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1941df61807cb4a99712d25115704fda9a0f8b25 |
# Dataset Card for "FUNSD"
## 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
### Dataset Summary
The [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, with one difference compared to the original dataset, each document image is resized to 224x224.
The FUNSD dataset is a collection of annotated forms.
This dataset loading script is taken from the [official LayoutLMv2 implementation](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/data/datasets/funsd.py), and updated to not include any Detectron2 dependencies.
### 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
#### conll2000
- **Size of downloaded dataset files:** 3.32 MB
- **Size of the generated dataset:** 6.25 MB
- **Total amount of disk used:** 9.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 13, 11, 12, 21, 22, 22, 22, 22, 11, 12, 12, 17, 11, 12, 13, 11, 0, 1, 13, 11, 11, 0, 21, 22, 22, 11, 12, 12, 13, 11, 12, 12, 11, 12, 12, 0],
"id": "0",
"pos_tags": [19, 14, 11, 19, 39, 27, 37, 32, 34, 11, 15, 19, 14, 19, 22, 14, 20, 5, 15, 14, 19, 19, 5, 34, 32, 34, 11, 15, 19, 14, 20, 9, 20, 24, 15, 22, 6],
"tokens": "[\"Confidence\", \"in\", \"the\", \"pound\", \"is\", \"widely\", \"expected\", \"to\", \"take\", \"another\", \"sharp\", \"dive\", \"if\", \"trade\", \"figur..."
}
```
### Data Fields
The data fields are the same among all splits.
### Data Splits
## 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{DBLP:journals/corr/abs-1905-13538,
author = {Guillaume Jaume and
Hazim Kemal Ekenel and
Jean{-}Philippe Thiran},
title = {{FUNSD:} {A} Dataset for Form Understanding in Noisy Scanned Documents},
journal = {CoRR},
volume = {abs/1905.13538},
year = {2019},
url = {http://arxiv.org/abs/1905.13538},
archivePrefix = {arXiv},
eprint = {1905.13538},
timestamp = {Mon, 03 Jun 2019 13:42:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-13538.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset. | nielsr/FUNSD_layoutlmv2 | [
"language:en",
"arxiv:1905.13538",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"language": ["en"], "paperswithcode_id": "funsd"} | 2022-10-25T08:51:20+00:00 |
e4806d1eaf1dcab208ac30a6c921c710bc104374 | preprocessed removing mojibake texts | nlpufg/brwac-pt | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-07-12T22:25:24+00:00 |
73c8546913e7e34d1378c8ac74795539d55aa837 | preprocessed removing mojibake texts | nlpufg/oscar-pt | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-07-12T22:26:15+00:00 |
5331951940a16c65ff8a1bfaff0724d2944065a9 | nlpyeditepe/tr-qnli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|glue",
"license:mit",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["found"], "language_creators": ["machine-generated"], "language": ["tr-TR"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["extended|glue"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "QNLI for Turkish"} | 2022-07-01T14:28:44+00:00 |
|
0996e5fe4b8d41d9ce4c899a0f04d2f801f1fc33 | nlpyeditepe/tr_rte | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|glue",
"license:mit",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["found"], "language_creators": ["machine-generated"], "language": ["tr-TR"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["extended|glue"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "RTE for Turkish"} | 2022-07-01T14:28:27+00:00 |
|
f9172c55616145ca31d33872003249043c8f805c | annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
languages:
- en
licenses:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: squad
pretty_name: SQuAD
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa | nntadotzip/iuQAchatbot | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-01-20T07:25:26+00:00 |
65563031418b17855bf1f0c5252faa7c674109f0 |
# Dataset Card for notional-python
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [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://notional.ai/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models.
Follow [our repo]() to do the model evaluation using notional-python dataset.
### Languages
Python
## Dataset Creation
### Curation Rationale
Notional-python was built to provide a dataset for testing the ability of the machine to generate python code.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by filtering code from [Google Bigquery Github data](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code)
In order to improve the quality of the dataset, only python code files that meet the below conditions are added to the dataset:
- Code with more than 60% of executable lines
- Code with logic, not config files or comment-only files
- Code with more than 30% of attribute declaration lines (E.G.: Some files contain just only class names and their class attributes, usually used for configuration of the project, these files were not selected)
- Code without `TODO` and `FIXME`.
#### Who are the source language producers?
The producers are users of github.
| notional/notional-python | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:py",
"license:unknown",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["py"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["code-generation", "conditional-text-generation"], "task_ids": ["language-modeling", "code-generation"]} | 2022-10-21T12:39:56+00:00 |
7314a5b74f065dca9d690a5494d3145b531f1d85 | # Ansanb done vwa an Kreyòl pou antrene DeepSPeech.
Dataset sa a gen plis pase 7 è tan anrejistreman vwa ak prèske 100 moun an Kreyòl pou bati sistèm ASR ak TTS pou lang Kreyòl la.
Pifò nan done yo soti nan "CMU Haitian Creole Speech Recognition Database" la.
Done sa yo gentan filtre epi òganize pou ka antrene modèl DeepSPeech Mozilaa a.
Si toutfwa ou ta bezwen jwenn plis enfòmasyon sou jan done yo ranje a epi kisa ou ka fè avèk yo, tcheke [DeepSpeech Readme](https://deepspeech.readthedocs.io/en/r0.9/TRAINING.html).
| nucklehead/ht-voice-dataset | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-04-14T11:34:47+00:00 |
a5c0dce3bed22d5a2fffb5a26b6f9c349e6b8f6c | egrfdfaffd | oelkrise/CRT | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-03-28T14:32:38+00:00 |
2b4f7179a68a023d210c30b5b093b178b5686760 | ## Novel Aggressive Text Dataset in Bengali
## Tackling Cyber-Aggression: Identification and Fine-Grained Categorization of Aggressive Texts on Social Media using Weighted Ensemble of Transformers
**Author:** Omar Sharif and Mohammed Moshiul Hoque
**Related Papers:**
[Paper1 in Neurocomputing Journal](https://www.sciencedirect.com/science/article/abs/pii/S0925231221018567)
[Paper2 in CONSTRAINT@AAAI-2021](https://link.springer.com/chapter/10.1007%2F978-3-030-73696-5_2)
[Paper3 in LTEDI@EACL-2021](https://link.springer.com/chapter/10.1007%2F978-3-030-73696-5_2)
## Abstract
The pervasiveness of aggressive content in social media has become a serious concern for government organizations and tech companies because of its pernicious societal effects. In recent years, social media has been repeatedly used as a tool to incite communal aggression, spread distorted propaganda, damage social harmony and demean the identity of individuals or a community in the public spaces. Therefore, restraining the proliferation of aggressive content and detecting them has become an urgent duty. Studies of the identification of aggressive content have mostly been done for English and other resource-high languages. Automatic systems developed for those languages can not accurately identify detrimental contents written in regional languages like Bengali. To compensate this insufficiency, this work presents a novel Bengali aggressive text dataset (called ‘BAD’) with two-level annotation. In level-A, 14158 texts are labeled as either aggressive or non-aggressive. While in level-B, 6807 aggressive texts are categorized into religious, political, verbal and gendered aggression classes each having 2217, 2085, 2043 and 462 texts respectively. This paper proposes a weighted ensemble technique including m-BERT, distil-BERT, Bangla-BERT and XLM-R as the base classifiers to identify and classify the aggressive texts in Bengali. The proposed model can readdress the softmax probabilities of the participating classifiers depending on their primary outcomes. This weighting technique has enabled the model to outdoes the simple average ensemble and all other machine learning (ML), deep learning (DL) baselines. It has acquired the highest weighted f1-score of 93.43% in the identification task and 93.11% in the categorization task.
## Contribution
Major contributions of this work can be illustrated in the following:
- Dataset:present a new Bengali aggressive text dataset which contains 6807 aggressive and 7351 non-aggressive texts. Furthermore, by employing a hierarchical annotation schema, aggressive texts are annotated into religious, political, verbal and gendered aggression classes.
- Insights: provide useful insights and detailed statistics of the data that ensure the quality of the dataset.
- Model: develop a weighted ensemble model using m-BERT, distil-BERT, Bangla-BERT, XLM-R to identify and categorize aggressive Bengali texts. The proposed model emphasizes the participating classifiers' softmax probabilities based on their previous performance on the dataset. This weighting technique outperforms the simple average ensemble approach and enhances the classifier performance in the developed dataset.
- Benchmarking: investigate and compare the performance of the proposed model with other ML, DL baselines and existing techniques, thus setting up a benchmark work to compare in the future.
- Error analysis: deeply analyze the results and errors of the proposed model. Presents qualitative and quantitative analysis that shed light on the reasons behind some of the errors and provide a few directions that might help to mitigate the system's deficiency.
This research is one of the pioneering works that aims to identify and classify aggressive texts in Bengali as per our exploration. We expect that the resources developed in this work will pave the way for aggressive text classification researchers in Bengali.
## Ackonwlegements
We sincerely acknowledge the anonymous reviewers for their insightful suggestions, which help to improve the work. This work was supported by the ICT Innovation Fund, ICT Division and Directorate of Research & Extension, CUET. Thanks to [Prof. Dr. Mohammed Moshiul Hoque](https://www.researchgate.net/profile/Moshiul_Hoque) sir for his valuable guidance.
## Cite this work
If you find this repository helpful in your work please cite the following
```
@article{SHARIF2021,
title = {Tackling Cyber-Aggression: Identification and Fine-Grained Categorization of Aggressive Texts on Social Media using Weighted Ensemble of Transformers},
journal = {Neurocomputing},
year = {2021},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2021.12.022},
url = {https://www.sciencedirect.com/science/article/pii/S0925231221018567},
author = {Omar Sharif and Mohammed Moshiul Hoque},
keywords = {Natural language processing, Aggressive text classification, Low resource language, Bengali aggressive text corpus, Deep learning, Transformers, Ensemble},
abstract = {The pervasiveness of aggressive content in social media has become a serious concern for government organizations and tech companies because of its pernicious societal effects. In recent years, social media has been repeatedly used as a tool to incite communal aggression, spread distorted propaganda, damage social harmony and demean the identity of individuals or a community in the public spaces. Therefore, restraining the proliferation of aggressive content and detecting them has become an urgent duty. Studies of the identification of aggressive content have mostly been done for English and other resource-high languages. Automatic systems developed for those languages can not accurately identify detrimental contents written in regional languages like Bengali. To compensate this insufficiency, this work presents a novel Bengali aggressive text dataset (called ‘BAD’) with two-level annotation. In level-A, 14158 texts are labeled as either aggressive or non-aggressive. While in level-B, 6807 aggressive texts are categorized into religious, political, verbal and gendered aggression classes each having 2217, 2085, 2043 and 462 texts respectively. This paper proposes a weighted ensemble technique including m-BERT, distil-BERT, Bangla-BERT and XLM-R as the base classifiers to identify and classify the aggressive texts in Bengali. The proposed model can readdress the softmax probabilities of the participating classifiers depending on their primary outcomes. This weighting technique has enabled the model to outdoes the simple average ensemble and all other machine learning (ML), deep learning (DL) baselines. It has acquired the highest weighted f1-score of 93.43% in the identification task and 93.11% in the categorization task.}
}
@InProceedings{sharif2021constraint,
author="Sharif, Omar
and Hoque, Mohammed Moshiul",
editor="Chakraborty, Tanmoy and et al.",
title="Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation",
booktitle="Combating Online Hostile Posts in Regional Languages during Emergency Situation",
year="2021",
publisher="Springer Nature Switzerland AG",
pages="1--12",
doi = {https://doi.org/10.1007/978-3-030-73696-5_2},
}
@inproceedings{sharif-etal-2021-nlp,
title = "{NLP}-{CUET}@{D}ravidian{L}ang{T}ech-{EACL}2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers",
author = "Sharif, Omar and
Hossain, Eftekhar and
Hoque, Mohammed Moshiul",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dravidianlangtech-1.35",
pages = "255--261",
abstract = "The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or contents. Most of such offensive posts are written in a cross-lingual manner and can easily evade the online surveillance systems. This paper presents an automated system that can identify offensive text from multilingual code-mixed data. In the task, datasets provided in three languages including Tamil, Malayalam and Kannada code-mixed with English where participants are asked to implement separate models for each language. To accomplish the tasks, we employed two machine learning techniques (LR, SVM), three deep learning (LSTM, LSTM+Attention) techniques and three transformers (m-BERT, Indic-BERT, XLM-R) based methods. Results show that XLM-R outperforms other techniques in Tamil and Malayalam languages while m-BERT achieves the highest score in the Kannada language. The proposed models gained weighted f{\_}1 score of 0.76 (for Tamil), 0.93 (for Malayalam ), and 0.71 (for Kannada) with a rank of 3rd, 5th and 4th respectively.",
}
```
## Note
`If you find any anomaly or have any query/suggestion feel free to ping.
| omar-sharif/BAD-Bengali-Aggressive-Text-Dataset | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-02-24T15:42:02+00:00 |
6bd475ffbfaef79351083a387c49bb03fc4575d7 | [Needs More Information]
# Dataset Card for EUMETSAT UK HRV
## 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://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:MSG15-RSS
- **Repository:** https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Jacob Bieker](mailto:[email protected])
### Dataset Summary
<p>The EUMETSAT Spinning Enhanced Visible and InfraRed Imager (SEVIRI) rapid scanning service (RSS) takes an image of the northern third of the Meteosat disc every five minutes (see <a href="https://www.eumetsat.int/rapid-scanning-service">the EUMETSAT website for more information on SEVIRI RSS</a>). The <a href="https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:MSG15-RSS">original EUMETSAT dataset</a> contains data from 2008 to the present day from 12 channels, and for a wide geographical extent covering North Africa, Saudi Arabia, all of Europe, and Western Russia. In contrast, this dataset on Google Cloud is a small subset of the entire SEVIRI RSS dataset: This Google Cloud dataset is from a single channel: the "high resolution visible" (HRV) channel; and contains data from January 2020 to November 2021. The geographical extent of this dataset on Google Cloud is a small subset of the total SEVIRI RSS extent: This Google Cloud dataset includes data over the United Kingdom and over North Western Europe.</p>
<p>This dataset is slightly transformed: It does not contain the original numerical values. See the "samples" section for more technical detail about the dataset.</p>
<p>The original data is copyright <a href="https://www.eumetsat.int">EUMETSAT</a>. EUMETSAT has given permission to redistribute this transformed data. The data was transformed by <a href="https://openclimatefix.org/">Open Climate Fix</a> using <a href="https://github.com/openclimatefix/Satip">satip</a>.</p>
### 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
This dataset was originally created for helping improve national PV output forecasts in combination with Numerical Weather Predictions and other data sources.
### Source Data
#### Initial Data Collection and Normalization
Data generated by <a href="https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:MSG15-RSS">EUMETSAT Rapid Scan High Rate SEVIRI Level 1.5 Image Data MSG</a> with preparation by <a href="https://openclimatefix.org/">Open Climate Fix</a>
#### 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
This dataset only includes the High Resolution Visible channel, and covers only the area around the UK. Additionally, the RSS service is shut down for 1 month each year, and so data for most of
February 2020 and 2021 does not exist.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
<p>Cite EUMETSAT as the data source. This data is redistributed with permission from EUMETSAT under the terms of the <a href="https://www.eumetsat.int/eumetsat-data-licensing">EUMETSAT Data Policy for SEVIRI data with a latency of >3 hours</a>.
### Citation Information
Cite EUMETSAT as the data source. | openclimatefix/eumetsat_uk_hrv | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-08-04T10:40:24+00:00 |
b99847afc7bd0a90dece766d0dee1c0ae4d420c4 | openclimatefix/gfs | [
"license:mit",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"license": "mit"} | 2022-02-14T13:09:42+00:00 |
|
544df619b21086e1046b3f3f9aea7a48d371e53e | # Dataset Card for Goes-MRMS
## 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
This dataset is a combination of GOES-16 data and MRMS radar precipitation data to roughly match the unreleased dataset used to train Google Research's MetNet. In the papers they used GOES-16 satellite imagery, MultiRadar/Multi-System (MRMS) instantaneous precipitation, hourly cumulative precipitation, and High Resolution Rapid Refresh NWP initializations as inputs to predict future MRMS precipitation rates. The precipitation rates were binned into 0.2mm/hr bins to make the output a classification task, and allow for the models to predict a probability distribution over the region of interest.
Additionally, the input image patches are much larger than the target image patches. For MetNet, the input images covered 512x512 km area, while the target was the center 64x64 km crop. For MetNet-2 the input covered 2048x2048 km with the target being the central 512x512 km.
### 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
MetNet (January 2018-July 2019) (16 days training, 2 days validation, 2 days test)
MetNet-2 (July 2017-August 2020) (Non-overlapping time ranges with 12 hour black outs in between)
Full (July 2017-January 2022) (Train: 2017-2020. except for first of the month, Validation: first of the month July 2017-2020, Test: 2021-2022)
## Dataset Creation
### Curation Rationale
The original curation rationale was for forecasting precipitation rate in a probabilistic way. This dataset covers a different time period than in the original paper, going from July 2017 through December 2021. There is a split available to match the temporal coverage of the original MetNet paper, (Janurary 2018 to July 2019) or the MetNet-2 paper (July 2017 to August 2020).
### Source Data
#### Initial Data Collection and Normalization
From the MetNet paper: "For both MRMS and GOES we acquired data for the period January 2018 through July 2019. We split the data temporally into three non-overlapping data sets by repeatedly using approximately 16 days for training followed by two days for validation and two days for testing. From these temporal splits we randomly extracted 13,717 test and validation samples and kept increasing the training set size until we observed no over-fitting at 1.72 million training samples."
From the MetNet-2 paper: "The training data consists of 1,230,585 patches of size 2048 km x 2048 km at the input and targets of size 512 km x 512 km including all 360 (2 to 720 minutes) time slices. The training area covers a region of 7000x2500 kilometers. We sample target patches from the input context region minus an all around border of 512 km. The input context is padded for all regions outside of the 7000x2500 CONUS. The validation data used for developing the models consists of 11,991 patches and the test data of 39,864 patches. The training, validation and test data are drawn from non-overlapping ranges of hours, with black out periods of 12 hours in between, over a period of observations of 3 years from July 2017 to August 2020. This ensures that the model does not learn any spurious training and evaluation correlations within any single day. HRRR only generates forecasts starting at full hours."
#### 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
Jacob Bieker ([email protected])
MetNet-1 split: MetNet Authors
MetNet-2 split: MetNet-2 Authors
### Licensing Information
All data is open and without restrictions from NOAA.
### Citation Information
Please cite NOAA as the data provider. | openclimatefix/goes-mrms | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2023-05-12T07:56:03+00:00 |
f433a990cca7574d4ed4687e7fa969ccad0dbeb3 | [Needs More Information]
# Dataset Card for UK Nimrod 1km Rainfall Radar 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:** https://github.com/deepmind/deepmind-research/tree/master/nowcasting
- **Repository:** https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km
- **Paper:** [Skillful Precipitation Nowcasting using Deep Generative Models of Radar, Ravuri et al. 2021](https://www.nature.com/articles/s41586-021-03854-z)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Jacob Bieker](mailto:[email protected])
### Dataset Summary
This dataset contains UK Nimrod rainfall radar data for 2016-2019 as used in the Skillful Precipitation Nowcasting Using Deep Generative Model of Radar paper by DeepMind. This dataset is an unofficial mirror of the open sourced dataset available here: gs://dm-nowcasting/datasets/nowcasting_open_source_osgb/nimrod_osgb_1000m_yearly_splits/radar/20200718
### 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
The train data is all days except the first of each month for 2016-2018. The validation is the first of every month for 2016-2018. The test data is all of 2019.
## Dataset Creation
### Curation Rationale
This dataset was originally created for training a generative model for forecasting rainfall percipitation.
### Source Data
#### Initial Data Collection and Normalization
DeepMind initially collected the data from the UK Met Office and post processed it into this dataset.
#### 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
The provided post-processed nowcasting dataset is licensed under a Creative Commons Attribution 4.0 International License and it contains public sector information licensed by the Met Office under the Open Government Licence v3.0.
### Citation Information
Cite DeepMind, and the authors of [Skillful Precipitation Nowcasting using Deep Generative Models of Radar, Ravuri et al. 2021](https://www.nature.com/articles/s41586-021-03854-z). | openclimatefix/nimrod-uk-1km | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-06-08T13:49:03+00:00 |
af9b3be1603dbb85d9b98d3b8db844ed317c85e5 | orisuchy/Descriptive_Sentences_He | [
"license:afl-3.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"license": "afl-3.0"} | 2022-03-03T10:19:56+00:00 |
|
ff99fc6fcee76887657c0b002350d81f13a38a9e |
# Dataset Card for "oscar"
## 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://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [github.com/oscar-corpus/corpus](https://github.com/oscar-corpus/corpus)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [ungoliant](https://github.com/oscar-corpus/ungoliant) architecture. Data is distributed by language in both original and deduplicated form.
### Supported Tasks and Leaderboards
OSCAR is mainly inteded to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 168 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
OSCAR 21.09 has known issues regarding specific languages.
Note that other issues may (and could) be present in other languages.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
|Language code|Language|Issues|
|-------------|--------|------|
|`tg`|Tajik|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Atg+label%3Aver%3A21.09)|
|`tr`|Turkish|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Atr+label%3Aver%3A21.09)|
|`vls`|West Flemish|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aopen+label%3Alang%3Avls+label%3Aver%3A21.09)|
|`wuu`|Wu Chinese|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Awuu+label%3Aver%3A21.09)|
|`nap`|Neapolitan|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Anap+label%3Aver%3A21.09)|
|`so`|Somali|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Aso+label%3Aver%3A21.09)|
|`frr`|Northern Frisian|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Afrr+label%3Aver%3A21.09)|
|`cbk`|Chavacano|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Acbk+label%3Aver%3A21.09)|
|`sco`|Scots|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Asco+label%3Aver%3A21.09)|
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
<details>
<summary>Click to expand the Data/size information for each language (deduplicated)</summary>
#### deduplicated_af
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3287,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BUOBNDDY3VZKNNUOY33PAWBXEVNDCDJK',
'warc-date': '2021-03-09T04:21:33Z',
'warc-identified-content-language': 'afr,eng',
'warc-record-id': '<urn:uuid:dece1e30-a099-411a-87fd-483791342d48>',
'warc-refers-to': '<urn:uuid:5a35e8b2-0fcb-4600-9d15-f5c6469ddf01>',
'warc-target-uri': 'http://www.northwestnewspapers.co.za/gemsbok/2015-06-18-10-02-17/hoe-om-n-ad-te-plaas/1907-man-betrap-met-jagluiperd-en-leeu-bene',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Stap 2: Tik jou ad in die teks boksie, jy sal sien dat die prys aan '
'die regterkant van die boksie verander volgens di...'}
```
#### deduplicated_als
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4607,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:URQ53Z4I4KGPHICZYLW2ZOX7OWWCGZUA',
'warc-date': '2021-03-03T16:09:20Z',
'warc-identified-content-language': 'deu,eng',
'warc-record-id': '<urn:uuid:134499db-d54a-4c29-9517-350cacc3d29d>',
'warc-refers-to': '<urn:uuid:073aeb77-b4ed-47eb-b955-27031963acf4>',
'warc-target-uri': 'https://als.m.wikipedia.org/wiki/Neukaledonien',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'D Wirtschaft bestoot vor allem us Handwärk, Bärgbau, Industrii und '
'Turismus. 40 Kilometer vo dr Hauptstadt Nouméa äwä...'}
```
#### deduplicated_am
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9679,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YADJOQVUOQHUKJ7BXCKKU4LRFKE3JPOA',
'warc-date': '2021-03-09T04:16:32Z',
'warc-identified-content-language': 'amh,eng',
'warc-record-id': '<urn:uuid:fa02fe22-c72e-42e8-9cb3-89da85a80941>',
'warc-refers-to': '<urn:uuid:ff89f862-5e6a-41aa-bc40-ef1d2f91d258>',
'warc-target-uri': 'http://ethioforum.ethiopiaforums.com/viewtopic.php?f=6&t=3874&p=6511',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': '(ፍኖተ ነፃነት) በኢትዮጵያ የአዉሮፓ ሕብረት ልኡካን ቡድን መሪ አምባሳደር ቻንታል ሔበሬሽ፣ በአዉሮፓ '
'ሕብረት የአፍሪካ ቀንድ እና የሕንድ ዉቂያኖስ አካባቢ ዴስክ ኦፌሴር ቪክቶሪያ ጋርሲ...'}
```
#### deduplicated_an
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 134014,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OG2T3MJFSLSH33PVI7D3WPXVE6ZFLZ4Z',
'warc-date': '2021-03-08T00:58:33Z',
'warc-identified-content-language': 'ara,fra',
'warc-record-id': '<urn:uuid:0ef1d002-86e7-49c1-ac8a-8ba933d190ee>',
'warc-refers-to': '<urn:uuid:5071f1f7-3350-406d-ad97-f292fe7a2ff0>',
'warc-target-uri': 'http://dorous.ek.la/1-5-a6032874?reply_comm=68653652',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو...'}
```
#### deduplicated_ar
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12677,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NFDDUGANGSGSFXIQAXEGIVHGRLFCUW55',
'warc-date': '2021-03-04T02:22:39Z',
'warc-identified-content-language': 'ara,eng',
'warc-record-id': '<urn:uuid:3ea1e651-68f3-4dde-bfea-7a12e5331084>',
'warc-refers-to': '<urn:uuid:dcecf9ad-1797-44d0-b06a-010c424ba396>',
'warc-target-uri': 'https://elmgals.net/?p=62804',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'مطحنة الكرة في ماسبات - orioloingeu. مطاحن الفرينة في مطحنة الكرة '
'مراكز بيع الة طحن التوابل بيع ألات لرحي اسعار بيع ا...'}
```
#### deduplicated_arz
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9603,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6O2LEGAWXAWYSRH2TQNYOWX47ZFWTKRC',
'warc-date': '2021-03-09T03:51:17Z',
'warc-identified-content-language': 'ara',
'warc-record-id': '<urn:uuid:0578411b-367f-4d52-b85c-56b4bb64c0be>',
'warc-refers-to': '<urn:uuid:8777119c-434c-49a1-80a8-f2b23fa0e21c>',
'warc-target-uri': 'https://www.hko-ommen.nl/Nov_01/605.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'مستعملة 4265 كسارات للبيع - كسارة الحجر. كسارات مستعمله للبيع فى '
'مصر. للبيع كسارات فى مصرمطلوب كسارات حجر مستعملة للب...'}
```
#### deduplicated_as
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9280,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DORQKORQ4TURDN35T75TW72IZ7IZIEFG',
'warc-date': '2021-03-03T15:06:57Z',
'warc-identified-content-language': 'asm,eng',
'warc-record-id': '<urn:uuid:fd6c3650-f91f-4f03-ae7a-bea654e043bb>',
'warc-refers-to': '<urn:uuid:48f057d6-f642-42d2-8de1-fec8e4fca4d4>',
'warc-target-uri': 'https://assam.nenow.in/%E0%A6%95%E0%A6%BE%E0%A6%87%E0%A6%B2%E0%A7%88%E0%A7%B0-%E0%A6%AA%E0%A7%B0%E0%A6%BE-%E0%A6%AF%E0%A7%8B%E0%A7%B0%E0%A6%B9%E0%A6%BE%E0%A6%9F%E0%A6%A4-%E0%A6%86%E0%A7%B0%E0%A6%AE%E0%A7%8D%E0%A6%AD/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'যোৰহাট জিলাৰ এন আৰ চি উন্নিতকৰণৰ প্ৰথম পৰ্য্যায়ৰ বংশবৃক্ষ পৰীক্ষণৰ '
'কাম কাইলৈৰ পৰা পৰীক্ষামূলকভাৱে আৰু ১৯ ফেব্ৰুৱাৰিৰ ...'}
```
#### deduplicated_ast
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3752,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BU44BHPYU2BOWH4TUAY7ZOEBFVQ6KD44',
'warc-date': '2021-03-01T15:56:44Z',
'warc-identified-content-language': 'spa',
'warc-record-id': '<urn:uuid:2b3ca12f-6614-4662-a4e9-16e1ce13a8b0>',
'warc-refers-to': '<urn:uuid:0e132db0-e0f4-44c5-ab63-48b7594a35a6>',
'warc-target-uri': 'https://elsummum.es/tag/dial-traxel-pais/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Esta ye la galería d’imáxenes de los participantes nel concursu, el '
'xuráu y dellos miembros de la organización de la ...'}
```
#### deduplicated_av
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2012,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EULKS66PQCWWVXHNRPSISI72G3GFJD7L',
'warc-date': '2021-03-01T10:13:53Z',
'warc-identified-content-language': 'rus,eng',
'warc-record-id': '<urn:uuid:c2986179-7947-4184-9df5-dca05c987055>',
'warc-refers-to': '<urn:uuid:8b3e82e1-0964-4677-8b39-9bd3c67be25b>',
'warc-target-uri': 'http://gazetalevashi.ru/articles/media/2019/10/25/diktant-tiobitiana/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Дагъистаналъул жамгIият рахьдал мацIал цIуниялде ва '
'церетIезариялде, тарих, гIадатал, маданият ва дагъистаналъул '
'халк...'}
```
#### deduplicated_az
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 59868,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LDASIZ5NDJU6NRCJW7XCCI4QRLFIZZQX',
'warc-date': '2021-02-26T04:13:32Z',
'warc-identified-content-language': 'aze',
'warc-record-id': '<urn:uuid:a35cc521-926e-442d-b285-299ea4a3b72a>',
'warc-refers-to': '<urn:uuid:b60fd7ea-7056-4ebb-8ae5-eb02617ca8cd>',
'warc-target-uri': 'https://azrefs.org/iqtisadi-tesebbuslere-yardim-ictimai-birliyi-yerli-seviyyede-i.html',
'warc-type': 'conversion'},
'nb_sentences': 70,
'offset': 0},
'text': 'İQTİsadi TƏŞƏBBÜSLƏRƏ yardim iCTİMAİ BİRLİYİ Yerli səviyyədə içməli '
'su təchizatı sisteminin idarə olunması\n'
'Az1009, Az...'}
```
#### deduplicated_azb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5245,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XWTKHZGKVJI6ZAIKSTOA4AOP5PCWI2SH',
'warc-date': '2021-03-05T13:35:27Z',
'warc-identified-content-language': 'fas,uzb,eng',
'warc-record-id': '<urn:uuid:41816fd7-985e-4e35-b79b-bf471e68dd80>',
'warc-refers-to': '<urn:uuid:5717a90d-021c-428b-a69d-45d6cb2fc692>',
'warc-target-uri': 'https://azb.wikipedia.org/wiki/%D8%A2%D9%85%D8%B3%D8%AA%D8%B1%D8%AF%D8%A7%D9%85_%D8%A8%DB%8C%D9%84%DB%8C%D9%85%E2%80%8C%DB%8C%D9%88%D8%B1%D8%AF%D9%88',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'یازی Creative Commons Attribution-ShareAlike '
'License;آلتیندا\u200cدیر آرتیق شرطلر آرتیریلا بیلر. آرتیق ایطلاعات '
'اوچون ایشل...'}
```
#### deduplicated_ba
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9444,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NRTIKDSYAPTPQ64CKKLNR6TFVUYG7CLR',
'warc-date': '2021-03-09T04:46:56Z',
'warc-identified-content-language': 'uig,eng',
'warc-record-id': '<urn:uuid:b69f43f4-0e19-4cad-b083-fce91a40f64b>',
'warc-refers-to': '<urn:uuid:3176da53-14ff-4f65-91e4-4d209e9c7190>',
'warc-target-uri': 'https://uyghurix.net/archives/date/2016/05?uls=us',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'линакис системисиниң көрүнмә йүзи барғансери ишлитишкә қулайлиқ '
'болуп, кәң ишлитиливатқан болсиму, әмили хизмәттә йән...'}
```
#### deduplicated_bar
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 105623,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:L7EXHEWTVKPV7BWPZJFKHM2TZ3ZNKPWC',
'warc-date': '2021-03-07T18:33:16Z',
'warc-identified-content-language': 'fra',
'warc-record-id': '<urn:uuid:578af8ce-2149-42e3-978c-5191caaaca8c>',
'warc-refers-to': '<urn:uuid:a7afc792-983c-43b7-9b5b-75b2dc5fcd77>',
'warc-target-uri': 'https://fr.readkong.com/page/automne-hiver-printemps-2017-8342349',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': ' '
'vo\n'
' ...'}
```
#### deduplicated_be
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3159,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TEJML7M4S55254DZU43DXXORKPZMKGUL',
'warc-date': '2021-03-09T05:47:09Z',
'warc-identified-content-language': 'bel,eng',
'warc-record-id': '<urn:uuid:e22883c9-5622-4a0e-b259-b5265e6e345a>',
'warc-refers-to': '<urn:uuid:7ec2102d-2645-4fd9-89b8-557762996439>',
'warc-target-uri': 'https://be-tarask.wikipedia.org/wiki/%D0%9A%D0%B0%D1%82%D1%8D%D0%B3%D0%BE%D1%80%D1%8B%D1%8F:%D0%9F%D1%80%D1%8D%D1%81%D0%BD%D0%B0%D1%8F_%D0%B2%D0%B0%D0%B4%D0%B0',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Гэты тэкст даступны на ўмовах ліцэнзіі Creative Commons '
'Attribution/Share-Alike 3.0; у асобных выпадках могуць ужывац...'}
```
#### deduplicated_bg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 23651,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QDAV5ZVRR2IGND4ANWTVOBPNO2POZUEQ',
'warc-date': '2021-03-08T21:47:20Z',
'warc-identified-content-language': 'bul',
'warc-record-id': '<urn:uuid:0e422a1d-ac8c-4f21-bb71-e5c65282f30c>',
'warc-refers-to': '<urn:uuid:0109dba6-8f1a-4047-bdd5-cbcc38de63a8>',
'warc-target-uri': 'http://europe.bg/bg/bulgariya-poluchava-resor-inovacii-i-mladezh',
'warc-type': 'conversion'},
'nb_sentences': 37,
'offset': 0},
'text': 'От хилядите кубинци и другите граждани на страните от СИВ, '
'командировани на строежа на АЕЦ-а, в Белене е останал само...'}
```
#### deduplicated_bh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9021,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IN7PHDOP7MZD6RHN6KIJ7SXTY7VC76SK',
'warc-date': '2021-03-08T22:57:31Z',
'warc-identified-content-language': 'hin,eng',
'warc-record-id': '<urn:uuid:62e18c96-cd2c-461b-93d9-900d95eec89e>',
'warc-refers-to': '<urn:uuid:73ee6388-6f0a-460d-ac2e-bbc1a2b63bb4>',
'warc-target-uri': 'https://bh.wikipedia.org/wiki/%E0%A4%B6%E0%A5%8D%E0%A4%B0%E0%A5%87%E0%A4%A3%E0%A5%80:%E0%A4%B5%E0%A4%BF%E0%A4%95%E0%A4%BF%E0%A4%AA%E0%A5%80%E0%A4%A1%E0%A4%BF%E0%A4%AF%E0%A4%BE_%E0%A4%97%E0%A5%88%E0%A4%B0-%E0%A4%AE%E0%A5%81%E0%A4%95%E0%A5%8D%E0%A4%A4_%E0%A4%AB%E0%A4%BE%E0%A4%87%E0%A4%B2_%E0%A4%B5%E0%A5%88%E0%A4%A7_%E0%A4%AC%E0%A5%88%E0%A4%95%E0%A4%B2%E0%A4%BF%E0%A4%82%E0%A4%95_%E0%A4%95%E0%A5%87_%E0%A4%B8%E0%A4%BE%E0%A4%A5?from=Ea',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ई एगो छुपावल गइल श्रेणी बाटे। ई पन्ना सभ पर तबले ना लउकी जबले कि '
