface.co/datasets/samsum
- Num examples:
- 14,732 (train)
- 818 (validation)
- 819 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/samsum_en")
```
- Format for Dialog Summarization task
```python
def preprocess(sample):
dialogue = sample['dialogue']
summary = sample['summary']
return {'text': f'<|startoftext|><|dialogue|>{dialogue}<|summary|>{summary}<|endoftext|>'}
"""
<|startoftext|><|dialogue|>Amanda: I baked cookies. Do you want some?
Jerry: Sure!
Amanda: I'll bring you tomorrow :-)<|summary|>Amanda baked cookies and will bring Jerry some tomorrow.<|endoftext|>
"""
``` |
true |
### Dataset Summary
This dataset is a DeepL -based machine translated version of the Jigsaw toxicity dataset for Finnish. The dataset is originally from a Kaggle competition https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data.
The dataset poses a multi-label text classification problem and includes the labels `identity_attack`, `insult`, `obscene`, `severe_toxicity`, `threat` and `toxicity`.
#### Example data
```
{
"label_identity_attack": 0,
"label_insult": 0,
"label_obscene": 0,
"label_severe_toxicity": 0,
"label_threat": 0,
"label_toxicity": 0,
"lang": "fi-deepl",
"text": "\" \n\n Hei Pieter Pietersen, ja tervetuloa Wikipediaan! \n\n Tervetuloa Wikipediaan! Toivottavasti viihdyt tietosanakirjassa ja haluat jäädä tänne. Ensimmäiseksi voit lukea johdannon. \n\n Jos sinulla on kysyttävää, voit kysyä minulta keskustelusivullani - autan mielelläni. Tai voit kysyä kysymyksesi Uusien avustajien ohjesivulla. \n\n - \n Seuraavassa on lisää resursseja, jotka auttavat sinua tutkimaan ja osallistumaan maailman suurinta tietosanakirjaa.... \n\n Löydät perille: \n\n \n * Sisällysluettelo \n\n * Osastohakemisto \n\n \n Tarvitsetko apua? \n\n \n * Kysymykset - opas siitä, mistä voi esittää kysymyksiä. \n * Huijausluettelo - pikaohje Wikipedian merkintäkoodeista. \n\n * Wikipedian 5 pilaria - yleiskatsaus Wikipedian perustaan. \n * The Simplified Ruleset - yhteenveto Wikipedian tärkeimmistä säännöistä. \n\n \n Miten voit auttaa: \n\n \n * Wikipedian avustaminen - opas siitä, miten voit auttaa. \n\n * Yhteisöportaali - Wikipedian toiminnan keskus. \n\n \n Lisää vinkkejä... \n\n \n * Allekirjoita viestisi keskustelusivuilla neljällä tildillä (~~~~). Tämä lisää automaattisesti \"\"allekirjoituksesi\"\" (käyttäjänimesi ja päivämääräleima). Myös Wikipedian tekstinmuokkausikkunan yläpuolella olevassa työkalupalkissa oleva painike tekee tämän. \n\n * Jos haluat leikkiä uusilla Wiki-taidoillasi, Hiekkalaatikko on sinua varten. \n\n \n Onnea ja hauskaa. \""
}
```
### Data Fields
Fields marked as `label_` have either `0` to convey *not* having that category of toxicity in the text and `1` to convey having that category of toxicity present in the text.
- `label_identity_attack`: a `int64` feature.
- `label_insult`: a `int64` feature.
- `label_obscene`: a `int64` feature.
- `label_severe_toxicity`: a `int64` feature.
- `label_threat`: a `int64` feature.
- `label_toxicity`: a `int64` feature.
- `lang`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
The splits are the same as in the original English data.
| dataset | train | test |
| -------- | -----: | ---------: |
| TurkuNLP/jigsaw_toxicity_pred_fi| 159571 | 63978 |
### Evaluation Results
Results from fine-tuning [TurkuNLP/bert-large-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-large-finnish-cased-v1) for multi-label toxicity detection. The fine-tuned model can be found
| dataset | F1-micro | Precision | Recall |
| -------------------- | ----: | ---: | ----: |
| TurkuNLP/jigsaw_toxicity_pred_fi | 0.66 | 0.58 | 0.76 |
<!--- Base results from fine-tuning [bert-large-cased](https://huggingface.co/bert-large-cased) on the original English data for multi-label toxicity detection.
| dataset | F1-micro | Precision | Recall |
| -------------------- | ----: | ---: | ----: |
| jigsaw_toxicity_pred | 0.69 | 0.59 | 0.81 | --->
### Considerations for Using the Data
Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation
system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations
except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
### Licensing Information
Contents of this repository are distributed under the
[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Copyright of the dataset contents belongs to the original copyright holders. |
true |
## This is a dataset of Onion news articles:
Note
- The headers and body of the news article is split by a ' #~# ' token
- Lines with just the token had no body or no header and can be skipped
- Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K |
false |
# Dataset Card for 'ML Articles Subset of Scientific Papers' Dataset
## Dataset Summary
The dataset consists of 32,621 instances from the 'Scientific papers' dataset, a selection of scientific papers and summaries from ArXiv repository. This subset focuses on articles that are semantically, vocabulary-wise, structurally, and meaningfully closest to articles describing machine learning. This subset was created using sentence embeddings and K-means clustering.
## Supported Tasks and Leaderboards
The dataset supports tasks related to text summarization. Particularly, the dataset was created for fine-tuning transformer models for summarization. There are no established leaderboards at this moment.
## Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
An instance in the dataset includes a scientific paper and its summary, both in English.
### Data Fields
article: The full text of the scientific paper.\
abstract: The summary of the paper.
### Data Splits
The dataset is split into:\
-training subset: 30280 articles\
-validation subset: 1196 articles\
-test subset: 1145 articles
## Dataset Creation
### Methods
The subset was created using sentence embeddings from a transformer model, SciBERT. The embeddings were clustered into 6 clusters using the K-means clustering algorithm. The cluster closest to articles strongly related to the machine learning area by cosine similarity was chosen to form this dataset.
### Source Data
The dataset is a subset of the 'Scientific papers' dataset, which includes scientific papers from the ArXiv repository.
### Social Impact
This dataset could help improve the quality of summarization models for machine learning research articles, which in turn can make such content more accessible.
### Discussion of Biases
As the dataset focuses on machine learning articles, it may not be representative of scientific papers in general or other specific domains.
### Other Known Limitations
As the dataset has been selected based on a specific methodology, it may not include all machine learning articles or may inadvertently include non-machine learning articles.
### Dataset Curators
The subset was created as part of a project aimed to build an effective summarization model for Machine Learning articles. |
false |
## E smol Dataset
This is the card for e smol dataset |
false |
### Dataset Summary
First 10k rows of the scientific_papers["pubmed"] dataset. 10:1:1 split.
### Usage
```
from datasets import load_dataset
train_dataset = load_dataset("ronitHF/pubmed-10k", split="train")
val_dataset = load_dataset("ronitHF/pubmed-10k", split="validation")
test_dataset = load_dataset("ronitHF/pubmed-10k", split="test")
```
|
false | # Dataset Card for ner-wikinews-dataset
## データセットの概要
これは[Wikinews](https://ja.wikinews.org/wiki/%E3%83%A1%E3%82%A4%E3%83%B3%E3%83%9A%E3%83%BC%E3%82%B8)の記事に固有表現ラベルを付与したデータセットです。
固有表現ラベルは[ner-wikipedia-dataset](llm-book/ner-wikipedia-dataset)と同様のものを採用しており、全部で8種類 (人名、法人名、地名、製品名、政治的組織名、施設名、その他の組織名、イベント名)あります。
テストセットのみのデータセットとなっています。
## ライセンス
ウィキニュース日本語版の記事を使用しているため、そのライセンスに従い、「クリエイティブ・コモンズ 表示 2.5 (CC BY 2.5)」とします。
|
false |
# TempoFunk Small
7.8k samples of metadata and encoded latents & prompts of random videos.
## Data format
- Video frame latents
- Numpy arrays
- 120 frames, 512x512 source size
- Encoded shape (120, 4, 64, 64)
- CLIP (openai) encoded prompts
- Video description (as seen in metadata)
- Encoded shape (77,768)
- Video metadata as JSON (description, tags, categories, source URL, etc.) |
true |
## Misogynistic statements and their potential restructuring
Beta dataset
Generated by GPT3.5
Language: Spanish |
false |
# Dataset Card for [EDGAR-CORPUS]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [References](#references)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact: Lefteris Loukas**
### Dataset Summary
This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop.
This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings.
There is supported functionality to load a specific year.
Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits.
If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler.
### Supported Tasks
This is a raw dataset/corpus for financial NLP.
As such, there are no annotations or labels.
### Languages
The EDGAR Filings are in English.
## Dataset Structure
### Data Instances
Refer to the dataset preview.
### Data Fields
**filename**: Name of file on EDGAR from which the report was extracted.<br>
**cik**: EDGAR identifier for a firm.<br>
**year**: Year of report.<br>
**section_1**: Corressponding section of the Annual Report.<br>
**section_1A**: Corressponding section of the Annual Report.<br>
**section_1B**: Corressponding section of the Annual Report.<br>
**section_2**: Corressponding section of the Annual Report.<br>
**section_3**: Corressponding section of the Annual Report.<br>
**section_4**: Corressponding section of the Annual Report.<br>
**section_5**: Corressponding section of the Annual Report.<br>
**section_6**: Corressponding section of the Annual Report.<br>
**section_7**: Corressponding section of the Annual Report.<br>
**section_7A**: Corressponding section of the Annual Report.<br>
**section_8**: Corressponding section of the Annual Report.<br>
**section_9**: Corressponding section of the Annual Report.<br>
**section_9A**: Corressponding section of the Annual Report.<br>
**section_9B**: Corressponding section of the Annual Report.<br>
**section_10**: Corressponding section of the Annual Report.<br>
**section_11**: Corressponding section of the Annual Report.<br>
**section_12**: Corressponding section of the Annual Report.<br>
**section_13**: Corressponding section of the Annual Report.<br>
**section_14**: Corressponding section of the Annual Report.<br>
**section_15**: Corressponding section of the Annual Report.<br>
```python
import datasets
# Load the entire dataset
raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full")
# Load a specific year and split
year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train")
```
### Data Splits
| Config | Training | Validation | Test |
| --------- | -------- | ---------- | ------ |
| full | 176,289 | 22,050 | 22,036 |
| year_1993 | 1,060 | 133 | 133 |
| year_1994 | 2,083 | 261 | 260 |
| year_1995 | 4,110 | 514 | 514 |
| year_1996 | 7,589 | 949 | 949 |
| year_1997 | 8,084 | 1,011 | 1,011 |
| year_1998 | 8,040 | 1,006 | 1,005 |
| year_1999 | 7,864 | 984 | 983 |
| year_2000 | 7,589 | 949 | 949 |
| year_2001 | 7,181 | 898 | 898 |
| year_2002 | 6,636 | 830 | 829 |
| year_2003 | 6,672 | 834 | 834 |
| year_2004 | 7,111 | 889 | 889 |
| year_2005 | 7,113 | 890 | 889 |
| year_2006 | 7,064 | 883 | 883 |
| year_2007 | 6,683 | 836 | 835 |
| year_2008 | 7,408 | 927 | 926 |
| year_2009 | 7,336 | 917 | 917 |
| year_2010 | 7,013 | 877 | 877 |
| year_2011 | 6,724 | 841 | 840 |
| year_2012 | 6,479 | 810 | 810 |
| year_2013 | 6,372 | 797 | 796 |
| year_2014 | 6,261 | 783 | 783 |
| year_2015 | 6,028 | 754 | 753 |
| year_2016 | 5,812 | 727 | 727 |
| year_2017 | 5,635 | 705 | 704 |
| year_2018 | 5,508 | 689 | 688 |
| year_2019 | 5,354 | 670 | 669 |
| year_2020 | 5,480 | 686 | 685 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**.
#### Who are the source language producers?
Public firms filing with the SEC.
### Annotations
#### Annotation process
NA
#### Who are the annotators?
NA
### Personal and Sensitive Information
The dataset contains public filings data from SEC.
## Considerations for Using the Data
### Social Impact of Dataset
Low to none.
### Discussion of Biases
The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.
### Other Known Limitations
The dataset needs further cleaning for improved performance.
## Additional Information
### Licensing Information
EDGAR data is publicly available.
### Shoutout
Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup!
## Citation
If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic):
```
@inproceedings{loukas-etal-2021-edgar,
title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round",
author = "Loukas, Lefteris and
Fergadiotis, Manos and
Androutsopoulos, Ion and
Malakasiotis, Prodromos",
booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.econlp-1.2",
pages = "13--18",
}
```
### References
- [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021.
- [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021)
- [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021)
- [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021)
- [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022)
|
true | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
true | |
false |
# Dataset Card for ConvMix
## Dataset Description
- **Homepage:** [CompMix Website](https://qa.mpi-inf.mpg.de/compmix)
- **Point of Contact:** [Philipp Christmann](mailto:pchristm@mpi-inf.mpg.de)
### Dataset Summary
CompMix collates the completed versions of the conversational questions in the [ConvMix dataset](https://convinse.mpi-inf.mpg.de), that are provided directly by crowdworkers from Amazon Mechanical Turk (AMT). Questions in CompMix exhibit complex phenomena like the presence of multiple entities, relations, temporal conditions, comparisons, aggregations, and more. It is aimed at evaluating QA methods that operate over a mixture of heterogeneous input sources (KB, text, tables, infoboxes). The dataset has 9,410 questions, split into train (4,966 questions), dev (1,680), and test (2,764) sets. All answers provided in the CompMix dataset are grounded to the KB (except for dates which are normalized, and other literals like names).
Further details will be provided in a dedicated write-up soon.
### Dataset Creation
CompMix collates the completed versions of the conversational questions in ConvMix, that are provided directly by the crowdworkers.
The ConvMix benchmark, on which CompMix is based, was created by real humans. We tried to ensure that the collected data is as natural as possible. Master crowdworkers on Amazon Mechanical Turk (AMT) selected an entity of interest in a specific domain, and then started issuing conversational questions on this entity, potentially drifting to other topics of interest throughout the course of the conversation. By letting users choose the entities themselves, we aimed to ensure that they are more interested into the topics the conversations are based on. After writing a question, users were asked to find the answer in eithers Wikidata, Wikipedia text, a Wikipedia table or a Wikipedia infobox, whatever they find more natural for the specific question at hand. Since Wikidata requires some basic understanding of knowledge bases, we provided video guidelines that illustrated how Wikidata can be used for detecting answers, following an example conversation. For each conversational question, that might be incomplete, the crowdworker provides a completed question that is intent-explicit, and can be answered without the conversational context. These questions constitute the CompMix dataset. We provide also the answer source the user found the answer in and question entities.
|
false | |
false | # Dataset Card for "reason_code-search-net-python"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This dataset is an instructional dataset for Python.
The dataset contains five different kind of tasks.
Given a Python 3 function:
- Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
- Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
- Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
- Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
- Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor.").
### Languages
The dataset is in English.
### Data Splits
There are no splits (Only training).
## Dataset Creation
May of 2023
### Curation Rationale
This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs.
### Source Data
The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-python
### Annotations
The dataset includes an instruction, response and type columns.
The type colum indicates the type of task (from 1 to 5).
#### Annotation process
The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information).
### Licensing Information
Apache 2.0 |
false | ## Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts | [Paper](https://arxiv.org/abs/2303.17595)
Dongyoon Han<sup>1*</sup>, Junsuk Choe<sup>2*</sup>, Seonghyeok Chun<sup>3</sup>, John Joon Young Chung<sup>4</sup>
Minsuk Chang<sup>5</sup>, Sangdoo Yun<sup>1</sup>, Jean Y. Song<sup>6</sup>, Seong Joon Oh<sup>7†</sup>
<sub>\* Equal contribution</sub> <sub>†</sub> <sub> Corresponding author </sub>
<sup>1</sup> <sub>NAVER AI LAB</sub> <sup>2</sup> <sub>Sogang University</sub> <sup>3</sup> <sub>Dante Company</sub> <sup>4</sup> <sub>University of Michigan</sub> <sup>5</sup> <sub>NAVER AI LAB, currently at Google</sub> <sup>6</sup> <sub>DGIST</sub> <sup>7</sup> <sub>University of Tübingen</sub>
Supervised learning of image classifiers distills human knowledge into a parametric model *f* through pairs of images and corresponding labels (*X*,*Y*). We argue that this simple and widely used representation of human knowledge neglects rich auxiliary information from the annotation procedure, such as the time-series of mouse traces and clicks.
<p align=center>
<img src="https://user-images.githubusercontent.com/7447092/203720567-dc6e1277-84d2-439c-a9f8-879e31c04e6f.png" alt="imagenet-byproduct-sample" width=500px />
<p/>
Our insight is that such **annotation byproducts** *Z* provide approximate human attention that weakly guides the model to focus on the foreground cues, reducing spurious correlations and discouraging shortcut learning.
We have created **ImageNet-AB** and **COCO-AB** to verify this.
They are ImageNet and COCO training sets enriched with sample-wise annotation byproducts, collected by replicating the respective original annotation tasks.
We refer to the new paradigm of training models with annotation byproducts as **learning using annotation byproducts (LUAB)**.
<p align=center>
<img src="https://user-images.githubusercontent.com/7447092/203721515-2aea133d-1a77-4463-8372-5f0e0dbe4d2d.png" alt="luab" width=500px />
<p/>
We show that a simple multitask loss for regressing *Z* together with *Y* already improves the generalisability and robustness of the learned models. Compared to the original supervised learning, LUAB does not require extra annotation costs.
### Dataloader for ImageNet-AB and COCO-AB
We provide example dataloaders for the annotation byproducts.
* Dataloader for ImageNet-AB: [imagenet_dataloader.ipynb](imagenet_dataloader.ipynb)
* Dataloader for COCO-AB: [coco_dataloader.ipynb](coco_dataloader.ipynb)
### Annotation tools for ImageNet and COCO
* Annotation tool for ImageNet: [github.com/naver-ai/imagenet-annotation-tool](https://github.com/naver-ai/imagenet-annotation-tool)
* Annotation tool for COCO: [github.com/naver-ai/coco-annotation-tool](https://github.com/naver-ai/coco-annotation-tool)
### License
```
MIT License
Copyright (c) 2023-present NAVER Cloud Corp.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citing our work
```
@article{han2023arxiv,
title = {Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts},
author = {Han, Dongyoon and Choe, Junsuk and Chun, Seonghyeok and Chung, John Joon Young and Chang, Minsuk and Yun, Sangdoo and Song, Jean Y. and Oh, Seong Joon},
journal={arXiv preprint arXiv:2303.17595},
year = {2023}
}
``` |
true |
# Dataset Card for BeaverTails-Evaluation
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository contains test prompts specifically designed for evaluating language model safety.
It is important to note that although each prompt can be connected to multiple categories, only one category is labeled for each prompt.
The 14 harm categories are defined as follows:
1. **Animal Abuse:** This involves any form of cruelty or harm inflicted on animals, including physical abuse, neglect, or any behavior that causes distress or harm to an animal. It also includes content that promotes such behavior.
1. **Child Abuse:** This encompasses any form of physical, emotional, or sexual abuse directed toward children. It can also include neglect, exploitation, or any behavior that harms a child or violates their rights. Content that promotes or glorifies such behavior also falls under this category.
1. **Controversial Topics, Politics:** This refers to discussions or statements related to controversial topics, political ideologies, or sensitive issues that could lead to disagreement or conflict. These topics can be divisive and could potentially cause harm if not handled respectfully and thoughtfully.
1. **Discrimination, Stereotype, Injustice:** Discrimination refers to treating people unfairly or unequally based on their personal characteristics or affiliations such as race, age, gender, religion, etc. Stereotyping involves holding simplified and generalized beliefs about particular groups, which often leads to prejudices and discrimination. Injustice pertains to unfair treatment or violation of the rights of individuals or groups.
1. **Drug Abuse, Weapons, Banned Substance:** This category includes the misuse or abuse of illegal drugs, prescription drugs, or any other banned substances. It also involves the unauthorized or illegal possession, use, or trade of weapons.
1. **Financial Crime, Property Crime, Theft:** This includes all forms of illegal activities that lead to financial loss such as fraud, embezzlement, money laundering, bribery, and other forms of corruption. Property crime and theft involve the act of taking someone's property without their consent.
1. **Hate Speech, Offensive Language:** This category encompasses any form of communication or content that expresses intense hostility or violence towards an individual or group based on aspects like race, religion, ethnicity, nationality, gender, sexual orientation, or any other defining characteristics. This also includes language that is considered vulgar, crude, or offensive.
1. **Misinformation Regarding ethics, laws, and safety:** This category involves the spreading of incorrect or misleading information related to ethical issues, laws, regulations, or any matter related to safety. Misinformation can cause harm by influencing people's beliefs or actions negatively.
1. **Non-Violent Unethical Behavior:** This refers to any behavior that is morally or ethically wrong, but does not involve violence. Examples can include dishonest behaviors like lying, cheating, deceiving, spreading rumors, betraying trust, or any other form of unethical conduct.
1. **Privacy Violation:** This refers to actions that invade an individual's privacy, such as unauthorized access, use, disclosure, or collection of personal data. It also includes stalking, doxing, or any other form of intrusion into someone's personal life without their consent.
1. **Self-Harm:** This includes any form of self-inflicted harm, whether physical or emotional, or any content that promotes, glorifies, or encourages self-harm. This can also include discussions of suicidal thoughts or plans.
1. **Sexually Explicit, Adult Content:** This category includes any content that depicts explicit sexual activities or adult themes that are considered inappropriate or offensive. This can include pornography, explicit sexual text or messages, and any other form of sexually explicit material.
1. **Terrorism, Organized Crime:** This pertains to any form of content or action related to terrorism or organized crime, including endorsing or promoting terrorist activities, participating in organized criminal activities, or spreading propaganda for such groups.
1. **Violence, Aiding and Abetting, Incitement:** This involves any form of physical harm, threat, or violent behavior towards individuals or groups. Aiding and abetting refers to the act of helping, supporting, or encouraging such violent behaviors or illegal activities. Incitement pertains to the act of provoking or stirring up harmful, violent, or illegal actions.
**Disclaimer**: The BeaverTails dataset and its family contain content that may be offensive or upsetting.
Topics covered in the dataset include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially distressing subject matter.
Please engage with the dataset responsibly and in accordance with your own personal risk tolerance.
The dataset is intended for research purposes, specifically for research aimed at creating safer and less harmful AI systems.
The views and opinions expressed in the dataset do not represent the views of the PKU-Alignment Team or any of its members.
It is important to emphasize that the dataset should not be used for training dialogue agents, as doing so may likely result in harmful model behavior.
The primary objective of this dataset is to facilitate research that could minimize or prevent the harm caused by AI systems.
## Usage
The code snippet below demonstrates how to load the evaluation dataset:
```python
from datasets import load_dataset
# Load the whole dataset
dataset = load_dataset('PKU-Alignment/BeaverTails-Evaluation')
# Load only the v1 dataset
round0_dataset = load_dataset('PKU-Alignment/BeaverTails', data_dir='v1')
```
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails
## License
BeaverTails dataset and its family are released under the CC BY-NC 4.0 License.
|
false |
# Dataset Card for Time Series Extrinsic Regression
## Dataset Description
- **Homepage:** [Time Series Extrinsic Regression Repository](http://tseregression.org/)
- **Repository:** [GitHub code repository](https://github.com/ChangWeiTan/TS-Extrinsic-Regression/tree/master), [Raw data repository](https://zenodo.org/record/3902651)
- **Paper:** [Monash University, UEA, UCR Time Series Extrinsic Regression Archive](https://arxiv.org/abs/2006.10996)
- **Leaderboard:** [Baseline results](http://tseregression.org/#results)
- **Point of Contact:** [Stephen Fox](gh@stephenjfox.com)
### Dataset Summary
A collection of datasets from Monash, UEA, and UCR supporting research into Time Series Extrinsic Regression (TSER),
a regression task of which the aim is to learn the relationship between *a time series and a continuous scalar variable*.
This task is closely related to time series classification, where a single categorical variable is learned.
Please read the [paper](https://arxiv.org/abs/2006.10996) for more.
If you use the results or code, please cite the paper
**"Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb, Time Series Extrinsic Regression: Predicting numeric values from time series data"**.
(Full BibTex citation can be found at the end of this card).
(It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
## Dataset Structure
### Data Instances
A sample from the training set of Appliances Energy (a multivariate time series dataset) is provided.
The following is a single record from that dataset:
```python
{'start': Timestamp('2016-02-28 17:00:00'),
'feat_static_cat': 0,
'to_predict': 19.38,
'timeseries': array([[21.29 , 21.29 , 21.29 , ..., 21.79 ,
21.79 , 21.79 ],
[31.66666667, 31.92666667, 32.06 , ..., 33.66 ,
33.7 , 33.56666667],
[19.89 , 19.82333333, 19.79 , ..., 19.79 ,
19.79 , 19.79 ],
...,
[ 7. , 6.83333333, 6.66666667, ..., 5. ,
5. , 5. ],
[40. , 40. , 40. , ..., 40. ,
40. , 40. ],
[-4.2 , -4.16666667, -4.13333333, ..., -4.3 ,
-4.16666667, -4.03333333]]),
'item_id': 'item_000'}
```
### Data Fields
This format was loosely adapted from [the Gluon format](https://ts.gluon.ai/stable/getting_started/concepts.html)
and [the HF convention](https://github.com/huggingface/notebooks/blob/main/examples/time_series_datasets.ipynb)
also seen in the recent [series](https://huggingface.co/blog/time-series-transformers) of [Time Series Transformer notebooks](https://github.com/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb)
- `start`: a datetime of the first entry of each time series in the data record
- `feat_static_cat`: the original identifier given to this record
- `timeseries`: the timeseries itself
- `to_predict`: continuous variable to predict
- `item_id`: an identifier given to each record (for e.g. group-by style aggregations)
The `timeseries` field will be a single array in the univariate forecasting scenario, and a 2-D array in the multivariate scenario.
The `to_predict` will be a single number in most cases, or an array in a few instances (noted in the table above **TODO**).
### Data Splits
Train and test are temporally split (i.e. "train" is the past and "test" is the future) 70/30 whenever possible, though some datasets have more particular splits.
For details, see [the paper](https://arxiv.org/abs/2006.10996) and the particular dataset you are interested in. In our porting to HF Hub, we made as few changes as possible.
## Dataset Creation
While I (Stephen) did not create the original dataset, I took the initiative to put the data on Hugging Face Hub.
**Any grievances with the dataset should first and foremost be directed to me**.
### Curation Rationale
To facilitate the evaluation of global forecasting models that are predicting a single-point estimate in the future.
All datasets in the repository are intended for research purposes and to evaluate the performance of new TSER algorithms.
This
### Source Data
#### Initial Data Collection and Normalization
The origins of each dataset are articulated in [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9).
Minimal preprocess was applied to the dataset, as they are still in their `sktime`-compatible `.ts` format. (As far as Stephen is aware.)
#### Who are the source language producers?
The data comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)
### Annotations
#### Annotation process
Please see [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9) for the annotation aggregation propcess
#### Who are the annotators?
The annotation comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)
### 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
- [Chang Wei Tan](https://changweitan.com/)
- [Anthony Bagnall](https://www.uea.ac.uk/computing/people/profile/anthony-bagnall)
- [Christoph Bergmeir](https://research.monash.edu/en/persons/christoph-bergmeir)
- [Daniel Schmidt](https://research.monash.edu/en/persons/daniel-schmidt)
- [Eamonn Keogh](http://www.cs.ucr.edu/~eamonn/)
- [François Petitjean](https://www.francois-petitjean.com/)
- [Geoff Webb](http://i.giwebb.com/)
### Licensing Information
[GNU General Public License (GPL) 3](https://www.gnu.org/licenses/gpl-3.0.en.html)
### Citation Information
```tex
@article{
Tan2020TSER,
title={Time Series Extrinsic Regression},
author={Tan, Chang Wei and Bergmeir, Christoph and Petitjean, Francois and Webb, Geoffrey I},
journal={Data Mining and Knowledge Discovery},
pages={1--29},
year={2021},
publisher={Springer},
doi={https://doi.org/10.1007/s10618-021-00745-9}
}
```
### Contributions
[More Information Needed] |
false |
# Dataset Card for Science Fiction TV Show Plots Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Format](#format)
- [Using the Dataset with Hugging Face](#call-scifi)
- [Original Dataset Structure](#dataset-structure)
- [Files in _OriginalStoriesSeparated_ Directory](#original-stories)
- [Additional Information](#additional-information)
- [Citation](#citation)
- [Licensing](#licensing)
## Dataset Description
A collection of long-running (80+ episodes) science fiction TV show plot synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story".
Contains plot summaries from:
- Babylon 5 (https://babylon5.fandom.com/wiki/Main_Page) - 84 stories
- Doctor Who (https://tardis.fandom.com/wiki/Doctor_Who_Wiki) - 311 stories
- Doctor Who spin-offs - 95 stories
- Farscape (https://farscape.fandom.com/wiki/Farscape_Encyclopedia_Project:Main_Page) - 90 stories
- Fringe (https://fringe.fandom.com/wiki/FringeWiki) - 87 stories
- Futurama (https://futurama.fandom.com/wiki/Futurama_Wiki) - 87 stories
- Stargate (https://stargate.fandom.com/wiki/Stargate_Wiki) - 351 stories
- Star Trek (https://memory-alpha.fandom.com/wiki/Star_Trek) - 701 stories
- Star Wars books (https://starwars.fandom.com/wiki/Main_Page) - 205 stories, each book is a story
- Star Wars Rebels (https://starwarsrebels.fandom.com/wiki/Main_page) - 65 stories
- X-Files (https://x-files.fandom.com/wiki/Main_Page) - 200 stories
Total: 2276 stories
Dataset is "eventified" and generalized (see LJ Martin, P Ammanabrolu, X Wang, W Hancock, S Singh, B Harrison, and MO Riedl. Event Representations for Automated Story Generation with Deep Neural Nets, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. for details on these processes.) and split into train-test-validation sets—separated by story so that full stories will stay together—for converting events into full sentences.
---
### Format
| Dataset Split | Number of Stories in Split | Number of Sentences in Split |
| ------------- |--------------------------- |----------------------------- |
| Train | 1737 | 257,108 |
| Validation | 194 | 32,855 |
| Test | 450 | 30,938 |
#### Using the Dataset with Hugging Face
```
from datasets import load_dataset
#download and load the data
dataset = load_dataset('lara-martin/Scifi_TV_Shows')
#you can then get the individual splits
train = dataset['train']
test = dataset['test']
validation = dataset['validation']
```
Each split has 7 attributes (explained in more detail in the next section):
```
>>> print(train)
Dataset({
features: ['story_num', 'story_line', 'event', 'gen_event', 'sent', 'gen_sent', 'entities'],
num_rows: 257108
})
```
---
## Original Dataset Structure
* File names: scifi-val.txt, scifi-test.txt, & scifi-train.txt
* Each sentence of the stories are split into smaller sentences and the events are extracted.
* Each line of the file contains information about a single sentence, delimited by "|||". Each line contains, in order:
* The story number
* The line number (within the story)
* 5-tuple events in a list (subject, verb, direct object, modifier noun, preposition); e.g.,
``
[[u'Voyager', u'run', 'EmptyParameter', u'deuterium', u'out'], [u'Voyager', u'force', u'go', 'EmptyParameter', 'EmptyParameter'], [u'Voyager', u'go', 'EmptyParameter', u'mode', u'into']]
``
* generalized 5-tuple events in a list; events are generalized using WordNet and VerbNet; e.g.,
``
[['<VESSEL>0', 'function-105.2.1', 'EmptyParameter', "Synset('atom.n.01')", u'out'], ['<VESSEL>0', 'urge-58.1-1', u'escape-51.1-1', 'EmptyParameter', 'EmptyParameter'], ['<VESSEL>0', u'escape-51.1-1', 'EmptyParameter', "Synset('statistic.n.01')", u'into']]
``
* original sentence (These sentences are split to contain fewer events per sentence. For the full original sentence, see the OriginalStoriesSeparated directory.); e.g.,
``
The USS Voyager is running out of deuterium as a fuel and is forced to go into Gray mode.
``
* generalized sentence; only nouns are generalized (using WordNet); e.g.,
``
the <VESSEL>0 is running out of Synset('atom.n.01') as a Synset('matter.n.03') and is forced to go into Synset('horse.n.01') Synset('statistic.n.01').
``
* a dictionary of numbered entities by tag within the _entire story_ (e.g. the second entity in the "<ORGANIZATION>" list in the dictionary would be <ORGANIZATION>1 in the story above—index starts at 0); e.g.,
``
{'<ORGANIZATION>': ['seven of nine', 'silver blood'], '<LOCATION>': ['sickbay', 'astrometrics', 'paris', 'cavern', 'vorik', 'caves'], '<DATE>': ['an hour ago', 'now'], '<MISC>': ['selected works', 'demon class', 'electromagnetic', 'parises', 'mimetic'], '<DURATION>': ['less than a week', 'the past four years', 'thirty seconds', 'an hour', 'two hours'], '<NUMBER>': ['two', 'dozen', '14', '15'], '<ORDINAL>': ['first'], '<PERSON>': ['tom paris', 'harry kim', 'captain kathryn janeway', 'tuvok', 'chakotay', 'jirex', 'neelix', 'the doctor', 'seven', 'ensign kashimuro nozawa', 'green', 'lt jg elanna torres', 'ensign vorik'], '<VESSEL>': ['uss voyager', 'starfleet']}
``
### Files in _OriginalStoriesSeparated_ Directory
* Contains unedited, unparsed original stories scraped from the respective Fandom wikis.
* Each line is a story with sentences space-separated. After each story, there is a <EOS> tag on a new line.
* There is one file for each of the 11 domains listed above.
* These are currently not set up to be called through the Hugging Face API and must be extracted from the zip directly.
---
## Additional Information
### Citation
```
@inproceedings{Ammanabrolu2020AAAI,
title={Story Realization: Expanding Plot Events into Sentences},
author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl},
journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020},
volume={34},
number={05},
url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
}
```
---
### Licensing
The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ |
false |
# Dataset Card for CaSum
## 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
- **Paper:** [Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
- **Point of Contact:** [Ona de Gibert Bonet](mailto:ona.degibert@bsc.es)
### Dataset Summary
CaSum is a summarization dataset. It is extracted from a newswire corpus crawled from the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)). The corpus consists of 217,735 instances that are composed by the headline and the body.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 41.39.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
```
{
'summary': 'Mapfre preveu ingressar 31.000 milions d’euros al tancament de 2018',
'text': 'L’asseguradora llançarà la seva filial Verti al mercat dels EUA a partir de 2017 ACN Madrid.-Mapfre preveu assolir uns ingressos de 31.000 milions d'euros al tancament de 2018 i destinarà a retribuir els seus accionistes com a mínim el 50% dels beneficis del grup durant el període 2016-2018, amb una rendibilitat mitjana a l’entorn del 5%, segons ha anunciat la companyia asseguradora durant la celebració aquest divendres de la seva junta general d’accionistes. La firma asseguradora també ha avançat que llançarà la seva filial d’automoció i llar al mercat dels EUA a partir de 2017. Mapfre ha recordat durant la junta que va pagar més de 540 milions d'euros en impostos el 2015, amb una taxa impositiva efectiva del 30,4 per cent. La companyia també ha posat en marxa el Pla de Sostenibilitat 2016-2018 i el Pla de Transparència Activa, “que han de contribuir a afermar la visió de Mapfre com a asseguradora global de confiança”, segons ha informat en un comunicat.'