'प्रयोगकर्ता के सेटिंग, छुपावल गइल श्रेणी देखावे खाति...'}
```
#### deduplicated_bn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 36198,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7QRYGJ3YDG7SBTFUVMMALFA6UWNDVLVY',
'warc-date': '2021-03-05T07:10:58Z',
'warc-identified-content-language': 'ben',
'warc-record-id': '<urn:uuid:050c0cdb-562c-49e5-bcb6-7e5350531ea6>',
'warc-refers-to': '<urn:uuid:a3749b59-4285-4e90-ba64-aa9d745c1f46>',
'warc-target-uri': 'https://www.kalerkantho.com/online/business/2020/12/06/982949',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'নিজস্ব সংবাদদাতা: গাড়ি নয় যেন মানুষের খাঁচা। নেই কোন ভালো বসার '
'আসন, যা আছে সেগুলো ভাঙ্গাচুরা, ময়লা ও ধুলাবালিতে ভর...'}
```
#### deduplicated_bo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5059,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XHKOQL5IQBLCVBANFVH66ZZXJZHEEMYW',
'warc-date': '2021-03-03T15:06:26Z',
'warc-identified-content-language': 'zho,bod',
'warc-record-id': '<urn:uuid:3a406f8f-58cd-4990-ae6f-f63dff7e06e3>',
'warc-refers-to': '<urn:uuid:806c4a11-f8cd-49e8-bc22-cae5e0cf6ef2>',
'warc-target-uri': 'http://tcansee.com/goods.php?id=392',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '所有分类 藏学名家名著 国内名家名著 国外名家名著政治 社会 法律 政治 法律 社会 经济文学 艺术 旅游 艺术 文学 旅游宗教 历史 '
'文化 宗教 历史 文化教育 童书 工具书 教辅 童书 工具书语言文字 语言研究 语言 文字期刊 社...'}
```
#### deduplicated_bpy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8270,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:POHCGWDC32KW74IE26NTJ2UMNX7QRBDB',
'warc-date': '2021-03-05T14:00:16Z',
'warc-identified-content-language': 'ben',
'warc-record-id': '<urn:uuid:d53007ee-ddbe-44e9-8253-235567d2960c>',
'warc-refers-to': '<urn:uuid:0409ce75-26bc-4a60-b08d-4e2b6174127e>',
'warc-target-uri': 'http://pobnapurup.gaibandha.gov.bd/site/page/5dc0a075-18fd-11e7-9461-286ed488c766/%E0%A6%95%E0%A6%BE%E0%A6%B0%E0%A7%8D%E0%A6%AF%E0%A6%BE%E0%A6%AC%E0%A6%B2%E0%A7%80',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'পবনাপুর ইউনিয়ন---কিশোরগাড়ী ইউনিয়নহোসেনপুর ইউনিয়নপলাশবাড়ী '
'ইউনিয়নবরিশাল ইউনিয়নমহদীপুর ইউনিয়নবেতকাপা ইউনিয়নপবনাপুর ইউনিয়...'}
```
#### deduplicated_br
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3134,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:U353JBWLMC22GRYEIDN4WOSBUOIUMYQT',
'warc-date': '2021-02-24T21:00:25Z',
'warc-identified-content-language': 'bre',
'warc-record-id': '<urn:uuid:49d1650d-aaf5-43b9-b340-326746e88b31>',
'warc-refers-to': '<urn:uuid:04877e5f-6b86-497e-b39c-30a72683261f>',
'warc-target-uri': 'https://br.m.wiktionary.org/wiki/dont',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Sellet e vez ouzh ar bajenn pe ar gevrenn-mañ evel un divraz da '
'glokaat e brezhoneg. Mar gouezit tra pe dra diwar-ben...'}
```
#### deduplicated_bs
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8483,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HS77KGP5HJKJASHMW6WSYV326BPGVM35',
'warc-date': '2021-02-24T18:13:58Z',
'warc-identified-content-language': 'bos,hrv',
'warc-record-id': '<urn:uuid:c12f1b14-4194-405e-a059-9af2f7146940>',
'warc-refers-to': '<urn:uuid:31bedcb4-265f-4aa3-8d2c-cfdc64c42325>',
'warc-target-uri': 'http://mojusk.ba/zastrasujuce-slike-tamnice-u-kojoj-je-skolski-domar-silovao-12-godisnjakinju/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Predsjednica Evropske centralne banke Christine Lagarde izjavila je '
'da njen najveći strah nije da će Evropska...'}
```
#### deduplicated_bxr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6751,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:RELUZWSMYT63FAPLHP55SMNNCSXIQEDX',
'warc-date': '2021-02-26T07:18:33Z',
'warc-identified-content-language': 'mon,rus',
'warc-record-id': '<urn:uuid:efe8d9fa-4329-4479-aa56-43938e8e5370>',
'warc-refers-to': '<urn:uuid:bba3bfb2-b7c7-4605-9f49-34598eac9a5b>',
'warc-target-uri': 'http://soyol.ru/bur/yoho-zanshal/hoityn/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Хүнэй бэе мүнхэ бэшэ. Һүнэһэнэй бэеымнай орхижо, түрэлөө '
'урилхадань, тэрэнэй хальһан боложо ябаһан бэемнай үхэнэ, газ...'}
```
#### deduplicated_ca
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 30591,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DJYNCXSBI5JH4V3LKGE7YNQBL34E3W5G',
'warc-date': '2021-03-02T21:39:28Z',
'warc-identified-content-language': 'cat,eng',
'warc-record-id': '<urn:uuid:ec350f95-900b-4164-aab3-8a6451228d5b>',
'warc-refers-to': '<urn:uuid:4c8e31b8-3011-4a21-9591-39be0942e121>',
'warc-target-uri': 'https://ca.m.wikipedia.org/wiki/Regne_d%27Ayutthaya',
'warc-type': 'conversion'},
'nb_sentences': 33,
'offset': 0},
'text': "El regne d'Ayutthaya va ser un estat a Tailàndia que va existir de "
'1351 a 1767 governat per un rei. El rei Rāmadhipat...'}
```
#### deduplicated_cbk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 151273,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JCULI5BTSXOFUJYKZPPLMU5BZEZJZEVJ',
'warc-date': '2021-03-04T21:00:26Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:ca25bd6b-9a5f-41b5-8b0f-ad437a545cee>',
'warc-refers-to': '<urn:uuid:ac67c26c-c62a-4c3d-9bd9-dd66a78a474f>',
'warc-target-uri': 'https://it.readkong.com/page/note-di-un-anno-di-lavoro-plural-3281543',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': ' '
'na '
'...'}
```
#### deduplicated_ce
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5944,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AXGWUWKZ5HO42LSEO32HWLT77MATHGXB',
'warc-date': '2021-03-03T14:41:28Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:1333c910-7921-4bdd-9bb9-1a8322dfa74b>',
'warc-refers-to': '<urn:uuid:9e976ac2-74e4-4e30-8c49-12f2dc1c257c>',
'warc-target-uri': 'https://www.radiomarsho.com/a/27368811.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Апти Бисултанов вина 1959 шарахь. Апти -- гоьваьлла нохчийн '
'кхузаманахьлера байтанча ву. 1983 шарахь цо чекхъяккхира ...'}
```
#### deduplicated_ceb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8799,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GSVQUFRLD3BYXEG2ASAEVHR2IH4D7A2S',
'warc-date': '2021-03-09T04:28:21Z',
'warc-identified-content-language': 'ceb,eng',
'warc-record-id': '<urn:uuid:e53f5344-29f5-4e59-8dac-8fdc92d1758f>',
'warc-refers-to': '<urn:uuid:03c0e7e5-b84c-4205-80cc-c3fb3dc82406>',
'warc-target-uri': 'https://www.safesworld.com/ceb/safewell-17ef-small-combination-lock-digital-safe-box-with-electronic-combination.html',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '17EF SERYE Talagsaong design ug madanihon nga kolor naghimo 17EF '
'popular nga sa taliwala sa mga anak ug mga babaye, k...'}
```
#### deduplicated_ckb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8668,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XZOIJPSX5QTL5QQPQMXEVADFHZTXMP5I',
'warc-date': '2021-03-09T03:25:59Z',
'warc-identified-content-language': 'kur,eng',
'warc-record-id': '<urn:uuid:9fe2f7e9-c158-4b84-a4a3-24e51acbd69e>',
'warc-refers-to': '<urn:uuid:14902cc0-948b-4dcf-bde6-e687ba41212f>',
'warc-target-uri': 'https://www.dastihawkary.org/blog/portfolio/social-harms-of-drugs/?lang=en',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'وەبیرم دێ\u200c لە كۆتایی هەشتاكانی سەدەی ڕابردوو دیاردەیەك هەبوو '
'لەنێو گەنجە لادەرەكانی شاری هەولێر و سەرشەقام هەڵدەستان ...'}
```
#### deduplicated_cs
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17263,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EJZ477E7PWMVVVM777MHB5DMDHVYEWK6',
'warc-date': '2021-03-05T11:28:42Z',
'warc-identified-content-language': 'ces',
'warc-record-id': '<urn:uuid:6fc03e7f-9768-4f26-89ce-84fa4732e3c0>',
'warc-refers-to': '<urn:uuid:d78128e5-f667-4461-9f0c-2263d75b74a1>',
'warc-target-uri': 'https://www.lidovky.cz/relax/dobra-chut/mak-a-svestky-vyzkousejte-makovec-podle-romana-pauluse.A150427_125913_dobra-chut_ape?recommendationId=00000000-0000-5000-8000-000000000000',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'Porno motor vyhledávání o nové sedlo masáž se svou. pro měkký sex '
'voda učitelka kočička videa stránky Starý pár sex n...'}
```
#### deduplicated_cv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4133,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FKR5EKWIFACLGBIK6IKLHTHDNTEZNF3T',
'warc-date': '2021-03-03T14:25:27Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:8140dbf0-2fb0-48d8-a834-c1b052bcc72d>',
'warc-refers-to': '<urn:uuid:cca433fe-6646-4ab7-b5da-f8e17821b43d>',
'warc-target-uri': 'http://chuv-krarm.3dn.ru/blog/vladimir_leontev_savna_masharam_emer_perle_purnar_i/2013-02-08-47',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Сайт авторĕ тата модераторĕ- Михайлов Алексей, Чăваш Республикин '
'Президенчĕн 2010,2012 çулсенчи стипендиачĕ, Сайт адм...'}
```
#### deduplicated_cy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1967,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:RNFNJNY7RHGXN5NPEVF2PYNNIWOTDAMJ',
'warc-date': '2021-03-09T03:48:16Z',
'warc-identified-content-language': 'cym,eng',
'warc-record-id': '<urn:uuid:66f063ba-6a33-4f53-9cfb-7dc64a292e89>',
'warc-refers-to': '<urn:uuid:281f9c10-2d7d-4781-82f6-a504f27852a1>',
'warc-target-uri': 'https://cy.wikipedia.org/wiki/John_T._Koch',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Graddiodd o Brifysgol Harvard, gan gymeryd doethuriaeth mewn '
'Ieithoedd a Llenyddiaethau Celtaidd yn 1985. Bu hefyd yn...'}
```
#### deduplicated_da
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22154,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AF2FFBNZQ3TOEEZ3MFDU77CXZ6PVU3ZB',
'warc-date': '2021-03-01T12:49:13Z',
'warc-identified-content-language': 'dan',
'warc-record-id': '<urn:uuid:92fffabd-5d36-4539-b8eb-18a0f2554ddb>',
'warc-refers-to': '<urn:uuid:1970d6bb-474f-448b-a3e1-8a77c9a32cb6>',
'warc-target-uri': 'http://rosamundis.dk/thai-horsens-gode-parfumer-til-m%C3%A6nd/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'Mange praler af den sindsro, de har fundet i huler i det '
'norske/forfaldne franske ferielejligheder etc., hvor de har ...'}
```
#### deduplicated_de
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11180,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LLCPCA3RGKMXLYUEA3OZ2KFEEBNEOPE2',
'warc-date': '2021-03-09T01:22:52Z',
'warc-identified-content-language': 'eng,deu',
'warc-record-id': '<urn:uuid:0128ab60-86c8-4dc2-b1cf-57950654ae38>',
'warc-refers-to': '<urn:uuid:ff27032b-b843-4ba3-b1e2-377793173071>',
'warc-target-uri': 'http://bioconcepts.de/views/search.php?term=231&listed=y',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'Kreismeisterschaften bringen zahlreiche Sunderner Medaillengewinner '
'und Titelträger - Tischtennis im Sauerland\n'
'Am ver...'}
```
#### deduplicated_diq
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4196,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DTA56M722SM5BZLNADOCPXQGGT32J46O',
'warc-date': '2021-03-06T15:51:03Z',
'warc-identified-content-language': 'tur,srp,nno',
'warc-record-id': '<urn:uuid:b7dcd4a4-b130-4009-88d0-631ca51a7bcc>',
'warc-refers-to': '<urn:uuid:fe4e4ad7-3089-40d2-aa29-f675e3cea0dd>',
'warc-target-uri': 'https://diq.wikipedia.org/wiki/Z%C4%B1wan%C3%AA_Slawki',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê '
'Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki be...'}
```
#### deduplicated_dsb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20663,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WWZOAFJJLJ4OHG2PTVLCMP664OR26XCR',
'warc-date': '2021-02-27T22:03:14Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:239b7155-8f37-4889-bad8-5bdb0aaa83c2>',
'warc-refers-to': '<urn:uuid:2714b744-a080-4807-a29a-d8f99c80e49c>',
'warc-target-uri': 'https://dsb.m.wikipedia.org/wiki/P%C5%9Bed%C5%82oga:LocMap',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Mjaz tamnjejšej pśedłogu a </noinclude>-kodom mógu pśidatne '
'kategorije a cuzorěcne wótkaze stojaś. Ewentualne pśikład...'}
```
#### deduplicated_dv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7923,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ECFUNRNYICXFAZXP5TLM45DPGJX5AHOI',
'warc-date': '2021-02-24T19:53:40Z',
'warc-identified-content-language': 'div,eng',
'warc-record-id': '<urn:uuid:23e2557a-dacc-428c-99fc-e41d4ce2ed95>',
'warc-refers-to': '<urn:uuid:067b6719-0209-49df-8198-27b1954b61b4>',
'warc-target-uri': 'https://dhiislam.com/114288',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': 'މީސްތަކުންގެ ފިކުރާއި ކުޅެލުމަށްޓަކައި މިޒަމާނުގެ ވަސީލަތްތަކުގެ '
'ބޭނުން އެންމެ ރަނގަޅު ގޮތުގައި ހިފަމުންދޭ: ޝެއިޚް ފި...'}
```
#### deduplicated_el
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12604,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2LXNVVGR3C4G72RLJUJBKUWLZZJ53TPX',
'warc-date': '2021-03-03T11:34:34Z',
'warc-identified-content-language': 'ell,eng',
'warc-record-id': '<urn:uuid:d95ddbe8-2e54-4d61-a6af-227212090684>',
'warc-refers-to': '<urn:uuid:a0e15450-8455-4b2f-ad8f-3858873a538d>',
'warc-target-uri': 'https://www.androsportal.gr/category/topika/nea-syllogwn/',
'warc-type': 'conversion'},
'nb_sentences': 18,
'offset': 0},
'text': 'Η ραδιοφωνική διαφήμιση χαρακτηρίζεται από αμεσότητα και οικειότητα '
'λόγω της στενής σχέσης του μέσου με τους ακροατές...'}
```
#### deduplicated_eml
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11710,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OM2W34UTSIJJHAEXEX42BYMZWBB7U3FS',
'warc-date': '2021-03-05T23:48:29Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:26a267af-a6de-4e84-b945-411b78b4815a>',
'warc-refers-to': '<urn:uuid:656aaba2-ff1d-4d7c-915a-9a555533aa42>',
'warc-target-uri': 'https://eml.wikipedia.org/wiki/2_(n%C3%B9mer)',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "Al 2 'l è al prim nùmer prim ed tùta la séri ch'a s cata in di "
"nùmer naturèl e anc 'l ùnic ch'al sìa pèra:\n"
"Insèm a 'l..."}
```
#### deduplicated_en
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15201,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EIQTEGOE4V5SDID2OLTO4PWWCTW3AD5H',
'warc-date': '2021-03-03T18:20:30Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:7cec445b-76fe-4ce2-ab43-8a85de680c6f>',
'warc-refers-to': '<urn:uuid:1cf845b2-3015-4f01-abaf-262af4adeba5>',
'warc-target-uri': 'https://www.aqueencitysound.com/2016/05',
'warc-type': 'conversion'},
'nb_sentences': 28,
'offset': 0},
'text': 'But the term “extension” also means lengthening. EkhartYoga members '
'can get to k… Renforcement du dos (muscles para-v...'}
```
#### deduplicated_eo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27953,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YO4NP6746IFQDF5KISEPLNFA2QD3PTEO',
'warc-date': '2021-03-09T05:29:46Z',
'warc-identified-content-language': 'epo,eng',
'warc-record-id': '<urn:uuid:5e3bc7b3-723f-4de9-8202-790351a2253f>',
'warc-refers-to': '<urn:uuid:dd5e537a-f340-4418-bc07-487232ea197c>',
'warc-target-uri': 'http://kantaro.ikso.net/cxu?image=kis_kut.png&ns=&tab_details=view&do=media',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Iloj Montri paĝonMalnovaj reviziojRetroligoj Freŝaj '
'ŝanĝojMedio-administriloIndekso RegistriĝiEnsaluti'}
```
#### deduplicated_es
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8322,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXIQKIWES4PP64BTGK5BYTJ3TX4RVQSI',
'warc-date': '2021-03-03T23:27:45Z',
'warc-identified-content-language': 'spa,eng',
'warc-record-id': '<urn:uuid:4275a14a-f997-4e58-8cf6-046006d76dab>',
'warc-refers-to': '<urn:uuid:d54d1a7b-1316-4bd1-8147-7a44ec5b3803>',
'warc-target-uri': 'https://www.rcrperu.com/defensoria-del-pueblo-oficina-en-lima-sur-registro-mas-de-3000-casos-durante-el-2020/',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': 'Se prevé que a finales de mes haya llegado al 92,5 por ciento de '
'los centros, aquellos en los que no hay confirmados ...'}
```
#### deduplicated_et
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 57234,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JU7SWP3ZS36M3ABAEPNTFH37MVI2SLAF',
'warc-date': '2021-02-24T20:43:43Z',
'warc-identified-content-language': 'est',
'warc-record-id': '<urn:uuid:2bbcaa39-7336-4ade-accf-1b582785f731>',
'warc-refers-to': '<urn:uuid:849563c9-8549-4bdc-a09c-d179c8399ae0>',
'warc-target-uri': 'https://cardiaccareclinic.com/chto-luchshe-panangin-ili-kardiomagnil.html',
'warc-type': 'conversion'},
'nb_sentences': 129,
'offset': 0},
'text': 'Kas hirmu ei pruugi tekitada hoopis segadus? Näiteks võtame Ukraina '
'kogemuse. Järsku ilmusid välja lindikestega mehed...'}
```
#### deduplicated_eu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4248,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:STDEJOH35DPN5UB52OUZJJC4YCN7EH3N',
'warc-date': '2021-03-09T05:11:48Z',
'warc-identified-content-language': 'spa,eus',
'warc-record-id': '<urn:uuid:fb6752f7-5e91-4d0c-b022-71bd5d3ce910>',
'warc-refers-to': '<urn:uuid:faca7a42-20c2-4c4c-bd8a-6d4be5a1adb6>',
'warc-target-uri': 'http://intermedia.eus/la-comunicacion-imprescindible-lo-que-no-debemos-olvidar-de-2015-resumido-en-447/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Nesken artean bokazio zientifikoak eta teknologikoak sustatzeko '
'INSPIRA STEAM proiektua ia 120 ikastetxetako 5.000 ik...'}
```
#### deduplicated_fa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 10411,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VM7Q7TXNMU2SRNHFJSZMBCKU2YVRKI56',
'warc-date': '2021-03-02T11:23:27Z',
'warc-identified-content-language': 'fas',
'warc-record-id': '<urn:uuid:9f666d03-9592-4f59-9111-981a558b3a32>',
'warc-refers-to': '<urn:uuid:8daf3dc1-92dd-4dbf-a339-992c99f09112>',
'warc-target-uri': 'https://zhycan.com/concough/blog/%D9%86%D8%AD%D9%88%D9%87-%D8%AB%D8%A8%D8%AA-%D9%86%D8%A7%D9%85-%DA%A9%D9%86%DA%A9%D9%88%D8%B1-%D8%AF%DA%A9%D8%AA%D8%B1%DB%8C-97-%D8%A7%D8%B9%D9%84%D8%A7%D9%85-%D8%B4%D8%AF-%D8%A7%D9%85/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'انجمن دانشجویان پیام نور تبليغات تماس با ما تبلیغات دسته بندی باز / '
'بسته کردن دسته بندی ها . شرایط اختصاصی برای شغل د...'}
```
#### deduplicated_fi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 19216,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5OUEZDSL7KB2VHT2R67YZDER6UO5FHON',
'warc-date': '2021-03-05T00:14:23Z',
'warc-identified-content-language': 'fin,eng',
'warc-record-id': '<urn:uuid:61e0fc42-ceee-4026-ba76-3c8a8addd596>',
'warc-refers-to': '<urn:uuid:c4ba3c9f-5a6c-4de5-8f77-f5beb547315c>',
'warc-target-uri': 'https://kreditassms.eu/arvostelut-treffisivusto-py%C3%B6re%C3%A4-tanssi/',
'warc-type': 'conversion'},
'nb_sentences': 46,
'offset': 0},
'text': 'Facebook ulkomaiset morsiamet fantasia lähellä lohja mistä pillua '
'porno leffat sex treffit karvaiset tussut Thai mass...'}
```
#### deduplicated_fr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5274,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XUVXOZU2BIT4TIDEVHLLBLUIHRS4L7WV',
'warc-date': '2021-03-03T14:00:24Z',
'warc-identified-content-language': 'fra,eng',
'warc-record-id': '<urn:uuid:76252d00-9672-479c-9580-722614e078f9>',
'warc-refers-to': '<urn:uuid:4a6bde1e-9596-4388-9334-cc473a7c93ee>',
'warc-target-uri': 'https://www.cahier-des-charges.net/produit/modele-cahier-des-charges-de-logiciel-de-gestion-de-processus-metier/',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'Créée en 1765 par le duc de Villars, alors gouverneur de Provence, '
'l’École supérieure d’art d’Aix en Provence est un ...'}
```
#### deduplicated_frr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27381,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DJE2KO4YWWRERKS5JYSK5JCJWYZ6DJHM',
'warc-date': '2021-03-01T03:40:10Z',
'warc-identified-content-language': 'ell',
'warc-record-id': '<urn:uuid:3a2a34ae-1c42-4d2e-bb08-8dabc916ea30>',
'warc-refers-to': '<urn:uuid:caeb39b2-da76-463d-b80c-4917d3dca230>',
'warc-target-uri': 'https://www.sedik.gr/neo/el/%CE%B1%CF%81%CF%87%CE%B5%CE%AF%CE%BF-%CE%B5%CE%BB%CE%B1%CE%B9%CE%BF%CE%BD%CE%AD%CF%89%CE%BD/%CE%B1%CF%81%CF%87%CE%B5%CE%AF%CE%BF-%CE%B5%CE%BB%CE%B1%CE%B9%CE%BF%CE%BD%CE%AD%CF%89%CE%BD-2009/178-178-title',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ '
'’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’...'}
```
#### deduplicated_fy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1807,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JABSHFJ2L6SQOXPPTBYGZGR24GCEDTTM',
'warc-date': '2021-03-09T04:24:30Z',
'warc-identified-content-language': 'fry',
'warc-record-id': '<urn:uuid:fd1b28cb-20ce-4082-b1ca-40045ed6af73>',
'warc-refers-to': '<urn:uuid:bc50e1f0-6384-4054-8916-2a489e9a0ffd>',
'warc-target-uri': 'https://www.omropfryslan.nl/nijs/201805-gruttere-lisboksstal-tastien',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Melkfeehâlders yn Súdwest-Fryslân kinne tenei makliker '
"lisboksstâlen fergrutsje no't de gemeente de lanlike wet op st..."}
```
#### deduplicated_ga
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3296,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WF6SCFDXN3NOT7FPKTEFOAMMPKXSEZ2W',
'warc-date': '2021-03-09T04:37:11Z',
'warc-identified-content-language': 'gle',
'warc-record-id': '<urn:uuid:bff39289-dbf7-444c-8df1-382fd46c993d>',
'warc-refers-to': '<urn:uuid:e27ba1c5-5707-4e9f-8ba8-f42c67bd9fc9>',
'warc-target-uri': 'http://nos.ie/cultur/iarratais-a-lorg-don-slam-filiochta-agus-duaischiste-700-ann-i-mbliana/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Tá duaischiste £700 ar fáil do Slam Filíochta Liú Lúnasa a bheidh '
'ar siúl ar líne ag deireadh na míosa seo chugainn. ...'}
```
#### deduplicated_gd
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7659,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OO363HOO6EDDYSBTTYB6H4WYAJBBMJ6D',
'warc-date': '2021-03-03T15:22:11Z',
'warc-identified-content-language': 'gla',
'warc-record-id': '<urn:uuid:e24cc86f-ae2c-49f6-b668-cda4f514a34d>',
'warc-refers-to': '<urn:uuid:1739d2d8-974d-4c29-b8d0-3a3ef9082537>',
'warc-target-uri': 'http://gd.cnswmc.com/ty320-3-bulldozer-product/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Tha inneal-brathaidh TY320-3 crochte leth-chruaidh, gluasad '
'uisgeachaidh, inneal tarbh fo smachd seòrsa hydraulic. Ta...'}
```
#### deduplicated_gl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4202,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TIH7ARF4FNLH7VRGHXKOWVHNXNXC2HZX',
'warc-date': '2021-03-09T04:47:46Z',
'warc-identified-content-language': 'glg',
'warc-record-id': '<urn:uuid:983dd790-0846-4232-a7b4-3956af0982a8>',
'warc-refers-to': '<urn:uuid:b77207af-29d0-459f-9a55-0b25501d3e8b>',
'warc-target-uri': 'http://concellomuxia.com/item/outras-capelas/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'O templo actual é producto de diversas reconstrucións que se '
'realizaron a finais do século XVII e principios do XVIII...'}
```
#### deduplicated_gn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3873,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FWN62CTWNJKPWUARS4BMBUFU6OVHL6XP',
'warc-date': '2021-02-27T22:49:49Z',
'warc-identified-content-language': 'grn,eng,bih',
'warc-record-id': '<urn:uuid:b4954ced-abe0-487e-b5b0-a26beb751a02>',
'warc-refers-to': '<urn:uuid:be5468f1-47f0-4bd8-a177-3529a14dead7>',
'warc-target-uri': 'https://gn.wikipedia.org/wiki/Apere%27arusu',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ko ñe\'ẽ "apere\'arusu" ou avañe\'ẽ ñe\'ẽngue "apere\'a" he\'ise '
'India Tapiti, ha avañe\'ẽ ñe\'ẽngue "rusu" he\'iséva iguasúva.'}
```
#### deduplicated_gom
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8747,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CKNSFAH2KISLLR7222FSQSPENYHQTAX3',
'warc-date': '2021-03-01T11:10:29Z',
'warc-identified-content-language': 'mar',
'warc-record-id': '<urn:uuid:d4622a3e-1b0e-4775-b25d-273ee14ae176>',
'warc-refers-to': '<urn:uuid:9d00e57b-9031-4f86-a9c8-cc3c0c2213a7>',
'warc-target-uri': 'https://gom.m.wikipedia.org/wiki/%E0%A4%B5%E0%A5%80%E0%A4%9C',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'कांय वस्तू रगडल्यो तर तांचेकडेन हलक्यो वस्तू आकर्शित जाता हेंजेन्ना '
'पळयलें तेन्ना वीज हे ऊर्जेची कल्पना मनशाक आयली.हे...'}
```
#### deduplicated_gu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ધ્યાન આપો: જો તમે ગુજરા તી બોલતા હો, તો તમને ભા ષા કીય સહાય તા સેવા '
'ઓ વિ ના મૂલ્યે ઉપલબ્ધ છે. તમા રા આઈડી કાર ્ડ પર આ...'}
```
#### deduplicated_gv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 29707,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TIDW47D4MAHOLY6PQZ5SHLDYQIJ66REQ',
'warc-date': '2021-03-06T18:16:22Z',
'warc-identified-content-language': 'glv,eng',
'warc-record-id': '<urn:uuid:c7a5e531-487b-4e52-96ca-33b658691652>',
'warc-refers-to': '<urn:uuid:fa7285d4-126c-458f-9a72-d0d8615ce494>',
'warc-target-uri': 'https://gv.wikipedia.org/wiki/%C3%87hengoaylleeaght',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ta çhengoaylleeaght feamagh eiyrt er sheiltynyssyn çhengoaylleeagh '
'ayns ayrnyn myr ynsaghey çhengaghyn joaree, glare-...'}
```
#### deduplicated_he
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12254,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BL56ZUXYO5GLIO6YTBUWKPVYJN2BKCIM',
'warc-date': '2021-03-09T10:29:09Z',
'warc-identified-content-language': 'heb,eng',
'warc-record-id': '<urn:uuid:1ae77825-a836-424e-a8b1-1f9c985a41b9>',
'warc-refers-to': '<urn:uuid:fce3d3dc-979e-4603-82e3-027b75346e52>',
'warc-target-uri': 'https://shop.makeup.land/collections/frontpage',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'הולדת פג היא אירוע מטלטל לכל משפחה, אך הולדת פג בצל מגפת הקורונה '
'מאתגרת אף יותר? מהם האתגרים עמם מתמודדים ההורים והצו...'}
```
#### deduplicated_hi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7897,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VZCN5HXN57VQHZJT5G3NWV7RCIT4GP7T',
'warc-date': '2021-02-26T10:18:11Z',
'warc-identified-content-language': 'hin,eng',
'warc-record-id': '<urn:uuid:6cccccb7-be0e-4c16-83be-7b4150b107ac>',
'warc-refers-to': '<urn:uuid:41eda5d1-e2cf-44f4-9f5b-c074a2de89da>',
'warc-target-uri': 'https://36.gurturgoth.com/2019/11/blog-post_8.html',
'warc-type': 'conversion'},
'nb_sentences': 5,
'offset': 0},
'text': 'Bill Gates Biography in Hindi, विश्व के सबसे अमीर इंसान और '
'माइक्रोसॉफ्ट कंपनी के संस्थापक Bill Gates जिसने अपनी बुद्ध...'}
```
#### deduplicated_hr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 41545,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6NTZEPK7ETF4AOLM3YDZRLRGZAKH7XM3',
'warc-date': '2021-03-09T04:58:04Z',
'warc-identified-content-language': 'hrv,bos,eng',
'warc-record-id': '<urn:uuid:32361cc9-e12a-4861-978a-b94b84efe78c>',
'warc-refers-to': '<urn:uuid:f0476e5f-e04c-4741-94a6-ddbcfb25c17e>',
'warc-target-uri': 'http://mjesec.ffzg.hr/webpac/?rm=results&show_full=1&f=PersonalName&v=Sanader%20Mirjana',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Impresum: Pula : Sveučilište u Zagrebu, Međunarodno središte '
'hrvatskih sveučilišta u Istri, Međunarodni istraživački ...'}
```
#### deduplicated_hsb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3352,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:E5ZCT5OIZBDV2EFBNX3MSLFJKKMZWQWI',
'warc-date': '2021-03-08T22:15:50Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:374a31b4-d38f-4d94-b3df-59013b15e644>',
'warc-refers-to': '<urn:uuid:fa9b7b26-2b4c-4acc-a652-47047617b0c0>',
'warc-target-uri': 'https://www.serbske-nowiny.de/index.php/hsb/z-luzicy/lokalka/item/50643-jednotna-proty-ka-tr-bna',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Žonjace akciske tydźenje zahajene\tDźensniši Mjezynarodny dźeń '
'žonow je zazběh hač do 22. apryla trajacych ...\t\n'
'Wotstr...'}
```
#### deduplicated_ht
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17823,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LXQEYMTPIKHPAYKEKIZF6FCMC6WH66PW',
'warc-date': '2021-02-25T02:48:22Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:a5599306-82ad-4740-9c00-5bba34c96d54>',
'warc-refers-to': '<urn:uuid:2378d2f7-69a4-4f8a-ad03-4d556d031ebb>',
'warc-target-uri': 'http://mywebstores.ru/index.php?id_product=1841&controller=product',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'начать us $ nan us $ nan us $ nan us $ nan us $ nan us $ nan us $ '
'nan us $ nan us $ nan us $ nan us $ nan us $ nan us...'}
```
#### deduplicated_hu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 39801,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:B3XHZ4C4AJYQLVV3ESGOVZU6FZ5N5637',
'warc-date': '2021-02-26T07:03:18Z',
'warc-identified-content-language': 'hun',
'warc-record-id': '<urn:uuid:926ed467-3adb-44f5-b33c-63112879ba5a>',
'warc-refers-to': '<urn:uuid:9d9175b4-6b0a-45e8-961b-61e9d50eb684>',
'warc-target-uri': 'https://luminanz.eu/anya-hatartalan-ingyen-videok-pina-nagy-video-video-sex-szekx-hd-videa-nyelvu-%C3%B6reg/',
'warc-type': 'conversion'},
'nb_sentences': 104,
'offset': 0},
'text': 'A WordPress egy ingyenesen letölthető rendszer. Letöltés után csak '
'telepíteni kell a webszerverre és máris használhat...'}
```
#### deduplicated_hy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6269,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:42PWBXN2Q7PFCRFWIDLTW42KUUGAKQOE',
'warc-date': '2021-02-24T23:49:31Z',
'warc-identified-content-language': 'hye,eng',
'warc-record-id': '<urn:uuid:932d1903-aea7-4be9-abb4-6b3114592c9c>',
'warc-refers-to': '<urn:uuid:cecf676f-884a-4311-a0b5-45ade0f517b7>',
'warc-target-uri': 'https://www.usanogh.am/lur/tramp-amn-coronavirus/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'ՀՀ ԳԱԱ Զեկույցներ =Reports NAS RA կիրառում է «Ստեղծագործական '
'համայնքներ» հեղինակային իրավունքի արտոնագիրը համաձայն որ...'}
```
#### deduplicated_ia
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9479,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:4JBN4SUDHHRPZI3TAVTZ4JUYSSOGGRFX',
'warc-date': '2021-03-01T17:14:58Z',
'warc-identified-content-language': 'ron,eng',
'warc-record-id': '<urn:uuid:5abe05ff-7309-4c3f-8ccd-175a12a655a2>',
'warc-refers-to': '<urn:uuid:8dec50fd-2be1-4bcf-8bb2-8cb9826c2465>',
'warc-target-uri': 'https://www.monitorulsv.ro/Ultima-ora-local/2008-02-18/Campania-electorala-interzisa-in-Primaria-Suceava',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha '
'ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ...'}
```
#### deduplicated_id
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3080,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XU6GIUNYT5ELGH5XSZ4FUARC3YTJAD5P',
'warc-date': '2021-03-05T03:32:56Z',
'warc-identified-content-language': 'ind',
'warc-record-id': '<urn:uuid:2328da88-ee5f-4b4c-af3e-25dc4a574041>',
'warc-refers-to': '<urn:uuid:0781f7e2-f020-402b-b204-71fdf299f956>',
'warc-target-uri': 'https://sulsel.kemenag.go.id/berita/berita-kontributor/stqh-26-tingkat-kabupaten-jeneponto-siap-di-gelar',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': '* Masa berlaku normal poin 1 (satu) tahun dan masa berlaku bonus '
'poin sampai dengan 31 Desember 2020.\n'
'Diskon dari Ban...'}
```
#### deduplicated_ie
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16919,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:W7UDGWMCEYQFEIPJMFZKX72Z6MH4XCUP',
'warc-date': '2021-03-08T16:16:42Z',
'warc-identified-content-language': 'ron,eng',
'warc-record-id': '<urn:uuid:f5ba5473-8eb2-41f4-9e43-3d36f14243a1>',
'warc-refers-to': '<urn:uuid:d2784efa-8250-4370-a348-28c640195663>',
'warc-target-uri': 'https://rolabel.info/door/yX-WpseZpNycfXY/luis-gabriel-haziran-te-am-cautat-si-te-am-gasit-official-video.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Va iubesc mult mult mult mult mult mult mult mult mult mult mult '
'mult mult mult mult mult mult mult mult mult mult mu...'}
```
#### deduplicated_ilo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3511,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NLHH2LVPZTUZE37ET2FJIRZNOLPLKK4O',
'warc-date': '2021-03-03T15:52:32Z',
'warc-identified-content-language': 'tgl',
'warc-record-id': '<urn:uuid:2fb6a437-41c8-4c2c-9f5d-2e8c34df9f2b>',
'warc-refers-to': '<urn:uuid:bdc072a0-db63-4256-a96b-7515a2c4fdfd>',
'warc-target-uri': 'https://ilo.m.wikipedia.org/wiki/Amphibia',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Daytoy nga artikulo dagiti nangruna nga artikulo ket pungol. '
'Makatulongka iti Wikipedia babaen ti panagnayon iti daytoy.'}
```
#### deduplicated_io
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3586,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VUQPETM2PUWBL5AGADEVN2FPE7KURXG4',
'warc-date': '2021-03-03T15:22:41Z',
'warc-identified-content-language': 'ara',
'warc-record-id': '<urn:uuid:fd8a899b-d54a-424d-9955-a90b81e16439>',
'warc-refers-to': '<urn:uuid:c40226a6-6851-4009-a834-77a1a3e0c0f3>',
'warc-target-uri': 'https://io.wikipedia.org/wiki/New_Vienna,_Iowa',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Segun l'Usana Kontado Ministerio, l'urbo havas entote 1.2 km², "
'equivalanta a 0.4 mi², di qui 1.2 km² (0.4 mi²) esas l...'}
```
#### deduplicated_is
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1829,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXUGRT4OK7WRCOPGB7AAKLHPUDTBDRO2',
'warc-date': '2021-03-09T04:40:07Z',
'warc-identified-content-language': 'isl',
'warc-record-id': '<urn:uuid:6568bf31-b402-45b8-9ddb-6ce0f3d0a323>',
'warc-refers-to': '<urn:uuid:5daa12c0-604a-4233-9ed8-d4e245af4048>',
'warc-target-uri': 'http://hugvis.hi.is/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Vegna hertra aðgerða í bará ttunni við Covid19 munum við takmarka '
'gestafjölda í laugum okkar við 80 manns. Thank you ...'}
```
#### deduplicated_it
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MLJ4TW2HJZAPE2ORVARPJES6GRGO6ZLK',
'warc-date': '2021-03-05T13:56:32Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:31d7ebb5-c1f7-468b-92f8-b79b7c28af9f>',
'warc-refers-to': '<urn:uuid:f92f33a2-6940-49fd-a21e-228ee5d2efb1>',
'warc-target-uri': 'https://mauriziomezzetti.com/patologie-trattate/',
'warc-type': 'conversion'},
'nb_sentences': 47,
'offset': 0},
'text': 'Il Presidente del Caffè Letterario Quasimodo di Modica, Domenico '
'Pisana, sarà ospite a Taranto, il prossimo 4 maggio,...'}
```
#### deduplicated_ja
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16411,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XOFBBBX7LINQS3EZN5VH6OQ7PPFNRICJ',
'warc-date': '2021-03-09T01:09:27Z',
'warc-identified-content-language': 'jpn,eng,lat',
'warc-record-id': '<urn:uuid:5c0685f4-736d-4155-9153-56cf79462df4>',
'warc-refers-to': '<urn:uuid:88586e1b-926d-4291-910f-53680e3d6482>',
'warc-target-uri': 'http://flpj.karapyzi.ru/30',
'warc-type': 'conversion'},
'nb_sentences': 14,
'offset': 0},
'text': '番組『日本を元気に！スマイルサプライズ！』が、28日に放送（後7：00）。コロナ禍や自然災害など、日本が長いトンネルに入ってしまったような状態だが、「でも、きっとこの先に明るい出口がある！」と明るい未...\n'
'プリゲーム『ポケモンスマイ...'}
```
#### deduplicated_jbo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6970,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2EVVU2OCTSB5EYCHSV6Z7I3PMQSNNOED',
'warc-date': '2021-03-03T23:28:54Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:0d4387a2-391d-4e3e-8772-808face0ab78>',
'warc-refers-to': '<urn:uuid:4e45af2a-aea7-4f1a-af89-6ee5f69b7bfd>',
'warc-target-uri': 'https://jbo.m.wikipedia.org/wiki/mumyma%27i_7moi',
'warc-type': 'conversion'},
'nb_sentences': 26,
'offset': 0},
'text': "ni'o 7 la mumast. cu 7moi djedi fi'o masti la mumast. noi ke'a cu "
'mumoi masti .i 6 la mumast. cu purlamdei .ije 8 la ...'}
```
#### deduplicated_jv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8822,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NPQGATEVIAYLOSLDB22EB7IYDVBZ7N6Q',
'warc-date': '2021-03-09T11:14:25Z',
'warc-identified-content-language': 'jav',
'warc-record-id': '<urn:uuid:db7d8bd7-a3a3-4a30-8786-7efb2352285d>',
'warc-refers-to': '<urn:uuid:2cb85a37-545e-471a-b7e7-cb334112f0e3>',
'warc-target-uri': 'https://jv.wikipedia.org/wiki/Bon%C3%A9kah',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Yèn sadurungé golèkan digawé kanggo awaké dhéwé, wiwit jaman iki '
'dikomersialakaké. Fungsiné owah saka ritual lan mode...'}
```
#### deduplicated_ka
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 42480,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HHSMTLZXKA4SQDPDBWAOUFELXBUJZJKO',
'warc-date': '2021-03-06T15:33:35Z',
'warc-identified-content-language': 'kat,eng',
'warc-record-id': '<urn:uuid:7d931f2a-a6ef-4070-9277-2033e7e96b9b>',
'warc-refers-to': '<urn:uuid:89429497-9722-45e6-95a6-699ef7280e6c>',
'warc-target-uri': 'https://ka.m.wikipedia.org/wiki/%E1%83%93%E1%83%90%E1%83%A1%E1%83%A2%E1%83%98%E1%83%9C_%E1%83%B0%E1%83%9D%E1%83%A4%E1%83%9B%E1%83%90%E1%83%9C%E1%83%98',
'warc-type': 'conversion'},
'nb_sentences': 36,
'offset': 0},
'text': 'დასტინ ჰოფმანი[1] (ინგლ. Dustin Lee Hoffman დ. 8 აგვისტო, 1937) — '
'ორგზის კინოაკადემიის ოსკარისა და ექვსგზის ოქროს გლო...'}