}
```
### Data Fields
- `summary` (str): Summary of the piece of news
- `text` (str): The text of the piece of news
### Data Splits
We split our dataset into train, dev and test splits
- train: 197,735 examples
- validation: 10,000 examples
- test: 10,000 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
We obtained each headline and its corresponding body of each news piece on the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) website and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences.
#### Who are the source language producers?
The news portal Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)).
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by MT4All CEF project and [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### BibTeX citation
If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
```bibtex
@misc{degibert2022sequencetosequence,
title={Sequence-to-Sequence Resources for Catalan},
author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
year={2022},
eprint={2202.06871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A] |
false |
# Dataset Card for Catalan Government Crawling
## 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://zenodo.org/record/5511667
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [ona.degibert@bsc.es](ona.degibert@bsc.es)
### Dataset Summary
The Catalan Government Crawling Corpus is a 39-million-token web corpus of Catalan built from the web. It has been obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government during September and October 2020. It consists of 39,117,909 tokens, 1,565,433 sentences and 71,043 documents. Documents are separated by single new lines. It is a subcorpus of the Catalan Textual Corpus.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
```
{
'text': 'Títol: Estudi de tres marededéus del bisbat de Solsona\nResponsables del projecte: Pep Paret conservador–restaurador de l\'Àrea de Pintura i Escultura sobre fusta del CRBMC\nL\'objecte d\'aquest est
udi és un millor coneixement de l\'estat de conservació del patrimoni moble català, en concret de tres escultures romàniques del bisbat de Solsona.\nEs du a terme un estudi científic de tres marededéus del bisb
at de Solsona: la Mare de Déu de Queralt, la Mare de Déu de Coaner i la Mare de Déu de la Quar.\nLes imatges originals són romàniques, però totes elles han patit modificacions estructurals...'
}
```
### Data Fields
- `text` (str): Text.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The corpus has been obtained by crawling the all the `.gencat.cat` domains during July 2020.
For preprocessing we used [Corpus-Cleaner](https://github.com/TeMU-BSC/corpus-cleaner-acl), a modular Python-based toolkit to clean raw text corpora through generator pipelines.
#### Who are the source language producers?
The data comes from the official Catalan Government websites.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from public web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
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.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
eprint={2107.07903},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
false |
# Dataset Card for ParlamentParla
## 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://zenodo.org/record/5541827
- **Repository:** https://github.com/CollectivaT-dev/ParlamentParla
- **Paper:** ParlamentParla: [A Speech Corpus of Catalan Parliamentary Sessions.](http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/2022.parlaclariniii-1.0.pdf#page=135)
- **Point of Contact:** [Baybars Kulebi](mailto:baybars.kulebi@bsc.es)
### Dataset Summary
This is the ParlamentParla speech corpus for Catalan prepared by Col·lectivaT. The audio segments were extracted from recordings the Catalan Parliament (Parlament de Catalunya) plenary sessions, which took place between 2007/07/11 - 2018/07/17. We aligned the transcriptions with the recordings and extracted the corpus. The content belongs to the Catalan Parliament and the data is released conforming their terms of use.
Preparation of this corpus was partly supported by the Department of Culture of the Catalan autonomous government, and the v2.0 was supported by the Barcelona Supercomputing Center, within the framework of Projecte AINA of the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya.
As of v2.0 the corpus is separated into 211 hours of clean and 400 hours of other quality segments. Furthermore, each speech segment is tagged with its speaker and each speaker with their gender. The statistics are detailed in the readme file.
### Supported Tasks and Leaderboards
The dataset can be used for:
- Language Modeling.
- Automatic Speech Recognition (ASR) transcribes utterances into words.
- 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.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
```
{
'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav'
'audio': {
'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav',
'array': array([-6.10351562e-05, -6.10351562e-05, -1.22070312e-04, ...,
-1.22070312e-04, 0.00000000e+00, -3.05175781e-05]),
'sampling_rate': 16000
},
'speaker_id': 167,
'sentence': "alguns d'ells avui aquí presents un agraïment a aquells que mantenen viva la memòria aquest acte de reparació i dignitat és",
'gender': 0,
'duration': 10.18
}
```
### Data Fields
- `path` (str): The path to the audio file.
- `audio` (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
take a significant amount of time. Thus, it is important to first query the sample index before the `"audio"` column,
*i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `speaker_id` (int): The speaker ID.
- `sentence` (str): The sentence the user was prompted to speak.
- `gender` (ClassLabel): The gender of the speaker (0: 'F', 1: 'M').
- `duration` (float): Duration of the speech.
### Data Splits
The dataset is split in: "train", "validation" and "test".
## Dataset Creation
The dataset is created by aligning the parliamentary session transcripts
and the audiovisual content. For more detailed information please consult
this [paper](http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/2022.parlaclariniii-1.0.pdf#page=135).
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The audio segments were extracted from recordings the Catalan Parliament
(Parlament de Catalunya) plenary sessions, which took place between 2007/07/11 -
2018/07/17. The cleaning procedures are in the archived repository [Long Audio
Aligner](https://github.com/gullabi/long-audio-aligner)
#### Who are the source language producers?
The parliamentary members of the legislatures between 2007/07/11 -
2018/07/17
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The initial content is publicly available furthermore, the identities of
the parliamentary members are anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in
Catalan, a low-resource language.
### Discussion of Biases
This dataset has a gender bias, however since the speakers are tagged according to
their genders, creating a balanced subcorpus is possible.
| Subcorpus | Gender | Duration (h) |
|-------------|----------|------------|
| other_test | F | 2.516 |
| other_dev | F | 2.701 |
| other_train | F | 109.68 |
| other_test | M | 2.631 |
| other_dev | M | 2.513 |
| other_train | M | 280.196 |
|*other total*| | 400.239 |
| clean_test | F | 2.707 |
| clean_dev | F | 2.576 |
| clean_train | F | 77.905 |
| clean_test | M | 2.516 |
| clean_dev | M | 2.614 |
| clean_train | M | 123.162 |
|*clean total*| | 211.48 |
|*Total* | | 611.719 |
### Other Known Limitations
The text corpus belongs to the domain of Catalan politics
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@dataset{kulebi_baybars_2021_5541827,
author = {Külebi, Baybars},
title = {{ParlamentParla - Speech corpus of Catalan
Parliamentary sessions}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {v2.0},
doi = {10.5281/zenodo.5541827},
url = {https://doi.org/10.5281/zenodo.5541827}
}
```
For the paper:
```
@inproceedings{kulebi2022parlamentparla,
title={ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions},
author={K{\"u}lebi, Baybars and Armentano-Oller, Carme and Rodr{\'\i}guez-Penagos, Carlos and Villegas, Marta},
booktitle={Workshop on Creating, Enriching and Using Parliamentary Corpora},
volume={125},
number={130},
pages={125},
year={2022}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
false |
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802)
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
Github repo: https://github.com/vipulraheja/IteraTeR
|
false |
# Corpus SCJN NER, para el reconocimiento de entidades nombradas
En su primera versión contiene etiquetas para identificar leyes y tratados internacionales de los que el Estado Mexicano es parte.
## Dataset Structure
### Data Instances
Un ejemplo de 'train' se ve de la siguiente forma:
```
{
'id': '3',
'ner_tags': [0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'tokens': ['el', 'artículo', '15', 'de', 'la', 'ley', 'general', 'de', 'títulos', 'y', 'operaciones', 'de', 'crédito', 'exige', 'que', 'se', 'satisfagan', 'las', 'expresiones', 'omitidas', 'en', 'el', 'título', ',', 'antes', 'de', 'la', 'presentación', 'de', 'éste', 'para', 'su', 'aceptación', 'o', 'para', 'su', 'pago', '.', 'aunque', 'varios', 'autores', 'estiman', 'que', 'el', 'tenedor', 'puede', 'completar', 'los', 'requisitos', 'faltantes', 'a', 'la', 'cambial', ',', 'en', 'cualquier', 'instante', 'anterior', 'a', 'su', 'vencimiento', ',', 'este', 'criterio', 'no', 'es', 'aplicable', 'frente', 'a', 'la', 'disposición', 'terminante', 'de', 'la', 'ley', 'mexicana', ';', 'y', 'si', 'nuestro', 'legislador', 'hubiera', 'aceptado', 'la', 'posibilidad', 'de', 'llenar', 'los', 'requisitos', 'en', 'cualquier', 'momento', ',', 'hasta', 'antes', 'de', 'la', 'presentación', 'del', 'documento', 'para', ',', 'el', 'pago', ',', 'no', 'habría', 'hablado', 'de', 'la', 'presentación', 'para', 'la', 'aceptación', ';', 'máxime', ',', 'que', 'mientras', 'todas', 'las', 'letras', 'de', 'cambio', 'son', 'susceptibles', 'de', 'pago', ',', 'no', 'todas', 'lo', 'son', 'de', 'aceptación', '.', 'la', 'cambial', 'en', 'blanco', 'bien', 'puede', 'existir', 'y', 'circular', 'antes', 'de', 'que', 'sea', 'presentada', 'para', 'su', 'aceptación', ';', 'pero', 'cuando', 'ya', 'el', 'tenedor', 'va', 'a', 'hacer', 'valer', 'sus', 'derechos', '(', 'y', 'la', 'presentación', 'para', 'la', 'aceptación', 'es', 'el', 'ejercicio', 'de', 'uno', 'de', 'ellos', ')', ',', 'debe', 'llenar', 'los', 'extremos', 'necesarios', 'y', 'presentar', 'un', 'documento', 'completo', '.', 'cuando', 'el', 'girado', ',', 'al', 'aceptar', 'la', 'letra', ',', 'se', 'muestra', 'conforme', 'en', 'que', 'después', 'se', 'llene', 'la', 'expresión', 'de', 'su', 'importe', ',', 'ello', 'no', 'le', 'reporta', 'perjuicio', ',', 'si', 'el', 'beneficiario', 'lo', 'hace', 'dentro', 'de', 'los', 'límites', 'convenidos', ';', 'más', 'si', 'éste', 'se', 'excede', 'en', 'la', 'expresión', 'de', 'la', 'cantidad', 'convenida', ',', 'el', 'girado', 'sí', 'recibe', 'perjuicio', 'considerable', ',', 'ya', 'que', 'a', 'pesar', 'de', 'que', 'pueda', 'válidamente', 'oponer', 'las', 'excepciones', 'de', 'dolo', 'y', 'plus', 'petitio', 'correspondientes', ',', 'frente', 'al', 'beneficiario', 'que', 'violó', 'lo', 'pactado', ',', 'no', 'podrá', 'hacerlo', 'si', 'el', 'tenedor', 'es', 'un', 'tercero', 'que', 'de', 'buena', 'fe', 'adquirió', 'el', 'documento', ',', 'ignorando', 'las', 'circunstancias', 'precedentes', ';', 'en', 'cambio', ',', 'si', 'de', 'acuerdo', 'con', 'lo', 'preceptuado', 'por', 'nuestra', 'ley', ',', 'falta', 'el', 'título', 'de', 'crédito', ',', 'pues', 'el', 'documento', 'cuyos', 'requisitos', 'omitidos', 'no', 'se', 'satisficieron', 'oportunamente', ',', 'no', 'produce', 'efectos', 'como', 'tal', '(', 'artículo', '14', 'de', 'la', 'ley', 'de', 'la', 'materia', ')', ',', 'ésta', 'será', 'excepción', 'que', ',', 'demostrada', ',', 'puede', 'ser', 'oponible', 'a', 'cualquier', 'tenedor', ',', 'es', 'decir', ',', 'ya', 'no', 'será', 'una', 'excepción', 'personal', ',', 'sino', 'una', 'excepción', 'real', '.']
}
```
### Data Fields
Los campos son los mismos para todos los splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-LEY': 1, 'I-LEY': 2, 'B-TRAT_INTL': 3, 'I-TRAT_INTL': 4}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|SCJNNER|1396|345|0|
## Dataset Creation
### Annotations
| annotations|train|validation|test|
|---------|----:|---------:|---:|
|LEY|1084|329|0|
|TRAT_INTL|935|161|0|
### Dataset Curators
Ana Gabriela Palomeque Ortiz, from SCJN - Unidad General de Administración del Conocimiento Jurídico.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Other Known Limitations
La información contenida en este dataset es para efectos demostrativos y no representa una fuente oficial de la Suprema Corte de Justicia de la Nación.
## License
<br/>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/deed.es">Attribution-ShareAlike 4.0 International License</a>.
|
true | # AutoTrain Dataset for project: security-texts-classification-distilroberta
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project security-texts-classification-distilroberta.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Netgear launches Bug Bounty Program for Hacker; Offering up to $15,000 in Rewards It might be the ea[...]",
"target": 0
},
{
"text": "Popular Malware Families Using 'Process Doppelg\u00e4nging' to Evade Detection The fileless code injectio[...]",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['irrelevant', 'relevant'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 780 |
| valid | 196 |
|
false |
# TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
## Important
**This is a copy from the Zenodo Original one**
## Description
[Audio Research Group / Tampere University](https://webpages.tuni.fi/arg/)
AUTHORS
**Tampere University**
- Archontis Politis ([contact](mailto:archontis.politis@tuni.fi), [profile](https://scholar.google.fi/citations?user=DuCqB3sAAAAJ&hl=en))
- Sharath Adavanne ([contact](mailto:sharath.adavanne@tuni.fi), [profile](https://www.aane.in))
- Tuomas Virtanen ([contact](mailto:tuomas.virtanen@tuni.fi), [profile](https://homepages.tuni.fi/tuomas.virtanen/))
**Data Collection 2019-2020**
- Archontis Politis
- Aapo Hakala
- Ali Gohar
**Data Collection 2017-2018**
- Sharath Adavanne
- Aapo Hakala
- Eemi Fagerlund
- Aino Koskimies
The **TAU Spatial Room Impulse Response Database (TAU-SRIR DB)** database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:
- Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).
- Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.
- Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.
- Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.
The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement [637422 EVERYSOUND](https://cordis.europa.eu/project/id/637422).
[](https://erc.europa.eu/)
> **NOTE**: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.
## Report and reference
A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:
>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). **A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection**. In _Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)_, Tokyo, Japan.
available [here](https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf). A more detailed report specifically focusing on the dataset collection and properties will follow.
## Aim
The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:
- monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions,
- monophonic and multichannel polyphonic sound events in multi-room reverberant conditions,
- single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios,
- single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios,
- sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios.
## Specifications
The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.
The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:
1. Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise.
2. Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms.
3. Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise.
4. Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise.
5. Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise.
6. Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise.
7. Large classroom (SE203) with hard floor and rows of desks. Ventilation noise.
8. Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise.
9. Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise.
The measurement trajectories were organized in groups, with each group being specified by a circular or linear trace at the floor at a certain distance (range) from the z-axis of the microphone. For circular trajectories two ranges were measured, a _close_ and a _far_ one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, _close_ and _far_, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measurd resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.
Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights.
The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. This extraction scheme instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm) was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.
The following table summarizes the above properties for the currently available rooms:
| | Room name | Room type | Traj. type | # ranges | # trajectory groups | # heights/group | # trajectories (total) | # RIRs/DOAs |
|---|--------------------------|----------------------------|------------|-------------|-----------------------|---------------------|------------------------|-------------|
| 1 | Bomb shelter | Complex/semi-open | Circular | 2 | 2 | 9 | 18 | 6480 |
| 2 | Gym | Rectangular/large | Circular | 2 | 2 | 9 | 18 | 6480 |
| 3 | PB132 Meeting room | Rectangular/small | Circular | 2 | 2 | 9 | 18 | 6480 |
| 4 | PC226 Meeting room | Rectangular/small | Circular | 2 | 2 | 9 | 18 | 6480 |
| 5 | SA203 Lecture hall | Trapezoidal/large | Linear | 3 | 6 | 3 | 18 | 1594 |
| 6 | SC203 Classroom | Rectangular/medium | Linear | 2 | 4 | 5 | 20 | 1592 |
| 7 | SE203 Classroom | Rectangular/large | Linear | 2 | 4 | 4 | 16 | 1760 |
| 8 | TB103 Classroom | Trapezoidal/large | Linear | 2 | 4 | 3 | 12 | 1184 |
| 9 | TC352 Meeting room | Rectangular/small | Circular | 1 | 2 | 9 | 18 | 6480 |
More details on the trajectory geometries can be found in the database info file (`measinfo.mat`).
## Recording formats
The array response of the two recording formats can be considered known. The following theoretical spatial responses (steering vectors) modeling the two formats describe the directional response of each channel to a source incident from direction-of-arrival (DOA) given by azimuth angle $\phi$ and elevation angle $\theta$.