```
#### deduplicated_kk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9197,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BJW4PLV2UOAJLJO6E55YH7DAEWQTFQUZ',
'warc-date': '2021-03-09T04:35:14Z',
'warc-identified-content-language': 'rus,kaz',
'warc-record-id': '<urn:uuid:ddd1d3e1-3bf3-4c4a-b722-8e293ab16f75>',
'warc-refers-to': '<urn:uuid:097c4f10-4bdc-400d-ab39-c04e4f98f51f>',
'warc-target-uri': 'http://blogs.kazakh.ru/blogs/index.php?page=group&gid=6&id=3&PAGEN_1=3%3Fid%3D2?id=6',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Бұрынғы жоғары лауазымды шенеунік Анатолий Шкарупа (сол жақта) '
'өзіне қарсы қозғалған қылмыстық іс бойынша өтіп жатқан...'}
```
#### deduplicated_km
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ការជូនដំណឹង៖ ប្រសិនប. ើអ្នកនិយាយភាសា ខ្មែរ សេ វាជំនួយភាសាឥតគិតថ្លៃ '
'គឺអាចរកបានសម្ រាប ់អ្នក។ សូមទូរស័ព្ទទ ៅផ ្នែ កសេ វ...'}
```
#### deduplicated_kn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8425,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TMWGSQVJMRPZCPMDM5D3AK2YKGMWBZZI',
'warc-date': '2021-03-09T04:21:39Z',
'warc-identified-content-language': 'kan,eng',
'warc-record-id': '<urn:uuid:ca35da96-ee3a-43ad-8082-a10b055200ca>',
'warc-refers-to': '<urn:uuid:a57cc8f6-c5ed-47a2-9322-2259687cdbde>',
'warc-target-uri': 'https://kannada.b4blaze.com/tag/rachitha-ram/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'ಅಡಿಗರು ಮತ್ತು ರಾಯರು ಚಾಪೆ ಹಾಸಿ ಸ್ವಲ್ಪ ಹೊತ್ತು ಮಲಗಿ ಕಾಫಿ ಕುಡಿದು '
'ಹೊರಟುಹೋದಿದ್ದರು. ಜಾತ್ರೆ ದಿನ ಜಗನ್ನಾಥನ ಮನೆಗೆ ಬರಬಹುದಾದ ನೂರಾರು...'}
```
#### deduplicated_ko
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2831,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DLTUACNWU3R5KYI7HMMZF4CYR4WGRMWU',
'warc-date': '2021-02-26T10:13:10Z',
'warc-identified-content-language': 'kor,eng',
'warc-record-id': '<urn:uuid:7f7727bf-bf3d-45c3-8e3c-b595f67f9d90>',
'warc-refers-to': '<urn:uuid:17735508-d2ce-4e0a-a3ba-86acb749b9a2>',
'warc-target-uri': 'http://excel2017.zz.am/entry/mousqul',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': '인류는 최근 수백년 동안 물질적 풍요를 행복의 최대 조건으로 믿고, 이를 추구해 왔다. 그러나 이 과정에서 사람들은 '
'상대방에게 사랑을 베풀기보다는 상처를 입히는 일이 많아졌고, 물질적 풍요는 내면의 충족을 동반...'}
```
#### deduplicated_krc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4806,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CWWWGTU7JCHS7SR5A7D7QMDTF4JBMCA6',
'warc-date': '2021-02-26T04:08:10Z',
'warc-identified-content-language': 'nno,bih',
'warc-record-id': '<urn:uuid:ef2175c0-4887-4006-9b21-374282abf2d2>',
'warc-refers-to': '<urn:uuid:d5aaef09-6f3c-427a-8c2f-664e639c2a0f>',
'warc-target-uri': 'https://krc.wikipedia.org/wiki/1606_%D0%B4%D0%B6%D1%8B%D0%BB',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Бу, тамамланмагъан статьяды. Сиз болушургъа боллукъсуз проектге, '
'тюзетиб эм информация къошуб бу статьягъа.'}
```
#### deduplicated_ku
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12767,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BQQEDD5HKU6LXDRIDLMWPIESOMEGIUX6',
'warc-date': '2021-03-09T04:11:10Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:5a67e5e4-f688-4aa1-a9a0-2e4f6217ef21>',
'warc-refers-to': '<urn:uuid:40fa61be-18d1-4bd5-9267-252720cd5b05>',
'warc-target-uri': 'http://www.peyamakurd.org/kurmanci/Kurdistan/gruben-smo-ye-bi-hawane-li-til-rifete-xistin-3-miri-u-6-birindar',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'PeyamaKurd – Grûbên bi ser Tirkiyê de li Binxetê li bajarokê Til '
'Rifetê bi hawanê lê dan û di encamê de 3 kes mirin û...'}
```
#### deduplicated_kv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14161,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JH3R64H4VMXQ3NRHTX3LO3B4VFN6IZ62',
'warc-date': '2021-03-03T15:09:36Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:a94b390c-8e72-475d-bf76-c523c20908ce>',
'warc-refers-to': '<urn:uuid:e11eee46-e68f-4e1b-b4a3-0b9eeb74a877>',
'warc-target-uri': 'https://kv.wikipedia.org/wiki/%D0%9C%D0%B8%D0%BA%D1%83%D1%88%D0%B5%D0%B2_%D0%90%D0%BD%D0%B0%D1%82%D0%BE%D0%BB%D0%B8%D0%B9_%D0%9A%D0%BE%D0%BD%D1%81%D1%82%D0%B0%D0%BD%D1%82%D0%B8%D0%BD%D0%BE%D0%B2%D0%B8%D1%87',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1947, моз тӧлысь–1950, кӧч тӧлысь – уджалiс велöдысьöн да '
'директорöн Сыктывдiн районса Ыб шöр школаын.'}
```
#### deduplicated_kw
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3496,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:S5H4MWHD4QTG74ZNJZ5X63W2XSLUJU7C',
'warc-date': '2021-02-26T18:49:31Z',
'warc-identified-content-language': 'cym',
'warc-record-id': '<urn:uuid:44d32e62-4240-413a-9f8a-562fe27223c6>',
'warc-refers-to': '<urn:uuid:7d95741c-6974-427f-80f7-d08559f799aa>',
'warc-target-uri': 'https://kw.m.wikipedia.org/wiki/Kembra',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Kembra yw konna-tir menydhek yn Howlsedhes Breten Veur. Glow hag '
'owr o poesek yn erbysieth Pow Kembra seulajydh, mes ...'}
```
#### deduplicated_ky
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 28946,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TVCYX44AC2J2TBVAYMQW62P4XYHWPSAH',
'warc-date': '2021-02-24T20:28:28Z',
'warc-identified-content-language': 'kir,eng',
'warc-record-id': '<urn:uuid:b0b897b8-5d55-4109-967f-9e368be6b7aa>',
'warc-refers-to': '<urn:uuid:b7ac5729-15cb-44c8-a0a2-096cb46cb1de>',
'warc-target-uri': 'http://mezgilnews.kg/tag/klip/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Мезгил. Ырчы Зерени соцтармактар аркылуу коркуткан белгисиз '
'адамдарды милиция издеп баштады. Чүй облустук ИИБинин маа...'}
```
#### deduplicated_la
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2647,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QXPYMWAXXOOHWKBNAYCNUODKWSB56XU4',
'warc-date': '2021-03-09T04:51:12Z',
'warc-identified-content-language': 'lat,eng',
'warc-record-id': '<urn:uuid:684bcdce-19ec-4a44-b814-949eb5ceff66>',
'warc-refers-to': '<urn:uuid:2cd40ddd-0087-41ba-8442-8b2b6b1bbcd2>',
'warc-target-uri': 'http://grhpay.es/index.php/about-us/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Nam libero tempore, cum soluta nobis est eligendi optio cumque '
'nihil impedit quo minus id quod maxime placeat facere ...'}
```
#### deduplicated_lb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2060,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5YXISU3T3UP7WKUDJ2W45OAKEFJ7ZD2T',
'warc-date': '2021-03-09T04:51:26Z',
'warc-identified-content-language': 'ltz',
'warc-record-id': '<urn:uuid:534e6ce8-782c-4813-9dfb-902736ffc141>',
'warc-refers-to': '<urn:uuid:5829843c-0428-4098-9213-52bb2fb319b2>',
'warc-target-uri': 'https://online-archive-extractor.com/lb/open-7z-file',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'Eis Online Archiv Extraiteren erlaabt Iech den Inhalt vu '
'kompriméierten Archiven direkt aus Ärem Browser ze extrahier...'}
```
#### deduplicated_lez
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6238,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:4MMTYN2QRKUOUZESCUL3AOZJTMDM5YSY',
'warc-date': '2021-03-02T18:06:44Z',
'warc-identified-content-language': 'nno,eng',
'warc-record-id': '<urn:uuid:78581b3a-c21f-46a2-b168-bff6f147c337>',
'warc-refers-to': '<urn:uuid:02f1447d-0b61-4ad5-ac56-0f42c2438e6b>',
'warc-target-uri': 'https://lez.wikipedia.org/wiki/1877_%D0%B9%D0%B8%D1%81',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1877 йис (са агъзурни муьжуьдвишни пудкъанницIеирид лагьай йис) — '
'григорийдин чIаваргандал гьалтайла ислендиз эгечӀза...'}
```
#### deduplicated_li
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2199,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IIZSY6KLHN5WSCCGU4NZ6K6WYLIMJP4I',
'warc-date': '2021-03-04T07:19:27Z',
'warc-identified-content-language': 'nld',
'warc-record-id': '<urn:uuid:c7eb18bb-ea03-43c2-a1e9-e8eb5b15e25b>',
'warc-refers-to': '<urn:uuid:486a5d06-6dd8-46d2-a93f-d798b8a5bd07>',
'warc-target-uri': 'https://li.m.wikipedia.org/wiki/Waterop',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Hoes Karsveld aan de Gulp sjtamp oet de 18e ièw. 't Kesjtièlechtig "
"hoes ies van mergel mèt 'ne trapgevel. 't Ies gebo..."}
```
#### deduplicated_lmo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6553,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DAJPSPBN7BVZNRWANXQAW2KP6LQEWNUW',
'warc-date': '2021-03-04T10:49:45Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:d9452b27-9a95-47e9-8274-518138812f56>',
'warc-refers-to': '<urn:uuid:4ff4e796-c685-4c81-adc9-fecbd50e79cb>',
'warc-target-uri': 'https://lmo.wikipedia.org/wiki/Antrenas',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "El sò teretóre el g'ha 'na superfìce de 17,55 km² e 'l và de 'na "
"altèsa mìnima de 720 méter a 'na altèsa màsima de 11..."}
```
#### deduplicated_lo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ຂໍ້ຄວນໃສ່ໃຈ: ຖ້າເຈົ້າເວົ້າພາສາລາວໄດ້, '
'ມີການບໍລິການຊ່ວຍເຫຼືອດ້ານພາສາໃຫ້ທ່ານໂດຍບໍ່ເສຍຄ່າ. ໂທ ຫາ '
'ຝ່າຍບໍລິການສະ ມາ ຊິກທີ່...'}
```
#### deduplicated_lrc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7958,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GTR6WCXERTVUI5RIKHE7MC7LTACF7R2W',
'warc-date': '2021-03-01T04:48:39Z',
'warc-identified-content-language': 'fas,eng',
'warc-record-id': '<urn:uuid:7ba618e0-f09e-48c2-a0be-a1b77ba5678a>',
'warc-refers-to': '<urn:uuid:2e4504e7-46c9-4aaa-818f-3077c73f1d97>',
'warc-target-uri': 'http://www.shaya.me/2013/01/blog-post_3.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار '
'یار یار یار یار یار یار یار یار یار'}
```
#### deduplicated_lt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 221005,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KSLULK6RGSIW43IBMSAEU4643LSRMW3V',
'warc-date': '2021-03-05T07:21:10Z',
'warc-identified-content-language': 'lit',
'warc-record-id': '<urn:uuid:fa6592a5-bc87-4683-88d6-37ce74af5058>',
'warc-refers-to': '<urn:uuid:d78122b4-90d8-4cdf-a205-579bcff9ec88>',
'warc-target-uri': 'https://apcis.ktu.edu/lt/site/katalogas?cat_id=132&type=2',
'warc-type': 'conversion'},
'nb_sentences': 219,
'offset': 0},
'text': 'Telšių apskritis – viena iš Lietuvos sričių, kuri turi ką parodyti '
'pasauliui, ir iš to galima pasiekti didelės naudos...'}
```
#### deduplicated_lv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NUB75CFJHUBI7HOED4HVCNHGQUIVCBO3',
'warc-date': '2021-03-09T03:46:31Z',
'warc-identified-content-language': 'lav,eng',
'warc-record-id': '<urn:uuid:9ad87feb-993f-45b9-bf1e-53a8185b3dc6>',
'warc-refers-to': '<urn:uuid:64eb85d8-c204-4cf8-a6c3-29760fe1f362>',
'warc-target-uri': 'http://igatesbaznica.lv/augupvrsta-stratijas-binr-opcijas.php',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': 'Latvijā šobrīd nav normatīvu aktu mājas un istabas dzīvnieku '
'vairotāju regulēšanai, jo vairākums audzētāju savu nodar...'}
```
#### deduplicated_mai
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3632,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OQRKDLTDWJCD37HVHGXYU7E3BXBR5NB3',
'warc-date': '2021-03-01T16:25:27Z',
'warc-identified-content-language': 'bih,hin,fra',
'warc-record-id': '<urn:uuid:da0cf739-4c6c-46d4-9c32-8e34a673fa26>',
'warc-refers-to': '<urn:uuid:0c39ca75-b871-431b-8c89-63d58ea0893f>',
'warc-target-uri': 'https://mai.m.wikipedia.org/wiki/%E0%A4%B0%E0%A4%BE%E0%A4%9C%E0%A4%A7%E0%A4%BE%E0%A4%A8%E0%A5%80',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'शब्द राजधानी संस्कृत सँ आएल अछि । राजधानी आम तौर पर सङ्घटक क्षेत्रक '
'सब सँ पैग सहर होएत अछि मुदा ई जरुरी नै अछि ।[१]'}
```
#### deduplicated_mg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2714,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OGAHJNKN3OSLXYKJKK2LQAFKAEM67DFQ',
'warc-date': '2021-03-03T15:32:59Z',
'warc-identified-content-language': 'mlg,nno',
'warc-record-id': '<urn:uuid:f5a6492f-29c4-4de9-baaa-12edb86d89cd>',
'warc-refers-to': '<urn:uuid:970362fe-4102-481e-8f4b-db5f3e8ce4db>',
'warc-target-uri': 'https://mg.wikipedia.org/wiki/Barro_Alto_(Bahia)',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "I Barro Alto (Bahia) dia kaominina ao Brazila, ao amin'i Bahia, ao "
"amin'i Centro-Norte Baiano, Irecê.\n"
'Ny velarantanin...'}
```
#### deduplicated_mhr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27685,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YJYVG5XEYRKALEYIO5PCK34QFNUO3JRD',
'warc-date': '2021-03-06T17:12:45Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:3405f528-672f-449c-a2a3-cfa73f5d17b0>',
'warc-refers-to': '<urn:uuid:dfe46be9-656c-4b02-9384-fd1e75987a15>',
'warc-target-uri': 'http://marisong.ru/mar/kalendar',
'warc-type': 'conversion'},
'nb_sentences': 31,
'offset': 0},
'text': '1982 — 1985 ийлаште — Палантай лӱмеш музыкальный училищыште баян '
'дене отделенийыште шинчымашым налын.\n'
'Тыгак шуко жап ...'}
```
#### deduplicated_min
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4309,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XV23LOBECSVNRXJ2NJTCZVJXOCVQ3BBR',
'warc-date': '2021-03-08T22:10:36Z',
'warc-identified-content-language': 'eng,spa',
'warc-record-id': '<urn:uuid:fdaddf50-1986-44b3-b84b-d9a5d0fa27f1>',
'warc-refers-to': '<urn:uuid:257f7969-3a19-42d6-ae1a-ddb5c0486bb8>',
'warc-target-uri': 'https://cookingwithmydoctor.com/?LOSS=danger-of-keto-diet%2F',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f...'}
```
#### deduplicated_mk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22483,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:SGEJ6O6XOEVCQXKXT2XRSRBOSH3ZDSVJ',
'warc-date': '2021-03-02T05:16:16Z',
'warc-identified-content-language': 'mkd,srp,eng',
'warc-record-id': '<urn:uuid:168d1661-a73f-4687-a614-e8cecf7a70a0>',
'warc-refers-to': '<urn:uuid:a61ec44e-a4c1-4b8e-837c-7adc80e853e2>',
'warc-target-uri': 'http://zenica.mk/2018/02/10/tri-dena-kultura-vo-karev-festival/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '„Три дена културa“ е настан кој ќе се одржи од 21-23 февруари '
'(среда, четврток и петок, 20:00ч.) во гимназијата „Нико...'}
```
#### deduplicated_ml
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20202,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ZOEIO7AIEAGDR2S6TOZYZOAQDOV6QJUE',
'warc-date': '2021-03-08T00:10:05Z',
'warc-identified-content-language': 'mal,eng',
'warc-record-id': '<urn:uuid:f19a2925-0064-47e2-9ec9-48b2786657bd>',
'warc-refers-to': '<urn:uuid:20c7b8fd-1909-480f-b36c-89cd1d0ee3c4>',
'warc-target-uri': 'https://boolokam.com/what-to-do-for-police-clearance-conduct-certificate-in-uae/227247',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'രണ്ടുപേര്\u200d തമ്മിലുള്ള സ്നേഹ ബന്ധം അവര്\u200dക്കിടയില്\u200d '
'പൊതുവായി കാണപ്പെടുന്ന മൂല്യങ്ങളുടെ അടിസ്ഥാനത്തില്\u200d '
'ആയിരിക്കും.\n'
'ഒരുവ...'}
```
#### deduplicated_mn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5616,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ILMC56UA63RNTABOJTVMUJQJHMKKC6QR',
'warc-date': '2021-03-09T04:20:37Z',
'warc-identified-content-language': 'mon,ell',
'warc-record-id': '<urn:uuid:07697b69-9e58-4e84-bc0e-a536bcc1ae11>',
'warc-refers-to': '<urn:uuid:704af2f1-3094-45dc-a1c5-63bd08d53069>',
'warc-target-uri': 'http://mn.uncyclopedia.info/index.php?title=%D0%A5%D1%8D%D1%80%D1%8D%D0%B3%D0%BB%D1%8D%D0%B3%D1%87:Mongol_Emperor&action=edit',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Анциклопедиа-д оруулсан бүх хувь нэмэр Creative Commons '
'Attribution-NonCommercial-ShareAlike-н хувьд (дэлгэрэнгүй мэд...'}
```
#### deduplicated_mr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11373,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:V3PQES342QGJGRFZ6QMXNB6RIX2ST3V5',
'warc-date': '2021-03-09T05:01:31Z',
'warc-identified-content-language': 'mar,eng',
'warc-record-id': '<urn:uuid:b96cf6ee-7cda-4a7a-9364-08b51284a05e>',
'warc-refers-to': '<urn:uuid:92e533ed-c2c7-4ac7-9b17-af780a503ce6>',
'warc-target-uri': 'https://marathi.thewire.in/devangana-kalita-uapa-bail-rejected-natasha-narwal',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'पुण्यातील कार्यक्रमांना स्थगिती:पुण्यातील अनेक सांस्कृतिक नियोजित '
'कार्यक्रमांना स्थगिती, कोरोनाच्या वाढत्या रुग्णांमु...'}
```
#### deduplicated_mrj
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3492,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7B242FKI45QVEGJQTF46YCRFYMYW6YFG',
'warc-date': '2021-03-03T05:03:02Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:bd7d5682-be60-4a00-9781-29b03a87b30e>',
'warc-refers-to': '<urn:uuid:49641a15-2834-4a72-a011-fdc9cd7273c7>',
'warc-target-uri': 'https://mrj.wikipedia.org/wiki/%D0%91%D0%B0%D1%80%D0%BA%D0%B5%D1%80%D0%B8',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Баркери (латинлӓ Barkeria) – Орхидейвлӓ (Orchidaceae) йыхыш пырышы '
'пеледшӹ кушкыш. Америкышты вӓшлиӓлтеш. Цилӓжӹ 15 й...'}
```
#### deduplicated_ms
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7939,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7BWXR4LQ6O2IBJLKLKWJKHTF3JBXB26T',
'warc-date': '2021-03-09T05:38:44Z',
'warc-identified-content-language': 'msa,eng',
'warc-record-id': '<urn:uuid:35a9d91c-3a64-4748-b135-3c467bfa403f>',
'warc-refers-to': '<urn:uuid:9cf4de91-0523-4327-9fcb-5c8f99956da0>',
'warc-target-uri': 'https://kheru2006.livejournal.com/1665383.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Bagaimanapun beliau memiliki satu lagi pandangan iaitu perkara '
'paling bodoh seseorang boleh lakukan ialah menjangka d...'}
```
#### deduplicated_mt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 98714,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HC75UY5ZHRC3AY4C2VHFR4JADUM2AZBH',
'warc-date': '2021-03-09T04:29:23Z',
'warc-identified-content-language': 'eng,mlt',
'warc-record-id': '<urn:uuid:45dec17d-a638-454e-a136-c45579517b53>',
'warc-refers-to': '<urn:uuid:c82d8d7c-05b6-43d8-be17-5072323aab01>',
'warc-target-uri': 'https://carmelcacopardo.wordpress.com/2015/07/28/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Kemmuna hi protetta bħala sit Natura 2000. Imma ma nistgħux '
'neskludu logħob tas-soltu biex iduru ma din il-protezzjon...'}
```
#### deduplicated_mwl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11598,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2A22BTIRZ4E5FI2FCG7AUCWJQTY2J4ST',
'warc-date': '2021-02-26T13:58:26Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:73a60756-1664-410f-bf62-ab44c88c074f>',
'warc-refers-to': '<urn:uuid:800d3642-449d-4be0-817c-edc7fb64c1b4>',
'warc-target-uri': 'https://mwl.wikipedia.org/wiki/R%C3%A1dio_(quemunica%C3%A7on)',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'La radioquemunicaçon ye un meio de quemunicaçon por trascepçon de '
'anformaçon, podendo ser rializada por Radiaçon eile...'}
```
#### deduplicated_my
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 237288,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:U2QEC6RSZR5UW5LXTNN6QRD47FHVYVJY',
'warc-date': '2021-02-27T06:07:58Z',
'warc-identified-content-language': 'mya,eng',
'warc-record-id': '<urn:uuid:817de4f8-0b7a-446e-bae2-8436019dd34f>',
'warc-refers-to': '<urn:uuid:b364cc33-c1bf-4adb-8317-1aad1cfd4aa0>',
'warc-target-uri': 'http://www.pnsjapan.org/2010/05/',
'warc-type': 'conversion'},
'nb_sentences': 248,
'offset': 0},
'text': 'စတိုင္လည္းက် စမတ္လည္းက်တဲ့ ေန႔စဥ္ လႈပ္ရွားမႈဘဝေလးေတြကို '
'ပိုင္ဆိုင္ႏိုင္ဖို႔အတြက္ Samsung ကေန မၾကာေသးခင္က ထုတ္လုပ္လိုက...'}
```
#### deduplicated_myv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11091,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IFCGUVXSCYHEFYLUVOQ5QMGJWYL2CTVJ',
'warc-date': '2021-03-02T21:05:00Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:ea77b8a6-e394-48c1-b865-3cea87e7b906>',
'warc-refers-to': '<urn:uuid:a4927904-4e3c-4f22-858a-adad9bbb1e63>',
'warc-target-uri': 'https://ru.m.wikinews.org/wiki/%D0%9E%D0%BC%D0%B1%D0%BE%D0%BC%D0%B0%D1%81%D1%82%D0%BE%D1%80%D1%81%D0%BE_%C2%AB%D0%90%D0%B7%D0%BE%D1%80%C2%BB_%D1%8D%D1%80%D0%B7%D1%8F%D0%BD%D1%8C_%D1%8D%D1%80%D1%8F%D0%BC%D0%B0%D1%80%D1%82%D0%BE%D0%BD%D1%82%D1%8C_%D0%B2%D0%B0%D1%81%D0%B5%D0%BD%D1%86%D0%B5_%D0%BD%D0%B5%D0%B2%D1%82%D0%B5%D0%BC%D0%B0%D1%81%D1%8C_%D1%8E%D1%82%D1%8B_%D0%A1%D1%83%D0%BE%D0%BC%D0%B8%D1%81%D1%81%D1%8D',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '«Азор» — васенце эрзянь кельсэ артонь эриванмо-фильманть теемстэ. '
'Орданьбуень Баеньбуе веле, Мордовиясо.'}
```
#### deduplicated_mzn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6193,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QVLHP3APVA34EQ4YFDRJWF2ODTQZ3QG6',
'warc-date': '2021-03-08T00:11:58Z',
'warc-identified-content-language': 'fas',
'warc-record-id': '<urn:uuid:c86dfe2b-795d-4e5d-aaa0-75c1e98690a6>',
'warc-refers-to': '<urn:uuid:b6258701-626d-4a7c-b79e-1c526f9892a6>',
'warc-target-uri': 'https://mzn.wikipedia.org/wiki/%D8%A7%D9%88%D8%B3%D9%88%DA%A9%DB%8C%D8%8C_%D8%A7%D9%88%D8%A6%DB%8C%D8%AA%D8%A7',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'اوسوکی اتا شهر نوم هسته که جاپون ِاوئیتا استان دله دره. ونه جمعیت '
'ره سال ۲۰۰۸ گادِر ۴۲٬۴۶۴ نفر اعلام هاکاردنه. این شه...'}
```
#### deduplicated_nah
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2517,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DSXC3C7F2LUL47USAV5ZRT4HMVQ4XGUI',
'warc-date': '2021-03-03T14:32:16Z',
'warc-identified-content-language': 'spa,ell',
'warc-record-id': '<urn:uuid:a305013e-01ba-49a3-89b9-027dc622576f>',
'warc-refers-to': '<urn:uuid:073b9e5a-a0d3-41c3-89bd-8f972b6a4154>',
'warc-target-uri': 'https://nah.wikipedia.org/wiki/%CF%98',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ϙ ītōcā inic cē huēhuehtlahtōl īpan '
'greciamachiyōtlahtōltecpantiliztli. Ītlahtōl nō ic 90 tlapōhualli.'}
```
#### deduplicated_nap
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2331,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EXGUINJCGD2K4E2IVQNJJAQLS4UDJ2TG',
'warc-date': '2021-03-07T13:12:47Z',
'warc-identified-content-language': 'cos,srp,lav',
'warc-record-id': '<urn:uuid:7362689d-31bc-492d-8e60-851c963b5313>',
'warc-refers-to': '<urn:uuid:ecd1bb5f-d247-4739-b9e9-4f93d46081d6>',
'warc-target-uri': 'https://nap.wikipedia.org/wiki/Priatorio',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "'Int'ô cattolicesimo, priatorio è 'o pruciesso 'e purefecazzione 'e "
"ll'aneme ca moreno 'into ll'amicizzia 'e Dio ma n..."}
```
#### deduplicated_nds
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5066,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:G2O2EJZLTIU5IDSXMYHPP3TMXVXMAZ3P',
'warc-date': '2021-03-08T22:13:48Z',
'warc-identified-content-language': 'nno,srp',
'warc-record-id': '<urn:uuid:d7f0c9a0-9c12-4d9a-ae5a-184bf7b59c5d>',
'warc-refers-to': '<urn:uuid:31f4d793-f3a4-4403-9c1f-a52f878b63c8>',
'warc-target-uri': 'https://nds.wikipedia.org/wiki/1763',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '7. Oktober: In London geiht en königliche Proklamatschoon rut, dat '
'vun nu af an in de Kolonien vun Amerika de Kamm vu...'}
```
#### deduplicated_ne
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17723,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AZ2CUDZ672TVV2R3O643TJAX7JGXASP2',
'warc-date': '2021-03-08T22:24:08Z',
'warc-identified-content-language': 'nep',
'warc-record-id': '<urn:uuid:fa642413-904a-4def-86fc-a4889e5e9e71>',
'warc-refers-to': '<urn:uuid:f7caed4f-c5ae-4f55-944a-1f06ed71e438>',
'warc-target-uri': 'https://postpati.com/2017/26/07/1353',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'युएइको दूतावास बिरुद्द युएइमा रहेका संघ संस्थाहरु द्वारा निरन्तर '
'दवाव आउने क्रमजारि रहेको छ। नेकपा माओबादी सम्बद्ध रह...'}
```
#### deduplicated_new
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2388,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:E6YZSKQK57PDBRG7VPE64CGOL3N4D63I',
'warc-date': '2021-03-09T04:24:48Z',
'warc-identified-content-language': 'nep,eng,bih',
'warc-record-id': '<urn:uuid:20692995-9d67-4b05-ba9b-9dbac80b4441>',
'warc-refers-to': '<urn:uuid:a8445a70-117a-42c1-89ca-aa5df0cc5616>',
'warc-target-uri': 'https://new.wikipedia.org/wiki/%E0%A4%A7%E0%A4%BE%E0%A4%AA%E0%A4%BE',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'धापा (अंग्रेजी भाय:Dhapa), नेपायागु कर्णाली अञ्चलयागु जुम्ला '
'जिल्लायागु गाँ विकास समिति खः। थ्व थासे231खा छेँ दु।'}
```
#### deduplicated_nl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 766978,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:77YAXN3F4IGI2CYBM3IESJRTCIB4WY2F',
'warc-date': '2021-02-25T16:49:18Z',
'warc-identified-content-language': 'nld',
'warc-record-id': '<urn:uuid:0b08e51a-1b82-4fb9-a420-8556f2fb47a3>',
'warc-refers-to': '<urn:uuid:dae7ca23-9b7e-45d1-9a1c-604942af8cb9>',
'warc-target-uri': 'https://www.delpher.nl/nl/tijdschriften/view?identifier=MMUBA13:001691001:00689&coll=dts',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1 Deze Duitse hond is nauw verwant aan de Duitse Brak, de '
'Westfaalse Dasbrak werd gefokt om op dieren te jagen, zoals...'}
```
#### deduplicated_nn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2770,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FLRYPK225URFXO3IG4LP6D5TI2WW7MNU',
'warc-date': '2021-03-09T03:50:05Z',
'warc-identified-content-language': 'nno',
'warc-record-id': '<urn:uuid:de821d19-abed-4a35-9284-91176a5428b9>',
'warc-refers-to': '<urn:uuid:7ed9913e-e7dd-496f-b0ef-e82098dd53ca>',
'warc-target-uri': 'https://www.avisa-hordaland.no/trafikk/tunell-pa-e16-stengd-2/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Bilføraren som vart stogga på E16 i helga hadde 2,28 i promille: – '
'Han var ikkje i stand til å ta vare på seg sjølv'}
```
#### deduplicated_no
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1329,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:G7JC2T5AD4YK4WWFGTYHHGP5VHB6M7KU',
'warc-date': '2021-03-08T13:17:52Z',
'warc-identified-content-language': 'nor',
'warc-record-id': '<urn:uuid:9e215de3-f988-4754-9ef5-6370121b9b5e>',
'warc-refers-to': '<urn:uuid:1facfcb5-da68-4122-9257-102271944050>',
'warc-target-uri': 'https://www.miljoindex.no/781825/nexans-norway-hovedkontor/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Utvikling, produksjon og markedsføring av kabler og '
'kablingssystemer, samt annen tilknyttet virksomhet, herunder del...'}
```
#### deduplicated_oc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20117,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2XDHRCL2CSS7YFAM2IAGQL6CSJJEQDXI',
'warc-date': '2021-03-03T15:40:21Z',
'warc-identified-content-language': 'oci',
'warc-record-id': '<urn:uuid:c9ebdec5-af68-4756-88c8-1df831621c5b>',
'warc-refers-to': '<urn:uuid:199db451-0e6f-4f75-ad81-2e7612295452>',
'warc-target-uri': 'https://oc.wikipedia.org/wiki/2',
'warc-type': 'conversion'},
'nb_sentences': 18,
'offset': 0},
'text': "8 : dins l'Empèri Part, assassinat dau rèi Orodes III, probablament "
'en causa de son autoritarisme, que foguèt remplaç...'}
```
#### deduplicated_or
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12859,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KQDIT6NHKBV43F56DTHTM5ZS3GHJT5SY',
'warc-date': '2021-03-09T05:25:21Z',
'warc-identified-content-language': 'ori,eng',
'warc-record-id': '<urn:uuid:e25e33da-92c5-42d6-aef8-c3465855312a>',
'warc-refers-to': '<urn:uuid:7457ac60-4aae-44ad-aaec-314795ea0708>',
'warc-target-uri': 'https://or.wikipedia.org/wiki/%E0%AC%A6%E0%AD%8D%E0%AD%B1%E0%AC%BF%E0%AC%A4%E0%AD%80%E0%AD%9F_%E0%AC%AC%E0%AC%BF%E0%AC%B6%E0%AD%8D%E0%AD%B1%E0%AC%AF%E0%AD%81%E0%AC%A6%E0%AD%8D%E0%AC%A7',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'ଇଉରୋପ, ପ୍ରଶାନ୍ତ ମହାସାଗର, ଆଟଲାଣ୍ଟିକ ମହାସାଗର, ଦକ୍ଷିଣ-ପୂର୍ବ ଏସିଆ, ଚୀନ, '
'ମଧ୍ୟପ୍ରାଚ୍ୟ, ଭୂମଧ୍ୟସାଗର, ଉତ୍ତର ଆଫ୍ରିକା, ପୂର୍ବ ଆଫ୍...'}
```
#### deduplicated_os
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7079,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:N7CKDF6E3SJBINW4SR6LIUNKLIJP2ROL',
'warc-date': '2021-03-08T22:01:32Z',
'warc-identified-content-language': 'nno',
'warc-record-id': '<urn:uuid:4cd86a68-815b-4539-84a8-bab850034e60>',
'warc-refers-to': '<urn:uuid:8774fb5e-b7fb-4feb-85e7-8c7b33f5980b>',
'warc-target-uri': 'https://os.wikipedia.org/wiki/%D0%9F%D1%83%D1%88%D0%BA%D0%B8%D0%BD,_%D0%A1%D0%B5%D1%80%D0%B3%D0%B5%D0%B9%D1%8B_%D1%84%D1%8B%D1%80%D1%82_%D0%90%D0%BB%D0%B5%D0%BA%D1%81%D0%B0%D0%BD%D0%B4%D1%80',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'Пушкин Александр Сергейы фырт (уырыс. Александр Сергеевич Пушкин; '
'райгуырдис 1799 азы 6 июны Мæскуыйы — амардис 1837 ...'}
```
#### deduplicated_pa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3990,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HBYN5XY3CD2KI4XIWMBJYPSV2ZPNBWUN',
'warc-date': '2021-03-09T05:05:20Z',
'warc-identified-content-language': 'pan,eng',
'warc-record-id': '<urn:uuid:1ac5c8d1-e750-492e-b35e-b9780bfd16fd>',
'warc-refers-to': '<urn:uuid:b4d8f997-8c9a-43cf-b16c-e8a77c209062>',
'warc-target-uri': 'https://pa.nhp.gov.in/Detail/getdirection?url=radha-krishna-nurshing-andmat-home-rae_bareli-uttar_pradesh',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ਇਹ ਪੋਰਟਲ ਰਾਸ਼ਟਰੀ ਸਿਹਤ ਪੋਰਟਲ ਦੇ ਸਿਹਤ ਸੂਚਨਾ ਕੇਂਦਰ (CHI) ਦੁਆਰਾ ਵਿਕਸਿਤ '
'ਤੇ ਤਿਆਰ ਕੀਤਾ ਗਿਆ ਹੈ ਅਤੇ ਸਿਹਤ ਤੇ ਪਰਿਵਾਰ ਭਲਾਈ ਮੰਤਰਾਲੇ...'}
```
#### deduplicated_pam
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4615,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WOAFTI75LXN3LAF6WFDRDHITPU33CZRK',
'warc-date': '2021-03-07T22:02:39Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:9d7a202a-0fec-4aac-9921-2ebf5aa7f9a2>',
'warc-refers-to': '<urn:uuid:70b6a707-77b1-4a0f-84e6-d75ed8d729ad>',
'warc-target-uri': 'https://toddlers.me/kpai-sarankan-gading-beri-penguatan-psikologi-untuk-gempi/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '“Káláu Gádìng tìdák mámpu melákukán ìtu, yá bìsá mìntá tolong '
'kepádá oráng yáng berkompeten, mìsálnyá psìkolog átáu s...'}
```
#### deduplicated_pl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 51849,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:25YENUTK4YA3ZYGCWQH5Z6YDINCMI6SI',
'warc-date': '2021-03-05T22:43:01Z',
'warc-identified-content-language': 'pol',
'warc-record-id': '<urn:uuid:753116b6-f680-448d-ae8a-8fc88ce061b1>',
'warc-refers-to': '<urn:uuid:926693c4-5b59-4f50-98b9-787576fc71d7>',
'warc-target-uri': 'https://igraszki-jezykowe.pl/category/tips-and-tricks-metodyka/',
'warc-type': 'conversion'},
'nb_sentences': 60,
'offset': 0},
'text': 'W niedzielę, 12 czerwca w Orlando na Florydzie islamski terrorysta, '
'powiązany z ISIS zastrzelił 50 osób i drugie tyle...'}
```
#### deduplicated_pms
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2620,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2T5H5XDLC3KPDB33XXVCTGNNYYDJXQWQ',
'warc-date': '2021-03-03T16:04:55Z',
'warc-identified-content-language': 'srp',
'warc-record-id': '<urn:uuid:952c2dda-041e-40ff-bf28-8a39075f53d9>',
'warc-refers-to': '<urn:uuid:6d526022-b736-4a51-9b9c-c5bdd5a546f9>',
'warc-target-uri': 'https://pms.wikipedia.org/wiki/Auer',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "Auer (Ora për j'italian) a l'é un comun ëd 3.025 abitant dla "
'provincia ëd Bolsan (Region Autònoma Trentin-Sud Tiròl)....'}
```
#### deduplicated_pnb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2896,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GWWDSJAQDB7JDQWV65CI6WT7E6C33DL4',
'warc-date': '2021-03-08T23:01:08Z',
'warc-identified-content-language': 'urd',
'warc-record-id': '<urn:uuid:8c385ca8-7561-4f47-b5a3-0862488eb948>',
'warc-refers-to': '<urn:uuid:837d621d-3540-44fd-a4d0-6cb3c6f2327f>',
'warc-target-uri': 'https://pnb.wikipedia.org/wiki/453%DA%BE',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'لکھت کریئیٹیو کامنز انتساب/ اکوجہے-شراکت لائسنس دے ہیٹھ دستیاب اے، '
'ہور شرطاں وی لاگو ہوسکدیاں نیں۔ ویروے لئی ورتن شرط...'}
```
#### deduplicated_ps
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2424,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CAUU5Y7TOTASV7WYKCYRCVXTZ7GGN2VO',
'warc-date': '2021-03-09T05:08:35Z',
'warc-identified-content-language': 'pus',
'warc-record-id': '<urn:uuid:d784cf7a-91e1-4c54-96a2-e41c67318548>',
'warc-refers-to': '<urn:uuid:98aed7d2-c3e3-4039-af83-f2c73a5c19f5>',
'warc-target-uri': 'https://www.mashaalradio.com/a/29821043.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'د افغانستان په فاریاب ولایت کې په یوه پارک کې ښځو په برقعو کې ورزش '
'کړی دی. د سیمې چارواکي وايي، د ښځو د ورزش لپاره ځا...'}
```
#### deduplicated_pt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 79931,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JYDP4XMEGW2XPPV6NAAF772KDH4X2CCF',
'warc-date': '2021-02-25T13:48:41Z',
'warc-identified-content-language': 'por',
'warc-record-id': '<urn:uuid:3b50f546-e03b-461f-98c8-5a38920d7c0a>',
'warc-refers-to': '<urn:uuid:564bfb21-0705-4997-bbb9-472f0cbcad3e>',
'warc-target-uri': 'http://www.artefazparte.com/',
'warc-type': 'conversion'},
'nb_sentences': 117,
'offset': 0},
'text': 'A reflexão sobre identidade de género anda a cansar muitos de nós. '
'Sobretudo os que não têm dúvidas e nela se sentem ...'}
```
#### deduplicated_qu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2630,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:34TX2UNXR2JLRLAFTE3ILOBMEBRMWIRH',
'warc-date': '2021-03-09T05:23:48Z',
'warc-identified-content-language': 'que',
'warc-record-id': '<urn:uuid:237398f6-a300-449b-9e09-7a1ed8cf1e97>',
'warc-refers-to': '<urn:uuid:84b20aab-d538-4efc-bc97-33d546d84802>',
'warc-target-uri': 'https://qu.wikipedia.org/wiki/Sapaq:HukchasqaTinkimuq/Chinchay_Chungcheong_pruwinsya',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Kay sapaq p'anqaqa t'inkisqa p'anqakunapi ñaqha hukchasqakunatam "
"rikuchin. Watiqasqayki p'anqakunaqa yanasapa qillqas..."}
```
#### deduplicated_rm
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 100558,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:Z7R6QV2K5FDIHR4QJH7F2NTXND6NDEFY',
'warc-date': '2021-02-27T13:53:32Z',
'warc-identified-content-language': 'deu',
'warc-record-id': '<urn:uuid:da3aec28-6c61-470c-a5d2-66710bc1fb35>',
'warc-refers-to': '<urn:uuid:9d04f371-89a7-4ac2-9b1e-883aa93e4ace>',
'warc-target-uri': 'http://lexbrowser.provinz.bz.it/doc/la/lp-2009-5/lege_provinzialadi_28_de_set_mber_dl_2009_n_5.aspx?view=1',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '(2) La prestaziun dla garanzia é sotmetüda al’aprovaziun di decunć '
'finanziars da pert dl’aministraziun dl consorz.'}
```
#### deduplicated_ro
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1677,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXKBGKXVETQLCHTHRMLLSWUPXTDNJDVV',
'warc-date': '2021-02-26T12:19:49Z',
'warc-identified-content-language': 'ron',
'warc-record-id': '<urn:uuid:2c20c06f-ca98-4118-9222-7b3b74bc760b>',
'warc-refers-to': '<urn:uuid:e77c028a-5857-4ec2-90db-58a9bb57c510>',
'warc-target-uri': 'https://ro.visafoto.com/es-visa-photo',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Căluşarii sau Boristenii, melodie culeasă din Braşov, în 1832, de '
'Canzler cav. de Ferio şi publicată târziu de Otto H...'}
```
#### deduplicated_ru
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14025,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2HSXIFOHEJZOTJV2EVDSZDVF26ATVATE',
'warc-date': '2021-03-07T02:45:16Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:aa9b3fc9-fb66-45fa-a064-62ae5fd67970>',
'warc-refers-to': '<urn:uuid:e9145f1e-4ce5-44db-a7d7-234842b31973>',
'warc-target-uri': 'http://budzdorov-kaluga.ru/statyi_i_materialy/o-grippe',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': '«Геро́й» (кит. 英雄) — исторический фильм режиссёра Чжана Имоу, '
'снятый в 2002 году. Продолжительность — 93 минуты (суще...'}
```
#### deduplicated_rue
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17472,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YBMO2PR3WF7WQ7UEU5YLRBI7BZ6IP6KB',
'warc-date': '2021-03-06T15:24:27Z',
'warc-identified-content-language': 'ukr,rus',
'warc-record-id': '<urn:uuid:ca71a8fe-adb9-4346-a5b4-7d283f1410f8>',
'warc-refers-to': '<urn:uuid:a609d9f9-5040-4ca5-80a8-aa2c4c7a3525>',
'warc-target-uri': 'https://rue.wikipedia.org/wiki/%D0%9F%D0%BE%D0%BC%D1%96%D1%87:%D0%9A%D0%B0%D1%82%D0%B5%D2%91%D0%BE%D1%80%D1%96%D1%97',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Наприклад можете едітовати Катеґорія:Фізіци і додати одказ '
'[[Катеґорія:Фізіка]]. Катеґорія Фізіци буде пікатеґоріёв к...'}
```
#### deduplicated_sa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4166,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ACZ66HH67HYSPS6I7YYQX64HRD4O5GIH',
'warc-date': '2021-02-24T20:35:30Z',
'warc-identified-content-language': 'san,eng',
'warc-record-id': '<urn:uuid:12bc2393-cb9b-492d-9398-f6b1090bd999>',
'warc-refers-to': '<urn:uuid:6e883bd6-350e-4280-94dc-ee84f44d2458>',
'warc-target-uri': 'https://sa.wikipedia.org/wiki/%E0%A4%B5%E0%A4%BF%E0%A4%B6%E0%A5%87%E0%A4%B7%E0%A4%83:%E0%A4%95%E0%A4%BF%E0%A4%AE%E0%A4%A4%E0%A5%8D%E0%A4%B0_%E0%A4%B8%E0%A4%81%E0%A4%B2%E0%A5%8D%E0%A4%B2%E0%A4%97%E0%A5%8D%E0%A4%A8%E0%A4%AE%E0%A5%8D/%E0%A4%B5%E0%A4%B0%E0%A5%8D%E0%A4%97%E0%A4%83:%E0%A5%A9%E0%A5%AC%E0%A5%A7',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'केभ्यः पृष्ठेभ्यः सम्बद्धम् पृष्ठम्: नामाकाशः : सर्वाणि (मुख्यम्) '
'सम्भाषणम् सदस्यः सदस्यसम्भाषणम् विकिपीडिया विकिपीडि...'}
```
#### deduplicated_sah
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1724,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5PKOMLENZCNOU6PT27NCNKTQFPRC37RQ',
'warc-date': '2021-03-03T15:19:03Z',
'warc-identified-content-language': 'ukr,rus',
'warc-record-id': '<urn:uuid:59b7bbeb-e375-4d8c-8b7c-fbe09e5ce21e>',
'warc-refers-to': '<urn:uuid:512d4df0-bd91-47aa-8f23-eb2a8d4b426e>',
'warc-target-uri': 'https://sah.m.wikipedia.org/wiki/%D0%A7%D0%B5%D1%80%D0%BD%D0%B8%D0%B3%D0%BE%D0%B2_%D1%83%D0%BE%D0%B1%D0%B0%D0%BB%D0%B0%D2%BB%D0%B0',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Тиэкис Creative Commons Attribution-ShareAlike лиссиэнсийэ '
'усулуобуйатынан тарҕанар, сорох түбэлтэҕэ эбии көрдөбүллэр...'}
```
#### deduplicated_scn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3622,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VGCXGU3B2WY722G2LRJ56RSYT4HSLUGI',
'warc-date': '2021-03-03T02:35:42Z',
'warc-identified-content-language': 'cos,ita',
'warc-record-id': '<urn:uuid:caeb7ba3-1bc2-4ef7-95cb-eb0d4d0792d6>',
'warc-refers-to': '<urn:uuid:19e33395-5981-4f6d-857b-12cf7d761b58>',
'warc-target-uri': 'https://scn.wikipedia.org/wiki/Canali_d%C3%A2_M%C3%A0nica',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Lu ripartu francisi dâ Mànica, chi cumprenni la pinìsula dû '
'Cotentin, chi si nesci ntô canali, pigghia lu sò nomu dû ...'}
```
#### deduplicated_sco
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 140370,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TRXAEE4XHP7FT4FCJF3DSEKD7YBPCFOR',
'warc-date': '2021-03-02T07:33:12Z',
'warc-identified-content-language': 'eng,vol',
'warc-record-id': '<urn:uuid:d406a6c9-dba6-4955-8ede-f8082f7da58f>',
'warc-refers-to': '<urn:uuid:155919e0-a689-415c-b2aa-eccd06021476>',
'warc-target-uri': 'https://baggato.com/fo',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'fowjo fowjp fowjq fowjr fowka fowkb fowkc fowkd fowke fowkf fowkg '
'fowkh fowki fowkj fowkk fowkl fowkm fowkn fowko fow...'}
```
#### deduplicated_sd
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17619,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DLWVP7WGNP64RB6ZLHDNQEJ7D24BYXOR',
'warc-date': '2021-02-24T20:04:37Z',
'warc-identified-content-language': 'snd,eng',
'warc-record-id': '<urn:uuid:8997e1c6-4d72-47f1-bffe-d18a00ae6b94>',
'warc-refers-to': '<urn:uuid:946e892e-46c3-4a68-8532-1eac8b65b76a>',
'warc-target-uri': 'https://sd.info-4all.ru/%D8%B1%D8%AA%D9%88%D9%BD%D9%88-%D8%A2%D8%A6%D9%8A%D8%B1%D8%B1%D8%A7/%DA%AA%D9%84%D8%A7%DA%AA/',
'warc-type': 'conversion'},
'nb_sentences': 21,
'offset': 0},
'text': 'بيلففيل ڪيئن ٿيو؟ پهرين توهان کي پنهنجو ضمير وڃائڻ جي ضرورت آهي. '
'اهي تعليم کان سواءِ صرف سست ماڻهو نه وٺندا آهن ، پر ...'}
```
#### deduplicated_sh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12517,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IH6O64JAV4PLXURRD5LKU6C46DGGXS27',
'warc-date': '2021-03-09T06:06:53Z',
'warc-identified-content-language': 'fra,hrv,eng',
'warc-record-id': '<urn:uuid:ddc0f982-aea2-4206-a431-02e6c89ab090>',
'warc-refers-to': '<urn:uuid:904a206d-515a-4f11-ad25-9035adbf0cfa>',
'warc-target-uri': 'https://sh.wikipedia.org/wiki/Cliponville',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Po podacima iz 1999. godine u opštini je živelo 245 stanovnika, a '
'gustina naseljenosti je iznosila 33 stanovnika/km²....'}
```
#### deduplicated_si
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 18426,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CZO426HASJ2VV5IMXEAHY2T53ZTDOZEP',
'warc-date': '2021-02-24T20:38:23Z',
'warc-identified-content-language': 'sin,eng',
'warc-record-id': '<urn:uuid:bec8b1fe-0659-4f47-b244-018b5dac9e30>',
'warc-refers-to': '<urn:uuid:1c918e04-8c2d-4bc0-bcfb-bf978ab0c0ea>',
'warc-target-uri': 'https://androidwedakarayo.com/before-you-look-for-a-job-please-fix-your-facebook-account/',
'warc-type': 'conversion'},
'nb_sentences': 19,
'offset': 0},
'text': 'ඉස්සර තමයි අපි සෝෂල්මීඩියා පාවිච්චි කරන්නේ අපි ආස නළු නිළියන්ගේ '
'ෆොටෝ, හදපු කෑම, ඩ්\u200dරින්ක් එකක් දාන්න සෙට් වෙච්චි වෙලා...'}
```
#### deduplicated_sk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 37910,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ODXVMZXR34B45NQTMJIKKK2VGBGRXKEA',
'warc-date': '2021-03-01T16:29:19Z',
'warc-identified-content-language': 'slk',
'warc-record-id': '<urn:uuid:6a22612f-9bbf-4f74-8cca-0457f069baa4>',
'warc-refers-to': '<urn:uuid:3981cb48-fadf-463f-9fc9-a6d717b9dc71>',
'warc-target-uri': 'http://www.tomsta.sk/',
'warc-type': 'conversion'},
'nb_sentences': 56,
'offset': 0},
'text': 'Keďže všade naokolo sú iba kopce, mohol byť jedine horský. Dnes je '
'z toho najlepší horský triatlon na Slovensku, ktor...'}
```
#### deduplicated_sl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8130,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:UFZ4P4LVU4TXYJIHZULTCIVJ4GA3JT54',
'warc-date': '2021-03-07T14:50:23Z',
'warc-identified-content-language': 'slv,eng',
'warc-record-id': '<urn:uuid:e50a528d-ebd3-46dc-92d7-af394aaa896a>',
'warc-refers-to': '<urn:uuid:dbfe8ac4-b415-45a8-a16c-c168ed5ce37b>',
'warc-target-uri': 'https://www.edi-nm.com/si/varicosen-mnenja-cena-lekarna/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Po najnovejših raziskavah v Sloveniji vsaka 4. oseba med 36. in 95. '
'letom trpi zaradi kronične venske insuficience – ...'}
```
#### deduplicated_so
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17837,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WIS4GECYGJYMTZMVFOUVUMRWTAPFZUSK',
'warc-date': '2021-03-03T20:11:46Z',
'warc-identified-content-language': 'bul,eng,srp',
'warc-record-id': '<urn:uuid:976de977-97b9-4517-8a42-2fc82fdda461>',
'warc-refers-to': '<urn:uuid:a0f1fbd0-b2cb-495f-93f3-53e77acae3f5>',
'warc-target-uri': 'https://studioqueens.bgnick.info/l4fOorCpgdutsnY/igra-na.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххх...'}
```
#### deduplicated_sq
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6129,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:D3PWGEKLJKJEGOTQLYVQNUV4URWEFH2P',
'warc-date': '2021-03-09T03:17:23Z',
'warc-identified-content-language': 'sqi',
'warc-record-id': '<urn:uuid:3299bc56-c7fb-4655-bebd-393510d89aaa>',
'warc-refers-to': '<urn:uuid:1416a2ad-d319-4c60-b663-29239ff79154>',
'warc-target-uri': 'http://ata.gov.al/2019/11/03/video-u-prek-nga-termeti-ndertohet-nga-e-para-banesa-e-familjes-stafa-ne-petrele/',
'warc-type': 'conversion'},
'nb_sentences': 11,
'offset': 0},
'text': 'TIRANË, 3 nëntor/ATSH/- Në Petrelë të Tiranës ka nisur puna për '
'ndërtimin nga e para të shtëpisë së familjes Stafa, e...'}
```
#### deduplicated_sr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7735,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7LKRS7R2L2K53YTV5CYR2IAJRNIQKGBJ',
'warc-date': '2021-03-03T11:23:25Z',
'warc-identified-content-language': 'srp,eng',
'warc-record-id': '<urn:uuid:8ade8406-bedb-41a7-b854-8429b6b21214>',
'warc-refers-to': '<urn:uuid:cca5c75c-7221-4247-a51e-f7be99661793>',
'warc-target-uri': 'https://vojvodjanske.rs/40-jubilarni-somborski-polumaraton-u-nedelju-19-maja/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '„У недељу 19. маја, у Сомбору се одржава јубиларна 40. најстарија '
'улична трка у Републици Србији, Сомборски полумарат...'}
```
#### deduplicated_su
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14013,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IMFFV646FPXSYLMOATX7O6CDMKUU4BFL',
'warc-date': '2021-03-09T10:29:19Z',
'warc-identified-content-language': 'sun,ind',
'warc-record-id': '<urn:uuid:02eb1f6f-7040-4b8f-b995-7c547196da4b>',
'warc-refers-to': '<urn:uuid:4a9807f7-0c98-493f-ab84-8fafc61a1e50>',
'warc-target-uri': 'https://www.masdinko.com/2019/04/soal-utspts-bahasa-sunda-sd-kelas-4.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Pikeun urang lembur, daun seureuh téh geus teu anéh deui. Seureuh '
'mah mangrupa tangkal nu ngarémbét kana tangkal séjéna.'}
```
#### deduplicated_sv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 87099,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TKLP6CG56M45ABZQGDD7EDTCQMKTSAVS',
'warc-date': '2021-03-05T20:01:45Z',
'warc-identified-content-language': 'swe',
'warc-record-id': '<urn:uuid:97860695-1688-46ef-93db-5e15742820af>',
'warc-refers-to': '<urn:uuid:7c924b0e-39e1-4921-a561-52dc5453b886>',
'warc-target-uri': 'https://fortretligheter.blogspot.com/2011/01/',
'warc-type': 'conversion'},
'nb_sentences': 255,
'offset': 0},
'text': 'Svenska trupper hade en kväll för flera hundra år sedan när Sverige '
'och Danmark låg i Krig med varandra kommit med sk...'}
```
#### deduplicated_sw
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2098,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FPGJP34F47FJQSZF62PELBLYNJ4RTCSE',
'warc-date': '2021-03-03T15:24:39Z',
'warc-identified-content-language': 'swa',
'warc-record-id': '<urn:uuid:d42018de-64be-41f9-b4b6-700dd0051ce3>',
'warc-refers-to': '<urn:uuid:a40c8328-ab33-4113-9ea1-8c35967b0bde>',
'warc-target-uri': 'http://mwanza.go.tz/videos/78',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Mkuu wa Mkoa wa Mwanza Mhe.John Mongella akifungua Baraza la '
'biashara katika kikao kilichofanyika kwenye ukumbi wa mk...'}
```
#### deduplicated_ta
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 49341,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FQEPDKJ7AYCAEVL5SRUQ5QOULOOSHECD',
'warc-date': '2021-03-09T04:15:52Z',
'warc-identified-content-language': 'tam',
'warc-record-id': '<urn:uuid:2fa70e6a-a31a-4359-b4ff-54ce7f5d6200>',
'warc-refers-to': '<urn:uuid:92eb01ff-4f82-438b-8d1f-1722fe23285a>',
'warc-target-uri': 'https://thiru2050.blogspot.com/2019_05_26_archive.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': '... 2017 adimmix psychic leah அறிவுரை கும்பம் மேஷம் ஜோதிடம் '
'புற்றுநோய் மகர படிக குழந்தைகள் மனநோய் புத்தகங்கள் முன்அ...'}
```
#### deduplicated_te
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 31516,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MG3MFYW5T6XSW3XYZ4ZIKGJW5XAY2RCG',
'warc-date': '2021-03-06T18:07:45Z',
'warc-identified-content-language': 'tel',
'warc-record-id': '<urn:uuid:238b108b-d16e-41d2-b06e-464267352b0e>',
'warc-refers-to': '<urn:uuid:3663318c-d256-4c97-b71b-e4eeb2e6b58a>',
'warc-target-uri': 'https://telugu.greatandhra.com/articles/mbs/ammo-ativa-01-114908.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': 'అది 1868. ఇంగ్లండ్\u200cలోని బ్రైటన్\u200cలో క్రిస్టియానా ఎడ్మండ్స్ '
'అనే 40 ఏళ్ల మహిళ వుండేది. పెళ్లి కాలేదు. తల్లితో కలిసి ఒక ఎ...'}
```
#### deduplicated_tg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LDBVTK3U6MY7J475ZR4LRLFK2CC2QWG5',
'warc-date': '2021-03-09T03:53:03Z',
'warc-identified-content-language': 'tgk,tat,rus',
'warc-record-id': '<urn:uuid:b2519476-6812-4a38-8522-f5292b95e73a>',
'warc-refers-to': '<urn:uuid:f11fa878-d4c6-4e56-bc50-a76554b7d811>',
'warc-target-uri': 'http://hamsafon.tj/2784-imr1263z-1203avoi-1207um1203ur1251-sofu-be1171ubor-meshavad.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': 'ДУШАНБЕ, 10.01.2017/АМИТ «Ховар»/. 10 январ дар пойтахти кишвар '
'ҳавои тағйирёбандаи бебориш дар назар дошта шудааст. ...'}
```
#### deduplicated_th
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 50841,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MESEMAONUQXZZEA6IKBT3VCUZ43ZP4B7',
'warc-date': '2021-02-28T15:41:47Z',
'warc-identified-content-language': 'tha,eng',
'warc-record-id': '<urn:uuid:46495e6b-f22f-4dc6-86ab-3bbed66ce7e4>',
'warc-refers-to': '<urn:uuid:10946c1b-9dc5-4afb-bc74-d6baf9793a03>',
'warc-target-uri': 'https://www.thaicsr.com/2009/02/blog-post_08.html',
'warc-type': 'conversion'},
'nb_sentences': 34,
'offset': 0},
'text': 'ปี พ.ศ. 2521 '
'พระบาทสมเด็จพระเจ้าอยู่หัวเสด็จเยี่ยมราษฎรบ้านพระบาทห้วยต้ม '
'ทรงทอดพระเนตรเห็นสภาพพื้นที่และชีวิตความเป็น...'}
```
#### deduplicated_tk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22486,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VNR5UQCQIGPEZQBZL4VAOQDASFOVNRDL',
'warc-date': '2021-03-03T15:07:09Z',
'warc-identified-content-language': 'eng,rus',
'warc-record-id': '<urn:uuid:b514b9c5-1ccd-4cf0-bea7-ea38a5aef686>',
'warc-refers-to': '<urn:uuid:edf1f6cb-9f46-4790-8256-eb984db0f0d5>',
'warc-target-uri': 'http://www.newscentralasia.net/2020/12/02/move-forward-with-universal-right-and-responsibility/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Türkmenistanyň Daşary işler ministriniň Owganystanyň Milli Yslam '
'Hereketi partiýasynyň ýolbaşçysy bilen duşuşygy'}
```
#### deduplicated_tl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'PAUNAWA: Kung nagsasalita ka ng wikang Tagalog, mayroon kang '
'magagamit na mga libreng serbisyo para sa tulong sa wika...'}
```
#### deduplicated_tr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14815,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GVNKVEGK7TMZGXIIMLV2O2YWYJRAKBO2',
'warc-date': '2021-03-04T00:44:44Z',
'warc-identified-content-language': 'tur,eng',
'warc-record-id': '<urn:uuid:7acbe6a8-83c4-4ebd-8d29-62cb0b150b2f>',
'warc-refers-to': '<urn:uuid:038ffe28-2fd1-49b9-a5c6-3dddd1af6318>',
'warc-target-uri': 'https://www.kadikoygitarkursum.com/search/label/g%C3%B6ztepe%20gitar%20dersi',
'warc-type': 'conversion'},
'nb_sentences': 5,
'offset': 0},
'text': 'İlk olarak, bir tek siyah kirpik takımı için fiyat belirleyin, '
"örneğin, 4000 ruble'ye eşittir. Artık bir müşteriyle ç..."}
```
#### deduplicated_tt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 26112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FAPA2JNYP6OL53T6OIL3SR3EGMX2R4XY',
'warc-date': '2021-03-09T04:42:07Z',
'warc-identified-content-language': 'tat,rus',
'warc-record-id': '<urn:uuid:5cac6257-fa6c-4e67-9ba1-8e7d7424ef54>',
'warc-refers-to': '<urn:uuid:52642c8d-da35-462f-9776-ccfa88353466>',
'warc-target-uri': 'http://saby-rt.ru/news/konkurslar/fotokonkurs',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'Хөрмәтле хатын-кызларбыз! Сезне чын күңелдән 8 Март бәйрәме белән '
'тәбрик итәбез! Яраткан әниләребез, әбиләребез, гоме...'}
```
#### deduplicated_tyv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7766,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:L5GRAANBGMGNYXDFF3ECSWJ5Q6D4QFHS',
'warc-date': '2021-02-28T07:20:44Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:238082a9-0adf-4c8c-b749-1a523c91e229>',
'warc-refers-to': '<urn:uuid:4bfd0ca2-52bb-4ece-9ccf-cdcee0b30ee9>',
'warc-target-uri': 'https://tyv.wikipedia.org/wiki/%D0%A1%D0%B0%D1%80%D0%BB%D1%8B%D0%BA',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Сарлык бызаазы – ниити ады, назыны бир хар чедир, сарлыктың эр '
'бызаазы аза сарлыктың кыс бызаазы деп чугаалаар.'}
```
#### deduplicated_ug
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 19089,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DHYFNWWKECLR6BHWF763HC62JRCASMGH',
'warc-date': '2021-03-09T04:33:38Z',
'warc-identified-content-language': 'uig',
'warc-record-id': '<urn:uuid:d1185989-9cd6-40f2-ad63-003e405c9141>',
'warc-refers-to': '<urn:uuid:923ac168-6484-49ea-807d-be3ced85a885>',
'warc-target-uri': 'https://www.akademiye.org/ug/?p=10959',
'warc-type': 'conversion'},
'nb_sentences': 30,
'offset': 0},
'text': 'شەرقىي تۈركىستانئاكادېمىيە ھەققىدەئەزالىقتەۋپىق '
'مۇكاپاتىئىئانەئالاقەTürkçeEnglishئۇيغۇرچەУйғурчәUyghurche\n'
'مىللىي مەۋج...'}
```
#### deduplicated_uk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16706,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:46XDNKJUJSG22BA4B6DDET2R5GMBU3LV',
'warc-date': '2021-02-26T22:04:41Z',
'warc-identified-content-language': 'ukr,eng',
'warc-record-id': '<urn:uuid:a3c68b5a-f9e8-41b6-b2bb-3d43e4d7a117>',
'warc-refers-to': '<urn:uuid:6a35e918-42ce-4349-9a6c-edcd22f07254>',
'warc-target-uri': 'https://www.interesniy.kiev.ua/vasil-boroday-korifey-mistetstva-pla/',
'warc-type': 'conversion'},
'nb_sentences': 14,
'offset': 0},
'text': 'На Женевському міжнародному автосалоні 2017 бренд Fiat буде '
'показувати дві свої душі, які співіснують у великій повні...'}
```
#### deduplicated_ur
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9450,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:3SZ3UYOSHTRE3W3PDZXRO7DDSLRKENV2',
'warc-date': '2021-03-09T03:21:23Z',
'warc-identified-content-language': 'eng,urd,bos',
'warc-record-id': '<urn:uuid:0ded0cb4-2f73-41a7-a093-5dcfed204738>',
'warc-refers-to': '<urn:uuid:6b380ef1-fec4-4f48-bcdc-86700c508dfc>',
'warc-target-uri': 'http://www.khanaghar.org/?p=50',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'اتراکھنڈ کے سلماتا گاؤں کی لڑائیتی دیوی ایک پُر اعتماد اور عقلمند '
'مجاہد ہیں، جن کی طرف دیگر خواتین بھی دیکھ رہی ہیں۔ ...'}
```
#### deduplicated_uz
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3808,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FYYLFGJTK74HXE2LRJOAR5E6BPGCQ5NU',
'warc-date': '2021-03-09T04:38:24Z',
'warc-identified-content-language': 'uzb,ben,ltz',
'warc-record-id': '<urn:uuid:2a56bf64-042e-47fa-9abb-819b13bf7920>',
'warc-refers-to': '<urn:uuid:155b1e81-dc6e-46dc-9544-5a6a97c05118>',
'warc-target-uri': 'https://uz.wikipedia.org/wiki/1408',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Matn Creative Commons Attribution-ShareAlike litsenziyasi boʻyicha '
'ommalashtirilmoqda, alohida holatlarda qoʻshimcha ...'}
```
#### deduplicated_vec
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7088,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CX2L4ZL4I4OLXG7YJTXLRKNFHE7RIHRX',
'warc-date': '2021-02-24T19:06:44Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:abc5a544-7009-407a-a5a3-5c2145195bd5>',
'warc-refers-to': '<urn:uuid:4a956690-536a-437b-afe2-50dc7ac54b39>',
'warc-target-uri': 'https://vec.wikipedia.org/wiki/Utensa:Aelwyn',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Łe parołe che vien dal łatin -TAS, TATIS łe termina par -DÁ. Łe '
'parołe che łe vien da -ICUS łe tèrmina par -ÉGO. Łe p...'}
```
#### deduplicated_vi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7845,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CCXAI5SV5PFLNPSMP4UF4SQGGSYN37AP',
'warc-date': '2021-03-03T02:43:13Z',
'warc-identified-content-language': 'vie',
'warc-record-id': '<urn:uuid:7ce27f30-a1eb-4978-83d0-5110421393b0>',
'warc-refers-to': '<urn:uuid:5dad988d-2426-402c-ac0c-1fa811ed96dc>',
'warc-target-uri': 'http://httlvinhphuoc.org/vi/duong-linh/Hoc-Kinh-Thanh-hang-ngay/Lam-Dieu-Thien-Bang-Tinh-Yeu-Thuong-6521/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'Bitcoin và tiền kỹ thuật số nói chung đang dần xâm nhập vào các '
'thị trường tài chính khi ngày càng có nhiều nhà đ...'}
```
#### deduplicated_vls
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 78684,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VQNDJYOQXZLCLMDXIFCT4BHSW6LVTJQE',
'warc-date': '2021-02-28T16:16:27Z',
'warc-identified-content-language': 'fra,eng',
'warc-record-id': '<urn:uuid:266acc08-1c69-449f-95ad-0dcc82565788>',
'warc-refers-to': '<urn:uuid:c45dcd64-1b20-4ffc-bdd7-7dbff4f0a726>',
'warc-target-uri': 'https://fr.readkong.com/page/livret-des-licences-faculte-des-sciences-et-des-techniques-7906239',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': ' '
'...'}
```
#### deduplicated_vo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1937,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VPG56ZACAOAZTXHSSXFJOBBH44NWUSJW',
'warc-date': '2021-03-09T06:02:56Z',
'warc-identified-content-language': 'vol,eng,srp',
'warc-record-id': '<urn:uuid:2cb96947-ee22-42a8-be36-31a03203efcc>',
'warc-refers-to': '<urn:uuid:da82b7d8-535b-4e39-8d9b-ea8c3d4a4460>',
'warc-target-uri': 'https://vo.wikipedia.org/wiki/Arnesano',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Arnesano binon zif in topäd: Puglia, in Litaliyän. Arnesano topon '
'videtü 40° 20’ N e lunetü 18° 6’ L.'}
```
#### deduplicated_wa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6518,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6NC6V46TRVMWTOHCPMTDVRTP7GGL3G3S',
'warc-date': '2021-02-26T09:47:28Z',
'warc-identified-content-language': 'wol',
'warc-record-id': '<urn:uuid:4d800a25-ccf5-4d55-9795-3f7974b988b1>',
'warc-refers-to': '<urn:uuid:87119673-154b-4246-8c39-35737821a7ff>',
'warc-target-uri': 'https://wa.wikipedia.org/wiki/Senegal',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est "
"djusse sibåtcheye, eyet co trop tene; et s' divreut..."}
```
#### deduplicated_war
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7356,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:SVXPIA63QN77O2IJXL4Q75LNVLDEBHYW',
'warc-date': '2021-03-09T05:49:57Z',
'warc-identified-content-language': 'war,tha,eng',
'warc-record-id': '<urn:uuid:a143ebc6-a7b4-4fa7-96b3-59ba2c1dd03c>',
'warc-refers-to': '<urn:uuid:571d090a-cb65-41e7-ae7c-d95588d41c28>',
'warc-target-uri': 'https://war.wikipedia.org/wiki/Chakri_nga_Dinastiya',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'An Chakri nga Dinastiya (Thai: ราชวงศ์จักรี: Rajawongse Chakri) '
'namuno ngan naghadi han Thailand tikang han hi hadi T...'}
```
#### deduplicated_wuu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 26503,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XAH2SJIYORGGSMLN4DNJZCNVG2FVWF3C',
'warc-date': '2021-03-09T04:09:05Z',
'warc-identified-content-language': 'jpn',
'warc-record-id': '<urn:uuid:8df3f922-fbbf-4733-a3a8-9f34b7505cbf>',
'warc-refers-to': '<urn:uuid:a55eb04e-3679-4817-b94b-e0317142ab2b>',
'warc-target-uri': 'https://wpedia.goo.ne.jp/wiki/%E4%BC%8A%E5%8D%81%E4%BA%94%E5%9E%8B%E6%BD%9C%E6%B0%B4%E8%89%A6',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '伊15 [I] | 伊17 | 伊19 | 伊21 | 伊23 | 伊25 | 伊26 | 伊27 | 伊28 | 伊29 | 伊30 '
'| 伊31 | 伊32 | 伊33 | 伊34 | 伊35 | 伊36 | 伊37 | 伊38 |...'}
```
#### deduplicated_xal
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8598,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KGZNUXNSFUSFYC45UQJRZPEHXNGK6C3H',
'warc-date': '2021-03-02T01:27:37Z',
'warc-identified-content-language': 'rus,spa',
'warc-record-id': '<urn:uuid:676f6ca8-706b-4f77-926f-bda90e3cd772>',
'warc-refers-to': '<urn:uuid:452efc2f-85ce-4e90-b268-2f46893172f8>',
'warc-target-uri': 'http://born.altnzam.com/2014/01/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Ааһ: Хоосн ааһ би, хагсхларн һанцардсн болҗ медгдҗәнә. Нанд усн йир '
'кергтә болҗана. Ус өгит, — эзнәсн сурна.\n'
'Ааһ ууль...'}
```
#### deduplicated_xmf
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7053,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OQKCWDGQCIJHXMM3SCUO2KPBMFCQACUJ',
'warc-date': '2021-03-03T14:27:35Z',
'warc-identified-content-language': 'kat',
'warc-record-id': '<urn:uuid:e701a584-a14f-49ac-80b3-a7604f98fc92>',
'warc-refers-to': '<urn:uuid:8fc0f735-6e2b-45b2-bee1-bf169e08433b>',
'warc-target-uri': 'https://xmf.wikipedia.org/wiki/%E1%83%99%E1%83%90%E1%83%A2%E1%83%94%E1%83%92%E1%83%9D%E1%83%A0%E1%83%98%E1%83%90:%E1%83%90%E1%83%94%E1%83%A0%E1%83%9D%E1%83%9E%E1%83%9D%E1%83%A0%E1%83%A2%E1%83%94%E1%83%A4%E1%83%98_%E1%83%90%E1%83%9C%E1%83%91%E1%83%90%E1%83%9C%E1%83%98%E1%83%A8_%E1%83%9B%E1%83%94%E1%83%AF%E1%83%98%E1%83%9C%E1%83%90%E1%83%97',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'მოჩამილი ტექსტი წჷმორინელი რე Creative Commons '
'Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ '
'არსებუა. კ...'}
```
#### deduplicated_yi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 10420,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CZAVPSCGNW77WY2V2IJNK7R2CCUEMZFB',
'warc-date': '2021-02-24T21:10:52Z',
'warc-identified-content-language': 'yid,eng',
'warc-record-id': '<urn:uuid:7aa9e375-726d-42bd-832a-deee6dce5e4a>',
'warc-refers-to': '<urn:uuid:53354991-7bca-4134-95ce-ce7edebf841b>',
'warc-target-uri': 'http://www.kaveshtiebel.com/viewtopic.php?p=237817',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': 'עמעזאן איז יעצט ארויסגעקומען מיט א נייע סמארט ספיקער סיסטעם. '
"ס'הייסט Echo. אין Echo דרייט זיך א ראבאטישקע זי הייסט אל..."}
```
#### deduplicated_yo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3627,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:UISXP36HUEMW2LBTMAR4CTISUYAVZZAD',
'warc-date': '2021-03-07T12:45:52Z',
'warc-identified-content-language': 'yor,eng',
'warc-record-id': '<urn:uuid:e67645e9-ee6c-4c88-9b27-a158dc7f83e9>',
'warc-refers-to': '<urn:uuid:07c8d83b-7840-4238-a3b4-edc3f98ecdd5>',
'warc-target-uri': 'https://edeyorubarewa.com/itelorun/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'A dá sílè fún àwọn ènìyàn tí wọn fẹ́ràn láti mò nípa èdè Yorùbá, '
'àṣà àti ìṣe ilẹ̀ kóòtù ojire. Kíkó àwọn ọmọ wa ni Èd...'}
```
#### deduplicated_zh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 108400,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:PP6MQUJB3F4G63HKKGKO2QJG7SMRMTFJ',
'warc-date': '2021-02-28T09:41:11Z',
'warc-identified-content-language': 'zho',
'warc-record-id': '<urn:uuid:132aab53-daff-4bae-83d0-a0cdb4039d00>',
'warc-refers-to': '<urn:uuid:2f26c020-f1fc-4216-a616-4683e0b25b1e>',
'warc-target-uri': 'http://www.yummtumm.com/offer',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': '久久精品视频在线看15_久久人人97超碰_久久爱 '
'人人澡超碰碰中文字幕,人人天天夜夜日日狠狠,久久人人97超碰,人人婷婷开心情五月,日日摸天天摸人人看,碰人人么免费视频,色综合天天综合网 '
'久久爱免费视频在线观看_久久爱视频_久久爱在线...'}
```
</details>
### Data Fields
* `id`: a `int64` feature.
* `meta`: Metadata
* `meta.headers`: WARC Headers
* `meta.headers.content-length`: `int64` Content length (in bytes) **before** cleaning
* `meta.headers.content-type`: `string` MIME type
* `meta.headers.warc-block-digest`:`string` Algorithm name and calculated value of a digest applied to the full block of the record
* `meta.headers.warc-date`: `string` Crawl date (YYYY-MM-DDThh:mm:ssZ)
* `meta.headers.warc-identified-content-language`: `string` Comma-separated list of language identifications done by CommonCrawl (uses CLD3)
* `meta.headers.warc-record-id`: `string` Record ID
* `meta.headers.warc-refers-to`: `string` Record-ID of a single record for which the present record holds additional content
* `meta.headers.warc-target-uri`: `string` URI from where the content has been fetched
* `meta.headers.warc-type`: `string` Type of the WARC Record
* `meta.nb_sentences`: `int64` Number of sentences in the text
* `meta.offset`: `int64` line offset where the related text begins. Should be used with `meta.nb_sentences` when reading the source files rather than using iterators to get related data.
* `text`: `string` content
See the [WARC Format standard](https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/#warc-type-mandatory) for more details.
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
## Table
| Language code | language | Size original | words original | size deduplicated | words deduplicated |
|:----|:----------------------------|:-------|:----------------|:---------------|:----------------|
| af | Afrikaans | 258MB | 44,628,392 | 157MB | 27,057,785 |
| als | Alemanic | 7MB | 1,212,699 | 5MB | 871,664 |
| am | Amharic | 405MB | 30,991,914 | 241MB | 18,326,043 |
| an | Aragonese | 1MB | 115,938 | 608KB | 89,043 |
| ar | Arabic | 69GB | 6,494,332,191 | 35GB | 3,365,025,866 |
| arz | Egyptian Arabic | 48MB | 4,998,963 | 21MB | 2,341,904 |
| ast | Asturian | 7MB | 1,085,670 | 4MB | 776,069 |
| as | Assamese | 135MB | 7,917,923 | 95MB | 5,605,207 |
| av | Avaric | 421KB | 25,104 | 325KB | 19,133 |
| azb | South Azerbaijani | 47MB | 3,595,569 | 29MB | 2,243,562 |
| az | Azerbaijani | 3GB | 344,187,319 | 1GB | 169,655,478 |
| bar | Bavarian | 2KB | 247 | 1KB | 245 |
| ba | Bashkir | 110MB | 8,121,603 | 77MB | 5,625,158 |
| be | Belarusian | 2GB | 168,911,341 | 1GB | 98,212,442 |
| bg | Bulgarian | 34GB | 2,994,775,106 | 15GB | 1,315,091,995 |
| bh | Bihari languages | 579KB | 46,436 | 120KB | 9,181 |
| bn | Bangla | 14GB | 814,550,777 | 7GB | 466,289,242 |
| bo | Tibetan | 439MB | 3,751,935 | 358MB | 2,797,085 |
| bpy | Bishnupriya | 11MB | 558,819 | 4MB | 280,825 |
| br | Breton | 49MB | 8,067,480 | 23MB | 4,032,467 |
| bs | Bosnian | 310KB | 50,266 | 175KB | 25,157 |
| bxr | Russia Buriat | 22KB | 1,625 | 18KB | 1,335 |
| ca | Catalan | 13GB | 2,110,833,307 | 6GB | 1,012,770,904 |
| cbk | Chavacano | 168B | 2 | 168B | 2 |
| ceb | Cebuano | 81MB | 12,921,589 | 58MB | 9,201,870 |
| ce | Chechen | 29MB | 2,283,093 | 20MB | 1,638,963 |
| ckb | Central Kurdish | 784MB | 63,417,572 | 367MB | 29,355,017 |
| cs | Czech | 72GB | 9,996,052,434 | 33GB | 4,739,928,730 |
| cv | Chuvash | 60MB | 4,592,449 | 41MB | 3,141,872 |
| cy | Welsh | 307MB | 50,606,998 | 180MB | 30,198,860 |
| da | Danish | 18GB | 2,892,004,180 | 10GB | 1,704,605,898 |
| de | German | 433GB | 58,716,727,164 | 184GB | 25,446,071,671 |
| diq | Dimli (individual language) | 294B | 38 | 147B | 19 |
| dsb | Lower Sorbian | 31KB | 4,115 | 14KB | 1,873 |
| dv | Divehi | 143MB | 8,293,093 | 111MB | 6,481,260 |
| el | Greek | 72GB | 6,024,414,850 | 30GB | 2,539,719,195 |
| eml | Unknown language [eml] | 22KB | 4,360 | 20KB | 3,876 |
| en | English | 2936GB | 488,723,815,522 | 1342GB | 223,669,114,922 |
| eo | Esperanto | 560MB | 84,432,772 | 390MB | 59,411,208 |
| es | Spanish | 342GB | 54,715,337,438 | 160GB | 25,877,724,186 |
| et | Estonian | 7GB | 954,732,803 | 3GB | 455,553,053 |
| eu | Basque | 900MB | 110,676,692 | 503MB | 62,812,888 |
| fa | Persian | 79GB | 8,566,653,720 | 35GB | 3,902,206,854 |
| fi | Finnish | 35GB | 4,074,911,658 | 20GB | 2,357,264,196 |
| frr | Northern Frisian | 7KB | 1,702 | 5KB | 1,267 |
| fr | French | 340GB | 52,839,365,242 | 161GB | 25,245,127,073 |
| fy | Western Frisian | 82MB | 13,094,538 | 57MB | 9,329,828 |
| ga | Irish | 131MB | 20,142,627 | 69MB | 10,835,410 |
| gd | Scottish Gaelic | 2MB | 332,946 | 1MB | 173,588 |
| gl | Galician | 989MB | 155,030,216 | 549MB | 87,015,417 |
| gn | Guarani | 32KB | 3,828 | 25KB | 3,056 |
| gom | Goan Konkani | 3MB | 177,357 | 2MB | 148,801 |
| gu | Gujarati | 1GB | 124,652,589 | 950MB | 63,150,641 |
| gv | Manx | 1KB | 264 | 907B | 141 |
| he | Hebrew | 29GB | 2,829,132,925 | 11GB | 1,156,588,919 |
| hi | Hindi | 26GB | 2,009,754,819 | 13GB | 1,038,914,735 |
| hr | Croatian | 361MB | 51,654,735 | 169MB | 24,583,270 |
| hsb | Upper Sorbian | 2MB | 305,176 | 1MB | 207,715 |
| ht | Haitian Creole | 2KB | 592 | 1KB | 351 |
| hu | Hungarian | 60GB | 7,415,936,687 | 29GB | 3,765,883,306 |
| hy | Armenian | 4GB | 322,429,587 | 1GB | 124,515,953 |
| ia | Interlingua | 291KB | 74,696 | 172KB | 41,625 |
| id | Indonesian | 40GB | 5,767,715,387 | 22GB | 3,126,926,138 |
| ie | Interlingue | 7KB | 1,432 | 2KB | 424 |
| ilo | Iloko | 1MB | 275,029 | 857KB | 140,579 |
| io | Ido | 276KB | 46,463 | 221KB | 36,976 |
| is | Icelandic | 2GB | 290,997,158 | 1GB | 176,018,529 |
| it | Italian | 192GB | 29,252,541,808 | 94GB | 14,426,829,908 |
| ja | Japanese | 208GB | 5,357,000,179 | 96GB | 1,319,938,248 |
| jbo | Lojban | 929KB | 179,684 | 731KB | 140,749 |
| jv | Javanese | 858KB | 121,271 | 728KB | 101,386 |
| ka | Georgian | 6GB | 304,329,117 | 2GB | 116,422,468 |
| kk | Kazakh | 3GB | 236,767,203 | 1GB | 126,886,720 |
| km | Khmer | 1GB | 28,188,612 | 860MB | 13,408,408 |
| kn | Kannada | 2GB | 111,460,546 | 1GB | 56,801,321 |
| ko | Korean | 35GB | 3,367,279,749 | 15GB | 1,475,474,588 |
| krc | Karachay-Balkar | 2MB | 193,207 | 2MB | 153,755 |
| ku | Kurdish | 152MB | 23,845,402 | 108MB | 17,264,310 |
| kv | Komi | 1MB | 89,105 | 588KB | 46,219 |
| kw | Cornish | 119KB | 20,775 | 72KB | 12,687 |
| ky | Kyrgyz | 485MB | 33,401,287 | 334MB | 23,102,129 |
| la | Latin | 103MB | 15,869,314 | 9MB | 1,488,545 |
| lb | Luxembourgish | 54MB | 7,953,887 | 37MB | 5,454,220 |
| lez | Lezghian | 2MB | 214,890 | 2MB | 198,433 |
| li | Limburgish | 76KB | 12,105 | 54KB | 8,472 |
| lmo | Lombard | 1MB | 203,002 | 1MB | 182,533 |
| lo | Lao | 287MB | 6,928,229 | 163MB | 3,620,360 |
| lrc | Northern Luri | 183B | 26 | 183B | 26 |
| lt | Lithuanian | 12GB | 1,573,926,673 | 5GB | 701,326,575 |
| lv | Latvian | 6GB | 799,923,431 | 2GB | 352,753,044 |
| mai | Maithili | 685KB | 144,859 | 24KB | 1,916 |
| mg | Malagasy | 59MB | 8,103,631 | 38MB | 5,220,655 |
| mhr | Eastern Mari | 15MB | 1,170,650 | 10MB | 784,071 |
| min | Minangkabau | 8MB | 451,591 | 1MB | 74,882 |
| mk | Macedonian | 3GB | 261,571,966 | 1GB | 134,544,934 |
| ml | Malayalam | 4GB | 182,898,691 | 2GB | 87,615,430 |
| mn | Mongolian | 1GB | 143,244,180 | 912MB | 71,138,550 |
| mrj | Western Mari | 645KB | 51,812 | 521KB | 41,950 |
| mr | Marathi | 3GB | 173,001,078 | 1GB | 99,858,901 |
| ms | Malay | 146MB | 20,433,250 | 60MB | 8,301,250 |
| mt | Maltese | 51MB | 6,162,888 | 26MB | 3,179,815 |
| mwl | Mirandese | 3KB | 419 | 2KB | 302 |
| my | Burmese | 2GB | 54,624,239 | 1GB | 35,969,724 |
| myv | Erzya | 29KB | 2,844 | 2KB | 236 |
| mzn | Mazanderani | 1MB | 134,128 | 1MB | 106,533 |
| nah | Nahuatl languages | 34KB | 3,664 | 21KB | 2,363 |
| nap | Neapolitan | 1KB | 550 | 1KB | 235 |
| nds | Low German | 25MB | 3,998,912 | 17MB | 2,868,608 |
| ne | Nepali | 3GB | 207,891,824 | 2GB | 142,087,100 |
| new | Newari | 6MB | 433,880 | 4MB | 254,711 |
| nl | Dutch | 97GB | 15,248,924,083 | 47GB | 7,584,055,321 |
| nn | Norwegian Nynorsk | 123MB | 20,629,675 | 66MB | 11,095,804 |
| no | Norwegian Bokmål | 9GB | 1,492,984,384 | 4GB | 776,354,517 |
| oc | Occitan | 12MB | 1,822,595 | 5MB | 834,187 |
| or | Odia | 538MB | 30,838,706 | 357MB | 20,357,839 |
| os | Ossetic | 11MB | 911,794 | 6MB | 536,525 |
| pam | Pampanga | 3KB | 405 | 3KB | 405 |
| pa | Punjabi | 769MB | 59,031,334 | 430MB | 33,413,527 |
| pl | Polish | 122GB | 16,120,806,481 | 48GB | 6,496,098,108 |
| pms | Piedmontese | 4MB | 804,600 | 3MB | 644,017 |
| pnb | Western Panjabi | 68MB | 7,757,785 | 45MB | 5,221,168 |
| ps | Pashto | 404MB | 49,643,597 | 286MB | 35,345,424 |
| pt | Portuguese | 159GB | 24,770,395,312 | 71GB | 11,190,148,216 |
| qu | Quechua | 322KB | 40,691 | 230KB | 29,108 |
| rm | Romansh | 3KB | 512 | 3KB | 429 |
| ro | Romanian | 37GB | 5,629,438,576 | 15GB | 2,387,230,734 |
| rue | Rusyn | 247B | 14 | 247B | 14 |
| ru | Russian | 1201GB | 89,568,364,811 | 542GB | 41,194,052,384 |
| sah | Sakha | 57MB | 2,600,989 | 39MB | 1,944,651 |
| sa | Sanskrit | 72MB | 3,288,786 | 43MB | 1,998,089 |
| scn | Sicilian | 4KB | 712 | 3KB | 516 |
| sco | Scots | 1KB | 523 | 1KB | 282 |
| sd | Sindhi | 75MB | 8,937,427 | 50MB | 6,064,102 |
| sh | Serbian (Latin) | 13MB | 2,164,175 | 9MB | 1,461,045 |
| si | Sinhala | 1GB | 91,456,436 | 791MB | 47,770,919 |
| sk | Slovak | 14GB | 2,002,088,524 | 6GB | 865,456,498 |
| sl | Slovenian | 4GB | 610,843,131 | 1GB | 288,222,997 |
| so | Somali | 15KB | 849 | 13KB | 449 |
| sq | Albanian | 3GB | 493,861,192 | 1GB | 257,278,518 |
| sr | Serbian | 6GB | 574,460,746 | 3GB | 289,211,579 |
| su | Sundanese | 397KB | 54,420 | 274KB | 37,082 |
| sv | Swedish | 43GB | 6,542,433,732 | 19GB | 2,964,887,952 |
| sw | Swahili | 11MB | 1,853,022 | 7MB | 1,279,350 |
| ta | Tamil | 10GB | 438,489,984 | 5GB | 215,856,584 |
| te | Telugu | 3GB | 182,268,133 | 1GB | 73,193,605 |
| tg | Tajik | 985MB | 79,016,232 | 321MB | 26,069,632 |
| th | Thai | 62GB | 1,694,658,532 | 26GB | 635,230,676 |
| tk | Turkmen | 25MB | 2,693,720 | 20MB | 2,221,760 |
| tl | Filipino | 699MB | 115,471,760 | 383MB | 62,473,283 |
| tr | Turkish | 73GB | 8,763,467,387 | 33GB | 3,950,989,357 |
| tt | Tatar | 947MB | 68,793,924 | 424MB | 31,485,000 |
| tyv | Tuvinian | 9KB | 638 | 7KB | 542 |
| ug | Uyghur | 187MB | 12,786,741 | 123MB | 8,410,269 |
| uk | Ukrainian | 53GB | 4,014,675,914 | 28GB | 2,131,491,321 |
| ur | Urdu | 2GB | 354,937,986 | 1GB | 234,111,239 |
| uz | Uzbek | 56MB | 6,237,371 | 28MB | 3,327,595 |
| vec | Venetian | 37KB | 6,694 | 28KB | 5,139 |
| vi | Vietnamese | 87GB | 14,523,772,784 | 42GB | 7,011,404,625 |
| vls | West Flemish | 134B | 2 | 134B | 2 |
| vo | Volapük | 2MB | 426,052 | 2MB | 410,688 |
| war | Waray | 4MB | 750,162 | 4MB | 702,336 |
| wa | Walloon | 511KB | 93,163 | 329KB | 59,906 |
| wuu | Wu Chinese | 145KB | 9,130 | 69KB | 3,031 |
| xal | Kalmyk | 62KB | 5,495 | 62KB | 5,495 |
| xmf | Mingrelian | 16MB | 807,158 | 10MB | 510,700 |
| yi | Yiddish | 199MB | 18,699,112 | 93MB | 8,716,366 |
| yo | Yoruba | 229KB | 34,468 | 120KB | 17,487 |
| zh | Chinese | 500GB | 10,118,381,906 | 266GB | 3,898,987,727 |
</details>
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
OSCAR 21.09 follows the [OSCAR Schema v1.1](https://oscar-corpus.com/post/oscar-schema-v1-1/), which adds metadata to each entry while staying backwards-compatible with OSCAR.
The order of operations is similar as in the goclassy pipeline, with optimisations regarding IO and a finer granlularity regarding multithreading.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering is done at line-level, removing lines shorter than 100 UTF-8 codepoints. While invalid UTF-8 characters are detected, they are not removed, but rather replaced with the [Replacement character](https://en.wikipedia.org/wiki/Special_(Unicode_block)#Replacement_character).