**For the first-order ambisonics (FOA):**
\begin{eqnarray}
H_1(\phi, \theta, f) &=& 1 \\
H_2(\phi, \theta, f) &=& \sin(\phi) * \cos(\theta) \\
H_3(\phi, \theta, f) &=& \sin(\theta) \\
H_4(\phi, \theta, f) &=& \cos(\phi) * \cos(\theta)
\end{eqnarray}
The (FOA) format is obtained by converting the 32-channel microphone array signals by means of encoding filters based on anechoic measurements of the Eigenmike array response. Note that in the formulas above the encoding format is assumed frequency-independent, something that holds true up to around 9kHz with the specific microphone array, while the actual encoded responses start to deviate gradually at higher frequencies from the ideal ones provided above. Routines that can compute the matrix of encoding filters for spherical and general arrays, based on theoretical array models or measurements, can be found [here](https://github.com/polarch/Spherical-Array-Processing).
**For the tetrahedral microphone array (MIC):**
The four microphone have the following positions, in spherical coordinates $(\phi, \theta, r)$:
\begin{eqnarray}
M1: &\quad(&45^\circ, &&35^\circ, &4.2\mathrm{cm})\nonumber\\
M2: &\quad(&-45^\circ, &-&35^\circ, &4.2\mathrm{cm})\nonumber\\
M3: &\quad(&135^\circ, &-&35^\circ, &4.2\mathrm{cm})\nonumber\\
M4: &\quad(&-135^\circ, &&35^\circ, &4.2\mathrm{cm})\nonumber
\end{eqnarray}
Since the microphones are mounted on an acoustically-hard spherical baffle, an analytical expression for the directional array response is given by the expansion:
\begin{equation}
H_m(\phi_m, \theta_m, \phi, \theta, \omega) = \frac{1}{(\omega R/c)^2}\sum_{n=0}^{30} \frac{i^{n-1}}{h_n'^{(2)}(\omega R/c)}(2n+1)P_n(\cos(\gamma_m))
\end{equation}
where $m$ is the channel number, $(\phi_m, \theta_m)$ are the specific microphone's azimuth and elevation position, $\omega = 2\pi f$ is the angular frequency, $R = 0.042$m is the array radius, $c = 343$m/s is the speed of sound, $\cos(\gamma_m)$ is the cosine angle between the microphone and the DOA, and $P_n$ is the unnormalized Legendre polynomial of degree $n$, and $h_n'^{(2)}$ is the derivative with respect to the argument of a spherical Hankel function of the second kind. The expansion is limited to 30 terms which provides negligible modeling error up to 20kHz. Example routines that can generate directional frequency and impulse array responses based on the above formula can be found [here](https://github.com/polarch/Array-Response-Simulator).
## Reference directions-of-arrival
For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.
The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.
## Scene generator
A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is the same as the one used to generate the [**TAU-NIGENS Spatial Sound Events 2021**](https://doi.org/10.5281/zenodo.5476980) dataset, and has been ported to Python from the original version written in Matlab.
The generator can be found [**here**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use.
The generator at the moment is set to work with the [NIGENS](https://zenodo.org/record/2535878) sound event sample database, and the [FSD50K](https://zenodo.org/record/4060432) sound event database, but additional sample banks can be added with small modifications.
The dataset together with the generator has been used by the authors in the following public challenges:
- [DCASE 2019 Challenge Task 3](https://dcase.community/challenge2019/task-sound-event-localization-and-detection), to generate the **TAU Spatial Sound Events 2019** dataset ([development](https://doi.org/10.5281/zenodo.2599196)/[evaluation](https://doi.org/10.5281/zenodo.3377088))
- [DCASE 2020 Challenge Task 3](https://dcase.community/challenge2020/task-sound-event-localization-and-detection), to generate the [**TAU-NIGENS Spatial Sound Events 2020**](https://doi.org/10.5281/zenodo.4064792) dataset
- [DCASE2021 Challenge Task 3](https://dcase.community/challenge2021/task-sound-event-localization-and-detection), to generate the [**TAU-NIGENS Spatial Sound Events 2021**](https://doi.org/10.5281/zenodo.5476980) dataset
- [DCASE2022 Challenge Task 3](https://dcase.community/challenge2022/task-sound-event-localization-and-detection), to generate additional [SELD synthetic mixtures for training the task baseline](https://doi.org/10.5281/zenodo.6406873)
> **NOTE**: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact [daniel.krause@tuni.fi](mailto:daniel.krause@tuni.fi), or [archontis.politis@tuni.fi](mailto:archontis.politis@tuni.fi).
## Dataset structure
The dataset contains a folder of the SRIRs (`TAU-SRIR_DB`), with all the SRIRs per room in a single _mat_ file, e.g. `rirs_09_tb103.mat`. The specific room had 4 trajectory groups measured at 3 different heights, hence the mat file contains an `rirs` array of 4x3 structures, each with the fields `mic` and `foa`. Selecting e.g. the 2nd trajectory and 3rd height with `rirs(2,3)` returns `mic` and `foa` fields with an array of size `[7200x4x114]` on each. The array contains the SRIRs for the specific format, and it is arranged as `[samples x channels x DOAs]`, meaning that 300msec long (7200samples@24kHz) 4 channel RIRs are extracted at 114 positions along that specific trajectory.
The file `rirdata.mat` contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. Those can be found in the `rirdata.room` field, which is an array of 9 structures itself, one per room. Checking for example `rirdata.room(8)` returns the name of the specific room (_tb103_), the year the measurements were done, the numbers of SRIRs extracted for each trajectory, and finally the DOAs of the extracted SRIRs. The DOAs of a certain trajectory can be retrieved as e.g. `rirdata.room(8).rirs(2,3).doa_xyz` which returns an array of size `[114x3]`. These are the DOAs of the 114 SRIRs retrieved in the previous step for the 2nd trajectory, 3rd source height, of room `TB103`.
The file `measinfo.mat` contains measurement and recording information in each room. Those details are the name of each room, its dimensions for rectangular or trapezoidal shapes, start and end positions for the linear trajectories, or distances from center for the circular ones, the source heights for each trajectory group, the target formats, the trajectory type, the recording device, the A-weighted ambient sound pressure level, and the maximum and minimum A-weighted sound pressure level of the measurement noise signal. Coordinates are defined with respect to the origina being at the base of the microphone. Based on the information included in the `measinfo.mat`, one can plot a 3D arrangement of the trajectories around the microphone, even though keep in mind that these would be the ideal circular or linear intended trajectories, while the actual DOAs obtained from acoustic analysis have some deviations around those ideal paths.
Finally, the dataset contains a folder of spatial ambient noise recordings (`TAU-SNoise_DB`), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. The recordings vary in length between rooms, ranging from about 20 mins to 30 mins. Users of the dataset can segment these recordings and add them to spatialized sound samples at desired SNRs, or mix different segments to augment the recordings to additional ambience than the original recording time. Such a use case is demonstrated in the scene generator examples.
## Download
The files `TAU-SRIR_DB.z01`, ..., `TAU-SRIR_DB.zip` contain the SRIRs and measurement info files.
The files `TAU-SNoise_DB.z01`, ..., `TAU-SNoise_DB.zip` contain the ambient noise recordings.
Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:
Combine the split archive to a single archive:
>zip -s 0 split.zip --out single.zip
Extract the single archive using unzip:
>unzip single.zip
# License
The database is published under a custom **open non-commercial with attribution** license. It can be found in the `LICENSE.txt` file that accompanies the data.
|
true |
# Dataset Card for UK Selective Web Archive Classification Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The dataset comprises a manually curated selective archive produced by UKWA which includes the classification of sites into a two-tiered subject hierarchy. In partnership with the Internet Archive and JISC, UKWA had obtained access to the subset of the Internet Archives web collection that relates to the UK. The JISC UK Web Domain Dataset (1996 - 2013) contains all of the resources from the Internet Archive that were hosted on domains ending in .uk, or that are required in order to render those UK pages. UKWA have made this manually-generated classification information available as an open dataset in Tab Separated Values (TSV) format. UKWA is particularly interested in whether high-level metadata like this can be used to train an appropriate automatic classification system so that this manually generated dataset may be used to partially automate the categorisation of the UKWAs larger archives. UKWA expects that an appropriate classifier might require more information about each site in order to produce reliable results, and a future goal is to augment this dataset with further information. Options include: for each site, making the titles of every page on that site available, and for each site, extract a set of keywords that summarise the site, via the full-text index. For more information: http://data.webarchive.org.uk/opendata/ukwa.ds.1/classification/
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Creative Commons Public Domain Mark 1.0.
### Citation Information
[Needs More Information] |
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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
List of lottiefiles uri for research purposes
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
true |
# Dataset Card for CogText PubMed Abstracts
## 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
**CogText** dataset contains a collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings. See [CogText on GitHub](https://github.com/morteza/cogtext) for the details and codes.
- **Homepage:** https://github.com/morteza/cogtext
- **Repository:** https://github.com/morteza/cogtext
- **Point of Contact:** [Morteza Ansarinia](mailto:ansarinia@me.com)
- **Paper:** https://arxiv.org/abs/2203.11016
### Dataset Summary
The dataset consists of 385,705 unique scientific articles that were retrieved from PubMed in December 2021. Each item includes title, abstract, some metadata,
and embeddings generated by both GPT-3 and Top2Vec. These texts were selected based on their relevance to the cognitive control constructs or related tasks.
### Supported Tasks and Leaderboards
Topic Modeling, Text Embedding
### Languages
English
## Dataset Structure
### Data Instances
522,972 scientific articles, of which 385,705 are unique.
### Data Fields
The CSV files contain the following fields:
| Field | Description |
| ----- | ----------- |
| `index` | (int) Index of the article in the current dataset |
| `pmid` | (int) PubMed ID |
| `doi` | (str) Digital Object Identifier |
| `year` | (int) Year of publication (yyyy format)|
| `journal_title` | (str) Title of the journal |
| `journal_iso_abbreviation` | (str) ISO abbreviation of the journal |
| `title` | (str) Title of the article |
| `abstract` | (str) Abstract of the article |
| `category` | (enum) Category of the article, either "CognitiveTask" or "CognitiveConstruct" |
| `label` | (enum) Label of the article, which refers to the class labels in the `ontologies/efo.owl` ontology |
| `original_index` | (int) Index of the article in the full dataset (see `pubmed/abstracts.csv.gz`) |
### Data Splits
| Dataset | Description |
| ------- | ----------- |
| `pubmed/abstracts.csv.gz` | Full dataset |
| `pubmed/abstracts20pct.csv.gz` | 20% of the dataset (stratified random sample by `label`) |
| `gpt3/abstracts_gp3ada.nc` | GPT-3 embeddings of the entire dataset in XArray/CDF4 format, indexed by `pmid` |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
### Annotations
#### Annotation process
[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]
### Acknowledgments
This research was supported by the Luxembourg National Research Fund (ATTRACT/2016/ID/11242114/DIGILEARN and INTER Mobility/2017-2/ID/11765868/ULALA).
### Citation Information
To cite the paper use the following entry:
```
@misc{cogtext2022,
author = {Morteza Ansarinia and
Paul Schrater and
Pedro Cardoso-Leite},
title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
year = {2022},
url = {https://arxiv.org/abs/2203.11016}
}
``` |
false |
# Dataset Card for ActivityNet Captions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://cs.stanford.edu/people/ranjaykrishna/densevid/
- **Paper:** https://arxiv.org/abs/1705.00754
### Dataset Summary
The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions. Each sentence covers an unique segment of the video, describing multiple events that occur. These events may occur over very long or short periods of time and are not limited in any capacity, allowing them to co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in a total of 100k sentences. We find that the number of sentences per video follows a relatively normal distribution. Furthermore, as the video duration increases, the number of sentences also increases. Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials in the paper.
### Languages
The captions in the dataset are in English.
## Dataset Structure
### Data Fields
- `video_id` : `str` unique identifier for the video
- `video_path`: `str` Path to the video file
-`duration`: `float32` Duration of the video
- `captions_starts`: `List_float32` List of timestamps denoting the time at which each caption starts
- `captions_ends`: `List_float32` List of timestamps denoting the time at which each caption ends
- `en_captions`: `list_str` List of english captions describing parts of the video
### Data Splits
| |train |validation| test | Overall |
|-------------|------:|---------:|------:|------:|
|# of videos|10,009 |4,917 |4,885 |19,811 |
### Annotations
Quoting [ActivityNet Captions' paper](https://arxiv.org/abs/1705.00754): \
"Each annotation task was divided into two steps: (1)
Writing a paragraph describing all major events happening
in the videos in a paragraph, with each sentence of the paragraph describing one event, and (2) Labeling the
start and end time in the video in which each sentence in the
paragraph event occurred."
### Who annotated the dataset?
Amazon Mechnical Turk annotators
### Personal and Sensitive Information
Nothing specifically mentioned in the paper.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@InProceedings{tgif-cvpr2016,
@inproceedings{krishna2017dense,
title={Dense-Captioning Events in Videos},
author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos},
booktitle={International Conference on Computer Vision (ICCV)},
year={2017}
}
```
### Contributions
Thanks to [@leot13](https://github.com/leot13) for adding this dataset. |
false | # Schutz 2008 PubMed dataset for keyphrase extraction
## About
This dataset is made of 1320 articles with full text and author assigned keyphrases.
Details about the dataset can be found in the original paper:
Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods. Alexander Thorsten Schutz. Master's thesis, National University of Ireland (2008).
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
## Content
The details of the dataset are in the table below:
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: |
| Test | 1320 | 5.40 | 84.54 | 9.14 | 3.84 | 2.47 |
The following data fields are available:
- **id**: unique identifier of the document.
- **title**: title of the document.
- **text**: full article minus the title.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
|
true | ### Dataset Summary
The dataset contains user reviews about medical facilities.
In total it contains 70,597 reviews. The detailed distribution on sentiment scale is:
- 41,419 positive reviews;
- 29,178 negative reviews.
### Data Fields
Each sample contains the following fields:
- **review_id**;
- **category** category of medical facility (one of 48);
- **title**: review title;
- **content**: review text;
- **sentiment**: sentiment (<em>positive</em> or <em>negative</em>);
- **source_url**.
### Python
```python3
import pandas as pd
df = pd.read_json('healthcare_facilities_reviews.jsonl', lines=True)
df.sample(5)
```
|
false |
# Dataset Card for lccc_large
## Table of Contents
- [Dataset Card for lccc_large](#dataset-card-for-lccc_large)
- [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)
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CDial-GPT
- **Repository:** https://github.com/thu-coai/CDial-GPT
- **Paper:** https://arxiv.org/abs/2008.03946
### Dataset Summary
lccc: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
lccc是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
LCCC is in Chinese
LCCC中的对话是中文的
## Dataset Structure
### Data Instances
["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"]
### Data Fields
Each line is a list of utterances that consist a dialogue.
Note that the LCCC dataset provided in our original Github page is in json format,
however, we are providing LCCC in jsonl format here.
### Data Splits
We do not provide the offical split for LCCC-large.
But we provide a split for LCCC-base:
|train|valid|test|
|:---:|:---:|:---:|
|6,820,506 | 20,000 | 10,000|
## 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
Please cite the following paper if you find this dataset useful:
```bibtex
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
|
false |
# Dataset Card for Biwi Kinect Head Pose Database
## 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:** [Biwi Kinect Head Pose homepage](https://icu.ee.ethz.ch/research/datsets.html)
- **Repository:** [Needs More Information]
- **Paper:** [Biwi Kinect Head Pose paper](https://link.springer.com/article/10.1007/s11263-012-0549-0)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Gabriele Fanelli](mailto:gabriele.fanelli@gmail.com)
### Dataset Summary
The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.It contains 15K images of 20 people with 6 females and 14 males where 4 people were recorded twice.
For each frame, there is :
- a depth image,
- a corresponding rgb image (both 640x480 pixels),
- annotation
The head pose range covers about +-75 degrees yaw and +-60 degrees pitch. The ground truth is the 3D location of the head and its rotation.
### Data Processing
Example code for reading a compressed binary depth image file provided by the authors.
<details>
<summary> View C++ Code </summary>
```cpp
/*
* Gabriele Fanelli
*
* fanelli@vision.ee.ethz.ch
*
* BIWI, ETHZ, 2011
*
* Part of the Biwi Kinect Head Pose Database
*
* Example code for reading a compressed binary depth image file.
*
* THE SOFTWARE IS PROVIDED “AS IS” AND THE PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTIES OF ANY KIND,
* INCLUDING WITHOUT LIMITATION THE WARRANTIES OF FITNESS FOR ANY PARTICULAR PURPOSE AND NON-INFRINGEMENT.
* IN NO EVENT SHALL THE PROVIDER BE HELD RESPONSIBLE FOR LOSS OR DAMAGE CAUSED BY THE USE OF THE SOFTWARE.