After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **February 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@ARTICLE{caswell-etal-2021-quality,
author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa},
title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
year = 2021,
month = mar,
eid = {arXiv:2103.12028},
pages = {arXiv:2103.12028},
archivePrefix = {arXiv},
eprint = {2103.12028},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210312028C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox), [@Uinelj](https://github.com/Uinelj) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
| oscar-corpus/OSCAR-2109 | [
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"license:cc0-1.0",
"arxiv:2010.14571",
"arxiv:2103.12028",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "als", "gsw", "am", "an", "ar", "arz", "as", "ast", "av", "az", "azb", "ba", "bar", "be", "bg", "bh", "bn", "bo", "bpy", "br", "bs", "bxr", "ca", "cbk", "ce", "ceb", "ckb", "cs", "cv", "cy", "da", "de", "diq", "dsb", "dv", "el", "eml", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "frr", "fy", "ga", "gd", "gl", "gn", "gom", "gu", "gv", "he", "hi", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ie", "ilo", "io", "is", "it", "ja", "jbo", "jv", "ka", "kk", "km", "kn", "ko", "krc", "ku", "kv", "kw", "ky", "la", "lb", "lez", "li", "lmo", "lo", "lrc", "lt", "lv", "mai", "mg", "mhr", "min", "mk", "ml", "mn", "mr", "mrj", "ms", "mt", "mwl", "my", "myv", "mzn", "nah", "nap", "nds", "ne", "new", "nl", "nn", "no", "oc", "or", "os", "pa", "pam", "pl", "pms", "pnb", "ps", "pt", "qu", "rm", "ro", "ru", "rue", "sa", "sah", "scn", "sco", "sd", "sh", "si", "sk", "sl", "so", 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"unshuffled_deduplicated_ia": ["n<1K"], "unshuffled_deduplicated_id": ["1M<n<10M"], "unshuffled_deduplicated_ie": ["n<1K"], "unshuffled_deduplicated_ilo": ["1K<n<10K"], "unshuffled_deduplicated_io": ["n<1K"], "unshuffled_deduplicated_is": ["100K<n<1M"], "unshuffled_deduplicated_it": ["10M<n<100M"], "unshuffled_deduplicated_ja": ["10M<n<100M"], "unshuffled_deduplicated_jbo": ["n<1K"], "unshuffled_deduplicated_jv": ["1K<n<10K"], "unshuffled_deduplicated_ka": ["100K<n<1M"], "unshuffled_deduplicated_kk": ["100K<n<1M"], "unshuffled_deduplicated_km": ["100K<n<1M"], "unshuffled_deduplicated_kn": ["100K<n<1M"], "unshuffled_deduplicated_ko": ["1M<n<10M"], "unshuffled_deduplicated_krc": ["1K<n<10K"], "unshuffled_deduplicated_ku": ["10K<n<100K"], "unshuffled_deduplicated_kv": ["n<1K"], "unshuffled_deduplicated_kw": ["n<1K"], "unshuffled_deduplicated_ky": ["10K<n<100K"], "unshuffled_deduplicated_la": ["10K<n<100K"], "unshuffled_deduplicated_lb": ["10K<n<100K"], "unshuffled_deduplicated_lez": ["1K<n<10K"], "unshuffled_deduplicated_li": ["n<1K"], "unshuffled_deduplicated_lmo": ["1K<n<10K"], "unshuffled_deduplicated_lo": ["10K<n<100K"], "unshuffled_deduplicated_lrc": ["n<1K"], "unshuffled_deduplicated_lt": ["1M<n<10M"], "unshuffled_deduplicated_lv": ["100K<n<1M"], "unshuffled_deduplicated_mai": ["n<1K"], "unshuffled_deduplicated_mg": ["10K<n<100K"], "unshuffled_deduplicated_mhr": ["1K<n<10K"], "unshuffled_deduplicated_min": ["n<1K"], "unshuffled_deduplicated_mk": ["100K<n<1M"], "unshuffled_deduplicated_ml": ["100K<n<1M"], "unshuffled_deduplicated_mn": ["100K<n<1M"], "unshuffled_deduplicated_mr": ["100K<n<1M"], "unshuffled_deduplicated_mrj": ["n<1K"], "unshuffled_deduplicated_ms": ["100K<n<1M"], "unshuffled_deduplicated_mt": ["10K<n<100K"], "unshuffled_deduplicated_mwl": ["n<1K"], "unshuffled_deduplicated_my": ["100K<n<1M"], "unshuffled_deduplicated_myv": ["n<1K"], "unshuffled_deduplicated_mzn": ["n<1K"], "unshuffled_deduplicated_nah": ["n<1K"], "unshuffled_deduplicated_nap": ["n<1K"], "unshuffled_deduplicated_nds": ["1K<n<10K"], "unshuffled_deduplicated_ne": ["100K<n<1M"], "unshuffled_deduplicated_new": ["1K<n<10K"], "unshuffled_deduplicated_nl": ["10M<n<100M"], "unshuffled_deduplicated_nn": ["100K<n<1M"], "unshuffled_deduplicated_no": ["1M<n<10M"], "unshuffled_deduplicated_oc": ["1K<n<10K"], "unshuffled_deduplicated_or": ["10K<n<100K"], "unshuffled_deduplicated_os": ["1K<n<10K"], "unshuffled_deduplicated_pa": ["10K<n<100K"], "unshuffled_deduplicated_pam": ["n<1K"], "unshuffled_deduplicated_pl": ["10M<n<100M"], "unshuffled_deduplicated_pms": ["1K<n<10K"], "unshuffled_deduplicated_pnb": ["1K<n<10K"], "unshuffled_deduplicated_ps": ["10K<n<100K"], "unshuffled_deduplicated_pt": ["10M<n<100M"], "unshuffled_deduplicated_qu": ["n<1K"], "unshuffled_deduplicated_rm": ["n<1K"], "unshuffled_deduplicated_ro": ["1M<n<10M"], "unshuffled_deduplicated_ru": ["100M<n<1B"], "unshuffled_deduplicated_sa": ["1K<n<10K"], "unshuffled_deduplicated_sah": ["1K<n<10K"], "unshuffled_deduplicated_scn": ["n<1K"], "unshuffled_deduplicated_sd": ["10K<n<100K"], "unshuffled_deduplicated_sh": ["10K<n<100K"], "unshuffled_deduplicated_si": ["100K<n<1M"], "unshuffled_deduplicated_sk": ["1M<n<10M"], "unshuffled_deduplicated_sl": ["100K<n<1M"], "unshuffled_deduplicated_so": ["n<1K"], "unshuffled_deduplicated_sq": ["100K<n<1M"], "unshuffled_deduplicated_sr": ["100K<n<1M"], "unshuffled_deduplicated_su": ["n<1K"], "unshuffled_deduplicated_sv": ["10M<n<100M"], "unshuffled_deduplicated_sw": ["10K<n<100K"], "unshuffled_deduplicated_ta": ["100K<n<1M"], "unshuffled_deduplicated_te": ["100K<n<1M"], "unshuffled_deduplicated_tg": ["10K<n<100K"], "unshuffled_deduplicated_th": ["1M<n<10M"], "unshuffled_deduplicated_tk": ["1K<n<10K"], "unshuffled_deduplicated_tl": ["100K<n<1M"], "unshuffled_deduplicated_tr": ["10M<n<100M"], "unshuffled_deduplicated_tt": ["10K<n<100K"], "unshuffled_deduplicated_tyv": ["n<1K"], "unshuffled_deduplicated_ug": ["10K<n<100K"], "unshuffled_deduplicated_uk": ["1M<n<10M"], "unshuffled_deduplicated_ur": ["100K<n<1M"], "unshuffled_deduplicated_uz": ["10K<n<100K"], "unshuffled_deduplicated_vec": ["n<1K"], "unshuffled_deduplicated_vi": ["1M<n<10M"], "unshuffled_deduplicated_vo": ["1K<n<10K"], "unshuffled_deduplicated_wa": ["n<1K"], "unshuffled_deduplicated_war": ["1K<n<10K"], "unshuffled_deduplicated_wuu": ["n<1K"], "unshuffled_deduplicated_xal": ["n<1K"], "unshuffled_deduplicated_xmf": ["1K<n<10K"], "unshuffled_deduplicated_yi": ["10K<n<100K"], "unshuffled_deduplicated_yo": ["n<1K"], "unshuffled_deduplicated_yue": ["n<1K"], "unshuffled_deduplicated_zh": ["10M<n<100M"], "unshuffled_original_af": ["100K<n<1M"], "unshuffled_original_als": ["1K<n<10K"], "unshuffled_original_am": ["10K<n<100K"], "unshuffled_original_an": ["1K<n<10K"], "unshuffled_original_ar": ["10M<n<100M"], "unshuffled_original_arz": ["100K<n<1M"], "unshuffled_original_as": ["10K<n<100K"], "unshuffled_original_ast": ["1K<n<10K"], "unshuffled_original_av": ["n<1K"], "unshuffled_original_az": ["100K<n<1M"], "unshuffled_original_azb": ["10K<n<100K"], "unshuffled_original_ba": ["10K<n<100K"], "unshuffled_original_bar": ["n<1K"], "unshuffled_original_bcl": ["n<1K"], "unshuffled_original_be": ["100K<n<1M"], "unshuffled_original_bg": ["1M<n<10M"], "unshuffled_original_bh": ["n<1K"], "unshuffled_original_bn": ["1M<n<10M"], "unshuffled_original_bo": ["10K<n<100K"], "unshuffled_original_bpy": ["1K<n<10K"], "unshuffled_original_br": ["10K<n<100K"], "unshuffled_original_bs": ["1K<n<10K"], "unshuffled_original_bxr": ["n<1K"], "unshuffled_original_ca": ["1M<n<10M"], "unshuffled_original_cbk": ["n<1K"], "unshuffled_original_ce": ["1K<n<10K"], "unshuffled_original_ceb": ["10K<n<100K"], "unshuffled_original_ckb": ["100K<n<1M"], "unshuffled_original_cs": ["10M<n<100M"], "unshuffled_original_cv": ["10K<n<100K"], "unshuffled_original_cy": ["100K<n<1M"], "unshuffled_original_da": ["1M<n<10M"], "unshuffled_original_de": ["100M<n<1B"], "unshuffled_original_diq": ["n<1K"], "unshuffled_original_dsb": ["n<1K"], "unshuffled_original_dv": ["10K<n<100K"], "unshuffled_original_el": ["10M<n<100M"], "unshuffled_original_eml": ["n<1K"], "unshuffled_original_en": ["100M<n<1B"], "unshuffled_original_eo": ["100K<n<1M"], "unshuffled_original_es": ["10M<n<100M"], "unshuffled_original_et": ["1M<n<10M"], "unshuffled_original_eu": ["100K<n<1M"], "unshuffled_original_fa": ["10M<n<100M"], "unshuffled_original_fi": ["1M<n<10M"], "unshuffled_original_fr": ["10M<n<100M"], "unshuffled_original_frr": ["n<1K"], "unshuffled_original_fy": ["10K<n<100K"], "unshuffled_original_ga": ["10K<n<100K"], "unshuffled_original_gd": ["1K<n<10K"], "unshuffled_original_gl": ["100K<n<1M"], "unshuffled_original_gn": ["n<1K"], "unshuffled_original_gom": ["n<1K"], "unshuffled_original_gu": ["100K<n<1M"], "unshuffled_original_he": ["1M<n<10M"], "unshuffled_original_hi": ["1M<n<10M"], "unshuffled_original_hr": ["100K<n<1M"], "unshuffled_original_hsb": ["1K<n<10K"], "unshuffled_original_ht": ["n<1K"], "unshuffled_original_hu": ["10M<n<100M"], "unshuffled_original_hy": ["100K<n<1M"], "unshuffled_original_ia": ["1K<n<10K"], "unshuffled_original_id": ["10M<n<100M"], "unshuffled_original_ie": ["n<1K"], "unshuffled_original_ilo": ["1K<n<10K"], "unshuffled_original_io": ["n<1K"], "unshuffled_original_is": ["100K<n<1M"], "unshuffled_original_it": ["10M<n<100M"], "unshuffled_original_ja": ["10M<n<100M"], "unshuffled_original_jbo": ["n<1K"], "unshuffled_original_jv": ["1K<n<10K"], "unshuffled_original_ka": ["100K<n<1M"], "unshuffled_original_kk": ["100K<n<1M"], "unshuffled_original_km": ["100K<n<1M"], "unshuffled_original_kn": ["100K<n<1M"], "unshuffled_original_ko": ["1M<n<10M"], "unshuffled_original_krc": ["1K<n<10K"], "unshuffled_original_ku": ["10K<n<100K"], "unshuffled_original_kv": ["1K<n<10K"], "unshuffled_original_kw": ["n<1K"], "unshuffled_original_ky": ["100K<n<1M"], "unshuffled_original_la": ["10K<n<100K"], "unshuffled_original_lb": ["10K<n<100K"], "unshuffled_original_lez": ["1K<n<10K"], "unshuffled_original_li": ["n<1K"], "unshuffled_original_lmo": ["1K<n<10K"], "unshuffled_original_lo": ["10K<n<100K"], "unshuffled_original_lrc": ["n<1K"], "unshuffled_original_lt": ["1M<n<10M"], "unshuffled_original_lv": ["1M<n<10M"], "unshuffled_original_mai": ["n<1K"], "unshuffled_original_mg": ["10K<n<100K"], "unshuffled_original_mhr": ["1K<n<10K"], "unshuffled_original_min": ["n<1K"], "unshuffled_original_mk": ["100K<n<1M"], "unshuffled_original_ml": ["100K<n<1M"], "unshuffled_original_mn": ["100K<n<1M"], "unshuffled_original_mr": ["100K<n<1M"], "unshuffled_original_mrj": ["n<1K"], "unshuffled_original_ms": ["100K<n<1M"], "unshuffled_original_mt": ["10K<n<100K"], "unshuffled_original_mwl": ["n<1K"], "unshuffled_original_my": ["100K<n<1M"], "unshuffled_original_myv": ["n<1K"], "unshuffled_original_mzn": ["1K<n<10K"], "unshuffled_original_nah": ["n<1K"], "unshuffled_original_nap": ["n<1K"], "unshuffled_original_nds": ["10K<n<100K"], "unshuffled_original_ne": ["100K<n<1M"], "unshuffled_original_new": ["1K<n<10K"], "unshuffled_original_nl": ["10M<n<100M"], "unshuffled_original_nn": ["100K<n<1M"], "unshuffled_original_no": ["1M<n<10M"], "unshuffled_original_oc": ["10K<n<100K"], "unshuffled_original_or": ["10K<n<100K"], "unshuffled_original_os": ["1K<n<10K"], "unshuffled_original_pa": ["100K<n<1M"], "unshuffled_original_pam": ["n<1K"], "unshuffled_original_pl": ["10M<n<100M"], "unshuffled_original_pms": ["1K<n<10K"], "unshuffled_original_pnb": ["1K<n<10K"], "unshuffled_original_ps": ["10K<n<100K"], "unshuffled_original_pt": ["10M<n<100M"], "unshuffled_original_qu": ["n<1K"], "unshuffled_original_rm": ["n<1K"], "unshuffled_original_ro": ["1M<n<10M"], "unshuffled_original_ru": ["100M<n<1B"], "unshuffled_original_sa": ["10K<n<100K"], "unshuffled_original_sah": ["10K<n<100K"], "unshuffled_original_scn": ["n<1K"], "unshuffled_original_sd": ["10K<n<100K"], "unshuffled_original_sh": ["10K<n<100K"], "unshuffled_original_si": ["100K<n<1M"], "unshuffled_original_sk": ["1M<n<10M"], "unshuffled_original_sl": ["1M<n<10M"], "unshuffled_original_so": ["n<1K"], "unshuffled_original_sq": ["100K<n<1M"], "unshuffled_original_sr": ["1M<n<10M"], "unshuffled_original_su": ["n<1K"], "unshuffled_original_sv": ["10M<n<100M"], "unshuffled_original_sw": ["10K<n<100K"], "unshuffled_original_ta": ["1M<n<10M"], "unshuffled_original_te": ["100K<n<1M"], "unshuffled_original_tg": ["10K<n<100K"], "unshuffled_original_th": ["1M<n<10M"], "unshuffled_original_tk": ["1K<n<10K"], "unshuffled_original_tl": ["100K<n<1M"], "unshuffled_original_tr": ["10M<n<100M"], "unshuffled_original_tt": ["100K<n<1M"], "unshuffled_original_tyv": ["n<1K"], "unshuffled_original_ug": ["10K<n<100K"], "unshuffled_original_uk": ["10M<n<100M"], "unshuffled_original_ur": ["100K<n<1M"], "unshuffled_original_uz": ["10K<n<100K"], "unshuffled_original_vec": ["n<1K"], "unshuffled_original_vi": ["10M<n<100M"], "unshuffled_original_vo": ["1K<n<10K"], "unshuffled_original_wa": ["1K<n<10K"], "unshuffled_original_war": ["1K<n<10K"], "unshuffled_original_wuu": ["n<1K"], "unshuffled_original_xal": ["n<1K"], "unshuffled_original_xmf": ["1K<n<10K"], "unshuffled_original_yi": ["10K<n<100K"], "unshuffled_original_yo": ["n<1K"], "unshuffled_original_yue": ["n<1K"], "unshuffled_original_zh": ["10M<n<100M"]}, "source_datasets": ["original"], "task_categories": ["sequence-modeling"], "task_ids": ["language-modeling"], "paperswithcode_id": "oscar", "pretty_name": "OSCAR"} | 2022-11-08T09:04:43+00:00 |
9eb87c887a17d18d82b6cdb6eacea73648aeb138 | # RAFT Submission Template
Welcome to the [RAFT benchmark](https://raft.elicit.org/)! RAFT is a few-shot classification benchmark that tests language models:
- across multiple domains (lit review, tweets, customer interaction, etc.)
- on economically valuable classification tasks (someone inherently cares about the task)
- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)
This repository can be used to generate a template so you can submit your predictions for evaluation on [the leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard).
## Quickstart
### 1. Create an account on the Hugging Face Hub
First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already!
### 2. Create a template repository on your machine
The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your predictions. The Hugging Face Hub uses [Git Large File Storage (LFS)](https://git-lfs.github.com) to manage large files, so first install it if you don't have it already. For example, on macOS you can run:
```bash
brew install git-lfs
git lfs install
```
Next, run the following commands to create the repository. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
```bash
# Create and activate a virtual environment
conda create -n raft python=3.8 && conda activate raft
# Install the following libraries
pip install cookiecutter huggingface-hub==0.13.4
# Create the template repository
cookiecutter git+https://huggingface.co/datasets/ought/raft-submission
```
This will ask you to specify your Hugging Face Hub username, a Hugging Face [access token](https://huggingface.co/settings/tokens)
with write permissions, and the name of the repository:
```
hf_hub_username [huggingface]:
hf_access_token [hf_access_token]:
repo_name [my-raft-submissions]:
```
This will trigger the following steps:
1. Create a private dataset repository on the Hugging Face Hub under `{hf_hub_username}/{repo_name}`
2. Clone the repository to your local machine
3. Add various template files and commit them locally to the repository
The resulting repository should have the following structure:
```
my-raft-submission
├── LICENSE
├── README.md <- The README with submission instructions
├── cli.py <- The CLI for validating predictions etc
├── data <- The predictions for each task
├── my-raft-submission.py <- Script to load predictions. Do not edit!
└── requirements.txt <- The requirements file for the submissions
```
### 3. Install the dependencies
The final step is to install the project's dependencies:
```bash
# Navigate to the template repository
cd my-raft-submissions
# Install dependencies
python -m pip install -r requirements.txt
```
That's it! You're now all set to start generating predictions - see the instructions below on how to submit them to the Hub.
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. Submissions are evaluated **every Sunday at 12:00 UTC.**
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names
tasks = get_dataset_config_names("ought/raft")
for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```
As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
```
data
├── ade_corpus_v2
│ ├── predictions.csv <- A CSV file of the predictions with `ID` and `Label` columns
│ └── task.json <- Configuration file for loading the predictions. Do not edit!
├── banking_77
│ ├── predictions.csv
│ └── task.json
├── neurips_impact_statement_risks
│ ├── predictions.csv
│ └── task.json
├── one_stop_english
│ ├── predictions.csv
│ └── task.json
├── overruling
│ ├── predictions.csv
│ └── task.json
├── semiconductor_org_types
│ ├── predictions.csv
│ └── task.json
├── systematic_review_inclusion
│ ├── predictions.csv
│ └── task.json
├── tai_safety_research
│ ├── predictions.csv
│ └── task.json
├── terms_of_service
│ ├── predictions.csv
│ └── task.json
├── tweet_eval_hate
│ ├── predictions.csv
│ └── task.json
└── twitter_complaints
├── predictions.csv
└── task.json
```
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
```
python cli.py validate
```
If everything is correct, you should see the following message:
```
All submission files validated! ✨ 🚀 ✨
Now you can make a submission 🤗
```
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
```
python cli.py submit
```
If there are no errors, you should see the following message:
```
Submission successful! 🎉 🥳 🎉
Your submission will be evaluated on Sunday 05 September 2021 at 12:00 UTC ⏳
```
where the evaluation is run every Sunday and your results will be visible on the leaderboard. | ought/raft-submission | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2023-04-27T21:20:54+00:00 |
9ee50172ea9afda2f1033c6f1b986e568b862fb3 |
# Dataset Card for RAFT
## 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://raft.elicit.org
- **Repository:** https://huggingface.co/datasets/ought/raft
- **Paper:** [arxiv.org](https://arxiv.org/abs/2109.14076)
- **Leaderboard:** https://huggingface.co/spaces/ought/raft-leaderboard
- **Point of Contact:** [Eli Lifland]([email protected])
### Dataset Summary
The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset.
### Supported Tasks and Leaderboards
- `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score.
### Languages
RAFT is entirely in American English (en-US).
## Dataset Structure
### Data Instances
| Dataset | First Example |
| ----------- | ----------- |
| Ade Corpus V2 | <pre>Sentence: No regional side effects were noted.<br>ID: 0<br>Label: 2</pre> |
| Banking 77 | <pre>Query: Is it possible for me to change my PIN number?<br>ID: 0<br>Label: 23<br></pre> |
| NeurIPS Impact Statement Risks | <pre>Paper title: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation...<br>Paper link: https://proceedings.neurips.cc/paper/2020/file/ec1f764517b7ffb52057af6df18142b7-Paper.pdf...<br>Impact statement: This work makes the first attempt to search for all key components of panoptic pipeline and manages to accomplish this via the p...<br>ID: 0<br>Label: 1</pre> |
| One Stop English | <pre>Article: For 85 years, it was just a grey blob on classroom maps of the solar system. But, on 15 July, Pluto was seen in high resolution ...<br>ID: 0<br>Label: 3<br></pre> |
| Overruling | <pre>Sentence: in light of both our holding today and previous rulings in johnson, dueser, and gronroos, we now explicitly overrule dupree....<br>ID: 0<br>Label: 2<br></pre> |
| Semiconductor Org Types | <pre>Paper title: 3Gb/s AC-coupled chip-to-chip communication using a low-swing pulse receiver...<br>Organization name: North Carolina State Univ.,Raleigh,NC,USA<br>ID: 0<br>Label: 3<br></pre> |
| Systematic Review Inclusion | <pre>Title: Prototyping and transforming facial textures for perception research...<br>Abstract: Wavelet based methods for prototyping facial textures for artificially transforming the age of facial images were described. Pro...<br>Authors: Tiddeman, B.; Burt, M.; Perrett, D.<br>Journal: IEEE Comput Graphics Appl<br>ID: 0<br>Label: 2</pre> |
| TAI Safety Research | <pre>Title: Malign generalization without internal search<br>Abstract Note: In my last post, I challenged the idea that inner alignment failures should be explained by appealing to agents which perform ex...<br>Url: https://www.alignmentforum.org/posts/ynt9TD6PrYw6iT49m/malign-generalization-without-internal-search...<br>Publication Year: 2020<br>Item Type: blogPost<br>Author: Barnett, Matthew<br>Publication Title: AI Alignment Forum<br>ID: 0<br>Label: 1</pre> |
| Terms Of Service | <pre>Sentence: Crowdtangle may change these terms of service, as described above, notwithstanding any provision to the contrary in any agreemen...<br>ID: 0<br>Label: 2<br></pre> |
| Tweet Eval Hate | <pre>Tweet: New to Twitter-- any men on here know what the process is to get #verified?...<br>ID: 0<br>Label: 2<br></pre> |
| Twitter Complaints | <pre>Tweet text: @HMRCcustomers No this is my first job<br>ID: 0<br>Label: 2</pre> |
### Data Fields
The ID field is used for indexing data points. It will be used to match your submissions with the true test labels, so you must include it in your submission. All other columns contain textual data. Some contain links and URLs to websites on the internet.
All output fields are designated with the "Label" column header. The 0 value in this column indicates that the entry is unlabeled, and should only appear in the unlabeled test set. Other values in this column are various other labels. To get their textual value for a given dataset:
```
# Load the dataset
dataset = datasets.load_dataset("ought/raft", "ade_corpus_v2")
# First, get the object that holds information about the "Label" feature in the dataset.
label_info = dataset.features["Label"]
# Use the int2str method to access the textual labels.
print([label_info.int2str(i) for i in (0, 1, 2)])
# ['Unlabeled', 'ADE-related', 'not ADE-related']
```
### Data Splits
There are two splits provided: train data and unlabeled test data.
The training examples were chosen at random. No attempt was made to ensure that classes were balanced or proportional in the training data -- indeed, the Banking 77 task with 77 different classes if used cannot fit all of its classes into the 50 training examples.
| Dataset | Train Size | Test Size | |
|--------------------------------|------------|-----------|---|
| Ade Corpus V2 | 50 | 5000 | |
| Banking 77 | 50 | 5000 | |
| NeurIPS Impact Statement Risks | 50 | 150 | |
| One Stop English | 50 | 516 | |
| Overruling | 50 | 2350 | |
| Semiconductor Org Types | 50 | 449 | |
| Systematic Review Inclusion | 50 | 2243 | |
| TAI Safety Research | 50 | 1639 | |
| Terms Of Service | 50 | 5000 | |
| Tweet Eval Hate | 50 | 2966 | |
| Twitter Complaints | 50 | 3399 | |
| **Total** | **550** | **28712** | |
## Dataset Creation
### Curation Rationale
Generally speaking, the rationale behind RAFT was to create a benchmark for evaluating NLP models that didn't consist of contrived or artificial data sources, for which the tasks weren't originally assembled for the purpose of testing NLP models. However, each individual dataset in RAFT was collected independently. For the majority of datasets, we only collected them second-hand from existing curated sources. The datasets that we curated are:
* NeurIPS impact statement risks
* Semiconductor org types
* TAI Safety Research
Each of these three datasets was sourced from our existing collaborators at Ought. They had used our service, Elicit, to analyze their dataset in the past, and we contact them to include their dataset and the associated classification task in the benchmark. For all datasets, more information is provided in our paper. For the ones which we did not curate, we provide a link to the dataset. For the ones which we did, we provide a datasheet that elaborates on many of the topics here in greater detail.
For the three datasets that we introduced:
* **NeurIPS impact statement risks** The dataset was created to evaluate the then new requirement for authors to include an "impact statement" in their 2020 NeurIPS papers. Had it been successful? What kind of things did authors mention the most? How long were impact statements on average? Etc.
* **Semiconductor org types** The dataset was originally created to understand better which countries’ organisations have contributed most to semiconductor R\&D over the past 25 years using three main conferences. Moreover, to estimate the share of academic and private sector contributions, the organisations were classified as “university”, “research institute” or “company”.
* **TAI Safety Research** The primary motivations for assembling this database were to: (1) Aid potential donors in assessing organizations focusing on TAI safety by collecting and analyzing their research output. (2) Assemble a comprehensive bibliographic database that can be used as a base for future projects, such as a living review of the field.
**For the following sections, we will only describe the datasets we introduce. All other dataset details, and more details on the ones described here, can be found in our paper.**
### Source Data
#### Initial Data Collection and Normalization
* **NeurIPS impact statement risks** The data was directly observable (raw text scraped) for the most part; although some data was taken from previous datasets (which themselves had taken it from raw text). The data was validated, but only in part, by human reviewers. Cf this link for full details:
* **Semiconductor org types** We used the IEEE API to obtain institutions that contributed papers to semiconductor conferences in the last 25 years. This is a random sample of 500 of them with a corresponding conference paper title. The three conferences were the International Solid-State Circuits Conference (ISSCC), the Symposia on VLSI Technology and Circuits (VLSI) and the International Electron Devices Meeting (IEDM).
* **TAI Safety Research** We asked TAI safety organizations for what their employees had written, emailed some individual authors, and searched Google Scholar. See the LessWrong post for more details: https://www.lesswrong.com/posts/4DegbDJJiMX2b3EKm/tai-safety-bibliographic-database
#### Who are the source language producers?
* **NeurIPS impact statement risks** Language generated from NeurIPS 2020 impact statement authors, generally the authors of submission papers.
* **Semiconductor org types** Language generated from IEEE API. Generally machine-formatted names, and title of academic papers.
* **TAI Safety Research** Language generated by authors of TAI safety research publications.
### Annotations
#### Annotation process
* **NeurIPS impact statement risks** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **Semiconductor org types** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **TAI Safety Research** N/A
#### Who are the annotators?
* **NeurIPS impact statement risks** Contractors paid by Ought performed the labeling of whether impact statements mention harmful applications. A majority vote was taken from 3 annotators.
* **Semiconductor org types** Contractors paid by Ought performed the labeling of organization types. A majority vote was taken from 3 annotators.
* **TAI Safety Research** The dataset curators annotated the dataset by hand.
### Personal and Sensitive Information
It is worth mentioning that the Tweet Eval Hate, by necessity, contains highly offensive content.
* **NeurIPS impact statement risks** The dataset contains authors' names. These were scraped from publicly available scientific papers submitted to NeurIPS 2020.
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
## Considerations for Using the Data
### Social Impact of Dataset
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Discussion of Biases
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Other Known Limitations
* **NeurIPS impact statement risks** This dataset has limitations that should be taken into consideration when using it. In particular, the method used to collect broader impact statements involved automated downloads, conversions and scraping and was not error-proof. Although care has been taken to identify and correct as many errors as possible, not all texts have been reviewed by a human. This means it is possible some of the broader impact statements contained in the dataset are truncated or otherwise incorrectly extracted from their original article.
* **Semiconductor org types** N/A
* **TAI Safety Research** Don't use it to create a dangerous AI that could bring the end of days.
## Additional Information
### Dataset Curators
The overall RAFT curators are Neel Alex, Eli Lifland, and Andreas Stuhlmüller.
* **NeurIPS impact statement risks** Volunteers working with researchers affiliated to Oxford's Future of Humanity Institute (Carolyn Ashurst, now at The Alan Turing Institute) created the impact statements dataset.
* **Semiconductor org types** The data science unit of Stiftung Neue Verantwortung (Berlin).
* **TAI Safety Research** Angelica Deibel and Jess Riedel. We did not do it on behalf of any entity.
### Licensing Information
RAFT aggregates many other datasets, each of which is provided under its own license. Generally, those licenses permit research and commercial use.
| Dataset | License |
| ----------- | ----------- |
| Ade Corpus V2 | Unlicensed |
| Banking 77 | CC BY 4.0 |
| NeurIPS Impact Statement Risks | MIT License/CC BY 4.0 |
| One Stop English | CC BY-SA 4.0 |
| Overruling | Unlicensed |
| Semiconductor Org Types | CC BY-NC 4.0 |
| Systematic Review Inclusion | CC BY 4.0 |
| TAI Safety Research | CC BY-SA 4.0 |
| Terms Of Service | Unlicensed |
| Tweet Eval Hate | Unlicensed |
| Twitter Complaints | Unlicensed |
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@neel-alex](https://github.com/neel-alex), [@uvafan](https://github.com/uvafan), and [@lewtun](https://github.com/lewtun) for adding this dataset. | ought/raft | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:extended|ade_corpus_v2",
"source_datasets:extended|banking77",
"language:en",
"license:other",
"arxiv:2109.14076",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated", "crowdsourced"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original", "extended|ade_corpus_v2", "extended|banking77"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Real-world Annotated Few-shot Tasks: RAFT", "language_bcp47": ["en-US"]} | 2022-10-25T08:54:19+00:00 |
aa56583bf2bc52b0565770607d6fc3faebecf9e2 |
# Dataset Card for Language Identification dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label.
This dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for language identification, which is a **multi-class text classification** task.
The model [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection), which is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), was trained on this dataset and currently achieves 99.6% accuracy on the test set.
### Languages
The Language Identification dataset contains text in 20 languages, which are:
`arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)`
## Dataset Structure
### Data Instances
For each instance, there is a string for the text and a string for the label (the language tag). Here is an example:
`{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}`
### Data Fields
- **labels:** a string indicating the language label.
- **text:** a string consisting of one or more sentences in one of the 20 languages listed above.
### Data Splits
The Language Identification dataset has 3 splits: *train*, *valid*, and *test*.
The train set contains 70k samples, while the validation and test sets 10k each.
All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500.
## Dataset Creation
### Curation Rationale
This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model.
### Source Data
The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models.
### Discussion of Biases
The possible biases correspond to those of the 3 datasets on which this dataset is based.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.
| papluca/language-identification | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|amazon_reviews_multi",
"source_datasets:extended|xnli",
"source_datasets:extended|stsb_multi_mt",
"language:ar",
"language:bg",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:it",
"language:ja",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"language:sw",
"language:th",
"language:tr",
"language:ur",
"language:vi",
"language:zh",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "it", "ja", "nl", "pl", "pt", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": [], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["extended|amazon_reviews_multi", "extended|xnli", "extended|stsb_multi_mt"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Language Identification dataset"} | 2022-07-15T09:11:23+00:00 |
34264380029d9aca8c6031b072d6fab6e1f97d10 |
## Sharif Emotional Speech Dataset (ShEMO)
## Dataset Summary
The dataset includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online Persian radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including <i>anger</i>, <i>fear</i>, <i>happiness</i>, <i>sadness</i> and <i>surprise</i>, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure,
the inter-annotator agreement is 64% which is interpreted as "substantial agreement".
## Languages
Persian (fa)
## Overview of ShEMO
Feature | Status
------------- | ----------
**license** | apache-2.0
**language** | Persian (fa)
**modality** | Speech
**duration** | 3 hours and 25 minutes
**#utterances** | 3000
**#speakers** | 87 (31 females, 56 males)
**#emotions** | 5 basic emotions (anger, fear, happiness, sadness and surprise) and neutral state
**orthographic transcripts** | Available
**phonetic transcripts** | Available
## Data Instances
Here is a sample of data instances:
```json
"F21N37": {
"speaker_id": "F21",
"gender": "female",
"emotion": "neutral",
"transcript": "مگه من به تو نگفته بودم که باید راجع به دورانت سکوت کنی؟",
"ipa": "mӕge mæn be to nægofte budӕm ke bɑyæd rɑdʒeʔ be dorɑnt sokut koni"
}
```
## Citation
If you use this dataset, please cite the following paper:
~~~~
@Article{MohamadNezami2019,
author = {Mohamad Nezami, Omid and Jamshid Lou, Paria and Karami, Mansoureh},
title = {ShEMO: a large-scale validated database for Persian speech emotion detection},
journal = {Language Resources and Evaluation},
year = {2019},
volume = {53},
number = {1},
pages = {1--16},
issn = {1574-0218},
doi = {10.1007/s10579-018-9427-x},
url = {https://doi.org/10.1007/s10579-018-9427-x}
}
~~~~
## Download Dataset
To download the dataset, please check the [ShEMO repo](https://github.com/pariajm/sharif-emotional-speech-database)! | pariajm/sharif_emotional_speech_dataset | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:radio-plays",
"language:fa",
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["radio-plays"], "task_categories": ["automatic-speech-recognition"], "task_ids": ["speech-recognition"], "pretty_name": "Sharif Emotional Speech Dataset (ShEMO)"} | 2022-10-24T15:49:19+00:00 |
18c0e0854a2e9e36b19f8524f272d861dbafc9ab | Best ayurvedic medicine for erectile dysfunction. More Info :- https://www.parivartanayurveda.com/male-sexual-problems.php | parivartanayurveda/Malesexproblemsayurvedictreatment | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-02-24T12:23:37+00:00 |
cca3cfb747db5bf97b95126ec79d5b7d743f9654 |
# XL-WiC
Huggingface dataset for the XL-WiC paper [https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf](https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf).
Please refer to the official [website](https://pilehvar.github.io/xlwic/) for more information.
## Configurations
When loading one of the XL-WSD datasets one has to specify the training language and the target language (on which dev and test will be performed).
Please refer to [Languages](#languages) section to see in which languages training data is available.
For example, we can load the dataset having English as training language and Italian as target language as follows:
```python
from datasets import load_dataset
dataset = load_dataset('pasinit/xlwic', 'en_it')
```
## Languages
**Training data**
- en (English)
- fr (French)
- de (German)
- it (Italian)
**Dev & Test data**
- fr (French)
- de (German)
- it (Italian)
- bg (Bulgarian)
- zh (Chinese)
- hr (Croatian)
- da (Danish)
- nl (Dutch)
- et (Estonian)
- fa (Farsi)
- ja (Japanesse)
- ko (Korean)
| pasinit/xlwic | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:bg",
"language:zh",
"language:hr",
"language:da",
"language:nl",
"language:et",
"language:fa",
"language:ja",
"language:ko",
"language:it",
"language:fr",
"language:de",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en", "bg", "zh", "hr", "da", "nl", "et", "fa", "ja", "ko", "it", "fr", "de"], "license": ["cc-by-nc-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "extended": ["original"]} | 2022-10-25T08:54:22+00:00 |
0f68047bb0d5d17e273ea7bd87b8964cdbe00028 |
# Dataset Card for equity-evaluation-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:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 Affect in Tweets. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `sentence`: a `string` feature.
- `template`: a `string` feature.
- `person`: a `string` feature.
- `race`: a `string` feature.
- `emotion`: a `string` feature.
- `emotion word`: a `string` feature.
### 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
[Needs More Information]
| peixian/equity_evaluation_corpus | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"gender-classification",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "tags": ["gender-classification"]} | 2022-10-20T22:35:15+00:00 |
74ef139a2d70372a878e406056ff37b1f0d561a5 |
# Dataset Card for rtGender
## 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
RtGender is a corpus for studying responses to gender online, including posts and responses from Facebook, TED, Fitocracy, and Reddit where the gender of the source poster/speaker is known.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `source`: a `string` feature.
- `op_gender`: a `string` feature.
- `post_text`: a `string` feature.
- `response_text`: a `string` feature.
- `sentiment`: a `string` feature.
- `relevance`: a `string` feature.
### 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
[Needs More Information] | peixian/rtGender | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"]} | 2022-10-25T08:54:24+00:00 |
c49b2d8fa0d6476520695c52207690b7ec854043 |
# Dataset Card for PersiNLU (Textual Entailment)
## Table of Contents
- [Dataset Card for PersiNLU (Textual Entailment)](#dataset-card-for-persi_nlu_entailment)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian textual entailment task (deciding `sent1` entails `sent2`).
The questions are partially translated from the SNLI dataset and partially generated by expert annotators.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"sent1": "سالها است که کنگره در تلاش است تا اثربخشی مدیریت اطلاعات و فناوری را در دولت فدرال افزایش دهد.",
"sent2": "کنگره بودجه ویژه ای برای مدیریت اطلاعات و فناوری در دولت فدرال دارد.",
"label": "n",
"category": "translation-train"
}
```
### Data Fields
- `sent1`: the first sentence.
- `sent2`: the second sentence.
- `source`: whether the questions are translated from MNLI (`translation-.`) or they're written by native speakers (`natural-.`).
- `label`: `e` if `sent2` is entailed from `sent1`; `c` if `sent2` is contradictory to `sent1`; `n` if the two sentences are neutral.
### Data Splits
The train/dev/test splits contains 756/271/1751 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_entailment | [
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|translated|mnli",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|translated|mnli"], "task_categories": ["textual-entailment", "natural-language-inference"], "task_ids": ["textual-entailment", "natural-language-inference"]} | 2022-10-22T14:13:00+00:00 |
ec675bb3ac50c1a52317c101fe1d724b4601f47a |
# Dataset Card for PersiNLU (Query Paraphrasing)
## Table of Contents
- [Dataset Card for PersiNLU (Query Paraphrasing)](#dataset-card-for-persi_nlu_query_paraphrasing)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian query paraphrasng task (deciding whether two questions are paraphrases of each other).
The questions are partially generated from Google auto-complete, and partially translated from the Quora paraphrasing dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"q1": "اعمال حج تمتع از چه روزی شروع میشود؟",
"q2": "ویار از چه روزی شروع میشود؟",
"label": "0",
"category": "natural"
}
```
### Data Fields
- `q1`: the first question.
- `q2`: the second question.
- `category`: whether the questions are mined from Quora (`qqp`) or they're extracted from Google auto-complete (`natural`).
- `label`: `1` if the questions are paraphrases; `0` otherwise.
### Data Splits
The train/dev/test splits contains 1830/898/1916 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_query_paraphrasing | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|quora|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|quora|google"], "task_categories": ["query-paraphrasing"], "task_ids": ["query-paraphrasing"]} | 2022-10-22T14:13:22+00:00 |
701cb4096c7e12695123c254f757ed56b12c49b8 |
# Dataset Card for PersiNLU (Reading Comprehension)
## Table of Contents
- [Dataset Card for PersiNLU (Reading Comprehension)](#dataset-card-for-persi_nlu_reading_comprehension)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{
'question': 'پیامبر در چه سالی به پیامبری رسید؟',
'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF',
'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.',
'answers': [
{'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'}
]
}
```
### Data Fields
- `question`: the question, mined using Google auto-complete.
- `passage`: the passage that contains the answer.
- `url`: the url from which the passage was mined.
- `answers`: a list of answers, containing the string and the index of the answer.
### Data Splits
The train/test split contains 600/575 samples.
## Dataset Creation
### Curation Rationale
The question were collected via Google auto-complete.
The answers were annotated by native speakers.
For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_reading_comprehension | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|wikipedia|google"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"]} | 2022-10-25T08:54:26+00:00 |
abecf6a01a45174b7aa9b861fcc4a586cc4c7f9d |
# Dataset Card for PersiNLU (Textual Entailment)
## Table of Contents
- [Dataset Card for PersiNLU (Sentiment Analysis)](#dataset-card-for-persi_sentiment)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian sentiment analysis dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"review": "خوب بود ولی خیلی گرون شده دیگه...فک نکنم به این قیمت ارزش خرید داشته باشد",
"review_id": "1538",
"example_id": "4",
"excel_id": "food_194",
"question": "نظر شما در مورد بسته بندی و نگهداری این حلوا شکری، ارده و کنجد چیست؟",
"category": "حلوا شکری، ارده و کنجد",
"aspect": "بسته بندی",
"label": "-3",
"guid": "food-dev-r1538-e4"
}
```
### Data Fields
- `review`: the review text.
- `review_id`: a unique id associated with the review.
- `example_id`: a unique id associated with a particular attribute being addressed about the review.
- `question`: a natural language question about a particular attribute.
- `category`: the subject discussed in the review.
- `aspect`: the aspect mentioned in the input question.
- `label`: the overall sentiment towards this particular subject, in the context of the mentioned aspect. Here are the definition of the labels:
```
'-3': 'no sentiment expressed',
'-2': 'very negative',
'-1': 'negative',
'0': 'neutral',
'1': 'positive',
'2': 'very positive',
'3': 'mixed',
```
### Data Splits
See the data.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_sentiment | [
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|translated|mnli",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|translated|mnli"], "task_categories": ["sentiment-analysis"], "task_ids": ["sentiment-analysis"]} | 2022-10-22T14:13:40+00:00 |
aac51e2d1d2d464c7c0a123ffbe66c43fb30c8e7 |
# Dataset Card for PersiNLU (Machine Translation)
## Table of Contents
- [Dataset Card for PersiNLU (Machine Translation)](#dataset-card-for-persi_nlu_machine_translation)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian translation dataset (English -> Persian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`) and English (`en`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"source": "how toil to raise funds, propagate reforms, initiate institutions!",
"targets": ["چه زحمتها که بکشد تا منابع مالی را تامین کند اصطلاحات را ترویج کند نهادهایی به راه اندازد."],
"category": "mizan_dev_en_fa"
}
```
### Data Fields
- `source`: the input sentences, in English.
- `targets`: the list of gold target translations in Persian.
- `category`: the source from which the dataset is mined.
### Data Splits
The train/de/test split contains 1,621,666/2,138/48,360 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_translation_en_fa | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:fa",
"multilinguality:en",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["fa", "en"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended"], "task_categories": ["translation"], "task_ids": ["translation"]} | 2022-10-24T15:50:37+00:00 |
a22208a3da5b794d4d5d472942327ca17ca0e806 |
# Dataset Card for PersiNLU (Machine Translation)
## Table of Contents
- [Dataset Card for PersiNLU (Machine Translation)](#dataset-card-for-persi_nlu_machine_translation)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian translation dataset (English -> Persian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`) and English (`en`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"source": "چه زحمتها که بکشد تا منابع مالی را تامین کند اصطلاحات را ترویج کند نهادهایی به راه اندازد.",
"targets": ["how toil to raise funds, propagate reforms, initiate institutions!"],
"category": "mizan_dev_en_fa"
}
```
### Data Fields
- `source`: the input sentences, in Persian.
- `targets`: the list of gold target translations in English.
- `category`: the source from which the example is mined.
### Data Splits
The train/dev/test split contains 1,622,281/2,138/47,745 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
| persiannlp/parsinlu_translation_fa_en | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:fa",
"multilinguality:en",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["fa"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["fa", "en"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended"], "task_categories": ["translation"], "task_ids": ["translation"]} | 2022-10-24T16:01:27+00:00 |
4996d1a68fc5c56f6b888180d6f4a7d98a0cd5e2 | annotations_creators:
- no-annotation
language_creators:
- found
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
pretty_name: Practice
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
- document-retrieval
# Dataset Card for [Needs More Information]
## 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://huggingface.co/datasets/peterhsu/
- **Repository:** github-issues
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
For Practice
### Supported Tasks and Leaderboards
Classification
### Languages
en
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
train
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | peterhsu/github-issues | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-01-07T09:16:29+00:00 |
8a731c1701fe9261accecdeee010c82202e7ef40 | phongdtd/VinDataVLSP | [
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"license": "apache-2.0"} | 2022-01-26T06:49:13+00:00 |
|
22ddd6021c9c6cae167842867026230685ce3973 |
# Dataset Card for common_voice
## 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:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
[Needs More Information]
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Vietnamese
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
`
{
'file_path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'script': 'Ik vind dat een dubieuze procedure.',
'audio': {'path': 'audio/_1OsFqkFI38_34.304_39.424.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000}
`
### Data Fields
file_path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
script: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train, test, validated.
The val, test, train are all data that has been reviewed, deemed of high quality and split into val, test and train.
## 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
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@datlq](https://github.com/datlq98) for adding this dataset.
| phongdtd/youtube_casual_audio | [
"task_categories:automatic-speech-recognition",
"source_datasets:extended|youtube",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"multilinguality": {"vi": ["190K<n<200K"]}, "source_datasets": ["extended|youtube"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "Pretty_name": "Youtube Casual Audio", "Annotations_creators": ["crowdsourced"], "Language_creators": ["datlq"], "Languages": ["vi"], "Licenses": ["cc0-1.0"]} | 2022-11-01T13:23:24+00:00 |
59d44d489b64b128c388a5f27c4fa66dd6c3a080 |
# Dataset Card for "LeNER-Br language modeling"
## Dataset Summary
The LeNER-Br language modeling dataset is a collection of legal texts in Portuguese from the [LeNER-Br](https://huggingface.co/datasets/lener_br) dataset ([official site](https://cic.unb.br/~teodecampos/LeNER-Br/)).
The legal texts were downloaded from this [link](https://cic.unb.br/~teodecampos/LeNER-Br/LeNER-Br.zip) (93.6MB) and processed to create a `DatasetDict` with train and validation dataset (20%).
The LeNER-Br language modeling dataset allows the finetuning of language models as BERTimbau [base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) and [large](https://huggingface.co/neuralmind/bert-large-portuguese-cased).