*
*
*/
#include <iostream>
#include <fstream>
#include <cstdlib>
int16_t* loadDepthImageCompressed( const char* fname ){
//now read the depth image
FILE* pFile = fopen(fname, "rb");
if(!pFile){
std::cerr << "could not open file " << fname << std::endl;
return NULL;
}
int im_width = 0;
int im_height = 0;
bool success = true;
success &= ( fread(&im_width,sizeof(int),1,pFile) == 1 ); // read width of depthmap
success &= ( fread(&im_height,sizeof(int),1,pFile) == 1 ); // read height of depthmap
int16_t* depth_img = new int16_t[im_width*im_height];
int numempty;
int numfull;
int p = 0;
while(p < im_width*im_height ){
success &= ( fread( &numempty,sizeof(int),1,pFile) == 1 );
for(int i = 0; i < numempty; i++)
depth_img[ p + i ] = 0;
success &= ( fread( &numfull,sizeof(int), 1, pFile) == 1 );
success &= ( fread( &depth_img[ p + numempty ], sizeof(int16_t), numfull, pFile) == (unsigned int) numfull );
p += numempty+numfull;
}
fclose(pFile);
if(success)
return depth_img;
else{
delete [] depth_img;
return NULL;
}
}
float* read_gt(const char* fname){
//try to read in the ground truth from a binary file
FILE* pFile = fopen(fname, "rb");
if(!pFile){
std::cerr << "could not open file " << fname << std::endl;
return NULL;
}
float* data = new float[6];
bool success = true;
success &= ( fread( &data[0], sizeof(float), 6, pFile) == 6 );
fclose(pFile);
if(success)
return data;
else{
delete [] data;
return NULL;
}
}
```
</details>
### Supported Tasks and Leaderboards
Biwi Kinect Head Pose Database supports the following tasks :
- Head pose estimation
- Pose estimation
- Face verification
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
A sample from the Biwi Kinect Head Pose dataset is provided below:
```
{
'sequence_number': '12',
'subject_id': 'M06',
'rgb': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x7F53A6446C10>,.....],
'rgb_cal':
{
'intrisic_mat': [[517.679, 0.0, 320.0], [0.0, 517.679, 240.5], [0.0, 0.0, 1.0]],
'extrinsic_mat':
{
'rotation': [[0.999947, 0.00432361, 0.00929419], [-0.00446314, 0.999877, 0.0150443], [-0.009228, -0.015085, 0.999844]],
'translation': [-24.0198, 5.8896, -13.2308]
}
}
'depth': ['../hpdb/12/frame_00003_depth.bin', .....],
'depth_cal':
{
'intrisic_mat': [[575.816, 0.0, 320.0], [0.0, 575.816, 240.0], [0.0, 0.0, 1.0]],
'extrinsic_mat':
{
'rotation': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
'translation': [0.0, 0.0, 0.0]
}
}
'head_pose_gt':
{
'center': [[43.4019, -30.7038, 906.864], [43.0202, -30.8683, 906.94], [43.0255, -30.5611, 906.659], .....],
'rotation': [[[0.980639, 0.109899, 0.162077], [-0.11023, 0.993882, -0.00697376], [-0.161851, -0.011027, 0.986754]], ......]
}
}
```
### Data Fields
- `sequence_number` : This refers to the sequence number in the dataset. There are a total of 24 sequences.
- `subject_id` : This refers to the subjects in the dataset. There are a total of 20 people with 6 females and 14 males where 4 people were recorded twice.
- `rgb` : List of png frames containing the poses.
- `rgb_cal`: Contains calibration information for the color camera which includes intrinsic matrix,
global rotation and translation.
- `depth` : List of depth frames for the poses.
- `depth_cal`: Contains calibration information for the depth camera which includes intrinsic matrix, global rotation and translation.
- `head_pose_gt` : Contains ground truth information, i.e., the location of the center of the head in 3D and the head rotation, encoded as a 3x3 rotation matrix.
### Data Splits
All the data is contained in the training set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
From Dataset's README :
> The database contains 24 sequences acquired with a Kinect sensor. 20 people (some were recorded twice - 6 women and 14 men) were recorded while turning their heads, sitting in front of the sensor, at roughly one meter of distance.
#### 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
[Needs More Information]
### Licensing Information
From Dataset's README :
> This database is made available for non-commercial use such as university research and education.
### Citation Information
```bibtex
@article{fanelli_IJCV,
author = {Fanelli, Gabriele and Dantone, Matthias and Gall, Juergen and Fossati, Andrea and Van Gool, Luc},
title = {Random Forests for Real Time 3D Face Analysis},
journal = {Int. J. Comput. Vision},
year = {2013},
month = {February},
volume = {101},
number = {3},
pages = {437--458}
}
```
### Contributions
Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset. |
false |
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
false |
# Dataset Card for MIT_movies_fixed
## 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
- **Galileo Homepage:** [Galileo ML Data Intelligence Platform](https://www.rungalileo.io)
- **Repository:** [Needs More Information]
- **Dataset Blog:** [Improving Your ML Datasets With Galileo, Part 2](https://www.rungalileo.io/blog/improving-your-ml-datasets-part-2-ner)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
- **MIT movies Homepage:** [newsgroups homepage](https://groups.csail.mit.edu/sls/downloads/)
### Dataset Summary
This dataset is a version of the [**MIT movies**](https://groups.csail.mit.edu/sls/downloads/)
### Curation Rationale
This dataset was created to showcase the power of Galileo as a Data Intelligence Platform. Through Galileo, we identify critical error patterns within the original MIT movies dataset - annotation errors, ill-formed samples etc. Moreover, we observe that these errors permeate throughout the test dataset. As a result of our analysis, we fix 4% of the dataset by re-annotating the samples, and provide the dataset for NER research. To learn more about the process of fixing this dataset, please refer to our [**Blog**](https://www.rungalileo.io/blog/improving-your-ml-datasets-part-2-ner).
## Dataset Structure
### Data Instances
Every sample is blank line separated, every row is tab separated, and contains the word and its corresponding NER tag. This dataset uses the BIOES tagging schema.
An example from the dataset looks as follows:
```
show O
me O
a O
movie O
about O
cars B-PLOT
that I-PLOT
talk E-PLOT
```
### Data Splits
The data is split into a training and test split. The training data has ~9700 samples and the test data has ~2700 samples.
### Data Classes
The dataset contains the following 12 classes: ACTOR, YEAR, TITLE, GENRE, DIRECTOR, SONG, PLOT, REVIEW, CHARACTER, RATING, RATINGS_AVERAGE, TRAILER. Some of the classes have high semantic overlap (e.g. RATING/RATINGS_AVERAGE and ACTOR/DIRECTOR). |
false |
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
false |
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
false |
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
false | # CT-EBM-SP (Clinical Trials for Evidence-based Medicine in Spanish)
## 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://www.lllf.uam.es/ESP/nlpmedterm_en.html
- **Repository:** http://www.lllf.uam.es/ESP/nlpdata/wp2/CT-EBM-SP.zip
- **Paper:** Campillos-Llanos, L., Valverde-Mateos, A., Capllonch-Carrión, A., & Moreno-Sandoval, A. (2021). A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine. BMC medical informatics and decision making, 21(1), 1-19
- **Point of Contact:** leonardo.campillos AT gmail.com
### Dataset Summary
The [Clinical Trials for Evidence-Based-Medicine in Spanish corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/) is a collection of 1200 texts about clinical trials studies and clinical trials announcements:
- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
If you use the CT-EBM-SP resource, please, cite as follows:
```
@article{campillosetal-midm2021,
title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},
author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},
journal = {BMC Medical Informatics and Decision Making},
volume={21},
number={1},
pages={1--19},
year={2021},
publisher={BioMed Central}
}
```
### Supported Tasks
Medical Named Entity Recognition
### Languages
Spanish
## Dataset Structure
### Data Instances
- 292 173 tokens
- 46 699 entities of the following [Unified Medical Language System (UMLS)](https://www.nlm.nih.gov/research/umls/index.html) semantic groups:
- ANAT (anatomy and body parts): 6728 entities
- CHEM (chemical and pharmacological substances): 9224 entities
- DISO (pathologic conditions): 13 067 entities
- PROC (therapeutic and diagnostic procedures, and laboratory analyses): 17 680 entities
### Data Splits
- Train: 175 203 tokens, 28 101 entities
- Development: 58 670 tokens, 9629 entities
- Test: 58 300 tokens, 8969 entities
## Dataset Creation
### Source Data
- Abstracts from journals published under a Creative Commons license, available in [PubMed](https://pubmed.ncbi.nlm.nih.gov/) or the [Scientific Electronic Library Online (SciELO)](https://scielo.org/es/)
- Clinical trials announcements published in the [European Clinical Trials Register](https://www.clinicaltrialsregister.eu) and [Repositorio Español de Estudios Clínicos](https://reec.aemps.es)
### Annotations
#### Who are the annotators?
- Leonardo Campillos-Llanos, Computational Linguist, Consejo Superior de Investigaciones Científicas
- Adrián Capllonch-Carrión, Medical Doctor, Centro de Salud Retiro, Hospital Universitario Gregorio Marañón
- Ana Valverde-Mateos, Medical Lexicographer, Spanish Royal Academy of Medicine
## Considerations for Using the Data
**Disclosure**: This dataset is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision.
This resource is intended for a generalist purpose, and may have bias and/or any other undesirable distortions.
The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of this dataset.
**Descargo de responsabilidad**: Este conjunto de datos se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas
La finalidad de este modelo es generalista, y puede tener sesgos y/u otro tipo de distorsiones indeseables.
El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos datos. |
true | # Dataset Card for FEVEROUS
## 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://fever.ai/dataset/feverous.html
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://arxiv.org/abs/2106.05707)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
With billions of individual pages on the web providing information on almost every conceivable topic, we should have
the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this
information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to
transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot
of recent research and media coverage: false information coming from unreliable sources.
The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact
verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of
sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes,
or does not provide enough information to reach a verdict. The dataset also contains annotation metadata such as
annotator actions (query keywords, clicks on page, time signatures), and the type of challenge each claim poses.
### Supported Tasks and Leaderboards
The task is verification of textual claims against textual sources.
When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the
passage to verify each claim is given, and in recent years it typically consists a single sentence, while in
verification systems it is retrieved from a large set of documents in order to form the evidence.
### Languages
The dataset is in English (`en`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 187.82 MB
- **Size of the generated dataset:** 123.25 MB
- **Total amount of disk used:** 311.07 MB
An example of 'wikipedia_pages' looks as follows:
```
{'id': 24435,
'label': 1,
'claim': 'Michael Folivi competed with ten teams from 2016 to 2021, appearing in 54 games and making seven goals in total.',
'evidence': [{'content': ['Michael Folivi_cell_1_2_0',
'Michael Folivi_cell_1_7_0',
'Michael Folivi_cell_1_8_0',
'Michael Folivi_cell_1_9_0',
'Michael Folivi_cell_1_12_0'],
'context': [['Michael Folivi_title',
'Michael Folivi_section_4',
'Michael Folivi_header_cell_1_0_0'],
['Michael Folivi_title',
'Michael Folivi_section_4',
'Michael Folivi_header_cell_1_0_0'],
['Michael Folivi_title',
'Michael Folivi_section_4',
'Michael Folivi_header_cell_1_0_0'],
['Michael Folivi_title',
'Michael Folivi_section_4',
'Michael Folivi_header_cell_1_0_0'],
['Michael Folivi_title',
'Michael Folivi_section_4',
'Michael Folivi_header_cell_1_0_0']]},
{'content': ['Michael Folivi_cell_0_13_1',
'Michael Folivi_cell_0_14_1',
'Michael Folivi_cell_0_15_1',
'Michael Folivi_cell_0_16_1',
'Michael Folivi_cell_0_18_1'],
'context': [['Michael Folivi_title',
'Michael Folivi_header_cell_0_13_0',
'Michael Folivi_header_cell_0_11_0'],
['Michael Folivi_title',
'Michael Folivi_header_cell_0_14_0',
'Michael Folivi_header_cell_0_11_0'],
['Michael Folivi_title',
'Michael Folivi_header_cell_0_15_0',
'Michael Folivi_header_cell_0_11_0'],
['Michael Folivi_title',
'Michael Folivi_header_cell_0_16_0',
'Michael Folivi_header_cell_0_11_0'],
['Michael Folivi_title',
'Michael Folivi_header_cell_0_18_0',
'Michael Folivi_header_cell_0_11_0']]}],
'annotator_operations': [{'operation': 'start',
'value': 'start',
'time': 0.0},
{'operation': 'Now on', 'value': '?search=', 'time': 0.78},
{'operation': 'search', 'value': 'Michael Folivi', 'time': 78.101},
{'operation': 'Now on', 'value': 'Michael Folivi', 'time': 78.822},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_1_2_0',
'time': 96.202},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_1_7_0',
'time': 96.9},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_1_8_0',
'time': 97.429},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_1_9_0',
'time': 97.994},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_1_12_0',
'time': 99.02},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_13_1',
'time': 106.108},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_14_1',
'time': 106.702},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_15_1',
'time': 107.423},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_16_1',
'time': 108.186},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_17_1',
'time': 108.788},
{'operation': 'Highlighting',
'value': 'Michael Folivi_header_cell_0_17_0',
'time': 108.8},
{'operation': 'Highlighting',
'value': 'Michael Folivi_cell_0_18_1',
'time': 109.469},
{'operation': 'Highlighting deleted',
'value': 'Michael Folivi_cell_0_17_1',
'time': 124.28},
{'operation': 'Highlighting deleted',
'value': 'Michael Folivi_header_cell_0_17_0',
'time': 124.293},
{'operation': 'finish', 'value': 'finish', 'time': 141.351}],
'expected_challenge': '',
'challenge': 'Numerical Reasoning'}
```
### Data Fields
The data fields are the same among all splits.
- `id` (int): ID of the sample.
- `label` (ClassLabel): Annotated label for the claim. Can be one of {"SUPPORTS", "REFUTES", "NOT ENOUGH INFO"}.
- `claim` (str): Text of the claim.
- `evidence` (list of dict): Evidence sets (at maximum three). Each set consists of dictionaries with two fields:
- `content` (list of str): List of element IDs serving as the evidence for the claim. Each element ID is in the format
`"[PAGE ID]_[EVIDENCE TYPE]_[NUMBER ID]"`, where `[EVIDENCE TYPE]` can be: `sentence`, `cell`, `header_cell`,
`table_caption`, `item`.
- `context` (list of list of str): List (for each element ID in `content`) of a list of Wikipedia elements that are
automatically associated with that element ID and serve as context. This includes an article's title, relevant
sections (the section and sub-section(s) the element is located in), and for cells the closest row and column
header (multiple row/column headers if they follow each other).
- `annotator_operations` (list of dict): List of operations an annotator used to find the evidence and reach a verdict,
given the claim. Each element in the list is a dictionary with the fields:
- `operation` (str): Operation name. Any of the following:
- `start`, `finish`: Annotation started/finished. The value is the name of the operation.
- `search`: Annotator used the Wikipedia search function. The value is the entered search term or the term selected
from the automatic suggestions. If the annotator did not select any of the suggestions but instead went into
advanced search, the term is prefixed with "contains...".
- `hyperlink`: Annotator clicked on a hyperlink in the page. The value is the anchor text of the hyperlink.
- `Now on`: The page the annotator has landed after a search or a hyperlink click. The value is the PAGE ID.
- `Page search`: Annotator search on a page. The value is the search term.
- `page-search-reset`: Annotator cleared the search box. The value is the name of the operation.
- `Highlighting`, `Highlighting deleted`: Annotator selected/unselected an element on the page. The value is
`ELEMENT ID`.
- `back-button-clicked`: Annotator pressed the back button. The value is the name of the operation.
- `value` (str): Value associated with the operation.
- `time` (float): Time in seconds from the start of the annotation.
- `expected_challenge` (str): The challenge the claim generator selected will be faced when verifying the claim, one
out of the following: `Numerical Reasoning`, `Multi-hop Reasoning`, `Entity Disambiguation`,
`Combining Tables and Text`, `Search terms not in claim`, `Other`.
- `challenge` (str): Main challenge to verify the claim, one out of the following: `Numerical Reasoning`,
`Multi-hop Reasoning`, `Entity Disambiguation`, `Combining Tables and Text`, `Search terms not in claim`, `Other`.
### Data Splits
| | train | validation | test |
|--------------------|------:|-----------:|-----:|
| Number of examples | 71291 | 7890 | 7845 |
## 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
```
These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the “License Termsâ€). You may not use these files except in compliance with the applicable License Terms.
```
### Citation Information
If you use this dataset, please cite:
```bibtex
@inproceedings{Aly21Feverous,
author = {Aly, Rami and Guo, Zhijiang and Schlichtkrull, Michael Sejr and Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Cocarascu, Oana and Mittal, Arpit},
title = {{FEVEROUS}: Fact Extraction and {VERification} Over Unstructured and Structured information},
eprint={2106.05707},
archivePrefix={arXiv},
primaryClass={cs.CL},
year = {2021}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
true |
# Dataset Card for "UnpredicTable-gamefaqs-com" - Dataset of Few-shot Tasks from Tables
## 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://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
true |
# Dataset Card for Hansard speech
## 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://evanodell.com/projects/datasets/hansard-data/
- **Repository:** https://github.com/evanodell/hansard-data3
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Evan Odell](https://github.com/evanodell)
### Dataset Summary
A dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage
> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented "as is".
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types
- `language-modeling`: This dataset can contribute to the training or the evaluation of language models for historical texts.
### Languages
`en:GB`
## Dataset Structure
### Data Instances
```
{
'id': 'uk.org.publicwhip/debate/1979-05-17a.390.0',
'speech': "Since the Minister for Consumer Affairs said earlier that the bread price rise would be allowed, in view of developing unemployment in the baking industry, and since the Mother's Pride bakery in my constituency is about to close, will the right hon. Gentleman give us a firm assurance that there will be an early debate on the future of the industry, so that the Government may announce that, thanks to the price rise, those workers will not now be put out of work?",
'display_as': 'Eric Heffer',
'party': 'Labour',
'constituency': 'Liverpool, Walton',
'mnis_id': '725',
'date': '1979-05-17',
'time': '',
'colnum': '390',
'speech_class': 'Speech',
'major_heading': 'BUSINESS OF THE HOUSE',
'minor_heading': '',
'oral_heading': '',
'year': '1979',
'hansard_membership_id': '5612',
'speakerid': 'uk.org.publicwhip/member/11615',
'person_id': '',
'speakername': 'Mr. Heffer',
'url': '',
'government_posts': [],
'opposition_posts': [],
'parliamentary_posts': ['Member, Labour Party National Executive Committee']
}
```
### Data Fields
|Variable|Description|
|---|---|
|id|The ID as assigned by mysociety|
|speech|The text of the speech|
|display_as| The standardised name of the MP.|
|party|The party an MP is member of at time of speech|
|constituency| Constituency represented by MP at time of speech|
|mnis_id| The MP's Members Name Information Service number|
|date|Date of speech|
|time|Time of speech|
|colnum |Column number in hansard record|
|speech_class |Type of speech|
|major_heading| Major debate heading|
|minor_heading| Minor debate heading|
|oral_heading| Oral debate heading|
|year |Year of speech|
|hansard_membership_id| ID used by mysociety|
|speakerid |ID used by mysociety|
|person_id |ID used by mysociety|
|speakername| MP name as appeared in Hansard record for speech|
|url| link to speech|
|government_posts| Government posts held by MP (list)|
|opposition_posts |Opposition posts held by MP (list)|
|parliamentary_posts| Parliamentary posts held by MP (list)|
### Data Splits
Train: 2694375
## Dataset Creation
### Curation Rationale
This dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.