## Language
Portuguese from Brazil.
## Blog post
[NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb) (29/12/2021)
## Dataset structure
```
DatasetDict({
validation: Dataset({
features: ['text'],
num_rows: 3813
})
train: Dataset({
features: ['text'],
num_rows: 15252
})
})
```
## Use
```
!pip install datasets
from datasets import load_dataset
dataset = load_dataset("pierreguillou/lener_br_finetuning_language_model")
``` | pierreguillou/lener_br_finetuning_language_model | [
"task_ids:language-modeling",
"multilinguality:monolingual",
"language:pt",
"lener_br",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"language": ["pt"], "multilinguality": ["monolingual"], "task_ids": ["language-modeling"], "paperswithcode_id": "lener-br", "pretty_name": "LeNER-Br language modeling", "datasets": ["lener_br"], "tags": ["lener_br"]} | 2022-10-25T08:54:32+00:00 |
d15dadc66c01f73d66f8b9947ebfc7db06cbb38e | CORD: A Consolidated Receipt Dataset for Post-OCR Parsing. | pierresi/cord | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-10-13T15:47:07+00:00 |
0dc9f2c26b42af4cb6330f36d6146e82f9117a3b |
# Dataset Card for Pile of Law
## 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://huggingface.co/datasets/pile-of-law/pile-of-law
- **Repository:** https://huggingface.co/datasets/pile-of-law/pile-of-law
- **Paper:** https://arxiv.org/abs/2207.00220
### Dataset Summary
We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.
### Supported Tasks and Leaderboards
See paper for details.
### Languages
Mainly English, but some other languages may appear in some portions of the data.
## Dataset Structure
### Data Instances
**courtListener_docket_entry_documents** : Docket entries in U.S. federal courts, including filed briefs from CourtListener RECAP archive.
**courtListener_opinions** : U.S. court opinions from CourtListener (synchronized as of 12/31/2022).
**atticus_contracts**: Unannotated contracts from the Atticus Project.
**federal_register**: The U.S. federal register where agencies file draft rulemaking.
**bva_opinions**: Bureau of Veterans Appeals opinions.
**us_bills**: Draft Bills from the United States Congress.
**cc_casebooks**: Educational Casebooks released under open CC licenses.
**tos**: Unannotated Terms of Service contracts.
**euro_parl**: European parliamentary debates.
**nlrb_decisions**: Decisions from the U.S. National Labor Review Board.
**scotus_oral_arguments**: U.S. Supreme Court Oral Arguments
**cfr**: U.S. Code of Federal Regulations
**state_codes**: U.S. State Codes
**scotus_filings**: Briefs and filings with the U.S. Supreme Court.
**exam_outlines**: Exam outlines available openly on the web.
**edgar**: Contracts filed with the SEC and made available on the SEC's Edgar tool.
**cfpb_creditcard_contracts**: Credit Card Contracts compiled by the U.S. Consumer Finance Protection Bureau.
**constitutions** : The World's constitutions.
**congressional_hearings** : U.S. Congressional hearing transcripts and statements.
**oig**: U.S. Office of Inspector general reports.
**olc_memos**: U.S. Office of Legal Counsel memos.
**uscode**: The United States Code (laws).
**founding_docs**: Letters from U.S. founders.
**ftc_advisory_opinions**: Advisory opinions by the Federal Trade Commission.
**echr** : European Court of Human Rights opinions.
**eurlex**: European Laws.
**tax_rulings**: Rulings from U.S. Tax court.
**un_debates**: U.N. General Debates
**fre**: U.S. Federal Rules of Evidence
**frcp** : U.S. Federal Rules of Civil Procedure
**canadian_decisions**: Canadian Court Opinions from ON and BC.
**eoir**: U.S. Executive Office for Immigration Review Immigration and Nationality Precedential Decisions
**dol_ecab**: Department of Labor Employees' Compensation Appeals Board decisions after 2006
**r_legaladvice** : Filtered data from the r/legaladvice and r/legaladviceofftopic subreddits in the format.
Title: [Post Title]
Question: [Post Content]
Topic: [Post Flair]
Answer \#[N]: [Top Answers]...
**acus_reports** : Reports from the Administrative Conference of the United States from 2010-2022.
**ed_policy_guidance** : Policy guidance documents from the U.S. Department of Education (2001-2022).
**uspto_office_actions** : Office Actions from the U.S. Patent and Trademark Office from 2019-2022.
**icj-pcij** : International Court of Justice and Permanent Court of International Justice opinions.
**hhs_alj_opinions** : Opinions from the U.S. Department of Health and Human Services Administrative Law Judges from 1985-2019.
**sec_administrative_proceedings**: Significant pleadings, orders and decisions for administrative proceedings from the U.S. Securities and Exchange Commission from 2005-2022.
**fmshrc_bluebooks**: Bluebooks from the U.S. Federal Mine Safety and Health Review Commission from 1979 (March) - 2022 (August).
**resource_contracts**: Resource Contracts collected by ResourceContracts.org
**medicaid_policy_guidance**: Policy guidance documents from the U.S. Department of Health and Human Services (1994-2022).
**irs_legal_advice_memos**: Legal Advice Memos and Chief Counsel Notices from the U.S. Internal Revenue Service.
**doj_guidance**: Guidance documents from the U.S. Department of Justice (2020-2022).
**1/23 update**: Data updated in 2023 included: syncing courtListener opinions, adding ACUS reports, USPTO office actions, Ed Policy Guidance, HHS ALJ opinions, SEC administrative proceedings, FMSHRC Bluebooks, Resource Contracts, and ICJ/PCIJ legal opinions. We also fixed OLC opinions which had some formatting inconsistencies and merged exam outlines into one file, adding some additional exam outlines.
On-disk sizes might vary due to caching and compression, but should be approximately as follows as of 1/7/2023.
```bash
% xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
183 181 9,631.2 KiB 35.0 MiB 0.268 CRC64 data/train.acus_reports.jsonl.xz
1 1 1,024.1 MiB 6,804.7 MiB 0.150 CRC64 data/train.atticus_contracts.0.jsonl.xz
1 1 1,024.1 MiB 6,781.1 MiB 0.151 CRC64 data/train.atticus_contracts.1.jsonl.xz
1 1 1,024.1 MiB 6,790.1 MiB 0.151 CRC64 data/train.atticus_contracts.2.jsonl.xz
1 1 1,024.1 MiB 6,759.2 MiB 0.152 CRC64 data/train.atticus_contracts.3.jsonl.xz
1 1 139.9 MiB 925.0 MiB 0.151 CRC64 data/train.atticus_contracts.4.jsonl.xz
1 1 1,564.6 MiB 12.5 GiB 0.123 CRC64 data/train.bva.jsonl.xz
1 1 29.8 MiB 154.3 MiB 0.193 CRC64 data/train.canadian_decisions.jsonl.xz
1 1 18.5 MiB 82.6 MiB 0.224 CRC64 data/train.cc_casebooks.jsonl.xz
1 1 3,427.3 KiB 67.2 MiB 0.050 CRC64 data/train.cfpb_cc.jsonl.xz
1 1 72.7 MiB 582.6 MiB 0.125 CRC64 data/train.cfr.jsonl.xz
1 1 1,056.1 MiB 4,941.9 MiB 0.214 CRC64 data/train.congressional_hearings.jsonl.xz
1 1 3,272.4 KiB 21.3 MiB 0.150 CRC64 data/train.constitutions.jsonl.xz
1 1 1,024.1 MiB 13.0 GiB 0.077 CRC64 data/train.courtlistenerdocketentries.0.jsonl.xz
1 1 1,024.3 MiB 13.3 GiB 0.075 CRC64 data/train.courtlistenerdocketentries.1.jsonl.xz
1 1 1,024.1 MiB 12.4 GiB 0.080 CRC64 data/train.courtlistenerdocketentries.2.jsonl.xz
1 1 635.2 MiB 8,671.6 MiB 0.073 CRC64 data/train.courtlistenerdocketentries.3.jsonl.xz
1 1 953.7 MiB 4,575.7 MiB 0.208 CRC64 data/train.courtlisteneropinions.0.jsonl.xz
1 1 953.7 MiB 4,356.2 MiB 0.219 CRC64 data/train.courtlisteneropinions.1.jsonl.xz
1 1 953.7 MiB 4,315.6 MiB 0.221 CRC64 data/train.courtlisteneropinions.10.jsonl.xz
1 1 953.7 MiB 4,650.3 MiB 0.205 CRC64 data/train.courtlisteneropinions.11.jsonl.xz
1 1 953.7 MiB 4,836.3 MiB 0.197 CRC64 data/train.courtlisteneropinions.12.jsonl.xz
1 1 953.7 MiB 4,644.9 MiB 0.205 CRC64 data/train.courtlisteneropinions.13.jsonl.xz
1 1 953.7 MiB 4,657.5 MiB 0.205 CRC64 data/train.courtlisteneropinions.14.jsonl.xz
1 1 539.2 MiB 2,621.8 MiB 0.206 CRC64 data/train.courtlisteneropinions.15.jsonl.xz
1 1 953.7 MiB 4,335.3 MiB 0.220 CRC64 data/train.courtlisteneropinions.2.jsonl.xz
1 1 953.7 MiB 4,352.0 MiB 0.219 CRC64 data/train.courtlisteneropinions.3.jsonl.xz
1 1 953.7 MiB 4,575.9 MiB 0.208 CRC64 data/train.courtlisteneropinions.4.jsonl.xz
1 1 953.7 MiB 4,382.6 MiB 0.218 CRC64 data/train.courtlisteneropinions.5.jsonl.xz
1 1 953.7 MiB 4,352.3 MiB 0.219 CRC64 data/train.courtlisteneropinions.6.jsonl.xz
1 1 953.7 MiB 4,462.4 MiB 0.214 CRC64 data/train.courtlisteneropinions.7.jsonl.xz
1 1 953.7 MiB 4,604.0 MiB 0.207 CRC64 data/train.courtlisteneropinions.8.jsonl.xz
1 1 953.7 MiB 4,612.0 MiB 0.207 CRC64 data/train.courtlisteneropinions.9.jsonl.xz
335 335 6,047.4 KiB 24.1 MiB 0.245 CRC64 data/train.doj_guidance.jsonl.xz
1 1 41.1 MiB 305.6 MiB 0.135 CRC64 data/train.dol_ecab.jsonl.xz
1 1 19.1 MiB 100.5 MiB 0.190 CRC64 data/train.echr.jsonl.xz
508 507 1,502.0 KiB 4,716.7 KiB 0.318 CRC64 data/train.ed_policy_guidance.jsonl.xz
1 1 1,372.0 MiB 9,032.6 MiB 0.152 CRC64 data/train.edgar.jsonl.xz
1 1 3,896.6 KiB 18.6 MiB 0.205 CRC64 data/train.eoir.jsonl.xz
1 1 140.3 MiB 1,154.7 MiB 0.121 CRC64 data/train.eurlex.jsonl.xz
1 1 51.4 MiB 239.4 MiB 0.215 CRC64 data/train.euro_parl.jsonl.xz
1 1 355.3 KiB 1,512.5 KiB 0.235 CRC64 data/train.examoutlines.jsonl.xz
1 1 20.7 MiB 131.7 MiB 0.157 CRC64 data/train.federal_register.jsonl.xz
396 396 43.9 MiB 175.7 MiB 0.250 CRC64 data/train.fmshrc.jsonl.xz
1 1 73.4 MiB 341.7 MiB 0.215 CRC64 data/train.founding_docs.jsonl.xz
1 1 324.2 KiB 1,459.4 KiB 0.222 CRC64 data/train.frcp.jsonl.xz
1 1 116.1 KiB 484.9 KiB 0.239 CRC64 data/train.fre.jsonl.xz
1 1 297.3 KiB 1,245.0 KiB 0.239 CRC64 data/train.ftc_advisory_opinions.jsonl.xz
2,084 2,083 13.4 MiB 42.2 MiB 0.318 CRC64 data/train.hhs_alj.jsonl.xz
1 1 29.5 MiB 157.4 MiB 0.188 CRC64 data/train.ijc.jsonl.xz
442 442 7,904.4 KiB 35.8 MiB 0.216 CRC64 data/train.irs_legal_advice_memos.jsonl.xz
658 658 3,403.1 KiB 10.6 MiB 0.314 CRC64 data/train.medicaid_policy_guidance.jsonl.xz
1 1 170.7 MiB 788.9 MiB 0.216 CRC64 data/train.nlrb_decisions.jsonl.xz
1 1 218.4 MiB 1,580.3 MiB 0.138 CRC64 data/train.oig.jsonl.xz
1 1 5,857.4 KiB 31.5 MiB 0.182 CRC64 data/train.olc_memos.jsonl.xz
1 1 58.6 MiB 234.5 MiB 0.250 CRC64 data/train.r_legaldvice.jsonl.xz
1,639 1,639 43.7 MiB 188.1 MiB 0.232 CRC64 data/train.resource_contracts.jsonl.xz
1 1 242.6 MiB 1,241.6 MiB 0.195 CRC64 data/train.scotus_docket_entries.jsonl.xz
1 1 68.5 MiB 323.2 MiB 0.212 CRC64 data/train.scotus_oral.jsonl.xz
10,805 10,805 40.7 MiB 118.4 MiB 0.344 CRC64 data/train.sec.jsonl.xz
1 1 705.0 MiB 5,019.9 MiB 0.140 CRC64 data/train.state_code.jsonl.xz
1 1 75.2 MiB 540.8 MiB 0.139 CRC64 data/train.taxrulings.jsonl.xz
1 1 273.6 KiB 1,318.5 KiB 0.207 CRC64 data/train.tos.jsonl.xz
1 1 22.6 MiB 108.1 MiB 0.209 CRC64 data/train.undebates.jsonl.xz
1 1 167.6 MiB 1,119.6 MiB 0.150 CRC64 data/train.us_bills.jsonl.xz
1 1 25.3 MiB 196.1 MiB 0.129 CRC64 data/train.uscode.jsonl.xz
1 1 1,713.2 MiB 33.7 GiB 0.050 CRC64 data/train.uspto_oab.jsonl.xz
54 54 2,960.9 KiB 11.0 MiB 0.264 CRC64 data/validation.acus_reports.jsonl.xz
1 1 1,024.1 MiB 6,797.1 MiB 0.151 CRC64 data/validation.atticus_contracts.0.jsonl.xz
1 1 374.6 MiB 2,471.7 MiB 0.152 CRC64 data/validation.atticus_contracts.1.jsonl.xz
1 1 523.0 MiB 4,258.9 MiB 0.123 CRC64 data/validation.bva.jsonl.xz
1 1 9.8 MiB 50.5 MiB 0.195 CRC64 data/validation.canadian_decisions.jsonl.xz
1 1 4,281.5 KiB 19.1 MiB 0.219 CRC64 data/validation.cc_casebooks.jsonl.xz
1 1 1,532.6 KiB 19.6 MiB 0.077 CRC64 data/validation.cfpb_cc.jsonl.xz
1 1 23.3 MiB 190.4 MiB 0.122 CRC64 data/validation.cfr.jsonl.xz
1 1 347.4 MiB 1,620.7 MiB 0.214 CRC64 data/validation.congressional_hearings.jsonl.xz
1 1 1,102.4 KiB 6,733.0 KiB 0.164 CRC64 data/validation.constitutions.jsonl.xz
1 1 1,024.1 MiB 10.7 GiB 0.094 CRC64 data/validation.courtlistenerdocketentries.0.jsonl.xz
1 1 473.7 MiB 5,225.2 MiB 0.091 CRC64 data/validation.courtlistenerdocketentries.1.jsonl.xz
1 1 953.7 MiB 4,391.3 MiB 0.217 CRC64 data/validation.courtlisteneropinions.0.jsonl.xz
1 1 953.7 MiB 4,406.9 MiB 0.216 CRC64 data/validation.courtlisteneropinions.1.jsonl.xz
1 1 953.8 MiB 4,436.7 MiB 0.215 CRC64 data/validation.courtlisteneropinions.2.jsonl.xz
1 1 953.7 MiB 4,476.9 MiB 0.213 CRC64 data/validation.courtlisteneropinions.3.jsonl.xz
1 1 953.7 MiB 4,618.0 MiB 0.207 CRC64 data/validation.courtlisteneropinions.4.jsonl.xz
1 1 238.5 MiB 1,147.4 MiB 0.208 CRC64 data/validation.courtlisteneropinions.5.jsonl.xz
100 100 1,778.7 KiB 7,371.5 KiB 0.241 CRC64 data/validation.doj_guidance.jsonl.xz
1 1 13.8 MiB 101.5 MiB 0.136 CRC64 data/validation.dol_ecab.jsonl.xz
1 1 4,132.1 KiB 20.8 MiB 0.194 CRC64 data/validation.echr.jsonl.xz
174 173 490.5 KiB 1,564.9 KiB 0.313 CRC64 data/validation.ed_policy_guidance.jsonl.xz
1 1 453.6 MiB 2,978.9 MiB 0.152 CRC64 data/validation.edgar.jsonl.xz
1 1 1,340.0 KiB 6,294.8 KiB 0.213 CRC64 data/validation.eoir.jsonl.xz
1 1 49.1 MiB 393.7 MiB 0.125 CRC64 data/validation.eurlex.jsonl.xz
1 1 17.0 MiB 79.0 MiB 0.215 CRC64 data/validation.euro_parl.jsonl.xz
1 1 103.7 KiB 547.9 KiB 0.189 CRC64 data/validation.examoutlines.jsonl.xz
1 1 7,419.0 KiB 45.7 MiB 0.158 CRC64 data/validation.federal_register.jsonl.xz
120 120 13.5 MiB 53.9 MiB 0.250 CRC64 data/validation.fmshrc.jsonl.xz
1 1 25.3 MiB 113.2 MiB 0.224 CRC64 data/validation.founding_docs.jsonl.xz
1 1 63.5 KiB 248.8 KiB 0.255 CRC64 data/validation.frcp.jsonl.xz
1 1 58.4 KiB 226.7 KiB 0.257 CRC64 data/validation.fre.jsonl.xz
1 1 117.4 KiB 419.1 KiB 0.280 CRC64 data/validation.ftc_advisory_opinions.jsonl.xz
722 721 4,900.2 KiB 15.1 MiB 0.318 CRC64 data/validation.hhs_alj.jsonl.xz
1 1 10.0 MiB 52.3 MiB 0.191 CRC64 data/validation.ijc.jsonl.xz
161 161 3,791.0 KiB 17.7 MiB 0.209 CRC64 data/validation.irs_legal_advice_memos.jsonl.xz
214 214 1,101.1 KiB 3,411.1 KiB 0.323 CRC64 data/validation.medicaid_policy_guidance.jsonl.xz
1 1 55.8 MiB 257.8 MiB 0.217 CRC64 data/validation.nlrb_decisions.jsonl.xz
1 1 80.0 MiB 603.7 MiB 0.132 CRC64 data/validation.oig.jsonl.xz
1 1 1,826.2 KiB 9,874.6 KiB 0.185 CRC64 data/validation.olc_memos.jsonl.xz
1 1 19.7 MiB 78.7 MiB 0.251 CRC64 data/validation.r_legaldvice.jsonl.xz
584 584 15.3 MiB 63.5 MiB 0.241 CRC64 data/validation.resource_contracts.jsonl.xz
1 1 86.4 MiB 422.5 MiB 0.204 CRC64 data/validation.scotus_docket_entries.jsonl.xz
1 1 23.1 MiB 109.0 MiB 0.212 CRC64 data/validation.scotus_oral.jsonl.xz
3,559 3,559 13.0 MiB 37.7 MiB 0.344 CRC64 data/validation.sec.jsonl.xz
1 1 371.8 MiB 2,678.4 MiB 0.139 CRC64 data/validation.state_code.jsonl.xz
1 1 24.8 MiB 177.4 MiB 0.140 CRC64 data/validation.taxrulings.jsonl.xz
1 1 92.7 KiB 381.6 KiB 0.243 CRC64 data/validation.tos.jsonl.xz
1 1 7,705.6 KiB 35.5 MiB 0.212 CRC64 data/validation.undebates.jsonl.xz
1 1 53.8 MiB 356.3 MiB 0.151 CRC64 data/validation.us_bills.jsonl.xz
1 1 15.2 MiB 117.5 MiB 0.129 CRC64 data/validation.uscode.jsonl.xz
1 1 885.5 MiB 11.2 GiB 0.077 CRC64 data/validation.uspto_oab.jsonl.xz
-------------------------------------------------------------------------------
22,839 22,833 41.0 GiB 291.5 GiB 0.141 CRC64 119 files
```
### Data Fields
- text: the document text
- created_timestamp: If the original source provided a timestamp when the document was created we provide this as well. Note, these may be inaccurate. For example CourtListener case opinions provide the timestamp of when it was uploaded to CourtListener not when the opinion was published. We welcome pull requests to correct this field if such inaccuracies are discovered.
- downloaded_timestamp: When the document was scraped.
- url: the source url
### Data Splits
There is a train/validation split for each subset of the data. 75%/25%. Note, we do not use the validation set for any downstream tasks nor do we filter out any data from downstream tasks. Please filter as needed before training models or feel free to use a different dataset split.
## Dataset Creation
### Curation Rationale
We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives. As such, data sources are curated to inform: (1) legal analysis, knowledge, or understanding; (2) argument formation; (3) privacy filtering standards. Sources like codes and laws tend to inform (1). Transcripts and court filings tend to inform (2). Opinions tend to inform (1) and (3).
### Source Data
#### Initial Data Collection and Normalization
We do not normalize the data, but we provide dataset creation code and relevant urls in https://github.com/Breakend/PileOfLaw
#### Who are the source language producers?
Varied (see sources above).
### Personal and Sensitive Information
This dataset may contain personal and sensitive information. However, this has been previously filtered by the relevant government and federal agencies that weigh the harms of revealing this information against the benefits of transparency. If you encounter something particularly harmful, please file a takedown request with the upstream source and notify us in the communities tab. We will then remove the content. We cannot enable more restrictive licensing because upstream sources may restrict using a more restrictive license. However, we ask that all users of this data respect the upstream licenses and restrictions. Per the standards of CourtListener, we do not allow indexing of this data by search engines and we ask that others do not also. Please do not turn on anything that allows the data to be easily indexed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset will provide more mechanisms for doing data work. As we describe in the paper, the internal variation allows contextual privacy rules to be learned. If robust mechanisms for this are developed they can applied more broadly. This dataset can also potentially be used for legal language model pretraining. As discussed in ``On the Opportunities and Risks of Foundation Models'', legal language models can help improve access to justice in various ways. But they can also be used in potentially harmful ways. While such models are not ready for most production environments and are the subject of significant research, we ask that model creators using this data, particularly when creating generative models, consider the impacts of their model and make a good faith effort to weigh the benefits against the harms of their method. Our license and many of the sub-licenses also restrict commercial usage.
### Discussion of Biases
The data reflects the biases of governments and courts. As we discuss in our work, these can be significant, though more recent text will likely be less overtly toxic. Please see the above statement and embark on any model uses responsibly.
### Other Known Limitations
We mainly focus on U.S. and English-speaking legal sources, though we include some European and Canadian resources.
## Additional Information
### Licensing Information
CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International. But individual sources may have other licenses. See paper for details. Some upstream data sources request that indexing be disabled. As such please **do not re-host any data in a way that can be indexed by search engines.**
### No Representations
We do not make any representation that the legal information provided here is accurate. It is meant for research purposes only. For the authoritative and updated source of information please refer directly to the governing body which provides the latest laws, rules, and regulations relevant to you.
### DMCA Takedown Requests
Pile of Law follows the notice and takedown procedures in the Digital Millennium Copyright Act (DMCA), 17 U.S.C. Section 512.
If you believe content on Pile of Law violates your copyright, please immediately notify its operators by sending a message with the information described below. Please use the subject "Copyright" in your message. If Pile of Law's operators act in response to an infringement notice, they will make a good-faith attempt to contact the person who contributed the content using the most recent email address that person provided to Pile of Law.
Under the DMCA, you may be held liable for damages based on material misrepresentations in your infringement notice. You must also make a good-faith evaluation of whether the use of your content is a fair use, because fair uses are not infringing. See 17 U.S.C. Section 107 and Lenz v. Universal Music Corp., No. 13-16106 (9th Cir. Sep. 14, 2015). If you are not sure if the content you want to report infringes your copyright, you should first contact a lawyer.
The DMCA requires that all infringement notices must include all of the following:
+ A signature of the copyright owner or a person authorized to act on the copyright owner's behalf
+ An identification of the copyright claimed to have been infringed
+ A description of the nature and location of the material that you claim to infringe your copyright, in sufficient detail to allow Pile of Law to find and positively identify that material
+ Your name, address, telephone number, and email address
+ A statement that you believe in good faith that the use of the material that you claim to infringe your copyright is not authorized by law, or by the copyright owner or such owner's agent
+ A statement, under penalty of perjury, that all of the information contained in your infringement notice is accurate
+ A statement, under penalty of perjury, that you are either the copyright owner or a person authorized to act on their behalf.
Pile of Law will respond to all DMCA-compliant infringement notices, including, as required or appropriate, by removing the offending material or disabling all links to it.
All received infringement notices may be posted in full to the Lumen database (previously known as the Chilling Effects Clearinghouse).
All takedown requests with the above information should be posted to the Communities tab.
This removal notice has been modified from the (CourtListener DMCA takedown notice)[https://www.courtlistener.com/terms/].
### Citation Information
For a citation to this work:
```
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
```
Since this dataset also includes several other data sources with citations, please refer to our paper and cite the additional relevant work in addition to our own work. | pile-of-law/pile-of-law | [
"task_categories:fill-mask",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2207.00220",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": [], "task_categories": ["fill-mask"], "task_ids": ["masked-language-modeling"], "pretty_name": "pile-of-law", "viewer": false} | 2023-01-08T03:10:35+00:00 |
3568620df66b9812dd9675c0a73c2c846f400bea |
# Dataset Card for PMC Open Access Subset
## 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.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [PubMed Central](mailto:[email protected])
### Dataset Summary
The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
license terms that allow reuse. Not all articles in PMC are available for text mining and other reuse, many have
copyright protection, however articles in the PMC Open Access Subset are made available under Creative Commons or
similar licenses that generally allow more liberal redistribution and reuse than a traditional copyrighted work. The
PMC Open Access Subset is one part of the PMC Article Datasets.
Within the PMC Open Access Subset, there are three groupings:
- Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses
- Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
- Other - no machine-readable Creative Commons license, no license, or a custom license.
### Supported Tasks and Leaderboards
- Language modeling
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{
'text': "==== Front\nPLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000005Research ArticleGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyPlasmodiumThe Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum\n P. falciparum IDC TranscriptomeBozdech Zbynek \n1\nLlinás Manuel \n1\nPulliam Brian Lee \n1\nWong Edith D \n1\nZhu Jingchun \n2\nDeRisi Joseph L [email protected]\n1\n1Department of Biochemistry and Biophysics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America2Department of Biological and Medical Informatics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America10 2003 18 8 2003 18 8 2003 1 1 e512 6 2003 25 7 2003 Copyright: ©2003 Bozdech et al.2003This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.\nMicroarray Analysis: Genome-Scale Hypothesis Scanning \n\nMonitoring Malaria: Genomic Activity of the Parasite in Human Blood Cells \n\nPlasmodium falciparum is the causative agent of the most burdensome form of human malaria, affecting 200–300 million individuals per year worldwide. The recently sequenced genome of P. falciparum revealed over 5,400 genes, of which 60% encode proteins of unknown function. Insights into the biochemical function and regulation of these genes will provide the foundation for future drug and vaccine development efforts toward eradication of this disease. By analyzing the complete asexual intraerythrocytic developmental cycle (IDC) transcriptome of the HB3 strain of P. falciparum, we demonstrate that at least 60% of the genome is transcriptionally active during this stage. Our data demonstrate that this parasite has evolved an extremely specialized mode of transcriptional regulation that produces a continuous cascade of gene expression, beginning with genes corresponding to general cellular processes, such as protein synthesis, and ending with Plasmodium-specific functionalities, such as genes involved in erythrocyte invasion. The data reveal that genes contiguous along the chromosomes are rarely coregulated, while transcription from the plastid genome is highly coregulated and likely polycistronic. Comparative genomic hybridization between HB3 and the reference genome strain (3D7) was used to distinguish between genes not expressed during the IDC and genes not detected because of possible sequence variations...
'pmid': '12929205',
'accession_id': 'PMC176545',
'license': 'CC BY',
'last_updated': '2021-01-05 08:21:03',
'retracted': 'no',
'citation': 'PLoS Biol. 2003 Oct 18; 1(1):e5'
}
```
### Data Fields
- `text`: Text content.
- `pmid`: PubMed ID.
- `accession_id`: Unique identifier for a sequence record.
- `license`: License type.
- `last_updated`: Date of last update.
- `retracted`: Whether retracted or not.
- `citation`: Citation reference.
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License terms vary. Please refer to the license statement in each article for specific terms of use.
Within the PMC Open Access Subset, there are three groupings based on available license terms:
- Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses;
- Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
- Other - no machine-readable Creative Commons license, no license, or a custom license.
### Citation Information
```
PMC Open Access Subset [Internet]. Bethesda (MD): National Library of Medicine. 2003 - [cited YEAR MONTH DAY]. Available from https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
| pmc/open_access | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"license:cc-by-4.0",
"license:cc-by-sa-4.0",
"license:cc-by-nd-4.0",
"license:cc-by-nc-4.0",
"license:cc-by-nc-sa-4.0",
"license:cc-by-nc-nd-4.0",
"license:other",
"license:unknown",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc0-1.0", "cc-by-4.0", "cc-by-sa-4.0", "cc-by-nd-4.0", "cc-by-nc-4.0", "cc-by-nc-sa-4.0", "cc-by-nc-nd-4.0", "other", "unknown"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "PMC Open Access"} | 2023-03-14T17:29:59+00:00 |
0bc9df68e92fd6bb54176bf7eb29e2b9e97cb218 |
# Dataset Card for Multilingual Spoken Words
## 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://mlcommons.org/en/multilingual-spoken-words/
- **Repository:** https://github.com/harvard-edge/multilingual_kws
- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken
words in 50 languages collectively spoken by over 5 billion people, for academic
research and commercial applications in keyword spotting and spoken term search,
licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords,
totaling 23.4 million 1-second spoken examples (over 6,000 hours). The dataset
has many use cases, ranging from voice-enabled consumer devices to call center
automation. This dataset is generated by applying forced alignment on crowd-sourced sentence-level
audio to produce per-word timing estimates for extraction.
All alignments are included in the dataset.
Data is provided in two formats: `wav` (16KHz) and `opus` (48KHz). Default configurations look like
`"{lang}_{format}"`, so to load, for example, Tatar in wav format do:
```python
ds = load_dataset("MLCommons/ml_spoken_words", "tt_wav")
```
To download multiple languages in a single dataset pass list of languages to `languages` argument:
```python
ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"])
```
To download a specific format pass it to the `format` argument (default format is `wav`):
```python
ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"], format="opus")
```
Note that each time you provide different sets of languages,
examples are generated from scratch even if you already provided one or several of them before
because custom configurations are created each time (the data is **not** redownloaded though).
### Supported Tasks and Leaderboards
Keyword spotting, Spoken term search
### Languages
The dataset is multilingual. To specify several languages to download pass a list of them to the
`languages` argument:
```python
ds = load_dataset("MLCommons/ml_spoken_words", languages=["ar", "tt", "br"])
```
The dataset contains data for the following languages:
Low-resourced (<10 hours):
* Arabic (0.1G, 7.6h)
* Assamese (0.9M, 0.1h)
* Breton (69M, 5.6h)
* Chuvash (28M, 2.1h)
* Chinese (zh-CN) (42M, 3.1h)
* Dhivehi (0.7M, 0.04h)
* Frisian (0.1G, 9.6h)
* Georgian (20M, 1.4h)
* Guarani (0.7M, 1.3h)
* Greek (84M, 6.7h)
* Hakha Chin (26M, 0.1h)
* Hausa (90M, 1.0h)
* Interlingua (58M, 4.0h)
* Irish (38M, 3.2h)
* Latvian (51M, 4.2h)
* Lithuanian (21M, 0.46h)
* Maltese (88M, 7.3h)
* Oriya (0.7M, 0.1h)
* Romanian (59M, 4.5h)
* Sakha (42M, 3.3h)
* Slovenian (43M, 3.0h)
* Slovak (31M, 1.9h)
* Sursilvan (61M, 4.8h)
* Tamil (8.8M, 0.6h)
* Vallader (14M, 1.2h)
* Vietnamese (1.2M, 0.1h)
Medium-resourced (>10 & <100 hours):
* Czech (0.3G, 24h)
* Dutch (0.8G, 70h)
* Estonian (0.2G, 19h)
* Esperanto (1.3G, 77h)
* Indonesian (0.1G, 11h)
* Kyrgyz (0.1G, 12h)
* Mongolian (0.1G, 12h)
* Portuguese (0.7G, 58h)
* Swedish (0.1G, 12h)
* Tatar (4G, 30h)
* Turkish (1.3G, 29h)
* Ukrainian (0.2G, 18h)
Hig-resourced (>100 hours):
* Basque (1.7G, 118h)
* Catalan (8.7G, 615h)
* English (26G, 1957h)
* French (9.3G, 754h)
* German (14G, 1083h)
* Italian (2.2G, 155h)
* Kinyarwanda (6.1G, 422h)
* Persian (4.5G, 327h)
* Polish (1.8G, 130h)
* Russian (2.1G, 137h)
* Spanish (4.9G, 349h)
* Welsh (4.5G, 108h)
## Dataset Structure
### Data Instances
```python
{'file': 'абзар_common_voice_tt_17737010.opus',
'is_valid': True,
'language': 0,
'speaker_id': '687025afd5ce033048472754c8d2cb1cf8a617e469866bbdb3746e2bb2194202094a715906f91feb1c546893a5d835347f4869e7def2e360ace6616fb4340e38',
'gender': 0,
'keyword': 'абзар',
'audio': {'path': 'абзар_common_voice_tt_17737010.opus',
'array': array([2.03458695e-34, 2.03458695e-34, 2.03458695e-34, ...,
2.03458695e-34, 2.03458695e-34, 2.03458695e-34]),
'sampling_rate': 48000}}
```
### Data Fields
* file: strinrelative audio path inside the archive
* is_valid: if a sample is valid
* language: language of an instance. Makes sense only when providing multiple languages to the
dataset loader (for example, `load_dataset("ml_spoken_words", languages=["ar", "tt"])`)
* speaker_id: unique id of a speaker. Can be "NA" if an instance is invalid
* gender: speaker gender. Can be one of `["MALE", "FEMALE", "OTHER", "NAN"]`
* keyword: word spoken in a current sample
* audio: a dictionary containing the relative path to the 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 data for each language is splitted into train / validation / test parts.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data comes form Common Voice dataset.
#### 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
he dataset consists of people who have donated their voice online.
You agree to not attempt to determine the identity of speakers.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) and can be used for academic
research and commercial applications in keyword spotting and spoken term search.
### Citation Information
```
@inproceedings{mazumder2021multilingual,
title={Multilingual Spoken Words Corpus},
author={Mazumder, Mark and Chitlangia, Sharad and Banbury, Colby and Kang, Yiping and Ciro, Juan Manuel and Achorn, Keith and Galvez, Daniel and Sabini, Mark and Mattson, Peter and Kanter, David and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
| MLCommons/ml_spoken_words | [
"task_categories:audio-classification",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:extended|common_voice",
"language:ar",
"language:as",
"language:br",
"language:ca",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:de",
"language:dv",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fr",
"language:fy",
"language:ga",
"language:gn",
"language:ha",
"language:ia",
"language:id",
"language:it",
"language:ka",
"language:ky",
"language:lt",
"language:lv",
"language:mn",
"language:mt",
"language:nl",
"language:or",
"language:pl",
"language:pt",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sk",
"language:sl",
"language:sv",
"language:ta",
"language:tr",
"language:tt",
"language:uk",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"other-keyword-spotting",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["ar", "as", "br", "ca", "cnh", "cs", "cv", "cy", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fr", "fy", "ga", "gn", "ha", "ia", "id", "it", "ka", "ky", "lt", "lv", "mn", "mt", "nl", "or", "pl", "pt", "rm", "ro", "ru", "rw", "sah", "sk", "sl", "sv", "ta", "tr", "tt", "uk", "vi", "zh"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|common_voice"], "task_categories": ["audio-classification"], "task_ids": [], "pretty_name": "Multilingual Spoken Words", "language_bcp47": ["fy-NL", "ga-IE", "rm-sursilv", "rm-vallader", "sv-SE", "zh-CN"], "tags": ["other-keyword-spotting"]} | 2022-12-06T11:11:02+00:00 |
c2280ffaf80629ba1b1be5dad6b08b93cd395371 | # Dataset Card for [pritamdeka/cord-19-abstract]
## Dataset Description
### Dataset Summary
This is a modified [cord19](https://huggingface.co/datasets/cord19) dataset which contains only the abstract field. This can be used directly for language modelling tasks.
### Languages
English
### Citation Information
```
@article{Wang2020CORD19TC,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and
Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and
D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| pritamdeka/cord-19-abstract | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-02-01T23:58:54+00:00 |
53f4c17ae8f06d9aabeae7230194e1528f9cd7aa | # Dataset Card for [pritamdeka/cord-19-fulltext]
## Dataset Description
### Dataset Summary
This is a modified [cord19](https://huggingface.co/datasets/cord19) dataset which contains only the fulltext field. This can be used directly for language modelling tasks.
### Languages
English
### Citation Information
```
@article{Wang2020CORD19TC,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and
Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and
D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
| pritamdeka/cord-19-fulltext | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-02-05T02:29:13+00:00 |
d1d759e8c2ab06e5958a2054d1987ea046f261c8 | priya3301/Graduation_admission | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-05-14T14:42:30+00:00 |
|
be260110deb051db63b66038bbb00a5ebfe996c6 | prk/testsq | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-02-25T13:52:13+00:00 |
|
d4ab94cacce28137358c0ad2de765e2fafa98653 | Common Voice 7
วันที่ 2021-07-21
ขนาด5 GB
รุ่น th_255h_2021-07-21
จำนวนชั่วโมงทั้งหมดที่ตรวจสอบ133
จำนวนชั่วโมงโดยรวม255
สัญญาอนุญาตCC-0
จำนวนเสียง7,212
รูปแบบเสียงMP3
| project2you/asr | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-12-02T08:08:08+00:00 |
aee489ef9560a5eb8adbf8c29317c0d43dc2069d |
# Dataset Card for AnCora-Ca-NER
## Dataset Description
- **Website:** https://zenodo.org/record/5036651
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Paper:** [AnCora: Multilevel Annotated Corpora for Catalan and Spanish](http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf)
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
This is a dataset for Named Entity Recognition (NER) in Catalan. It adapts <a href="http://clic.ub.edu/corpus/">AnCora corpus</a> for Machine Learning and Language Model evaluation purposes.
[AnCora corpus](http://clic.ub.edu/corpus/) is used under [CC-by](https://creativecommons.org/licenses/by/4.0/) licence.
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of the [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
### Supported Tasks and Leaderboards
Named Entities Recognition, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three two-column files, one for each split.
<pre>
Fundació B-ORG
Privada I-ORG
Fira I-ORG
de I-ORG
Manresa I-ORG
ha O
fet O
un O
balanç O
de O
l' O
activitat O
del O
Palau B-LOC
Firal I-LOC
</pre>
### Data Fields
Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one.
### Data Splits
We took the original train, dev and test splits from the [UD version of the corpus](https://huggingface.co/datasets/universal_dependencies)
- train: 10,630 examples
- validation: 1,429 examples
- test: 1,528 examples
## 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
[AnCora](http://clic.ub.edu/corpus/) consists of a CatCAalan corpus (AnCora-CA) and a Spanish corpus (AnCora-ES), each of them of 500,000 tokens (some multi-word). The corpora are annotated for linguistic phenomena at different levels.
AnCora corpus is mainly based on newswire texts. For more information, refer to Taulé, M., M.A. Martí, M. Recasens (2009): <a href="http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf">"AnCora: Multilevel Annotated Corpora for Catalan and Spanish”</a>, Proceedings of 6th International Conference on language Resources and Evaluation.
#### Who are the source language producers?
Catalan [AnCora corpus](http://clic.ub.edu/corpus/) is compiled from articles from the following news outlets: <a href="https://www.efe.com">EFE</a>, <a href="https://www.acn.cat">ACN</a>, <a href="https://www.elperiodico.cat/ca/">El Periodico</a>.
### Annotations
#### Annotation process
We adapted the NER labels from [AnCora corpus](http://clic.ub.edu/corpus/) to a token-per-line, multi-column format.
#### Who are the annotators?
Original annotators from [AnCora corpus](http://clic.ub.edu/corpus/).
### Personal and Sensitive Information
No personal or sensitive information included.
## 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
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
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/en/inici/index.html) 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/4.0/">Attribution 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.4529299)
### Contributions
[N/A] | projecte-aina/ancora-ca-ner | [
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-4.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": [], "task_ids": [], "pretty_name": "ancora-ca-ner"} | 2023-09-13T11:44:29+00:00 |
8d4bc89595621e6bcad68f150421425aa3bccef1 |
# 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:[email protected])
### 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-ES`).
## 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 ([email protected])
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] | projecte-aina/casum | [
"task_categories:summarization",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-4.0",
"arxiv:2202.06871",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["ca"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "casum"} | 2023-09-13T11:49:03+00:00 |
9d7fe8d6562025d7c09a53f87502e63754a176da |
# Dataset Card for Catalan General 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/5483031
- **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:** [[email protected]]([email protected])
### Dataset Summary
The Catalan General Crawling Corpus is a 435-million-token web corpus of Catalan built from the web. It has been obtained by crawling the 500 most popular .cat and .ad domains during July 2020. It consists of 434,817,705 tokens, 19,451,691 sentences and 1,016,114 documents. Documents are separated by single new lines. It is a subcorpus of the Catalan Textual Corpus.