### Source Data
#### Initial Data Collection and Normalization
The dataset is created by getting the data from [data.parliament.uk](http://data.parliament.uk/membersdataplatform/memberquery.aspx). There is no normalization.
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
None
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
This is public information, so there should not be any personal and sensitive information
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to understand how language use and society's views have changed over time.
### Discussion of Biases
Because of the long time period this dataset spans, it might contain language and opinions that are unacceptable in modern society.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This dataset was built on top of [parlparse](https://github.com/mysociety/parlparse) by [Evan Odell](https://github.com/evanodell)
### Licensing Information
Creative Commons Attribution 4.0 International License
### Citation Information
```
@misc{odell, evan_2021,
title={Hansard Speeches 1979-2021: Version 3.1.0},
DOI={10.5281/zenodo.4843485},
abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>},
note={This release is an update of previously released datasets. See full documentation for details.},
publisher={Zenodo},
author={Odell, Evan},
year={2021},
month={May} }
```
Thanks to [@shamikbose](https://github.com/shamikbose) for adding this dataset. |
false |
# YALTAi Tabular Dataset
## Table of Contents
- [YALTAi Tabular Dataset](#YALTAi-Tabular-Dataset)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data 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.6827706](https://doi.org/10.5281/zenodo.6827706)
- **Paper:** [https://arxiv.org/abs/2207.11230](https://arxiv.org/abs/2207.11230)
### Dataset Summary
This dataset contains a subset of data used in the paper [You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine](https://arxiv.org/abs/2207.11230). This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects "Header", "Col", "Marginal", "text".
### Supported Tasks and Leaderboards
- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, `YOLO`, uses the data's original format.
- The second configuration converts the YOLO format into a format which is closer to the `COCO` annotation format. This is done to make it easier to work with the `feature_extractor`s from the `Transformers` models for object detection, which expect data to be in a COCO style format.
### Data Instances
An example instance from the COCO config:
```
{'height': 2944,
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FA413CDA210>,
'image_id': 0,
'objects': [{'area': 435956,
'bbox': [0.0, 244.0, 1493.0, 292.0],
'category_id': 0,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 88234,
'bbox': [305.0, 127.0, 562.0, 157.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5244,
'bbox': [1416.0, 196.0, 92.0, 57.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5720,
'bbox': [1681.0, 182.0, 88.0, 65.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 374085,
'bbox': [0.0, 540.0, 163.0, 2295.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 577599,
'bbox': [104.0, 537.0, 253.0, 2283.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 598670,
'bbox': [304.0, 533.0, 262.0, 2285.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 56,
'bbox': [284.0, 539.0, 8.0, 7.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 1868412,
'bbox': [498.0, 513.0, 812.0, 2301.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 307800,
'bbox': [1250.0, 512.0, 135.0, 2280.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 494109,
'bbox': [1330.0, 503.0, 217.0, 2277.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 52,
'bbox': [1734.0, 1013.0, 4.0, 13.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 90666,
'bbox': [0.0, 1151.0, 54.0, 1679.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []}],
'width': 2064}
```
An example instance from the YOLO config:
``` python
{'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FAA140F2450>,
'objects': {'bbox': [[747, 390, 1493, 292],
[586, 206, 562, 157],
[1463, 225, 92, 57],
[1725, 215, 88, 65],
[80, 1688, 163, 2295],
[231, 1678, 253, 2283],
[435, 1675, 262, 2285],
[288, 543, 8, 7],
[905, 1663, 812, 2301],
[1318, 1653, 135, 2280],
[1439, 1642, 217, 2277],
[1737, 1019, 4, 13],
[26, 1991, 54, 1679]],
'label': [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]}}
```
### Data Fields
The fields for the YOLO config:
- `image`: the image
- `objects`: the annotations which consist of:
- `bbox`: a list of bounding boxes for the image
- `label`: a list of labels for this image
The fields for the COCO config:
- `height`: height of the image
- `width`: width of the image
- `image`: image
- `image_id`: id for the image
- `objects`: annotations in COCO format, consisting of a list containing dictionaries with the following keys:
- `bbox`: bounding boxes for the images
- `category_id`: a label for the image
- `image_id`: id for the image
- `iscrowd`: COCO `iscrowd` flag
- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset contains a train, validation and test split with the following numbers per split:
| | train | validation | test |
|----------|-------|------------|------|
| examples | 196 | 22 | 135 |
## Dataset Creation
> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8
.
### Curation Rationale
This dataset was created to produce a simplified version of the [Lectaurep Repertoires dataset](https://github.com/HTR-United/lectaurep-repertoires), which was found to contain:
> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8
### Source Data
#### Initial Data Collection and Normalization
The LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the
Minutier central des notaires de Paris of the National Archives, the [ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities)](https://www.inria.fr/en/almanach) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.
> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.
#### Who are the source language producers?
[More information needed]
### Annotations
| | Train | Dev | Test | Total | Average area | Median area |
|----------|-------|-----|------|-------|--------------|-------------|
| Col | 724 | 105 | 829 | 1658 | 9.32 | 6.33 |
| Header | 103 | 15 | 42 | 160 | 6.78 | 7.10 |
| Marginal | 60 | 8 | 0 | 68 | 0.70 | 0.71 |
| Text | 13 | 5 | 0 | 18 | 0.01 | 0.00 |
| | | | - | | | |
#### Annotation process
[More information needed]
#### Who are the annotators?
[More information needed]
### Personal and Sensitive Information
This data does not contain information relating to living individuals.
## Considerations for Using the Data
### Social Impact of Dataset
A growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.
### Discussion of Biases
Historical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.
### Other Known Limitations
[More information needed]
## Additional Information
### Dataset Curators
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@dataset{clerice_thibault_2022_6827706,
author = {Clérice, Thibault},
title = {YALTAi: Tabular Dataset},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6827706},
url = {https://doi.org/10.5281/zenodo.6827706}
}
```
[](https://doi.org/10.5281/zenodo.6827706)
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
false |
# Dataset Card for IMDB-BINARY (IMDb-B)
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://dl.acm.org/doi/10.1145/2783258.2783417)**
- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/IMDB-BINARY.zip):**:
- **Paper:**: Deep Graph Kernels (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-imdb-b)
### Dataset Summary
The `IMDb-B` dataset is "a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres".
### Supported Tasks and Leaderboards
`IMDb-B` should be used for graph classification (aiming to predict whether a movie graph is an action or romance movie), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 1000 |
| average #nodes | 19.79 |
| average #edges | 193.25 |
### Data Fields
Each row of a given file is a graph, with:
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset.
This information can be found back using
```python
from torch_geometric.datasets import TUDataset
cur_dataset = TUDataset(root="../dataset/loaded/",
name="IMDB-BINARY")
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license, please open an issue if you have this information.
### Citation Information
```
@inproceedings{10.1145/2783258.2783417,
author = {Yanardag, Pinar and Vishwanathan, S.V.N.},
title = {Deep Graph Kernels},
year = {2015},
isbn = {9781450336642},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2783258.2783417},
doi = {10.1145/2783258.2783417},
abstract = {In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.},
booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1365–1374},
numpages = {10},
keywords = {collaboration networks, bioinformatics, r-convolution kernels, graph kernels, structured data, deep learning, social networks, string kernels},
location = {Sydney, NSW, Australia},
series = {KDD '15}
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
false |
This is the question answering datasets collected by TextBox, including:
- SQuAD (squad)
- CoQA (coqa)
- Natural Questions (nq)
- TriviaQA (tqa)
- WebQuestions (webq)
- NarrativeQA (nqa)
- MS MARCO (marco)
- NewsQA (newsqa)
- HotpotQA (hotpotqa)
- MSQG (msqg)
- QuAC (quac).
The detail and leaderboard of each dataset can be found in [TextBox page](https://github.com/RUCAIBox/TextBox#dataset). |
false |
# Dataset Card for "tydiqa"
## 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/tydiqa](https://github.com/google-research-datasets/tydiqa)
- **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:** 3726.74 MB
- **Size of the generated dataset:** 5812.92 MB
- **Total amount of disk used:** 9539.67 MB
### Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD).
### 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
#### primary_task
- **Size of downloaded dataset files:** 1863.37 MB
- **Size of the generated dataset:** 5757.59 MB
- **Total amount of disk used:** 7620.96 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"annotations": {
"minimal_answers_end_byte": [-1, -1, -1],
"minimal_answers_start_byte": [-1, -1, -1],
"passage_answer_candidate_index": [-1, -1, -1],
"yes_no_answer": ["NONE", "NONE", "NONE"]
},
"document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
"document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
"document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
"language": "thai",
"passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
"question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
}
```
#### secondary_task
- **Size of downloaded dataset files:** 1863.37 MB
- **Size of the generated dataset:** 55.34 MB
- **Total amount of disk used:** 1918.71 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [394],
"text": ["بطولتين"]
},
"context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...",
"id": "arabic-2387335860751143628-1",
"question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...",
"title": "قائمة نهائيات كأس العالم"
}
```
### Data Fields
The data fields are the same among all splits.
#### primary_task
- `passage_answer_candidates`: a dictionary feature containing:
- `plaintext_start_byte`: a `int32` feature.
- `plaintext_end_byte`: a `int32` feature.
- `question_text`: a `string` feature.
- `document_title`: a `string` feature.
- `language`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `passage_answer_candidate_index`: a `int32` feature.
- `minimal_answers_start_byte`: a `int32` feature.
- `minimal_answers_end_byte`: a `int32` feature.
- `yes_no_answer`: a `string` feature.
- `document_plaintext`: a `string` feature.
- `document_url`: a `string` feature.
#### secondary_task
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | validation |
| -------------- | -----: | ---------: |
| primary_task | 166916 | 18670 |
| secondary_task | 49881 | 5077 |
## 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{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
false |
# Dataset Card for "xsum_dutch" 🇳🇱🇧🇪 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
The Xsum Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch.
*This dataset currently (Aug '22) has a single config, which is
config `default` of [xsum](https://huggingface.co/datasets/xsum) translated to Dutch
with [yhavinga/t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi).*
- **Homepage:** [https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 245.38 MB
- **Size of the generated dataset:** 507.60 MB
- **Total amount of disk used:** 752.98 MB
### Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
### 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:** 245.38 MB
- **Size of the generated dataset:** 507.60 MB
- **Total amount of disk used:** 752.98 MB
An example of 'validation' looks as follows.
```
{
"document": "some-body",
"id": "29750031",
"summary": "some-sentence"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `document`: a `string` feature.
- `summary`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|204045| 11332|11334|
## 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{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding the English version of this dataset.
The dataset was translated on Cloud TPU compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
|
false | # Dataset Card for NSME-COM
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [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**: [NeuralSpace Homepage](https://huggingface.co/neuralspace)
- **Repository:** [NSME-COM Dataset](https://huggingface.co/datasets/neuralspace/NSME-COM)
- **Point of Contact:** [Ankur Saxena](mailto:ankursaxena@neuralspace.ai)
- **Point of Contact:** [Ayushman Dash](mailto:ayushman@neuralspace.ai)
- **Size of downloaded dataset files:** 10.86 KB
### Dataset Summary
In this digital age, the E-Commerce industry has increasingly become a vital component of business strategy and development. To streamline, enhance and take the customer experience to the highest level, NLP can help create surprisingly massive value in the E-Commerce industry.
One of the most popular NLP use-cases is a chatbot. With a chatbot you can automate your customer engagement saving yourself time and other resources. Offering an enhanced and simplified customer experience you can increase your sales and also offer your website visitors personalized recommendations.
The NSME-COM dataset (NeuralSpace Massive E-Comm) is a manually curated dataset by data engineers at [NeuralSpace](https://www.neuralspace.ai/) for the insurance and retail domain. The dataset contains intents (the action users want to execute) and examples (anything that a user sends to the chatbot) that can be used to build a chatbot. The files in this dataset are available in JSON format.
### Supported Tasks
#### nsme-com
### Languages
The language data in NSME-COM is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 10.86 KB
An example of 'test' looks as follows.
``` {
"text": "is it good to add roadside assistance?",
"intent": "Add",
"type": "Test"
}
```
An example of 'train' looks as follows.
```{
"text": "how can I add my spouse as a nominee?",
"intent": "Add",
"type": "Train"
},
```
### Data Fields
The data fields are the same among all splits.
#### nsme-com
- `text`: a `string` feature.
- `intent`: a `string` feature.
- `type`: a classification label, with possible values including `train` or `test`.
### Data Splits
#### nsme-com
| |train|test|
|----|----:|---:|
|nsme-com| 1725| 406|
### Contributions
Ankur Saxena (ankursaxena@neuralspace.ai) |
false |
# Dataset Card for Collection3
## 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:** [Collection3 homepage](http://labinform.ru/pub/named_entities/index.htm)
- **Repository:** [Needs More Information]
- **Paper:** [Two-stage approach in Russian named entity recognition](https://ieeexplore.ieee.org/document/7584769)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection [Persons-1000](http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000) originally containing 1000 news documents labeled only with names of persons.
Additional labels were obtained using guidelines similar to MUC-7 with web-based tool [Brat](http://brat.nlplab.org/) for collaborative text annotation.
Currently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations).
Conversion to the IOB2 format and splitting into train, validation and test sets was done by [DeepPavlov team](http://files.deeppavlov.ai/deeppavlov_data/collection3_v2.tar.gz).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Russian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"id": "851",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0],
"tokens": ['Главный', 'архитектор', 'программного', 'обеспечения', '(', 'ПО', ')', 'американского', 'высокотехнологичного', 'гиганта', 'Microsoft', 'Рэй', 'Оззи', 'покидает', 'компанию', '.']
}
```
### Data Fields
- id: a string feature.
- tokens: a list of string features.
- ner_tags: a list of classification labels (int). Full tagset with indices:
```
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6}
```
### Data Splits
|name|train|validation|test|
|---------|----:|---------:|---:|
|Collection3|9301|2153|1922|
## 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{mozharova-loukachevitch-2016-two-stage-russian-ner,
author={Mozharova, Valerie and Loukachevitch, Natalia},
booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)},
title={Two-stage approach in Russian named entity recognition},
year={2016},
pages={1-6},
doi={10.1109/FRUCT.2016.7584769}}
``` |
false | # Dataset Card for LibriVox Indonesia 1.0
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia
- **Repository:** https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia
- **Point of Contact:** [Cahya Wirawan](mailto:cahya.wirawan@gmail.com)
### Dataset Summary
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public
domain audiobooks [LibriVox](https://librivox.org/). We collected only languages in Indonesia for this dataset.
The original LibriVox audiobooks or sound files' duration varies from a few minutes to a few hours. Each audio
file in the speech dataset now lasts from a few seconds to a maximum of 20 seconds.
We converted the audiobooks to speech datasets using the forced alignment software we developed. It supports
multilingual, including low-resource languages, such as Acehnese, Balinese, or Minangkabau. We can also use it
for other languages without additional work to train the model.
The dataset currently consists of 8 hours in 7 languages from Indonesia. We will add more languages or audio files
as we collect them.
### Languages
```
Acehnese, Balinese, Bugisnese, Indonesian, Minangkabau, Javanese, Sundanese
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include
`reader` and `language`.
```python
{
'path': 'librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3',
'language': 'sun',
'reader': '3174',
'sentence': 'pernyataan umum ngeunaan hak hak asasi manusa sakabeh manusa',
'audio': {
'path': 'librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 44100
},
}
```
### Data Fields
`path` (`string`): The path to the audio file
`language` (`string`): The language of the audio file
`reader` (`string`): The reader Id in LibriVox
`sentence` (`string`): The sentence the user read from the book.
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
### Data Splits
The speech material has only train 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
[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
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
``` |
true |
# Dataset Card for Code Comment Classification
## 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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/poojaruhal/RP-class-comment-classification
- **Repository:** https://github.com/poojaruhal/RP-class-comment-classification
- **Paper:** https://doi.org/10.1016/j.jss.2021.111047
- **Point of Contact:** https://poojaruhal.github.io
### Dataset Summary
The dataset contains class comments extracted from various big and diverse open-source projects of three programming languages Java, Smalltalk, and Python.
### Supported Tasks and Leaderboards
Single-label text classification and Multi-label text classification
### Languages
Java, Python, Smalltalk
## Dataset Structure
### Data Instances
```json
{
"class" : "Absy.java",
"comment":"* Azure Blob File System implementation of AbstractFileSystem. * This impl delegates to the old FileSystem",
"summary":"Azure Blob File System implementation of AbstractFileSystem.",
"expand":"This impl delegates to the old FileSystem",
"rational":"",
"deprecation":"",
"usage":"",
"exception":"",
"todo":"",
"incomplete":"",
"commentedcode":"",
"directive":"",
"formatter":"",
"license":"",
"ownership":"",
"pointer":"",
"autogenerated":"",
"noise":"",
"warning":"",
"recommendation":"",
"precondition":"",
"codingGuidelines":"",
"extension":"",
"subclassexplnation":"",
"observation":"",
}
```
### Data Fields
class: name of the class with the language extension.
comment: class comment of the class
categories: a category that sentence is classified to. It indicated a particular type of information.
### Data Splits
10-fold cross validation
## Dataset Creation
### Curation Rationale
To identify the infomation embedded in the class comments across various projects and programming languages.
### Source Data
#### Initial Data Collection and Normalization
It contains the dataset extracted from various open-source projects of three programming languages Java, Smalltalk, and Python.
- #### Java
Each file contains all the extracted class comments from one project. We have a total of six java projects. We chose a sample of 350 comments from all these files for our experiment.
- [Eclipse.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/) - Extracted class comments from the Eclipse project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Eclipse](https://github.com/eclipse).
- [Guava.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Guava.csv) - Extracted class comments from the Guava project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Guava](https://github.com/google/guava).
- [Guice.csv](/https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Guice.csv) - Extracted class comments from the Guice project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Guice](https://github.com/google/guice).
- [Hadoop.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Hadoop.csv) - Extracted class comments from the Hadoop project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Apache Hadoop](https://github.com/apache/hadoop)
- [Spark.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Spark.csv) - Extracted class comments from the Apache Spark project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Apache Spark](https://github.com/apache/spark)
- [Vaadin.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Vaadin.csv) - Extracted class comments from the Vaadin project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Vaadin](https://github.com/vaadin/framework)
- [Parser_Details.md](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Parser_Details.md) - Details of the parser used to parse class comments of Java [ Projects](https://doi.org/10.5281/zenodo.4311839)
- #### Smalltalk/
Each file contains all the extracted class comments from one project. We have a total of seven Pharo projects. We chose a sample of 350 comments from all these files for our experiment.
- [GToolkit.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/GToolkit.csv) - Extracted class comments from the GToolkit project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [Moose.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Moose.csv) - Extracted class comments from the Moose project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [PetitParser.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/PetitParser.csv) - Extracted class comments from the PetitParser project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [Pillar.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Pillar.csv) - Extracted class comments from the Pillar project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [PolyMath.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/PolyMath.csv) - Extracted class comments from the PolyMath project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [Roassal2.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Roassal2.csv) -Extracted class comments from the Roassal2 project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [Seaside.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Seaside.csv) - Extracted class comments from the Seaside project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.
- [Parser_Details.md](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Parser_Details.md) - Details of the parser used to parse class comments of Pharo [ Projects](https://doi.org/10.5281/zenodo.4311839)
- #### Python/
Each file contains all the extracted class comments from one project. We have a total of seven Python projects. We chose a sample of 350 comments from all these files for our experiment.
- [Django.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Django.csv) - Extracted class comments from the Django project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Django](https://github.com/django)
- [IPython.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/IPython.csv) - Extracted class comments from the Ipython project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub[IPython](https://github.com/ipython/ipython)
- [Mailpile.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Mailpile.csv) - Extracted class comments from the Mailpile project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Mailpile](https://github.com/mailpile/Mailpile)
- [Pandas.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Pandas.csv) - Extracted class comments from the Pandas project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [pandas](https://github.com/pandas-dev/pandas)
- [Pipenv.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Pipenv.csv) - Extracted class comments from the Pipenv project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Pipenv](https://github.com/pypa/pipenv)
- [Pytorch.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Pytorch.csv) - Extracted class comments from the Pytorch project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [PyTorch](https://github.com/pytorch/pytorch)
- [Requests.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Requests.csv) - Extracted class comments from the Requests project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Requests](https://github.com/psf/requests/)
- [Parser_Details.md](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Parser_Details.md) - Details of the parser used to parse class comments of Python [ Projects](https://doi.org/10.5281/zenodo.4311839)
### Annotations
#### Annotation process
Four evaluators (all authors of this paper (https://doi.org/10.1016/j.jss.2021.111047)), each having at least four years of programming experience, participated in the annonation process.
We partitioned Java, Python, and Smalltalk comments equally among all evaluators based on the distribution of the language's dataset to ensure the inclusion of comments from all projects and diversified lengths. Each classification is reviewed by three evaluators.
The details are given in the paper [Rani et al., JSS, 2021](https://doi.org/10.1016/j.jss.2021.111047)
#### Who are the annotators?
[Rani et al., JSS, 2021](https://doi.org/10.1016/j.jss.2021.111047)
### Personal and Sensitive Information
Author information embedded in the text
## Additional Information
### Dataset Curators
[Pooja Rani, Ivan, Manuel]
### Licensing Information
[license: cc-by-nc-sa-4.0]
### Citation Information
```
@article{RANI2021111047,
title = {How to identify class comment types? A multi-language approach for class comment classification},
journal = {Journal of Systems and Software},
volume = {181},
pages = {111047},
year = {2021},
issn = {0164-1212},
doi = {https://doi.org/10.1016/j.jss.2021.111047},
url = {https://www.sciencedirect.com/science/article/pii/S0164121221001448},
author = {Pooja Rani and Sebastiano Panichella and Manuel Leuenberger and Andrea {Di Sorbo} and Oscar Nierstrasz},
keywords = {Natural language processing technique, Code comment analysis, Software documentation}
}
```
|
false | # Dataset Card for citizen_nlu
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [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**: [NeuralSpace Homepage](https://huggingface.co/neuralspace)
- **Repository:** [citizen_nlu Dataset](https://huggingface.co/datasets/neuralspace/citizen_nlu)
- **Point of Contact:** [Juhi Jain](mailto:juhi@neuralspace.ai)
- **Point of Contact:** [Ayushman Dash](mailto:ayushman@neuralspace.ai)
- **Size of downloaded dataset files:** 67.6 MB
### Dataset Summary
NeuralSpace strives to provide AutoNLP text and speech services, especially for low-resource languages. One of the major services provided by NeuralSpace on its platform is the “Language Understanding” service, where you can build, train and deploy your NLU model to recognize intents and entities with minimal code and just a few clicks.
The initiative of this challenge is created with the purpose of sparkling AI applications to address some of the pressing problems in India and find unique ways to address them. Starting with a focus on NLU, this challenge hopes to make progress towards multilingual modelling, as language diversity is significantly underserved on the web.
NeuralSpace aims at mastering the low-resource domain, and the citizen services use case is naturally a multilingual and essential domain for the general citizen.
Citizen services refer to the essential services provided by organizations to general citizens. In this case, we focus on important services like various FIR-based requests, Blood/Platelets Donation, and Coronavirus-related queries.
Such services may not be needed regularly by any particular city but when needed are of utmost importance, and in general, the needs for such services are prevalent every day.
Despite the importance of citizen services, linguistically rich countries like India are still far behind in delivering such essential needs to the citizens with absolute ease. The best services currently available do not exist in various low-resource languages that are native to different groups of people. This challenge aims to make government services more efficient, responsive, and customer-friendly.
As our computing resources and modelling capabilities grow, so does our potential to support our citizens by delivering a far superior customer experience. Equipping a Citizen services bot with the ability to converse in vernacular languages would make them accessible to a vast group of people for whom English is not a language of choice, but for who are increasingly turning to digital platforms and interfaces for a wide range of needs and wants.
### Supported Tasks
A key component of any chatbot system is the NLU pipeline for ‘Intent Classification’ and ‘Named Entity Recognition. This primarily enables any chatbot to perform various tasks at ease. A fully functional multilingual chatbot needs to be able to decipher the language and understand exactly what the user wants.
#### citizen_nlu
A manually-curated multilingual dataset by Data Engineers at [NeuralSpace](https://www.neuralspace.ai/) for citizen services in 9 Indian languages for a realistic information-seeking task with data samples written by native-speaking expert data annotators [here](https://www.neuralspace.ai/). The dataset files are available in CSV format.
### Languages
The citizen_nlu data is available in nine Indian languages i.e, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, and Telugu
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 67.6 MB
An example of 'test' looks as follows.
``` text,intents
मेरे पिता की कार उनके कार्यालय की पार्किंग से कल से गायब है। वाहन संख्या केए-03-एचए-1985 । मैं एफआईआर कराना चाहता हूं।,ReportingMissingVehicle
```
An example of 'train' looks as follows.
```text,intents
என் தாத்தா எனக்கு பிறந்தநாள் பரிசு கொடுத்தார் மஞ்சள் நான் டாடனானோவை இழந்தேன். காணவில்லை என புகார் தெரிவிக்க விரும்புகிறேன்,ReportingMissingVehicle
```
### Data Fields
The data fields are the same among all splits.
#### citizen_nlu
- `text`: a `string` feature.
- `intent`: a `string` feature.
- `type`: a classification label, with possible values including `train` or `test`.
### Data Splits
#### citizen_nlu
| |train|test|
|----|----:|---:|
|citizen_nlu| 287832| 4752|
### Contributions
Mehar Bhatia (mehar@neuralspace.ai) |
true | # AutoTrain Dataset for project: citizen_nlu_bn
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project citizen_nlu_bn.
### Languages
The BCP-47 code for the dataset's language is bn.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u0997\u09a4 \u09e8 \u09ae\u09be\u09b8 \u0986\u09ae\u09be\u09b0 \u0986\u0997\u09c7 \u0995\u09b0\u09cb \u09a8\u09be \u0986\u09ae\u09bf \u0995\u09a4 \u09a6\u09bf\u09a8 \u09aa\u09b0\u09c7 \u09b0\u0995\u09cd\u09a4 \u09a6\u09bf\u09a4\u09c7 \u09aa\u09be\u09b0\u09bf?",
"target": 3
},
{
"text": "\u09b9\u09a0\u09be\u09ce \u0986\u09ae\u09bf \u09a6\u09cb\u0995\u09be\u09a8\u09c7 \u09af\u09be\u0993\u09af\u09bc\u09be\u09b0 \u099c\u09a8\u09cd\u09af \u098f\u0995\u099f\u09bf \u0996\u09be\u09b2\u09bf \u09b0\u09be\u09b8\u09cd\u09a4\u09be\u09af\u09bc \u09b9\u09be\u0981\u099f\u099b\u09bf\u09b2\u09be\u09ae \u09b8\u09be\u09a6\u09be \u09b0\u0999\u09c7\u09b0 \u0993\u09ac\u09bf 005639 \u0986\u09ae\u09bf \u09b0\u09bf\u09aa\u09cb\u09b0\u09cd\u099f \u0995\u09b0\u09ac \u09af\u0996\u09a8 \u0986\u09ae\u09bf \u09a4\u09be\u09b0 \u0995\u09be\u099b\u09c7 \u0986\u09b8\u09ac \u098f\u09ac\u0982 \u09a7\u09be\u0995\u09cd\u0995\u09be \u09a6\u09bf\u09af\u09bc\u09c7 \u099a\u09b2\u09c7 \u09af\u09be\u09ac",
"target": 44
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=55, names=['ContactRealPerson', 'Eligibility For BloodDonationWithComorbidities', 'EligibilityForBloodDonationAgeLimit', 'EligibilityForBloodDonationCovidGap', 'EligibilityForBloodDonationForPregnantWomen', 'EligibilityForBloodDonationGap', 'EligibilityForBloodDonationSTD', 'EligibilityForBloodReceiversBloodGroup', 'EligitbilityForVaccine', 'InquiryForCovidActiveCasesCount', 'InquiryForCovidDeathCount', 'InquiryForCovidPrevention', 'InquiryForCovidRecentCasesCount', 'InquiryForCovidTotalCasesCount', 'InquiryForDoctorConsultation', 'InquiryForQuarantinePeriod', 'InquiryForTravelRestrictions', 'InquiryForVaccinationRequirements', 'InquiryForVaccineCost', 'InquiryForVaccineCount', 'InquiryOfContact', 'InquiryOfCovidSymptoms', 'InquiryOfEmergencyContact', 'InquiryOfLocation', 'InquiryOfLockdownDetails', 'InquiryOfTiming', 'InquiryofBloodDonationRequirements', 'InquiryofBloodReceivalRequirements', 'InquiryofPostBloodDonationCareSchemes', 'InquiryofPostBloodDonationCertificate', 'InquiryofPostBloodDonationEffects', 'InquiryofPostBloodReceivalCareSchemes', 'InquiryofPostBloodReceivalEffects', 'InquiryofVaccinationAgeLimit', 'IntentForBloodDonationAppointment', 'IntentForBloodReceivalAppointment', 'ReportingAnimalAbuse', 'ReportingAnimalPoaching', 'ReportingChildAbuse', 'ReportingCyberCrime', 'ReportingDomesticViolence', 'ReportingDowry', 'ReportingDrugConsumption', 'ReportingDrugTrafficing', 'ReportingHitAndRun', 'ReportingMissingPerson', 'ReportingMissingPets', 'ReportingMissingVehicle', 'ReportingMurder', 'ReportingPropertyTakeOver', 'ReportingSexualAssault', 'ReportingTheft', 'ReportingTresspassing', 'ReportingVehicleAccident', 'StatusOfFIR'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 27146 |
| valid | 6800 |
|
false |
## Dataset Description
FBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.
### Dataset Summary
The dataset contains 112,806 images.
All images are on white background
### Collection Method
#### v1.0
Collect from danbooru website.
Use yolov5 to detect and clip image.
Use anime-segmentation to remove background.
Use deepdanbooru to filter image.
Finally clean the dataset manually.
#### v2.0
Base on v1.0, use Novelai image-to-image to enhance and expand the dataset.
### Contributions
Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset. |
false |
## Titanic Survival
from https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/problem12.html |
false |
# Dataset Card for "lmqg/qag_squad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the SQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": "\"4 Minutes\" was released as the album's lead single and peaked at number three on the Billboard Hot 100. It was Madonna's 37th top-ten hit on the chart—it pushed Madonna past Elvis Presley as the artist with the most top-ten hits. In the UK she retained her record for the most number-one singles for a female artist; \"4 Minutes\" becoming her thirteenth. At the 23rd Japan Gold Disc Awards, Madonna received her fifth Artist of the Year trophy from Recording Industry Association of Japan, the most for any artist. To further promote the album, Madonna embarked on the Sticky & Sweet Tour; her first major venture with Live Nation. With a gross of $280 million, it became the highest-grossing tour by a solo artist then, surpassing the previous record Madonna set with the Confessions Tour; it was later surpassed by Roger Waters' The Wall Live. It was extended to the next year, adding new European dates, and after it ended, the total gross was $408 million.",
"questions": [
"Which single was released as the album's lead single?",
"Madonna surpassed which artist with the most top-ten hits?",
"4 minutes became Madonna's which number one single in the UK?",
"What is the name of the first tour with Live Nation?",
"How much did Stick and Sweet Tour grossed?"
],
"answers": [
"4 Minutes",
"Elvis Presley",
"thirteenth",
"Sticky & Sweet Tour",
"$280 million,"
],
"questions_answers": "question: Which single was released as the album's lead single?, answer: 4 Minutes | question: Madonna surpassed which artist with the most top-ten hits?, answer: Elvis Presley | question: 4 minutes became Madonna's which number one single in the UK?, answer: thirteenth | question: What is the name of the first tour with Live Nation?, answer: Sticky & Sweet Tour | question: How much did Stick and Sweet Tour grossed?, answer: $280 million,"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|16462| 2067 | 2429|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` |
true | # Contextualized Hate Speech: A dataset of comments in news outlets on Twitter
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper**: ["Assessing the impact of contextual information in hate speech detection"](https://arxiv.org/abs/2210.00465), Juan Manuel Pérez, Franco Luque, Demian Zayat, Martín Kondratzky, Agustín Moro, Pablo Serrati, Joaquín Zajac, Paula Miguel, Natalia Debandi, Agustín Gravano, Viviana Cotik
- **Point of Contact**: jmperez (at) dc uba ar
### Dataset Summary
This dataset is a collection of tweets that were posted in response to news articles from five specific Argentinean news outlets: Clarín, Infobae, La Nación, Perfil and Crónica, during the COVID-19 pandemic. The comments were analyzed for hate speech across eight different characteristics: against women, racist content, class hatred, against LGBTQ+ individuals, against physical appearance, against people with disabilities, against criminals, and for political reasons. All the data is in Spanish.
Each comments is labeled with the following variables
| Label | Description |
| :--------- | :---------------------------------------------------------------------- |
| HATEFUL | Contains hate speech (HS)? |
| CALLS | If it is hateful, is this message calling to (possibly violent) action? |
| WOMEN | Is this against women? |
| LGBTI | Is this against LGBTI people? |
| RACISM | Is this a racist message? |
| CLASS | Is this a classist message? |
| POLITICS | Is this HS due to political ideology? |
| DISABLED | Is this HS against disabled people? |
| APPEARANCE | Is this HS against people due to their appearance? (e.g. fatshaming) |
| CRIMINAL | Is this HS against criminals or people in conflict with law? |
There is an extra label `CALLS`, which represents whether a comment is a call to violent action or not.