This work is licensed under a [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'text': 'Reduïu els costos dels processos administratius al vostre organisme públic\nEviteu els desplaçaments i pèrdua de temps als ciutadans en les seves gestions\nOferiu una administració més transparent a
ciutadans i empreses\nEns grans i petits experimenten aquesta transformació amb èxit, gràcies al suport de l\'AOC\nDepartament de Sistemes d\'Informació i Processos\n" Via Oberta ens ha permès fer efectiu el d
ret dels ciutadans a no aportar documents, eliminant paper i simplificant procediments"\n" e.FACT proporciona informació indispensable per a la realització de les auditories del registre comptable de factures d
e les Administracions Públiques Catalanes"\nCoordinador del departament d\'Informàtica\n"El servei VIA OBERTA és el que ha aportat majors avantatges per als ciutadans"\n"Amb l\' e-NOTUM hem escurçat els procedi
ments en 12 dies, quasi un 40% menys!"\nCoordinadora d\'organització de persones i e-administració\n" Via Oberta ofereix millores per als ciutadans al no haver d\'aportar cap document"\nResponsable d\'Informàti
ca i Administració Electrònica\n" e-TRAM ens ha permès implantar un servei de tramitació electrònica per als ciutadans de forma ràpida, senzilla i amb un cost reduït"\n"Els municipis amb pocs habitants trobem e
n els serveis de l\'AOC la gratuïtat i la comoditat necessàries per dur a terme el nostre dia a dia"\n"Les T-CAT han permès incorporar de forma segura la signatura electrònica dins dels nostres procediments afa
vorint la transformació digital de la nostra activitat"\nCap de Departament de Sistemes i Tecnologies de la Informació\n"Amb el desplegament de l\' idCAT hem apropat l\'Ajuntament a la ciutadania"\n"Mitjançant
els serveis de Govern Obert de l\'AOC hem pogut fer fàcil el que sembla difícil"\n"Al tauler electrònic pots penjar fins i tot el projecte sencer i al final et permet fer també la diligència"\nÀrea de Promoció
Econòmica, Administració i Hisenda\n"El Sobre Digital i la PSCP han aconseguit una comunió senzilla entre empreses i administració per universalitzar la compra pública electrònica"\n"L\' e-SET és la implantació
d\'un nou sistema de treball que facilita la feina del dia a dia"\nCap del servei de contractació i compres\n"El Sobre Digital, una experiència imprescindible per a la bona administració amb estalvi de recurso
s i millora de la seguretat jurídica i la transparència"\nÀrea d\'Organització i Administració Electrònica\n"El desplegament de la valisa electrònica ha estat clau en el procés de transformació digital dels nos
tres procediments interns"\n"L\' Hèstia permet el treball en temps real i des de qualsevol lloc, així com sistematitzar la pràctica professional, recollir la informació ordenadament i amb el mateix llenguatge"\
nConsulta els materials del Congrés de Govern Digital 2019\nGoverns transparents, fluids, dinàmics, líquids... un bon lema pel principal objectiu de la governança del segle XXI: democratitzar-ho tot.\nConfluènc
ies, rius, cooperació.\nCatalunya, Mediterrània, mar de drets.\nA favor: totes les Administracions movent-se per posar-se al dia i millorar, tot aprofitant la revolució digital.\nEn contra: quants cops estem re
inventant la roda i quantes quantes oportunitats perdudes de fer-ho una única vegada i de forma coordinada i col·laborativa?\n"La transparència és una oportunitat.\nHem de perdre tota por a explicar què fem": l
a conclusió de la taula d\'alcaldies de la Jornada de Govern Obert pic.twitter.com/ERbgLSIXZM\nEl director general de Participació Ciutadana ens convida a transformar les administracions públiques a partir de l
a participació ciutadana\nEns cal que allò que preocupa i ocupa els governants formi part d\'allò en què participa la ciutadania pic.twitter.com/NwQr4EZSCS: "A moltes institucions encara els sona xinés això de
les dades obertes i la transparència.\nDe que serveix que hi hagi un portal, si llavors no hi ha dades?\nLlavors l\'accés a la informació pels periodistes és molt parcial".\nOferim eines que, conjuntament amb l
a metodologia i el suport necessari, fan possible l\'assoliment d\'un govern digital\nPosem al vostre abast tot el coneixement: formació, guies, normatives, etc.\nTenim eines per gestionar àgilment part del pro
cés administratiu del vostre ens\nEl nostre equip farà tot el possible per resoldre les vostres incidències\nSabem que es tracta d\'una decisió molt important per al vostre ens i és per això que us ho volem pos
ar fàcil.\nLa selecció de l\'actualitat d\'Administració Oberta a la vostra safata.'
}
```
### 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 500 most popular .cat and .ad 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 multiple web pages in Catalan.
### 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 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 ([email protected])
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 [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
### 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.
| projecte-aina/catalan_general_crawling | [
"task_categories:fill-mask",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"license:cc-by-4.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "task_ids": [], "pretty_name": "Catalan General Crawling"} | 2023-11-25T04:56:29+00:00 |
30d1943cb3e086d51f35c91eb166f0c4ccc9587e |
# 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:** [[email protected]]([email protected])
### 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.
This work is licensed under a [Creative Commons CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) license.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-ES`).
## 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 ([email protected])
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. | projecte-aina/catalan_government_crawling | [
"task_categories:fill-mask",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ca",
"license:cc0-1.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ca"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "task_ids": [], "pretty_name": "Catalan Government Crawling"} | 2023-11-25T05:15:45+00:00 |
8f4c4c3062f52d1b76ada322e6a861a6895c05c6 |
# Dataset Card for Catalan Textual Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://zenodo.org/record/4519349
- **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:** [[email protected]]([email protected])
### Dataset Summary
The Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources.
It consists of 1,758,388,896 tokens, 73,172,152 sentences, and 12,556,365 documents. Documents are separated by single new lines. These boundaries have been preserved as long as the license allowed it.
This work is licensed under a [Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{'text': "L'operatiu continuarà durant aquest divendres."}
```
### 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 Catalan Textual Corpus is a 1760-million-token web corpus of Catalan built from several sources: existing corpora such as DOGC, CaWac (non-dedup version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia, and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
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 original data comes from various sources: existing corpora and crawlings from public websites.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
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 unreliable web pages and multilingual crawled corpora, 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 ([email protected])
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 Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/).
### 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. | projecte-aina/catalan_textual_corpus | [
"task_categories:fill-mask",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"source_datasets:extended|opus_dogc",
"source_datasets:extended|cawac",
"source_datasets:extended|oscar",
"source_datasets:extended|open_subtitles",
"source_datasets:extended|wikipedia",
"source_datasets:extended|projecte-aina/catalan_general_crawling",
"source_datasets:extended|projecte-aina/catalan_government_crawling",
"language:ca",
"license:cc-by-sa-4.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original", "extended|opus_dogc", "extended|cawac", "extended|oscar", "extended|open_subtitles", "extended|wikipedia", "extended|projecte-aina/catalan_general_crawling", "extended|projecte-aina/catalan_government_crawling"], "task_categories": ["fill-mask"], "task_ids": [], "pretty_name": "Catalan Textual Corpus"} | 2023-11-25T05:11:21+00:00 |
79d37c12b9cc8827e4351521ab8802325c9c3620 |
# 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:[email protected])
### 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-ES`).
## 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 ([email protected])
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.
| projecte-aina/parlament_parla | [
"task_categories:automatic-speech-recognition",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:speaker-identification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ca",
"license:cc-by-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": {"clean": ["10K<n<100K"], "other": ["100K<n<1M"]}, "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition", "text-generation"], "task_ids": ["language-modeling", "speaker-identification"], "pretty_name": "ParlamentParla"} | 2023-09-13T11:38:52+00:00 |
ee61638c447bc59e4f72877e90752cad957ac4fe |
# Dataset Card for STS-ca
## Dataset Description
- **Website:** https://zenodo.org/record/4761434
- **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]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
STS-ca corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
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>.
### Supported Tasks and Leaderboards
This dataset can be used to build and score semantic similarity models in Catalan.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Follows [SemEval challenges](https://www.aclweb.org/anthology/S13-1004.pdf):
* index (int)
* id (str): Unique ID assigned to the sentence pair.
* sentence 1 (str): First sentence of the pair.
* sentence 2 (str): Second sentence of the pair.
* avg (float): Gold truth
#### Example
| index | id | sentence 1 | sentence 2 | avg |
| ------- | ---- | ------------ | ------------ | ----- |
| 19 | ACN2_131 | Els manifestants ocupen l'Imperial Tarraco durant una hora fent jocs de taula | Els manifestants ocupen l'Imperial Tarraco i fan jocs de taula | 4 |
| 21 | TE2_80 | El festival comptarà amb cinc escenaris i se celebrarà entre el 7 i el 9 de juliol al Parc del Fòrum. | El festival se celebrarà el 7 i 8 de juliol al Parc del Fòrum de Barcelona | 3 |
| 23 | Oscar2_609 | Aleshores hi posarem un got de vi i continuarem amb la cocció fins que s'hagi evaporat el vi i ho salpebrarem. | Mentre, hi posarem el vi al sofregit i deixarem coure uns 7/8′, fins que el vi s'evapori. | 3 |
| 25 | Viqui2_48 | L'arboç grec (Arbutus andrachne) és un arbust o un petit arbre dins la família ericàcia. | El ginjoler ("Ziziphus jujuba") és un arbust o arbre petit de la família de les "Rhamnaceae". | 2.75 |
| 27 | ACN2_1072 | Mentre han estat davant la comandància, els manifestants han cridat consignes a favor de la independència i han cantat cançons com 'L'estaca'. | Entre les consignes que han cridat s'ha pogut escoltar càntics com 'els carrers seran sempre nostres' i contínues consignes en favor de la independència. | 3 |
| 28 | Viqui2_587 | Els cinc municipis ocupen una superfície de poc més de 100 km2 i conjuntament sumen una població total aproximada de 3.691 habitants (any 2019). | Té una població d'1.811.177 habitants (2005) repartits en 104 municipis d'una superfície total de 14.001 km2. | 2.67 |
### Data Fields
This dataset follows [SemEval](https://www.aclweb.org/anthology/S13-1004.pdf) challenges formats and conventions.
### Data Splits
- sts_cat_dev_v1.tsv (500 annotated pairs)
- sts_cat_train_v1.tsv (2073 annotated pairs)
- sts_cat_test_v1.tsv (500 annotated pairs)
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
Random sentences were extracted from 3 Catalan subcorpus from the [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs): [ACN](https://www.acn.cat/), [Oscar](https://oscar-corpus.com/) and [Wikipedia](https://ca.wikipedia.org/wiki/Portada).
We generated candidate pairs using a combination of metrics from Doc2Vec, Jaccard and a BERT-like model (“[distiluse-base-multilingual-cased-v2](https://huggingface.co/distilbert-base-multilingual-cased)”). Finally, we manually reviewed the generated pairs to reject non-relevant pairs (identical or ungrammatical sentences, etc.) before providing them to the annotation team.
The average of the four annotations was selected as a “ground truth” for each sentence pair, except when an annotator diverged in more than one unit from the average. In these cases, we discarded the divergent annotation and recalculated the average without it. We also discarded 45 sentence pairs because the annotators disagreed too much.
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
#### Who are the source language producers?
The [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs) is a 1760-million-token web corpus of Catalan built from several sources: existing corpus such as DOGC, CaWac (non-deduplicated version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia; and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
### Annotations
#### Annotation process
We comissioned the manual annotation of the similarity between the sentences of each pair, following the provided guidelines.
#### Who are the annotators?
A team of native language speakers from 2 different companies, working independently.
### 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 ([email protected])
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/en/inici/index.html) 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.4529183)
### Contributions
[N/A]
| projecte-aina/sts-ca | [
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-4.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-scoring", "text-scoring"], "pretty_name": "sts-ca"} | 2023-11-25T05:27:49+00:00 |
feff109328ea5c8d7c90b8ab81d85b20c37f371b |
# Dataset Card for TE-ca
## Dataset Description
- **Website:** https://zenodo.org/record/4761458
- **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]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
TE-ca is a dataset of textual entailment in Catalan, which contains 21,163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral).
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
This work is licensed under an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Example:
<pre>
{
"id": 3247,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "S'acorden unes recomanacions per les persones migrades a Marràqueix",
"label": "0"
},
{
"id": 2825,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "Les persones migrades seran acollides a Marràqueix",
"label": "1"
},
{
"id": 2431,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "L'acord impulsat per l'ONU lluny de tancar-se",
"label": "2"
},
</pre>
### Data Fields
- premise: text
- hypothesis: text related to the premise
- label: relation between premise and hypothesis:
* 0: entailment
* 1: neutral
* 2: contradiction
### Data Splits
* dev.json: 2116 examples
* test.json: 2117 examples
* train.json: 16930 examples
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
Source sentences are extracted from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349) and from [VilaWeb](https://www.vilaweb.cat) newswire.
#### Initial Data Collection and Normalization
12000 sentences from the BSC [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349), together with 6200 headers from the Catalan news site [VilaWeb](https://www.vilaweb.cat), were chosen randomly. We filtered them by different criteria, such as length and stand-alone intelligibility. For each selected text, we commissioned 3 hypotheses (one for each entailment category) to be written by a team of native annotators.
Some sentence pairs were excluded because of inconsistencies.
#### Who are the source language producers?
The Catalan Textual Corpus corpus consists of several corpora gathered from web crawling and public corpora. More information can be found [here](https://doi.org/10.5281/zenodo.4519349).
[VilaWeb](https://www.vilaweb.cat) is a Catalan newswire.
### Annotations
#### Annotation process
We commissioned 3 hypotheses (one for each entailment category) to be written by a team of annotators.
#### Who are the annotators?
Annotators are a team of native language collaborators from two independent companies.
### 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 ([email protected])
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 an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 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.4529183)
| projecte-aina/teca | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-nd-4.0",
"arxiv:2107.07903",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "teca"} | 2023-11-25T05:30:02+00:00 |
2364a2f052ddf75659c7912aa0f3e951b6075b1d |
# Dataset Card for TeCla
## Dataset Description
- **Website:** [Zenodo](https://zenodo.org/record/7334110)
- **Point of Contact:** [Irene Baucells de la Peña]([email protected]), [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
TeCla (Text Classification) is a Catalan News corpus for thematic multi-class Text Classification tasks. The present version (2.0) contains 113.376 articles classified under a hierarchical class structure consisting of a coarse-grained and a fine-grained class. Each of the 4 coarse-grained classes accept a subset of fine-grained ones, 53 in total.
The previous version (1.0.1) can still be found at https://zenodo.org/record/4761505
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three json files, one for each split.
### Data Fields
Each example contains the following 3 fields:
* text: the article text (string)
* label1: the coarse-grained class
* label2: the fine-grained class
#### Example:
<pre>
{"version": "2.0",
"data":
[
{
'sentence': "La setena edició del Festival Fantàstik inclourà les cintes 'Matar a dios' i 'Mandy' i un homenatge a 'Mi vecino Totoro'. Es projectaran 22 curtmetratges seleccionats d'entre més de 500 presentats a nivell internacional. El Centre Cultural de Granollers acull del 8 a l'11 de novembre la setena edició del Festival Fantàstik. El certamen, que s'allargarà un dia, arrencarà amb la projecció de la cinta de Caye Casas i Albert Pide 'Matar a Dios'. Els dos directors estaran presents en la inauguració de la cita. A més, els asssitents podran gaudir de 'Mandy', el darrer treball de Nicolas Cage. Altres llargmetratges seleccionats per aquest any són 'Aterrados' (2017), 'Revenge' (2017), 'A Mata Negra' (2018), 'Top Knot Detective' (2018) i 'La Gran Desfeta' (2018). A més, amb motiu del trentè aniversari de la pel·lícula 'El meu veí Totoro' es durà a terme l'exposició dedicada a aquest film '30 anys 30 artistes' comissariada per Jordi Pastor i Reinaldo Pereira. La mostra '30 anys 30 artistes' recull els treballs de trenta artistes d'estils diferents al voltant de la figura de Totoro i el seu director. Es podrà veure durant els dies de festival i es complementarà amb la projecció de la pel·lícula el diumenge 11 de novembre. Al llarg del festival també es projectaran els 22 curtmetratges prèviament seleccionats d'entre més de 500 presentats a nivell internacional. El millor tindrà una dotació de 1000 euros fruit de la unió de forces amb el Mercat Audiovisual de Catalunya.",
'label1': 'Cultura',
'label2': 'Cinema'
},
...
]
}
</pre>
#### Labels
* label1: 'Societat', 'Política', 'Economia', 'Cultura'
* label2: 'Llengua', 'Infraestructures', 'Arts', 'Parlament', 'Noves tecnologies', 'Castells', 'Successos', 'Empresa', 'Mobilitat', 'Teatre', 'Treball', 'Logística', 'Urbanisme', 'Govern', 'Entitats', 'Finances', 'Govern espanyol', 'Trànsit', 'Indústria', 'Esports', 'Exteriors', 'Medi ambient', 'Habitatge', 'Salut', 'Equipaments i patrimoni', 'Recerca', 'Cooperació', 'Innovació', 'Agroalimentació', 'Policial', 'Serveis Socials', 'Cinema', 'Memòria històrica', 'Turisme', 'Política municipal', 'Comerç', 'Universitats', 'Hisenda', 'Judicial', 'Partits', 'Música', 'Lletres', 'Religió', 'Festa i cultura popular', 'Unió Europea', 'Moda', 'Moviments socials', 'Comptes públics', 'Immigració', 'Educació', 'Gastronomia', 'Meteorologia', 'Energia'
### Data Splits
Train, development and test splits were created in a stratified fashion, following a 0.8, 0.05 and 0.15 proportion, respectively. The sizes of each split are the following:
* train.json: 90700 examples
* dev.json: 5669 examples
* test.json: 17007 examples
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The source data are crawled articles from the Catalan News Agency ([Agència Catalana de Notícies, ACN](https://www.acn.cat/)) site.
We crawled 219.586 articles from the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) newswire archive, the latest from October 11, 2020.
From the crawled data, we selected those articles whose 'section' and 'subsection' categories followed the expected codification combinations included in the ACN's style guide and whose 'section' complied the requirements of containing subsections and being thematically founded (in contrast to geographically defined categories such as 'Món' and 'Unió Europea'). The articles originally belonging to the 'Unió Europea' section, which were related to political organisms from the European Union, were included in the 'Política' coarse-grained category (within a fine-grained category named 'Unió Europea') due to its close proximity between some of the original subsections of 'Política' and those of 'Unió Europea', both defined by the specific political organism dealt with in the article.
The text field in each example is a concatenation of the original title, subtitle and body of the article (before the concatenation, both title and subtitle were added a final dot whenever they lacked one). The preprocessing of the texts was minimal and consisted in the removal of the pattern "ACN {location}.-" included before the body in each text as well as newlines originally used to divide the text in paragraphs.
#### Who are the source language producers?
The Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) is a news agency owned by the Catalan government via the public corporation Intracatalònia, SA. It is one of the first digital news agencies created in Europe and has been operating since 1999 (source: [wikipedia](https://en.wikipedia.org/wiki/Catalan_News_Agency)).
### Annotations
#### Annotation process
The crawled data contained the categories' annotations, which were then used to create this dataset with the mentioned criteria.
#### Who are the annotators?
Editorial staff classified the articles under the different thematic sections and subsections, and we extracted these from metadata.
### 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
Irene Baucells ([email protected]), Casimiro Pio Carrino ([email protected]), Carlos Rodríguez ([email protected]) and Carme Armentano ([email protected]), from [BSC-CNS](https://www.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-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Citation Information
[DOI]([https://doi.org/10.5281/zenodo.7334110])
| projecte-aina/tecla | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "tecla"} | 2023-11-25T06:24:24+00:00 |
358bb3c7433e1e7ce2e8fdb7e2eb60bf578a77b0 |
# Dataset Card for VilaQuAD
## 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://doi.org/10.5281/zenodo.4562337
- **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:[email protected]) and [Carme Armentano-Oller](mailto:[email protected])
### Dataset Summary
VilaQuAD, An extractive QA dataset for Catalan, from [VilaWeb](https://www.vilaweb.cat/) newswire text.
This dataset contains 2095 of Catalan language news articles along with 1 to 5 questions referring to each fragment (or context).
VilaQuad articles are extracted from the daily [VilaWeb](https://www.vilaweb.cat/) and used under [CC-BY-NC-SA-ND](https://creativecommons.org/licenses/by-nc-nd/3.0/deed.ca) licence.
This dataset can be used to build extractive-QA and Language Models.
### Supported Tasks and Leaderboards
Extractive-QA, Language Model.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'id': 'P_556_C_556_Q1',
'title': "El Macba posa en qüestió l'eufòria amnèsica dels anys vuitanta a l'estat espanyol",
'context': "El Macba ha obert una nova exposició, 'Gelatina dura. Històries escamotejades dels 80', dedicada a revisar el discurs hegemònic que es va instaurar en aquella dècada a l'estat espanyol, concretament des del començament de la transició, el 1977, fins a la fita de Barcelona 92. És una mirada en clau espanyola, però també centralista, perquè més enllà dels esdeveniments ocorreguts a Catalunya i els artistes que els van combatre, pràcticament només s'hi mostren fets polítics i culturals generats des de Madrid. No es parla del País Basc, per exemple. Però, dit això, l'exposició revisa aquesta dècada de la història recent tot qüestionant un triomfalisme homogeneïtzador, que ja se sap que va arrasar una gran quantitat de sectors crítics i radicals de l'àmbit social, polític i cultural. Com diu la comissària, Teresa Grandas, de l'equip del Macba: 'El relat oficial dels anys vuitanta a l'estat espanyol va prioritzar la necessitat per damunt de la raó i va consolidar una mirada que privilegiava el futur abans que l'anàlisi del passat recent, obviant qualsevol consideració crítica respecte de la filiació amb el poder franquista.",
'question': 'Com es diu la nova exposició que ha obert el Macba?',
'answers': [
{
'text': "'Gelatina dura. Històries escamotejades dels 80'",
'answer_start': 38
}
]
}
```
### 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 VilaWeb article.
- `context` (str): VilaWeb 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.json: 1295 contexts, 3882 questions
- dev.json: 400 contexts, 1200 questions
- test.json: 400 contexts, 1200 questions
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
- [VilaWeb site](https://www.vilaweb.cat/)
#### Initial Data Collection and Normalization
The source data are scraped articles from archives of Catalan newspaper website [Vilaweb](https://www.vilaweb.cat).
From a the online edition of the newspaper [VilaWeb](https://www.vilaweb.cat), 2095 articles were randomnly selected. These headlines were also used to create a Textual Entailment dataset. For the extractive QA dataset, creation of between 1 and 5 questions for each news context was commissioned, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). In total, 6282 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. We also created [another QA dataset with wikipedia](https://huggingface.co/datasets/projecte-aina/viquiquad) to ensure thematic and stylistic variety.
#### Who are the source language producers?
CA
Professional journalists from the Catalan newspaper [VilaWeb](https://www.vilaweb.cat/).
### Annotations
#### Annotation process
We comissioned 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 ([email protected])
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/en/inici/index.html) 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.4562337)
### Contributions
[N/A] | projecte-aina/vilaquad | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ca",
"license:cc-by-sa-4.0",
"arxiv:2107.07903",
"arxiv:1606.05250",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "VilaQuAD"} | 2023-11-25T05:21:59+00:00 |
1267b4234dc06bf4cdd85c110b319d5bc5225b9f |
# 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:[email protected])
### 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-ES`).
## 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 ([email protected])
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]
| projecte-aina/vilasum | [
"task_categories:summarization",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-4.0",
"arxiv:2202.06871",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["ca"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "casum"} | 2023-09-13T11:49:32+00:00 |
586b37f87c9fd0705b7364a8f127a3d278e28cb1 |
# 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:[email protected]) and [Carme Armentano-Oller](mailto:[email protected])
### 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-ES`).
## 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 inCA 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 ([email protected])
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] | projecte-aina/viquiquad | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ca",
"license:cc-by-sa-4.0",
"arxiv:2107.07903",
"arxiv:1606.05250",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "ViquiQuAD"} | 2023-09-13T11:44:04+00:00 |
ec69bc35b897f666902ab400cb18666aac276471 |
# WNLI-ca
## 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
- **Website:** https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd." Source: [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
The [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).
This dataset is a professional translation into Catalan of [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) as published in [GLUE Benchmark](https://gluebenchmark.com/tasks).
Both the original dataset and this translation are licenced under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model.
### Languages
The dataset is in Catalan (`ca-ES`)
## Dataset Structure
### Data Instances
Three tsv files.
### Data Fields
- index
- sentence 1: first sentence of the pair
- sentence 2: second sentence of the pair
- label: relation between the two sentences:
* 0: the second sentence does not entail a correct interpretation of the first one (neutral)
* 1: the second sentence entails a correct interpretation of the first one (entailment)
### Example
| index | sentence 1 | sentence 2 | label |
| ------- |----------- | --------- | ----- |
| 0 | Vaig clavar una agulla en una pastanaga. Quan la vaig treure, tenia un forat. | La pastanaga tenia un forat. | 1 |
| 1 | En Joan no podia veure l’escenari amb en Guillem davant seu perquè és molt baix. | En Joan és molt baix. | 1 |
| 2 | Els policies van arrestar tots els membres de la banda. Volien aturar el tràfic de drogues del barri. | Els policies volien aturar el tràfic de drogues del barri. | 1 |
| 3 | L’Esteve segueix els passos d’en Frederic en tot. L’influencia moltíssim. | L’Esteve l’influencia moltíssim. | 0 |
### Data Splits
- wnli-train-ca.csv: 636
- wnli-dev-ca.csv: 72
- wnli-test-shuffled-ca.csv: 147
## Dataset Creation
### Curation Rationale
We translated this dataset to contribute to the development of language models in Catalan, a low-resource language, and to allow inter-lingual comparisons.
### Source Data
- [GLUE Benchmark site](https://gluebenchmark.com)
#### Initial Data Collection and Normalization
This is a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Catalan, commissioned by BSC TeMU within the [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
#### Who are the source language producers?
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
### Annotations
#### Annotation process
We comissioned a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Catalan.
#### Who are the annotators?
Translation was commisioned to a professional translator.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
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 ([email protected]).
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/4.0/">CC Attribution 4.0 International License</a>.
### Contributions
[N/A]
| projecte-aina/wnli-ca | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|glue",
"language:ca",
"license:cc-by-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["extended|glue"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "wnli-ca"} | 2023-09-13T11:42:10+00:00 |
aa8d49172029aee66a874a7562429f5f8cf200f3 |
# Dataset Card for XQuAD-Ca
## 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/6669801
- **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]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
Professional translation into Catalan of [XQuAD dataset](https://github.com/deepmind/xquad).
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 ([Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250)) together with their professional translations into ten language: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Rumanian was added later. We added the 13th language to the corpus using also professional native Catalan translators.
XQuAD and XQuAD-Ca datasets are released under [CC-by-sa](https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence.
### Supported Tasks and Leaderboards
Cross-lingual-QA, Extractive-QA, Language Model
### Languages
The dataset is in Catalan (`ca-ES`)
## Dataset Structure
### Data Instances
One json file.
1189 examples.
<pre>
{
"data": [
{
"context": "Al llarg de la seva existència, Varsòvia ha estat una ciutat multicultural. Segons el cens del 1901, de 711.988 habitants, el 56,2 % eren catòlics, el 35,7 % jueus, el 5 % cristians ortodoxos grecs i el 2,8 % protestants. Vuit anys després, el 1909, hi havia 281.754 jueus (36,9 %), 18.189 protestants (2,4 %) i 2.818 mariavites (0,4 %). Això va provocar que es construïssin centenars de llocs de culte religiós a totes les parts de la ciutat. La majoria d’ells es van destruir després de la insurrecció de Varsòvia del 1944. Després de la guerra, les noves autoritats comunistes de Polònia van apocar la construcció d’esglésies i només se’n va construir un petit nombre.",
"qas": [
{
"answers": [
{
"text": "711.988",
"answer_start": 104
}
],
"id": "57338007d058e614000b5bdb",
"question": "Quina era la població de Varsòvia l’any 1901?"
},
{
"answers": [
{
"text": "56,2 %",
"answer_start": 126
}
],
"id": "57338007d058e614000b5bdc",
"question": "Dels habitants de Varsòvia l’any 1901, quin percentatge era catòlic?"
},
...
]
}
]
},
...
]
}
</pre>
### 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
- test.json: 1189 examples.
## Dataset Creation
### Curation RationaleCA
We created this dataset to contribute to the development of language models in Catalan, a low-resource language, and for compatibility with similar datasets in other languages, and to allow inter-lingual comparisons.
### Source Data
- [XQuAD's webpage](https://github.com/deepmind/xquad).
#### Initial Data Collection and Normalization
This dataset is a professional translation of [XQuAD](https://github.com/deepmind/xquad) into Catalan, commissioned by [BSC TeMU](https://temu.bsc.es/) within [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
For more information on how XQuAD was created, refer to the paper, On the [Cross-lingual Transferability of Monolingual Representations](https://arxiv.org/abs/1910.11856), or visit the [XQuAD's webpage](https://github.com/deepmind/xquad).
#### Who are the source language producers?
For more information on how XQuAD was created, refer to the paper, [On the Cross-lingual Transferability of Monolingual Representations ](https://arxiv.org/abs/1910.11856), or visit the [XQuAD's webpage](https://github.com/deepmind/xquad).
### Annotations
This is a professional translation of the XQuAD corpus and its annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
Translation was commissioned to a professional translation company.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
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 ([email protected])
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 [CC-by-sa](https://creativecommons.org/licenses/by-sa/3.0/legalcode) licence.
### 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.4526223)
### Contributions
[N/A] | projecte-aina/xquad-ca | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-sa-4.0",
"arxiv:2107.07903",
"arxiv:1606.05250",
"arxiv:1910.11856",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "xquad-ca"} | 2023-11-25T05:37:46+00:00 |
1df6ec7dce31491d28f5af112c6ad3a70716a159 | ## Latin part of cc100 corpus
This dataset contains parts of the Latin part of the [cc100](http://data.statmt.org/cc-100/) dataset. It was used to train a [RoBERTa-based LM model](https://huggingface.co/pstroe/roberta-base-latin-cased) with huggingface.
### Preprocessing
I undertook the following preprocessing steps:
- Removal of all "pseudo-Latin" text ("Lorem ipsum ...").
- Use of [CLTK](http://www.cltk.org) for sentence splitting and normalisation.
- Retaining only lines containing letters of the Latin alphabet, numerals, and certain punctuation (--> `grep -P '^[A-z0-9ÄÖÜäöüÆ挜ᵫĀāūōŌ.,;:?!\- Ęę]+$' la.nolorem.tok.txt`
- deduplication of the corpus
The result is a corpus of ~390 million tokens.
### Structure
The dataset is structured the following way:
```
{
"train": {
"text": "Solventibus autem illis pullum , dixerunt domini ejus ad illos : Quid solvitis pullum ?",
"text": "Errare humanum est ."
...
}
"test": {
"text": "Alia iacta est ."
...
}
}
```
### Contact
For contact, reach out to Phillip Ströbel [via mail](mailto:[email protected]) or [via Twitter](https://twitter.com/CLingophil). | pstroe/cc100-latin | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-11-02T14:28:12+00:00 |
1e7db1d2e6e0984ff24efa50133d3adc90205429 | puffy310/yandset | [
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"license": "apache-2.0"} | 2022-03-01T06:18:16+00:00 |
|
c8cfe7c55b5245ef9b48edd2ca37e1a1df6a04ff | COVID-19 image data collection | pulmo/chest_xray | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-07-25T14:10:08+00:00 |
f05fdf09596bd7fcf8b2c14cb93305e7b7a7fa54 | ## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)
### Train Sets
To create training set or entailment and QA tasks, download and convert the ShARC data using the following commands:
```
wget https://sharc-data.github.io/data/sharc1-official.zip
unzip sharc1-official.zip
python create_train_from_sharc.py -sharc_dev_path sharc1-official/json/sharc_dev.json -sharc_train_path sharc1-official/json/sharc_train.json
```
### Evaluation Sets
#### Entailment Data
The following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.
- __dev_entailment_qa4pc.json__
- __test_entailment_qa4pc.json__
#### QA Data
The following files contain the data for the QA task.
- __dev_sc_qa4pc.json__
- __test_sc_qa4pc.json__
The following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.
- __trees_dev_test_qa4pc.json__
| qa4pc/QA4PC | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-11-23T11:22:13+00:00 |
9a6b58c803ec27ad00117420022761b2a69cf526 |
# ANTILLES : An Open French Linguistically Enriched Part-of-Speech Corpus
## 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)
- [sent_id = fr-ud-dev_00005](#sent_id--fr-ud-dev_00005)
- [text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné.](#text--travail-de-trs-grande-qualit-excut-par-un-imprimeur-artisan-passionn)
- [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://qanastek.github.io/ANTILLES/
- **Repository:** https://github.com/qanastek/ANTILLES
- **Paper:** https://hal.archives-ouvertes.fr/hal-03696042/document
- **Leaderboard:** https://paperswithcode.com/dataset/antilles
- **Point of Contact:** [Yanis Labrak](mailto:[email protected])
### Dataset Summary
`ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb).
Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation script `transform.py`, we obtain 60 different classes which add semantic information such as: the gender, number, mood, person, tense or verb form given in the different CoNLL-U fields from the original corpora.
We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001.
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
### Supported Tasks and Leaderboards
`part-of-speech-tagging`: The dataset can be used to train a model for part-of-speech-tagging. The performance is measured by how high its F1 score is. A Flair Sequence-To-Sequence model trained to tag tokens from Wikipedia passages achieves a F1 score (micro) of 0.952.
### Languages
The text in the dataset is in French, as spoken by [Wikipedia](https://en.wikipedia.org/wiki/Main_Page) users. The associated [BCP-47](https://tools.ietf.org/html/bcp47) code is `fr`.
## Load the dataset
### HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/ANTILLES")
print(dataset)
```
### FlairNLP
```python
from flair.datasets import UniversalDependenciesCorpus
corpus: Corpus = UniversalDependenciesCorpus(
data_folder='ANTILLES',
train_file="train.conllu",
test_file="test.conllu",
dev_file="dev.conllu"
)
```
## Load the model
### Flair ([model](https://huggingface.co/qanastek/pos-french))
```python
from flair.models import SequenceTagger
tagger = SequenceTagger.load("qanastek/pos-french")
```
## HuggingFace Spaces
<table style="width: fit-content;">
<thead>
<tr>
<td>
<a href="https://huggingface.co/spaces/qanastek/French-Part-Of-Speech-Tagging">
<img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/en.png" width="160">
</a>
</td>
<td>
<a href="https://huggingface.co/spaces/qanastek/Etiqueteur-Morphosyntaxique-Etendu">
<img src="https://huggingface.co/datasets/qanastek/ANTILLES/raw/main/imgs/fr.png" width="160">
</a>
</td>
</tr>
</thead>
</table>
## Dataset Structure
### Data Instances
```plain
# sent_id = fr-ud-dev_00005
# text = Travail de trés grande qualité exécuté par un imprimeur artisan passionné.
1 Travail travail NMS _ Gender=Masc|Number=Sing 0 root _ wordform=travail
2 de de PREP _ _ 5 case _ _
3 trés trés ADV _ _ 4 advmod _ _
4 grande grand ADJFS _ Gender=Fem|Number=Sing 5 amod _ _
5 qualité qualité NFS _ Gender=Fem|Number=Sing 1 nmod _ _
6 exécuté exécuter VPPMS _ Gender=Masc|Number=Sing|Tense=Past|VerbForm=Part 1 acl _ _
7 par par PREP _ _ 9 case _ _
8 un un DINTMS _ Definite=Ind|Gender=Masc|Number=Sing|PronType=Art 9 det _ _
9 imprimeur imprimeur NMS _ Gender=Masc|Number=Sing 6 obl:agent _ _
10 artisan artisan NMS _ Gender=Masc|Number=Sing 9 nmod _ _
11 passionné passionné ADJMS _ Gender=Masc|Number=Sing 9 amod _ SpaceAfter=No
12 . . YPFOR _ _ 1 punct _ _
```
### Data Fields
| Abbreviation | Description | Examples | # tokens |
|:--------:|:--------:|:--------:|:--------:|
| PREP | Preposition | de | 63 738 |
| AUX | Auxiliary Verb | est | 12 886 |
| ADV | Adverb | toujours | 14 969 |
| COSUB | Subordinating conjunction | que | 3 007 |
| COCO | Coordinating Conjunction | et | 10 102 |
| PART | Demonstrative particle | -t | 93 |
| PRON | Pronoun | qui ce quoi | 667 |
| PDEMMS | Singular Masculine Demonstrative Pronoun | ce | 1 950 |
| PDEMMP | Plurial Masculine Demonstrative Pronoun | ceux | 108 |
| PDEMFS | Singular Feminine Demonstrative Pronoun | cette | 1 004 |
| PDEMFP | Plurial Feminine Demonstrative Pronoun | celles | 53 |
| PINDMS | Singular Masculine Indefinite Pronoun | tout | 961 |
| PINDMP | Plurial Masculine Indefinite Pronoun | autres | 89 |
| PINDFS | Singular Feminine Indefinite Pronoun | chacune | 136 |
| PINDFP | Plurial Feminine Indefinite Pronoun | certaines | 31 |
| PROPN | Proper noun | houston | 22 135 |
| XFAMIL | Last name | levy | 6 449 |
| NUM | Numerical Adjectives | trentaine vingtaine | 67 |
| DINTMS | Masculine Numerical Adjectives | un | 4 254 |
| DINTFS | Feminine Numerical Adjectives | une | 3 543 |
| PPOBJMS | Singular Masculine Pronoun complements of objects | le lui | 1 425 |
| PPOBJMP | Plurial Masculine Pronoun complements of objects | eux y | 212 |
| PPOBJFS | Singular Feminine Pronoun complements of objects | moi la | 358 |
| PPOBJFP | Plurial Feminine Pronoun complements of objects | en y | 70 |
| PPER1S | Personal Pronoun First Person Singular | je | 571 |
| PPER2S | Personal Pronoun Second Person Singular | tu | 19 |
| PPER3MS | Personal Pronoun Third Person Masculine Singular | il | 3 938 |
| PPER3MP | Personal Pronoun Third Person Masculine Plurial | ils | 513 |
| PPER3FS | Personal Pronoun Third Person Feminine Singular | elle | 992 |
| PPER3FP | Personal Pronoun Third Person Feminine Plurial | elles | 121 |
| PREFS | Reflexive Pronouns First Person of Singular | me m' | 120 |
| PREF | Reflexive Pronouns Third Person of Singular | se s' | 2 337 |
| PREFP | Reflexive Pronouns First / Second Person of Plurial | nous vous | 686 |
| VERB | Verb | obtient | 21 131 |
| VPPMS | Singular Masculine Participle Past Verb | formulé | 6 275 |
| VPPMP | Plurial Masculine Participle Past Verb | classés | 1 352 |
| VPPFS | Singular Feminine Participle Past Verb | appelée | 2 434 |
| VPPFP | Plurial Feminine Participle Past Verb | sanctionnées | 813 |
| VPPRE | Present participle | étant | 2 |
| DET | Determinant | les l' | 25 206 |
| DETMS | Singular Masculine Determinant | les | 15 444 |
| DETFS | Singular Feminine Determinant | la | 10 978 |
| ADJ | Adjective | capable sérieux | 1 075 |
| ADJMS | Singular Masculine Adjective | grand important | 8 338 |
| ADJMP | Plurial Masculine Adjective | grands petits | 3 274 |
| ADJFS | Singular Feminine Adjective | franéaise petite | 8 004 |
| ADJFP | Plurial Feminine Adjective | légéres petites | 3 041 |
| NOUN | Noun | temps | 1 389 |
| NMS | Singular Masculine Noun | drapeau | 29 698 |
| NMP | Plurial Masculine Noun | journalistes | 10 882 |
| NFS | Singular Feminine Noun | téte | 25 414 |
| NFP | Plurial Feminine Noun | ondes | 7 448 |
| PREL | Relative Pronoun | qui dont | 2 976 |
| PRELMS | Singular Masculine Relative Pronoun | lequel | 94 |
| PRELMP | Plurial Masculine Relative Pronoun | lesquels | 29 |
| PRELFS | Singular Feminine Relative Pronoun | laquelle | 70 |
| PRELFP | Plurial Feminine Relative Pronoun | lesquelles | 25 |
| PINTFS | Singular Feminine Interrogative Pronoun | laquelle | 3 |
| INTJ | Interjection | merci bref | 75 |
| CHIF | Numbers | 1979 10 | 10 417 |
| SYM | Symbol | é % | 705 |
| YPFOR | Endpoint | . | 15 088 |
| PUNCT | Ponctuation | : , | 28 918 |
| MOTINC | Unknown words | Technology Lady | 2 022 |
| X | Typos & others | sfeir 3D statu | 175 |
### Data Splits
| | Train | Dev | Test |
|:------------------:|:------:|:------:|:-----:|
| # Docs | 14 449 | 1 476 | 416 |
| Avg # Tokens / Doc | 24.54 | 24.19 | 24.08 |
## 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 corpora is free of personal or sensitive information since it has been based on `Wikipedia` articles content.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
The nature of the corpora introduce various biases such as the names of the streets which are temporaly based and can therefore introduce named entity like author or event names. For example, street names such as `Rue Victor-Hugo` or `Rue Pasteur` doesn't exist before the 20's century in France.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
__ANTILLES__: Labrak Yanis, Dufour Richard
__UD_FRENCH-GSD__: de Marneffe Marie-Catherine, Guillaume Bruno, McDonald Ryan, Suhr Alane, Nivre Joakim, Grioni Matias, Dickerson Carly, Perrier Guy
__Universal Dependency__: Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee
### Licensing Information
```plain
For the following languages
German, Spanish, French, Indonesian, Italian, Japanese, Korean and Brazilian
Portuguese
we will distinguish between two portions of the data.
1. The underlying text for sentences that were annotated. This data Google
asserts no ownership over and no copyright over. Some or all of these
sentences may be copyrighted in some jurisdictions. Where copyrighted,
Google collected these sentences under exceptions to copyright or implied
license rights. GOOGLE MAKES THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY
WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED.
2. The annotations -- part-of-speech tags and dependency annotations. These are
made available under a CC BY-SA 4.0. GOOGLE MAKES
THEM AVAILABLE TO YOU 'AS IS', WITHOUT ANY WARRANTY OF ANY KIND, WHETHER
EXPRESS OR IMPLIED. See attached LICENSE file for the text of CC BY-NC-SA.
Portions of the German data were sampled from the CoNLL 2006 Tiger Treebank
data. Hans Uszkoreit graciously gave permission to use the underlying
sentences in this data as part of this release.
Any use of the data should reference the above plus:
Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg,
Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang,
Oscar Tackstrom, Claudia Bedini, Nuria Bertomeu Castello and Jungmee Lee
Proceedings of ACL 2013
```
### Citation Information
Please cite the following paper when using this model.
ANTILLES extended corpus:
```latex
@inproceedings{labrak:hal-03696042,
TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}},
AUTHOR = {Labrak, Yanis and Dufour, Richard},
URL = {https://hal.archives-ouvertes.fr/hal-03696042},
BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}},
ADDRESS = {Brno, Czech Republic},
PUBLISHER = {{Springer}},
YEAR = {2022},
MONTH = Sep,
KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers},
PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf},
HAL_ID = {hal-03696042},
HAL_VERSION = {v1},
}
```
UD_French-GSD corpora:
```latex
@misc{
universaldependencies,
title={UniversalDependencies/UD_French-GSD},
url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
author={UniversalDependencies}
}
```
{U}niversal {D}ependency Annotation for Multilingual Parsing:
```latex
@inproceedings{mcdonald-etal-2013-universal,
title = "{U}niversal {D}ependency Annotation for Multilingual Parsing",
author = {McDonald, Ryan and
Nivre, Joakim and
Quirmbach-Brundage, Yvonne and
Goldberg, Yoav and
Das, Dipanjan and
Ganchev, Kuzman and
Hall, Keith and
Petrov, Slav and
Zhang, Hao and
T{\"a}ckstr{\"o}m, Oscar and
Bedini, Claudia and
Bertomeu Castell{\'o}, N{\'u}ria and
Lee, Jungmee},
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-2017",
pages = "92--97",
}
```
LIA TAGG:
```latex
@techreport{LIA_TAGG,
author = {Frédéric Béchet},
title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
institution = {Aix-Marseille University & CNRS},
year = {2001}
}
```
| qanastek/ANTILLES | [
"task_categories:token-classification",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:fr",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["fr"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["part-of-speech-tagging"], "pretty_name": "ANTILLES", "language_bcp47": ["fr-FR"]} | 2022-10-24T16:13:19+00:00 |
30a7e525efbb3094204e7e9a49bc46fd0ec7afb6 |
# 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:[email protected])
### 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}
}
```
| qanastek/ECDC | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:en-sv",
"multilinguality:en-pl",
"multilinguality:en-hu",
"multilinguality:en-lt",
"multilinguality:en-sk",
"multilinguality:en-ga",
"multilinguality:en-fr",
"multilinguality:en-cs",
"multilinguality:en-el",
"multilinguality:en-it",
"multilinguality:en-lv",
"multilinguality:en-da",
"multilinguality:en-nl",
"multilinguality:en-bg",
"multilinguality:en-is",
"multilinguality:en-ro",
"multilinguality:en-no",
"multilinguality:en-pt",
"multilinguality:en-es",
"multilinguality:en-et",
"multilinguality:en-mt",
"multilinguality:en-sl",
"multilinguality:en-fi",
"multilinguality:en-de",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:en",
"license:other",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["en-sv", "en-pl", "en-hu", "en-lt", "en-sk", "en-ga", "en-fr", "en-cs", "en-el", "en-it", "en-lv", "en-da", "en-nl", "en-bg", "en-is", "en-ro", "en-no", "en-pt", "en-es", "en-et", "en-mt", "en-sl", "en-fi", "en-de"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["translation", "machine-translation"], "task_ids": ["translation", "machine-translation"], "pretty_name": "ECDC"} | 2022-10-23T03:59:32+00:00 |
7f5633e7f9903947a9e51ab0e12ff483574aeebf |
# ELRC-Medical-V2 : European parallel corpus for healthcare machine translation
## 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://live.european-language-grid.eu/catalogue/project/2209
- **Repository:** https://github.com/qanastek/ELRC-Medical-V2/
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:[email protected])
### Dataset Summary
`ELRC-Medical-V2` is a parallel corpus for neural machine translation funded by the [European Commission](http://www.lr-coordination.eu/) and coordinated by the [German Research Center for Artificial Intelligence](https://www.dfki.de/web).