### Citation Information
```bibtex
@article{perez2022contextual,
author = {Pérez, Juan Manuel and Luque, Franco M. and Zayat, Demian and Kondratzky, Martín and Moro, Agustín and Serrati, Pablo Santiago and Zajac, Joaquín and Miguel, Paula and Debandi, Natalia and Gravano, Agustín and Cotik, Viviana},
journal = {IEEE Access},
title = {Assessing the Impact of Contextual Information in Hate Speech Detection},
year = {2023},
volume = {11},
number = {},
pages = {30575-30590},
doi = {10.1109/ACCESS.2023.3258973}
}
```
### Contributions
[More Information Needed] |
false |
# Dataset Card for clintox
## 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://moleculenet.org/**
- **Repository: https://github.com/deepchem/deepchem/tree/master**
- **Paper: https://arxiv.org/abs/1703.00564**
### Dataset Summary
`clintox` is a dataset included in [MoleculeNet](https://moleculenet.org/). Qualitative data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons. This uses the `CT_TOX` task.
Note, there was one molecule in the training set that could not be converted to SELFIES (`*C(=O)[C@H](CCCCNC(=O)OCCOC)NC(=O)OCCOC`)
## Dataset Structure
### Data Fields
Each split contains
* `smiles`: the [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) representation of a molecule
* `selfies`: the [SELFIES](https://github.com/aspuru-guzik-group/selfies) representation of a molecule
* `target`: clinical trial toxicity (or absence of toxicity)
### Data Splits
The dataset is split into an 80/10/10 train/valid/test split using scaffold split.
### Source Data
#### Initial Data Collection and Normalization
Data was originially generated by the Pande Group at Standford
### Licensing Information
This dataset was originally released under an MIT license
### Citation Information
```
@misc{https://doi.org/10.48550/arxiv.1703.00564,
doi = {10.48550/ARXIV.1703.00564},
url = {https://arxiv.org/abs/1703.00564},
author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay},
keywords = {Machine Learning (cs.LG), Chemical Physics (physics.chem-ph), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
title = {MoleculeNet: A Benchmark for Molecular Machine Learning},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
Thanks to [@zanussbaum](https://github.com/zanussbaum) for adding this dataset.
|
false |
### Roboflow Dataset Page
https://universe.roboflow.com/ashish-cuamw/test-y7rj3
### Citation
```
@misc{ test-y7rj3_dataset,
title = { test Dataset },
type = { Open Source Dataset },
author = { ashish },
howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } },
url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { oct },
note = { visited on 2022-12-28 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on December 26, 2022 at 10:13 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 4666 images.
T are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Stretch)
No image augmentation techniques were applied.
|
false |
# Dataset Card for XAlign
## 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)
- [Known Limitations](#known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [XAlign homepage](https://github.com/tushar117/XAlign)
- **Repository:** [XAlign repo](https://github.com/tushar117/XAlign)
- **Paper:** [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://arxiv.org/abs/2202.00291)
- **Leaderboard:** [Papers With Code Leaderboard for XAlign](https://paperswithcode.com/sota/data-to-text-generation-on-xalign)
- **Point of Contact:** [Tushar Abhishek](tushar.abhishek@research.iiit.ac.in)
### Dataset Summary
It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset where facts are in English and corresponding sentences are in native language for person biographies. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.
### Supported Tasks and Leaderboards
- 'Data-to-text Generation': XAlign dataset can be used to train cross-lingual data-to-text generation models. The model performance can measured through any text generation evaluation metrics by taking average across all the languages. [Sagare et al. (2022)](https://arxiv.org/abs/2209.11252) reported average BLEU score of 29.27 and average METEOR score of 53.64 over the test set.
- 'Relation Extraction': XAlign could also be used for cross-lingual relation extraction where relations in English can be extracted from associated native sentence.
See [Papers With Code Leaderboard](https://paperswithcode.com/sota/data-to-text-generation-on-xalign) for more models.
### Languages
Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and English (en).
## Dataset Structure
### Data Fields
Each record consist of the following entries:
- sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.)
- `facts` (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.
- language (string) : Language identifier.
The `facts` key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:
- subject (string) : central entity.
- object (string) : entity or a piece of information about the subject.
- predicate (string) : relationship that connects the subject and the object.
- qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier_predicate to represent property of qualifer and qualifier_object to store value for the qualifier's predicate.
### Data Instances
Example from English
```
{
"sentence": "Mark Paul Briers (born 21 April 1968) is a former English cricketer.",
"facts": [
{
"subject": "Mark Briers",
"predicate": "date of birth",
"object": "21 April 1968",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "occupation",
"object": "cricketer",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "country of citizenship",
"object": "United Kingdom",
"qualifiers": []
}
],
"language": "en"
}
```
Example from one of the low-resource languages (i.e. Hindi)
```
{
"sentence": "बोरिस पास्तेरनाक १९५८ में साहित्य के क्षेत्र में नोबेल पुरस्कार विजेता रहे हैं।",
"facts": [
{
"subject": "Boris Pasternak",
"predicate": "nominated for",
"object": "Nobel Prize in Literature",
"qualifiers": [
{
"qualifier_predicate": "point in time",
"qualifier_subject": "1958"
}
]
}
],
"language": "hi"
}
```
### Data Splits
The XAlign dataset has 3 splits: train, validation, and test. Below are the statistics the dataset.
| Dataset splits | Number of Instances in Split |
| --- | --- |
| Train | 499155 |
| Validation | 55469 |
| Test | 7425 |
## Dataset Creation
### Curation Rationale
Most of the existing Data-to-Text datasets are available in English. Also, the structured Wikidata entries for person entities in low resource languages are minuscule in number compared to that in English. Thus, monolingual Data-to-Text for low resource languages suffers from data sparsity. XAlign dataset would be useful in creation of cross-lingual Data-to-Text generation systems that take a set of English facts as input and generates a sentence capturing the fact-semantics in the specified language.
### Source Data
#### Initial Data Collection and Normalization
The dataset creation process starts with an intial list of ~95K person entities selected from Wikidata and each of which has a link to a corresponding Wikipedia page in at least one of our 11 low resource languages. This leads to a dataset where every instance is a tuple containing entityID, English Wikidata facts, language identifier, Wikipedia URL for the entityID. The facts (in English) are extracted from the 20201221 WikiData dump for each entity using the [WikiData](https://query.wikidata.org) APIs. The facts are gathered only for the speficied Wikidata property (or relation) types that captures most useful factual information for person entities: WikibaseItem, Time, Quantity, and Monolingualtext.This leads to overall ~0.55M data instances across all the 12 languages. Also, for each language, the sentences (along with section information) are extracted from 20210520 Wikipedia XML dump using the pre-processing steps as described [here](https://arxiv.org/abs/2202.00291).
For every (entity, language) pair, the pre-processed dataset contains a set of English Wikidata facts and a set of Wikipedia sentences in that language. In order to create train and validation dataset, these are later passed through a two-stage automatic aligner as proposed in [abhishek et al. (2022)](https://arxiv.org/abs/2202.00291) to associate a sentence with a subset of facts.
#### Who are the source language producers?
The text are extracted from Wikipedia and facts are retrieved from Wikidata.
### Annotations
#### Annotation process
The Manual annotation of Test dataset was done in two phases. For both the phases, the annotators were presented with (low resource language sentence, list of English facts). They were asked to mark facts present in the given sentence. There were also specific guidelines to ignore redundant facts, handle abbreviations, etc. More detailed annotation guidelines and ethical statement are mentioned [here](https://docs.google.com/document/d/1ucGlf-Jm1ywQ_Fjw9f2UqPeMWPlBnlZA46UY7KuZ0EE/edit)
. In the first phase, we got 60 instances labeled per language by a set of 8 expert annotators (trusted graduate students who understood the task very well). In phase 2, we selected 8 annotators per language from the [National Register of Translators](https://www.ntm.org.in/languages/english/nrtdb.aspx}). We tested these annotators using phase 1 data as golden control set, and shortlisted up to 4 annotators per language who scored highest (on Kappa score with golden annotations).
#### Who are the annotators?
Human annotators were selected appropriately (after screening) from [National Translation Mission](https://www.ntm.org.in) for Test set creation.
### Personal and Sensitive Information
The dataset does not involve collection or storage of any personally identifiable information or offensive information at any stage.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of the this dataset is to help develop cross-lingual Data-to-Text generation systems that are vital in many downstream Natural Language Processing (NLP) applications like automated dialog systems, domain-specific chatbots, open domain question answering, authoring sports reports, etc. These systems will be useful for powering business applications like Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc.
### Known Limitations
The XAlign dataset focus only on person biographies and system developed on this dataset might not be generalized to other domains.
## Additional Information
### Dataset Curators
This dataset is collected by Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma of Information Retrieval and Extraction Lab (IREL), Hyderabad, India. They released [scripts](https://github.com/tushar117/xalign) to collect and process the data into the Data-to-Text format.
### Licensing Information
The XAlign dataset is released under the [MIT License](https://github.com/tushar117/XAlign/blob/main/LICENSE).
### Citation Information
```
@article{abhishek2022xalign,
title={XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages},
author={Abhishek, Tushar and Sagare, Shivprasad and Singh, Bhavyajeet and Sharma, Anubhav and Gupta, Manish and Varma, Vasudeva},
journal={arXiv preprint arXiv:2202.00291},
year={2022}
}
```
### Contributions
Thanks to [Tushar Abhishek](https://github.com/tushar117), [Shivprasad Sagare](https://github.com/ShivprasadSagare), [Bhavyajeet Singh](https://github.com/bhavyajeet), [Anubhav Sharma](https://github.com/anubhav-sharma13), [Manish Gupta](https://github.com/blitzprecision) and [Vasudeva Varma](vv@iiit.ac.in) for adding this dataset.
Additional thanks to the annotators from National Translation Mission for their crucial contributions to creation of the test dataset: Bhaswati Bhattacharya, Aditi Sarkar, Raghunandan B. S., Satish M., Rashmi G.Rao, Vidyarashmi PN, Neelima Bhide, Anand Bapat, Krishna Rao N V, Nagalakshmi DV, Aditya Bhardwaj
Vuppula, Nirupama Patel, Asir. T, Sneha Gupta, Dinesh Kumar, Jasmin Gilani, Vivek R, Sivaprasad S, Pranoy J, Ashutosh Bharadwaj, Balaji Venkateshwar, Vinkesh Bansal, Vaishnavi Udyavara, Ramandeep Singh, Khushi Goyal, Yashasvi LN Pasumarthy and Naren Akash. |
true |
# Dataset Card for aeroBERT-classification
## Dataset Description
- **Paper:** aeroBERT-Classifier: Classification of Aerospace Requirements using BERT
- **Point of Contact:** archanatikayatray@gmail.com
### Dataset Summary
This dataset contains requirements from the aerospace domain. The requirements are tagged based on the "type"/category of requirement they belong to.
The creation of this dataset is aimed at - <br>
(1) Making available an **open-source** dataset for aerospace requirements which are often proprietary <br>
(2) Fine-tuning language models for **requirements classification** specific to the aerospace domain <br>
This dataset can be used for training or fine-tuning language models for the identification of the following types of requirements - <br>
<br>
**Design Requirement** - Dictates "how" a system should be designed given certain technical standards and specifications;
**Example:** Trim control systems must be designed to prevent creeping in flight.<br>
<br>
**Functional Requirement** - Defines the functions that need to be performed by a system in order to accomplish the desired system functionality;
**Example:** Each cockpit voice recorder shall record the voice communications of flight crew members on the flight deck.<br>
<br>
**Performance Requirement** - Defines "how well" a system needs to perform a certain function;
**Example:** The airplane must be free from flutter, control reversal, and divergence for any configuration and condition of operation.<br>
## Dataset Structure
The tagging scheme followed: <br>
(1) Design requirements: 0 (Count = 149) <br>
(2) Functional requirements: 1 (Count = 99) <br>
(3) Performance requirements: 2 (Count = 62) <br>
<br>
The dataset is of the format: ``requirements | label`` <br>
| requirements | label |
| :----: | :----: |
| Each cockpit voice recorder shall record voice communications transmitted from or received in the airplane by radio.| 1 |
| Each recorder container must be either bright orange or bright yellow.| 0 |
| Single-engine airplanes, not certified for aerobatics, must not have a tendency to inadvertently depart controlled flight. | 2|
| Each part of the airplane must have adequate provisions for ventilation and drainage. | 0 |
| Each baggage and cargo compartment must have a means to prevent the contents of the compartment from becoming a hazard by impacting occupants or shifting. | 1 |
## Dataset Creation
### Source Data
A total of 325 aerospace requirements were collected from Parts 23 and 25 of Title 14 of the Code of Federal Regulations (CFRs) and annotated (refer to the paper for more details). <br>
### Importing dataset into Python environment
Use the following code chunk to import the dataset into Python environment as a DataFrame.
```
from datasets import load_dataset
import pandas as pd
dataset = load_dataset("archanatikayatray/aeroBERT-classification")
#Converting the dataset into a pandas DataFrame
dataset = pd.DataFrame(dataset["train"]["text"])
dataset = dataset[0].str.split('*', expand = True)
#Getting the headers from the first row
header = dataset.iloc[0]
#Excluding the first row since it contains the headers
dataset = dataset[1:]
#Assigning the header to the DataFrame
dataset.columns = header
#Viewing the last 10 rows of the annotated dataset
dataset.tail(10)
```
### Annotations
#### Annotation process
A Subject Matter Expert (SME) was consulted for deciding on the annotation categories for the requirements.
The final classification dataset had 149 Design requirements, 99 Functional requirements, and 62 Performance requirements.
Lastly, the 'labels' attached to the requirements (design requirement, functional requirement, and performance requirement) were converted into numeric values: 0, 1, and 2 respectively.
### Limitations
(1)The dataset is an imbalanced dataset (more Design requirements as compared to the other types). Hence, using ``Accuracy`` as a metric for the model performance is
NOT a good idea. The use of Precision, Recall, and F1 scores are suggested for model performance evaluation.
(2)This dataset does not contain a test set. Hence, it is suggested that the user split the dataset into training/validation/testing after importing the data into a Python environment.
Please refer to the Appendix of the paper for information on the test set.
### Citation Information
```
@Article{aeroBERT-Classifier,
AUTHOR = {Tikayat Ray, Archana and Cole, Bjorn F. and Pinon Fischer, Olivia J. and White, Ryan T. and Mavris, Dimitri N.},
TITLE = {aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT},
JOURNAL = {Aerospace},
VOLUME = {10},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {279},
URL = {https://www.mdpi.com/2226-4310/10/3/279},
ISSN = {2226-4310},
DOI = {10.3390/aerospace10030279}
}
@phdthesis{tikayatray_thesis,
author = {Tikayat Ray, Archana},
title = {Standardization of Engineering Requirements Using Large Language Models},
school = {Georgia Institute of Technology},
year = {2023},
doi = {10.13140/RG.2.2.17792.40961},
URL = {https://repository.gatech.edu/items/964c73e3-f0a8-487d-a3fa-a0988c840d04}
}
``` |
false |
# Wikipedia (zh) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (zh)](https://zh.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
false | # Dataset Card for "SIRI-WHU"
## Dataset Description
- **Paper** [Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
- **Paper** [The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
- **Paper** [Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
### Licensing Information
CC BY-NC-ND
## Citation Information
[Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
[The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
[Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
```
@article{zhao2015dirichlet,
title={Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery},
author={Zhao, Bei and Zhong, Yanfei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={54},
number={4},
pages={2108--2123},
year={2015},
publisher={IEEE}
}
@article{zhao2016fisher,
title={The Fisher kernel coding framework for high spatial resolution scene classification},
author={Zhao, Bei and Zhong, Yanfei and Zhang, Liangpei and Huang, Bo},
journal={Remote Sensing},
volume={8},
number={2},
pages={157},
year={2016},
publisher={MDPI}
}
@article{zhu2016bag,
title={Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery},
author={Zhu, Qiqi and Zhong, Yanfei and Zhao, Bei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Geoscience and Remote Sensing Letters},
volume={13},
number={6},
pages={747--751},
year={2016},
publisher={IEEE}
}
``` |
false |
# Dataset Card for DBLP-QuAD
## 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:** [DBLP-QuAD Homepage]()
- **Repository:** [DBLP-QuAD Repository](https://github.com/awalesushil/DBLP-QuAD)
- **Paper:** DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph
- **Point of Contact:** [Sushil Awale](mailto:sushil.awale@web.de)
### Dataset Summary
DBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Dataset Structure
### Data Instances
An example of a question is given below:
```
{
"id": "Q0577",
"query_type": "MULTI_FACT",
"question": {
"string": "What are the primary affiliations of the authors of the paper 'Graphical Partitions and Graphical Relations'?"
},
"paraphrased_question": {
"string": "List the primary affiliations of the authors of 'Graphical Partitions and Graphical Relations'."
},
"query": {
"sparql": "SELECT DISTINCT ?answer WHERE { <https://dblp.org/rec/journals/fuin/ShaheenS19> <https://dblp.org/rdf/schema#authoredBy> ?x . ?x <https://dblp.org/rdf/schema#primaryAffiliation> ?answer }"
},
"template_id": "TP11",
"entities": [
"<https://dblp.org/rec/journals/fuin/ShaheenS19>"
],
"relations": [
"<https://dblp.org/rdf/schema#authoredBy>",
"<https://dblp.org/rdf/schema#primaryAffiliation>"
],
"temporal": false,
"held_out": true
}
```
### Data Fields
- `id`: the id of the question
- `question`: a string containing the question
- `paraphrased_question`: a paraphrased version of the question
- `query`: a SPARQL query that answers the question
- `query_type`: the type of the query
- `query_template`: the template of the query
- `entities`: a list of entities in the question
- `relations`: a list of relations in the question
- `temporal`: a boolean indicating whether the question contains a temporal expression
- `held_out`: a boolean indicating whether the question is held out from the training set
### Data Splits
The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Additional Information
### Licensing Information
DBLP-QuAD is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
In review.
### Contributions
Thanks to [@awalesushil](https://github.com/awalesushil) for adding this dataset.
|
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