### 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 23 differents languages from the European Union (EU) with as source language in each cases english (EN).
**List of languages :** `Bulgarian (bg)`,`Czech (cs)`,`Danish (da)`,`German (de)`,`Greek (el)`,`Spanish (es)`,`Estonian (et)`,`Finnish (fi)`,`French (fr)`,`Irish (ga)`,`Croatian (hr)`,`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
NAME = "qanastek/ELRC-Medical-V2"
dataset = load_dataset(NAME, use_auth_token=True)
print(dataset)
dataset_train = load_dataset(NAME, "en-es", split='train[:90%]')
dataset_test = load_dataset(NAME, "en-es", split='train[10%:]')
print(dataset_train)
print(dataset_train[0])
print(dataset_test)
```
## Dataset Structure
### Data Instances
```plain
id,lang,source_text,target_text
1,en-bg,"TOC \o ""1-3"" \h \z \u Introduction 3","TOC \o ""1-3"" \h \z \u Въведение 3"
2,en-bg,The international humanitarian law and its principles are often not respected.,Международното хуманитарно право и неговите принципи често не се зачитат.
3,en-bg,"At policy level, progress was made on several important initiatives.",На равнище политики напредък е постигнат по няколко важни инициативи.
```
### Data Fields
**id** : The document identifier of type `Integer`.
**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 | # Docs | Avg. # Source Tokens | Avg. # Target Tokens |
|--------|-----------|------------------------|------------------------|
| bg | 13 149 | 23 | 24 |
| cs | 13 160 | 23 | 21 |
| da | 13 242 | 23 | 22 |
| de | 13 291 | 23 | 22 |
| el | 13 091 | 23 | 26 |
| es | 13 195 | 23 | 28 |
| et | 13 016 | 23 | 17 |
| fi | 12 942 | 23 | 16 |
| fr | 13 149 | 23 | 28 |
| ga | 412 | 12 | 12 |
| hr | 12 836 | 23 | 21 |
| hu | 13 025 | 23 | 21 |
| it | 13 059 | 23 | 25 |
| lt | 12 580 | 23 | 18 |
| lv | 13 044 | 23 | 19 |
| mt | 3 093 | 16 | 14 |
| nl | 13 191 | 23 | 25 |
| pl | 12 761 | 23 | 22 |
| pt | 13 148 | 23 | 26 |
| ro | 13 163 | 23 | 25 |
| sk | 12 926 | 23 | 20 |
| sl | 13 208 | 23 | 21 |
| sv | 13 099 | 23 | 21 |
|||||
| Total | 277 780 | 22.21 | 21.47 |
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://elrc-share.eu/repository/search/?q=mfsp%3A87ef9e5e8ac411ea913100155d026706e19a1a9f908b463c944490c36ba2f454&page=3).
### Source Data
#### Initial Data Collection and Normalization
The acquisition of bilingual data (from multilingual websites), normalization, cleaning, deduplication and identification of parallel documents have been done by [ILSP-FC tool](http://nlp.ilsp.gr/redmine/projects/ilsp-fc/wiki/Introduction). [Maligna aligner](https://github.com/loomchild/maligna) was used for alignment of segments. Merging/filtering of segment pairs has also been applied.
#### Who are the source language producers?
Every data of this corpora as been uploaded by [Vassilis Papavassiliou](mailto:[email protected]) on [ELRC-Share](https://elrc-share.eu/repository/browse/bilingual-corpus-from-the-publications-office-of-the-eu-on-the-medical-domain-v2-en-fr/6b31b32e8ac411ea913100155d0267061547d9b3ec284584af19a2953baa8937/).
### 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
__ELRC-Medical-V2__: Labrak Yanis, Dufour Richard
__Bilingual corpus from the Publications Office of the EU on the medical domain v.2 (EN-XX) Corpus__: [Vassilis Papavassiliou](mailto:[email protected]) and [others](https://live.european-language-grid.eu/catalogue/project/2209).
### Licensing Information
<a rel="license" href="https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf"><img alt="Attribution 4.0 International (CC BY 4.0) License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf">Attribution 4.0 International (CC BY 4.0) License</a>.
### Citation Information
Please cite the following paper when using this model.
```latex
@inproceedings{losch-etal-2018-european,
title = European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management,
author = {
L'osch, Andrea and
Mapelli, Valérie and
Piperidis, Stelios and
Vasiljevs, Andrejs and
Smal, Lilli and
Declerck, Thierry and
Schnur, Eileen and
Choukri, Khalid and
van Genabith, Josef
},
booktitle = Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018),
month = may,
year = 2018,
address = Miyazaki, Japan,
publisher = European Language Resources Association (ELRA),
url = https://aclanthology.org/L18-1213,
}
```
| qanastek/ELRC-Medical-V2 | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:en",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["en", "bg", "cs", "da", "de", "el", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["translation"], "task_ids": ["translation"], "pretty_name": "ELRC-Medical-V2"} | 2022-10-24T16:15:17+00:00 |
783edb3e7341c61ec455b253654550c6bdbdfa89 |
# 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:[email protected])
### Dataset Summary
`EMEA-V3` is a parallel corpus for neural machine translation collected and aligned by [Tiedemann, Jorg](mailto:[email protected]) 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:[email protected]) 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:[email protected]).
<!-- ### 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.,
}
```
| qanastek/EMEA-V3 | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:bg",
"multilinguality:cs",
"multilinguality:da",
"multilinguality:de",
"multilinguality:el",
"multilinguality:en",
"multilinguality:es",
"multilinguality:et",
"multilinguality:fi",
"multilinguality:fr",
"multilinguality:hu",
"multilinguality:it",
"multilinguality:lt",
"multilinguality:lv",
"multilinguality:mt",
"multilinguality:nl",
"multilinguality:pl",
"multilinguality:pt",
"multilinguality:ro",
"multilinguality:sk",
"multilinguality:sl",
"multilinguality:sv",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "multilinguality": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["translation", "machine-translation"], "task_ids": ["translation", "machine-translation"], "pretty_name": "EMEA-V3"} | 2022-10-22T14:18:02+00:00 |
d74986fdd2f8aa542ca4b875d9fd37979518a027 |
# 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:[email protected])
### 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,
}
```
| qanastek/WMT-16-PubMed | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["translation", "machine-translation"], "task_ids": ["translation", "machine-translation"], "pretty_name": "WMT-16-PubMed"} | 2022-10-22T14:20:12+00:00 |
39c7dcea4794e2224243c87a81b9ccf21ae3b417 |
# Dataset Card for "squad_fr"
## 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
- **Paper:** [On the Usability of Transformers-based models for a French Question-Answering task](https://hal.archives-ouvertes.fr/hal-03336060)
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
### Dataset Summary
SQuAD-fr:
- a translated version of the Stanford Question Answering Dataset (SQuAD) into French
- obtained through automatic translation of the English dataset
- a reading comprehension dataset, consisting of approximately 90K factoid questions on Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage
- serves as a means of data augmentation on FQuAD and PIAF benchmarks
### Supported Tasks and Leaderboards
- `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks.
### Languages
This dataset is exclusively in French.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `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|
|----------|----:|---------:|
|1.1.0|87514| 17492|
## Dataset Creation
### Curation Rationale
Usability of Transformer-based models, instability relating to data scarcity, investigation of data augmentation, hyperparameters optimization and cross-lingual transfer on the performance of a question-answering task in French.
### Source Data
#### Initial Data Collection and Normalization
validation: manually collected gold standards, chrf scores and bleu evaluation
#### 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
Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
### Citation Information
```
@inproceedings{cattan:hal-03336060,
TITLE = {{On the Usability of Transformers-based models for a French Question-Answering task}},
AUTHOR = {Cattan, Oralie and Servan, Christophe and Rosset, Sophie},
URL = {https://hal.archives-ouvertes.fr/hal-03336060},
BOOKTITLE = {{Recent Advances in Natural Language Processing (RANLP)}},
ADDRESS = {Varna, Bulgaria},
YEAR = {2021},
MONTH = Sep,
PDF = {https://hal.archives-ouvertes.fr/hal-03336060/file/RANLP_2021_transformers_usability.pdf},
HAL_ID = {hal-03336060},
HAL_VERSION = {v1},
}
``` | qwant/squad_fr | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:fr",
"license:cc-by-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["fr"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual", "translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|squad"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "closed-domain-qa"], "paperswithcode_id": "squad", "pretty_name": "SQuAD-fr"} | 2023-04-19T13:37:09+00:00 |
56645be151b61e1143597f922ccf666b43a5c02b |
# Itihāsa
Itihāsa is a Sanskrit-English translation corpus containing 93,000 Sanskrit shlokas and their English translations extracted from M. N. Dutt's seminal works on The Rāmāyana and The Mahābhārata. The paper which introduced this dataset can be found [here](https://aclanthology.org/2021.wat-1.22/).
This repository contains the randomized train, development, and test sets. The original extracted data can be found [here](https://github.com/rahular/itihasa/tree/gh-pages/res) in JSON format. If you just want to browse the data, you can go [here](http://rahular.com/itihasa/).
## Usage
```
>> from datasets import load_dataset
>> dataset = load_dataset("rahular/itihasa")
>> dataset
DatasetDict({
train: Dataset({
features: ['translation'],
num_rows: 75162
})
validation: Dataset({
features: ['translation'],
num_rows: 6149
})
test: Dataset({
features: ['translation'],
num_rows: 11722
})
})
>> dataset['train'][0]
{'translation': {'en': 'The ascetic Vālmīki asked Nārada, the best of sages and foremost of those conversant with words, ever engaged in austerities and Vedic studies.',
'sn': 'ॐ तपः स्वाध्यायनिरतं तपस्वी वाग्विदां वरम्। नारदं परिपप्रच्छ वाल्मीकिर्मुनिपुङ्गवम्॥'}}
```
## Citation
If you found this dataset to be useful, please consider citing the paper as follows:
```
@inproceedings{aralikatte-etal-2021-itihasa,
title = "Itihasa: A large-scale corpus for {S}anskrit to {E}nglish translation",
author = "Aralikatte, Rahul and
de Lhoneux, Miryam and
Kunchukuttan, Anoop and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.22",
pages = "191--197",
abstract = "This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.",
}
``` | rahular/itihasa | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:sa",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["sa", "en"], "license": ["apache-2.0"], "multilinguality": ["translation"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Itihasa", "metrics": ["bleu", "sacrebleu", "rouge", "ter", "chrF"], "tags": ["conditional-text-generation"]} | 2022-10-24T17:06:01+00:00 |
7aa5ac224f3acc8600c6c8c648c18b5dd6d3cf41 | ## IndicNLP News Article Classification Dataset
We used the IndicNLP text corpora to create classification datasets comprising news articles and their categories for 9 languages. The dataset is balanced across classes. The following table contains the statistics of our dataset:
| Language | Classes | Articles per Class |
| --------- | ------------------------------------------- | ------------------ |
| Bengali | entertainment, sports | 7K |
| Gujarati | business, entertainment, sports | 680 |
| Kannada | entertainment, lifestyle, sports | 10K |
| Malayalam | business, entertainment, sports, technology | 1.5K |
| Marathi | entertainment, lifestyle, sports | 1.5K |
| Oriya | business, crime, entertainment, sports | 7.5K |
| Punjabi | business, entertainment, sports, politics | 780 |
| Tamil | entertainment, politics, sport | 3.9K |
| Telugu | entertainment, business, sports | 8K |
## Citing
If you are using any of the resources, please cite the following article:
```
@article{kunchukuttan2020indicnlpcorpus,
title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}
```
| rajeshradhakrishnan/malayalam_news | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-07-04T04:57:19+00:00 |
e4fbbe300e28a65c40334241aa4e9f1c4e155852 |
# Dataset Card for [Malayalam Wiki - common crawl malayalam]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository: https://github.com/qburst/common-crawl-malayalam**
- **Paper: None**
- **Leaderboard:**
- **Point of Contact: [@RRaajjesshh](https://twitter.com/RRaajjesshh)**
### Dataset Summary
Created from the files extract using Useful tools for extracting malayalam text from the Common Crawl Dataset.
https://github.com/qburst/common-crawl-malayalam
### 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
[qburst](https://github.com/qburst), have run scripts on some months of the Common Crawl archives and are made it publicly available. This dataset is from cleaned up corpus from [QBurst common-crawl-malayalam](https://github.com/qburst/common-crawl-malayalam)
#### 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
https://github.com/qburst/common-crawl-malayalam, contains the Useful tools to extract malayalam text from the Common Crawl Datasets.
### Licensing Information
[More Information Needed]
### Citation Information
@article{
qburst,
title={Common Crawl - Malayalam},
journal={arXiv preprint arXiv:2005.00085},
year={2020}\n}
]
### Contributions
Thanks to [rajeshradhakrishnanmvk](https://github.com/rajeshradhakrishnanmvk) for adding this dataset.
| rajeshradhakrishnan/malayalam_wiki | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "wikitext-2", "pretty_name": "rajeshradhakrishnan/malayalam_wiki"} | 2022-07-04T11:21:06+00:00 |
036d6b4f0077262f485de3d16085244408af2430 |
***This dataset contains 1.5k Algerian Arabic sentiment comments classified into two classes
subjective positive, subjective negative.
***This dataset is collected and annotated by RANIM for Arabic NLP Solutions, feel free to use it.
***We appreciate citing our company name "RANIM for Arabic NLP Solutions" when using this dataset.
***For more data/information visit our website : https://ranim-for-nlp.web.app
or contact us : [email protected]
*******************************"RANIM for Arabic NLP Solutions"********************************** | ranim/Algerian-Arabic | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-11-04T18:17:42+00:00 |
584b85c66dda5e43f64964267554329ec0675694 | rays2pix/example | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-07-05T10:29:59+00:00 |
|
5345895c56a601afe1a98519ce3199be60a27dba |
# Dataset Card for DiaBLa: Bilingual dialogue parallel evaluation set
## 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:** [almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html](http://almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html)
- **Repository:** [github.com/rbawden/DiaBLa-dataset](https://github.com/rbawden/DiaBLa-dataset)
- **Paper:** [Bawden et al. (2021). DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation. Language Resources and Evaluation(55). Pages 635–660. Springer Verlag. 10.1007/s10579-020-09514-4.](https://hal.inria.fr/hal-03021633)
- **Point of contact:** rachel.bawden[at]inria.fr
### Dataset Summary
The dataset is an English-French dataset for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue.
The dataset contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. See below for some basic statistics. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. See here for information about evaluation.
The motivation for the corpus is two-fold: to provide:
- a unique resource for evaluating MT models for dialogue (i.e. in context)
- a corpus for the analysis of MT-mediated communication
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (mainly UK) and French
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 37 MB
- **Number of parallel utterances:** 5748
Each example is highly annotated and is associated with dialogue context. An example from the test set looks as follows (only the first and last utterances of the dialogue history are shown for readability purposes):
```
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_25",
"mt": "Tu m'en veux pour \u00e7a ?",
"norm": "",
"orig": "Are you blaming me for this?",
"ref": "C'est moi que vous critiquez pour \u00e7a\u00a0?",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"coherence"
],
"lang": "english"
},
"dialogue_meta": {
"start_time": "2018-04-25T16:20:36.087170",
"end_time": "",
"translation_model": "baseline",
"final_evaluation_user1": {
"style": "average",
"coherence": "average",
"grammaticality": "good",
"meaning": "average",
"word_choice": "average"
},
"final_evaluation_user2": {
"style": "",
"coherence": "",
"grammaticality": "",
"meaning": "",
"word_choice": ""
},
"scenario": [
[
"You are both stuck in a lift at work.",
"Vous \u00eates tous les deux bloqu\u00e9(e)s dans un ascenseur au travail."
],
[
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
[
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
]
],
"user1": {
"role_num": 1,
"role": [
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
"initiated_dialogue": true,
"turn_number": 2,
"lang": "french"
},
"user2": {
"role_num": 2,
"role": [
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
],
"initiated_dialogue": false,
"turn_number": 1,
"lang": "english"
}
},
"dialogue_history": [
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_0",
"orig": "We appear to have stopped moving.",
"norm": "",
"mt": "On semble avoir arr\u00eat\u00e9 de bouger.",
"ref": "J'ai l'impression qu'on s'est arr\u00eat\u00e9s.",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"style"
],
"lang": "english"
}
},
[...]
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_24",
"orig": "La sonnerie s'est arr\u00eat\u00e9, je pense que personne ne va nous r\u00e9pondre.",
"norm": "",
"mt": "The ringing stopped, and I don't think anyone's gonna answer us.",
"ref": "It stopped ringing. I don't think anybody's going to reply.",
"utterance_meta": {
"eval_judgment": "perfect",
"eval_verbatim": "",
"eval_problems": [],
"lang": "french"
}
}
]
}
```
### Data Fields
#### plain_text
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
- `dialogue_meta`: a dictionary feature containing:
- `start_time` : a `string` feature.
- `end_time`: a `string` feature.
- `translation_model`: a `string` feature.
- `final_evaluation_user1`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `final_evaluation_user2`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `scenario`: a list feature containing
- 3 lists each containing 2 `string` features.
- `user1`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `user2`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `dialogue_history`: a list feature containing:
- dictionary features containing:
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
### Data Splits
DiaBLa is a test set only.
| name |test |
|----------|------:|
|plain_text| 5748|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Original data was collected through a [dedicated online chat platform](https://github.com/rbawden/diabla-chat-interface) and involved native speakers of English and of French. As well as producing the original text, participants also annotated the quality of the machine-translated outputs of their partners' utterances (which they saw instead of their partners' original text) based on their monolingual intuitions and the dialogue context.
Each dialogue is assigned one of 12 role-play scenarios and where appropriate each participant is assigned a role to play in the dialogue.
#### Who are the source language producers?
The source text producers were native French and native English volunteers (mainly British English). See the paper for very basic information concerning their backgrounds (age categories and experience in NLP).
### Annotations
#### Annotation process
On top of the original dialogue text (a mixture of utterances in English and in French), the following "annotations" are provided:
- machine translated version of the original text (produced in real time and presented during the dialogue), produced by one of two MT systems, both trained using [Marian](https://github.com/marian-nmt/marian).
- judgments of MT quality by participants (overall quality, particular problems, verbatim comments)
- manually produced normalised version of the original text (for spelling mistakes, grammatical errors, missing punctuation, etc.)
- manually produced reference translations
#### Who are the annotators?
The judgments of MT quality were produced by the dialogue participants themselves in real time. The normalised version of the text and the reference translations were manually produced by the authors of the paper. Translations were always done into the translator's native language and all translations were verified and post-edited by a bilingual English-French speaker.
### Personal and Sensitive Information
A priori the dataset does not contain personal and sensitive information. Participants were instructed not to give any personal information and to assume the roles assigned in the role play scenario. Usernames were anonymised prior to distribution and any mention of either usernames or real names in the dialogues were replaced by generic names of the same gender as the participant. Only basic user information was collected to get an idea of the distribution of participants and to potentially see how multilingual ability influences quality judgments (rough age categories, experience in NLP or research, native languages, familiarity with the other language (either English or French), other languages spoken and gender). Gender was included because it is an important factor in translation (particularly for the direction English-to-French), and this was explained in advance to the participants in the FAQs.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was collected by Rachel Bawden, Eric Bilinski, Thomas Lavergne and Sophie Rosset (see citation below).
### Licensing Information
The dataset is available under a CC BY-SA 4.0 licence.
### Citation Information
If you use or are inspired by this dataset, please cite:
```
@article{bawden_DiaBLa:-A-Corpus-of_2021,
author = {Bawden, Rachel and Bilinski, Eric and Lavergne, Thomas and Rosset, Sophie},
doi = {10.1007/s10579-020-09514-4},
title = {DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation},
year = {2021},
journal = {Language Resources and Evaluation},
publisher = {Springer Verlag},
volume = {55},
pages = {635--660},
url = {https://hal.inria.fr/hal-03021633},
pdf = {https://hal.inria.fr/hal-03021633/file/diabla-lre-personal-formatting.pdf},
}
```
### Contributions
This dataset was added by Rachel Bawden [@rbawden](https://github.com/rbawden). | rbawden/DiaBLa | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "fr"], "license": ["cc-by-sa-4.0"], "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "DiaBLa", "language_bcp47": ["en-UK", "fr-FR"]} | 2022-10-25T13:21:10+00:00 |
6a4e89d29202fab0ded138253c6193f1ebd98c45 | # Test Dataset
This is a test dataset | robz/test | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-02-17T13:54:07+00:00 |
d4431eb9768d77852272755a3679b1fc28a45062 | A collection of >200k screenshots from the Sims 4 character creator (face and upper-torso only), using the randomize button.
* There are ~100k masculine faces (`masc` folder), ~100k feminine faces (`fem` folder), ~12k faces with a masculine physical frame and feminine attire/makeup (`masc2fem` folder).
* All images are 917x917.
* Each image is about 40kb.
* The examples below are cropped slightly off-center, but in the actual data the characters are more centered.
* The files are named from `1.jpg` through to `N.jpg` (no zero-padding). For `fem`, `N=101499`. For `masc`, `N=103615`. For `masc2fem`, `N=12123`.
## fem examples:
## masc examples:
## masc2fem examples:
| rocca/sims4-faces | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-03-12T06:58:39+00:00 |
1bc98b7baa0108710ff2c0cca45bdf13451fb492 | #this is a test dataset and should not be used by anyone
#i am not the owner of the data
| ronaldvanos/testdata | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-11-09T12:56:07+00:00 |
40779edab2b798158e00080373e75c506e7da8c5 | rookieguy12/dataset | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-11-23T09:00:07+00:00 |
|
1ad7203a10de7e474ea9d3f8030207ee46b19c5a | # Dataset Summary
We present *rosetta-balcanica* a manually extracted multilingual machine translation dataset for low resource
western Balkan languages. The documents were sourced from Organization for Security and Co-operation in Europe (OSCE)
website by applying appropriate language filters. Filtered list of documents can be found [here](https://www.osce.org/resources/documents?filters=%20sm_translations%3A%28sq%29&solrsort=score%20desc&rows=10).
# Languages Supported
Currently, our dataset has documents sourced from [Macedonian](https://github.com/ebegoli/rosetta-balcanica) and [Albanian](https://en.wikipedia.org/wiki/Albanian_language)(also known as Shqip).
| rosettarandd/rosetta_balcanica | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-11-14T17:45:31+00:00 |
5214b2a66405abf87fd229e5c1007985501ffe3e | # DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
The data is based on the original distribution ([link to original website](http://yanran.li/dailydialog)) ([link to paper](https://aclanthology.org/I17-1099/)).
It is created as a convenience to enablefaster prototyping.
# License
DailyDialog dataset is licensed under CC BY-NC-SA 4.0.
If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. Any third party annotation is welcome. Note the dataset may not be adopted for commercial use. | roskoN/dailydialog | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-08-06T13:14:18+00:00 |
b43be7fa91a2d03f72682cca175ec5271d89b880 | # DSTC8 Reddit Corpus
The data is based of the following repository:
> [https://github.com/microsoft/dstc8-reddit-corpus](https://github.com/microsoft/dstc8-reddit-corpus)
The dataset is created is a convenience to enable skipping the lengthy extraction process. | roskoN/dstc8-reddit-corpus | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-04-22T23:19:35+00:00 |
07ad52a2252150dda5dda2ab234915574d6c46b6 | s-myk/test | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-09-27T08:55:17+00:00 |
|
e52b561f896d97568d9c10ecae2816729b2a6036 | s50227harry/test1 | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-03-01T13:15:42+00:00 |
|
00355bee8104a40d80665be0e4570f4a8b2c96f7 |
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/mcobzarenco/mctest/)
- **Paper:** [MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text](https://www.aclweb.org/anthology/D13-1020.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Microsoft Research License Agreement.
### Citation Information
[More Information Needed]
### Contributions
| sagnikrayc/mctest | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"explanations-in-question-answering",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mctest", "language_bcp47": ["en-US"], "tags": ["explanations-in-question-answering"]} | 2022-10-24T23:16:37+00:00 |
ef167bca1e2bd18115fb6b6d58e5c888b30f7fde |
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/bdhingra/quasar)
- **Paper:** [Quasar: Datasets for Question Answering by Search and Reading](https://arxiv.org/abs/1707.03904)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
| sagnikrayc/quasar | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"license:bsd-3-clause",
"arxiv:1707.03904",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en-US"], "license": ["bsd-3-clause"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "extractive-qa"], "paperswithcode_id": "quasar-1"} | 2022-10-25T08:54:36+00:00 |
71a7c86c0432a0320f2b825c4064d00e79c4705b |
# Dataset Card for [author_profiling]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/sag111/Author-Profiling
- **Repository:** https://github.com/sag111/Author-Profiling
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Sboev Alexander](mailto:[email protected])
### Dataset Summary
The corpus for the author profiling analysis contains texts in Russian-language which labeled for 5 tasks:
1) gender -- 13448 texts with the labels, who wrote this: text female or male;
2) age -- 13448 texts with the labels, how old the person who wrote the text. This is a number from 12 to 80. In addition, for the classification task we added 5 age groups: 0-19; 20-29; 30-39; 40-49; 50+;
3) age imitation -- 8460 texts, where crowdsource authors is asked to write three texts:
a) in their natural manner,
b) imitating the style of someone younger,
c) imitating the style of someone older;
4) gender imitation -- 4988 texts, where the crowdsource authors is asked to write texts: in their origin gender and pretending to be the opposite gender;
5) style imitation -- 4988 texts, where crowdsource authors is asked to write a text on behalf of another person of your own gender, with a distortion of the authors usual style.
Dataset is collected sing the Yandex.Toloka service [link](https://toloka.yandex.ru/en).
You can read the data using the following python code:
```
def load_jsonl(input_path: str) -> list:
"""
Read list of objects from a JSON lines file.
"""
data = []
with open(input_path, 'r', encoding='utf-8') as f:
for line in f:
data.append(json.loads(line.rstrip('\n|\r')))
print('Loaded {} records from {}/n'.format(len(data), input_path))
return data
path_to_file = "./data/train.jsonl"
data = load_jsonl(path_to_file)
```
or you can use HuggingFace style:
```
from datasets import load_dataset
train_df = load_dataset('sagteam/author_profiling', split='train')
valid_df = load_dataset('sagteam/author_profiling', split='validation')
test_df = load_dataset('sagteam/author_profiling', split='test')
```
#### Here are some statistics:
1. For Train file:
- No. of documents -- 9564;
- No. of unique texts -- 9553;
- Text length in characters -- min: 197, max: 2984, mean: 500.5;
- No. of documents written -- by men: 4704, by women: 4860;
- No. of unique authors -- 2344; men: 1172, women: 1172;
- Age of the authors -- min: 13, max: 80, mean: 31.2;
- No. of documents by age group -- 0-19: 813, 20-29: 4188, 30-39: 2697, 40-49: 1194, 50+: 672;
- No. of documents with gender imitation: 1215; without gender imitation: 2430; not applicable: 5919;
- No. of documents with age imitation -- younger: 1973; older: 1973; without age imitation: 1973; not applicable: 3645;
- No. of documents with style imitation: 1215; without style imitation: 2430; not applicable: 5919.
2. For Valid file:
- No. of documents -- 1320;
- No. of unique texts -- 1316;
- Text length in characters -- min: 200, max: 2809, mean: 520.8;
- No. of documents written -- by men: 633, by women: 687;
- No. of unique authors -- 336; men: 168, women: 168;
- Age of the authors -- min: 15, max: 79, mean: 32.2;
- No. of documents by age group -- 1-19: 117, 20-29: 570, 30-39: 339, 40-49: 362, 50+: 132;
- No. of documents with gender imitation: 156; without gender imitation: 312; not applicable: 852;
- No. of documents with age imitation -- younger: 284; older: 284; without age imitation: 284; not applicable: 468;
- No. of documents with style imitation: 156; without style imitation: 312; not applicable: 852.
3. For Test file:
- No. of documents -- 2564;
- No. of unique texts -- 2561;
- Text length in characters -- min: 199, max: 3981, mean: 515.6;
- No. of documents written -- by men: 1290, by women: 1274;
- No. of unique authors -- 672; men: 336, women: 336;
- Age of the authors -- min: 12, max: 67, mean: 31.8;
- No. of documents by age group -- 1-19: 195, 20-29: 1131, 30-39: 683, 40-49: 351, 50+: 204;
- No. of documents with gender imitation: 292; without gender imitation: 583; not applicable: 1689;
- No. of documents with age imitation -- younger: 563; older: 563; without age imitation: 563; not applicable: 875;
- No. of documents with style imitation: 292; without style imitation: 583; not applicable: 1689.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class and multi-label text classification.
The baseline models currently achieve the following F1-weighted metrics scores (table):
| Model name | gender | age_group | gender_imitation | age_imitation | style_imitation | no_imitation | average |
| ------------------- | ------ | --------- | ---------------- | ------------- | --------------- | ------------ | ------- |
| Dummy-stratified | 0.49 | 0.29 | 0.56 | 0.32 | 0.57 | 0.55 | 0.46 |
| Dummy-uniform | 0.49 | 0.23 | 0.51 | 0.32 | 0.51 | 0.51 | 0.43 |
| Dummy-most_frequent | 0.34 | 0.27 | 0.53 | 0.17 | 0.53 | 0.53 | 0.40 |
| LinearSVC + TF-IDF | 0.67 | 0.37 | 0.62 | 0.72 | 0.71 | 0.71 | 0.63 |
### Languages
The text in the dataset is in Russian.
## Dataset Structure
### Data Instances
Each instance is a text in Russian with some author profiling annotations.
An example for an instance from the dataset is shown below:
```
{
'id': 'crowdsource_4916',
'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.',
'account_id': 'account_#1239',
'author_id': 411,
'age': 22,
'age_group': '20-29',
'gender': 'male',
'no_imitation': 'with_any_imitation',
'age_imitation': 'None',
'gender_imitation': 'with_gender_imitation',
'style_imitation': 'no_style_imitation'
}
```
### Data Fields
Data Fields includes:
- id -- unique identifier of the sample;
- text -- authors text written by a crowdsourcing user;
- author_id -- unique identifier of the user;
- account_id -- unique identifier of the crowdsource account;
- age -- age annotations;
- age_group -- age group annotations;
- no_imitation -- imitation annotations.
Label codes:
- 'with_any_imitation' -- there is some imitation in the text;
- 'no_any_imitation' -- the text is written without any imitation
- age_imitation -- age imitation annotations.
Label codes:
- 'younger' -- someone younger than the author is imitated in the text;
- 'older' -- someone older than the author is imitated in the text;
- 'no_age_imitation' -- the text is written without age imitation;
- 'None' -- not supported (the text was not written for this task)
- gender_imitation -- gender imitation annotations.
Label codes:
- 'no_gender_imitation' -- the text is written without gender imitation;
- 'with_gender_imitation' -- the text is written with a gender imitation;
- 'None' -- not supported (the text was not written for this task)
- style_imitation -- style imitation annotations.
Label codes:
- 'no_style_imitation' -- the text is written without style imitation;
- 'with_style_imitation' -- the text is written with a style imitation;
- 'None' -- not supported (the text was not written for this task).
### Data Splits
The dataset includes a set of train/valid/test splits with 9564, 1320 and 2564 texts respectively.
The unique authors do not overlap between the splits.
## Dataset Creation
### Curation Rationale
The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided.
#### Who are the source language producers?
Russian-speaking Yandex.Toloka users.
### Annotations
#### Annotation process
We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language.
For age imitation task the respondents are to choose a
topic out of a few suggested, and write three texts on it:
1) Text in their natural manner;
2) Text imitating the style of someone younger;
3) Text imitating the style of someone older.
For gender and style imitation task each author wrote three texts in certain different styles:
1) Text in the authors natural style;
2) Text imitating other gender style;
3) Text in a different style but without gender imitation.
The topics to choose from are the following.
- An attempt to persuade some arbitrary listener to meet the respondent at their place;
- A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy;
- A story about oneself or about someone else, aiming to please the listener and win their favour;
- A description of oneself and one’s potential partner for a dating site;
- An attempt to persuade an unfamiliar person to come;
- A negative tour review.
The task does not pass checking and is considered improper work if it contains:
- Irrelevant answers to the questionnaire;
- Incoherent jumble of words;
- Chunks of text borrowed from somewhere else;
- Texts not conforming to the above list of topics.
Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task.
#### Who are the annotators?
Russian-speaking Yandex.Toloka users.
### Personal and Sensitive Information
All personal data was anonymized. Each author has been assigned an impersonal, unique identifier.
## 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
Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/).
### Licensing Information
Apache License 2.0.
### Citation Information
If you have found our results helpful in your work, feel free to cite our publication.
```
@article{сбоев2022сравнение,
title={СРАВНЕНИЕ ТОЧНОСТЕЙ МЕТОДОВ НА ОСНОВЕ ЯЗЫКОВЫХ И ГРАФОВЫХ НЕЙРОСЕТЕВЫХ МОДЕЛЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ПРИЗНАКОВ АВТОРСКОГО ПРОФИЛЯ ПО ТЕКСТАМ НА РУССКОМ ЯЗЫКЕ},
author={Сбоев, АГ and Молошников, ИА and Рыбка, РБ and Наумов, АВ and Селиванов, АА},
journal={Вестник Национального исследовательского ядерного университета МИФИ},
volume={10},
number={6},
pages={529--539},
year={2021},
publisher={Общество с ограниченной ответственностью МАИК "Наука/Интерпериодика"}
}
```
### Contributions
Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.
| sagteam/author_profiling | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ru"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "multi-label-classification"], "pretty_name": "The Corpus for the analysis of author profiling in Russian-language texts."} | 2022-08-09T11:33:07+00:00 |
34e1bfc950680dbd24f664e30b877e8bbe935c31 | samarlune/Holy_Coran | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-12-07T17:16:23+00:00 |
|
38bfb57d96df0df3b254f0dcde663b6e8d7e4b5a | For details, please refer to the following links.
Github repo: https://github.com/amazon-research/SC2QA-DRIL
Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf) | sc2qa/sc2q_commoncrawl | [
"arxiv:2109.04689",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-03-30T17:34:35+00:00 |
0d9e409f4d7d4b3d14e05c5470fdb89ab1520cca | For details, please refer to the following links.
Github repo: https://github.com/amazon-research/SC2QA-DRIL
Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf) | sc2qa/sc2q_commoncrawl_large | [
"arxiv:2109.04689",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-03-30T17:34:11+00:00 |
e3428b7136488e54a1a3ea6c9390f4d4c1267179 | For details, please refer to the following links.
Github repo: https://github.com/amazon-research/SC2QA-DRIL
Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf) | sc2qa/sc2qa_commoncrawl | [
"arxiv:2109.04689",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-03-30T17:34:27+00:00 |
1204f20946363d1dca1619042fc675852a26998d | sdfufygvjh/fgghuviugviu | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-04-08T21:56:44+00:00 |
|
5d2ac65ef982b54e00944e3f6455b8201c149886 |
# TripClick Baselines with Improved Training Data
*Establishing Strong Baselines for TripClick Health Retrieval* Sebastian Hofstätter, Sophia Althammer, Mete Sertkan and Allan Hanbury
https://arxiv.org/abs/2201.00365
**tl;dr** We create strong re-ranking and dense retrieval baselines (BERT<sub>CAT</sub>, BERT<sub>DOT</sub>, ColBERT, and TK) for TripClick (health ad-hoc retrieval). We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the re-ranking and retrieval setting on TripClick, which were not achieved with the original baselines. We publish the improved training files for everyone to use.
If you have any questions, suggestions, or want to collaborate please don't hesitate to get in contact with us via [Twitter](https://twitter.com/s_hofstaetter) or mail to [email protected]
**Please cite our work as:**
````
@misc{hofstaetter2022tripclick,
title={Establishing Strong Baselines for TripClick Health Retrieval},
author={Sebastian Hofst{\"a}tter and Sophia Althammer and Mete Sertkan and Allan Hanbury},
year={2022},
eprint={2201.00365},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
````
## Published Training Files
We publish the improved training files without the text content instead using the ids from TripClick (with permission from the TripClick owners); for the text content please get the full TripClick dataset from [the TripClick Github page](https://github.com/tripdatabase/tripclick).
Our training file **improved_tripclick_train_triple-ids.tsv** has the format ``query_id pos_passage_id neg_passage_id`` (with tab separation).
----
For more information on how to use the training files see: https://github.com/sebastian-hofstaetter/tripclick | sebastian-hofstaetter/tripclick-training | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:other",
"annotations_creators:clicks",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:tripclick",
"license:apache-2.0",
"arxiv:2201.00365",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["other", "clicks"], "language_creators": ["other"], "language": ["en-US"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["tripclick"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "tripclick-training"} | 2022-07-26T12:16:46+00:00 |
9c33630210cfdc58ab3680f425d44b79c4d03c53 |
# Dataset Card for sidewalk-semantic
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Categories](#data-categories)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Dataset homepage on Segments.ai](https://segments.ai/segments/sidewalk-imagery/)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Bert De Brabandere](mailto:[email protected])
### Dataset Summary
A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on [segments.ai](https://segments.ai/?utm_source=hf&utm_medium=hf-ds&utm_campaign=sidewalk)
### Supported Tasks and Leaderboards
- `semantic-segmentation`: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its [mean IoU (intersection over union)](https://huggingface.co/metrics/mean_iou) to the reference is.
## Dataset Structure
### Data categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 0 | unlabeled | - |
| 1 | flat-road | - |
| 2 | flat-sidewalk | - |
| 3 | flat-crosswalk | - |
| 4 | flat-cyclinglane | - |
| 5 | flat-parkingdriveway | - |
| 6 | flat-railtrack | - |
| 7 | flat-curb | - |
| 8 | human-person | - |
| 9 | human-rider | - |
| 10 | vehicle-car | - |
| 11 | vehicle-truck | - |
| 12 | vehicle-bus | - |
| 13 | vehicle-tramtrain | - |
| 14 | vehicle-motorcycle | - |
| 15 | vehicle-bicycle | - |
| 16 | vehicle-caravan | - |
| 17 | vehicle-cartrailer | - |
| 18 | construction-building | - |
| 19 | construction-door | - |
| 20 | construction-wall | - |
| 21 | construction-fenceguardrail | - |
| 22 | construction-bridge | - |
| 23 | construction-tunnel | - |
| 24 | construction-stairs | - |
| 25 | object-pole | - |
| 26 | object-trafficsign | - |
| 27 | object-trafficlight | - |
| 28 | nature-vegetation | - |
| 29 | nature-terrain | - |
| 30 | sky | - |
| 31 | void-ground | - |
| 32 | void-dynamic | - |
| 33 | void-static | - |
| 34 | void-unclear | - |
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
This dataset only contains one split.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | segments/sidewalk-semantic | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["crowdsourced", "expert-generated"], "language_creators": ["expert-generated"], "license": "cc-by-nc-4.0", "multilinguality": [], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "pretty_name": "sidewalk-semantic"} | 2023-07-10T07:09:07+00:00 |
f0952284922903339c620681b8506e31884c63b4 | # Training Data for Text Embedding Models
This repository contains training files to train text embedding models, e.g. using [sentence-transformers](https://www.SBERT.net).
## Data Format
All files are in a `jsonl.gz` format: Each line contains a JSON-object that represent one training example.
The JSON objects can come in different formats:
- **Pairs:** `["text1", "text2"]` - This is a positive pair that should be close in vector space.
- **Triplets:** `["anchor", "positive", "negative"]` - This is a triplet: The `positive` text should be close to the `anchor`, while the `negative` text should be distant to the `anchor`.
- **Sets:** `{"set": ["text1", "text2", ...]}` A set of texts describing the same thing, e.g. different paraphrases of the same question, different captions for the same image. Any combination of the elements is considered as a positive pair.
- **Query-Pairs:** `{"query": "text", "pos": ["text1", "text2", ...]}` A query together with a set of positive texts. Can be formed to a pair `["query", "positive"]` by randomly selecting a text from `pos`.
- **Query-Triplets:** `{"query": "text", "pos": ["text1", "text2", ...], "neg": ["text1", "text2", ...]}` A query together with a set of positive texts and negative texts. Can be formed to a triplet `["query", "positive", "negative"]` by randomly selecting a text from `pos` and `neg`.
## Available Datasets
**Note: I'm currently in the process to upload the files. Please check again next week to get the full list of datasets**
We measure the performance for each training dataset by training the [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model on it with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss), a batch size of 256, for 2000 training steps. The performance is then averaged across 14 sentence embedding benchmark datasets from diverse domains (Reddit, Twitter, News, Publications, E-Mails, ...).
| Dataset | Description | Size (#Lines) | Performance | Reference |
| --- | --- | :---: | :---: | --- |
| [gooaq_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/gooaq_pairs.jsonl.gz) | (Question, Answer)-Pairs from Google auto suggest | 3,012,496 | 59.06 | [GooAQ](https://github.com/allenai/gooaq)
| [yahoo_answers_title_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_title_answer.jsonl.gz) | (Title, Answer) pairs from Yahoo Answers | 1,198,260 | 58.65 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)
| [msmarco-triplets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/msmarco-triplets.jsonl.gz) | (Question, Answer, Negative)-Triplets from MS MARCO Passages dataset | 499,184 | 58.76 | [MS MARCO Passages](https://github.com/microsoft/MSMARCO-Passage-Ranking)
| [stackexchange_duplicate_questions_title_title.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/stackexchange_duplicate_questions_title_title.jsonl.gz) | (Title, Title) pairs of duplicate questions from StackExchange | 304,525 | 58.47 | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963)
| [eli5_question_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/eli5_question_answer.jsonl.gz) | (Question, Answer)-Pairs from ELI5 dataset | 325,475 | 58.24 | [ELI5](https://huggingface.co/datasets/eli5)
| [yahoo_answers_title_question.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_title_question.jsonl.gz) | (Title, Question_Body) pairs from Yahoo Answers | 659,896 | 58.05 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)
| [squad_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/squad_pairs.jsonl.gz) | (Question, Answer_Passage) Pairs from SQuAD dataset | 87,599 | 58.02 | [SQuAD](https://huggingface.co/datasets/squad)
| [yahoo_answers_question_answer.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/yahoo_answers_question_answer.jsonl.gz) | (Question_Body, Answer) pairs from Yahoo Answers | 681,164 | 57.74 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset)
| [wikihow.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/wikihow.jsonl.gz) | (Summary, Text) from WikiHow | 128,542 | 57.67 | [WikiHow](https://github.com/pvl/wikihow_pairs_dataset)
| [amazon_review_2018.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon_review_2018.jsonl.gz) | (Title, review) pairs from Amazon | 87,877,725 | 57.65 | [Amazon review data (2018)](http://deepyeti.ucsd.edu/jianmo/amazon/index.html)
| [NQ-train_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/NQ-train_pairs.jsonl.gz) | Training pairs (query, answer_passage) from the NQ dataset | 100,231 | 57.48 | [Natural Questions](https://ai.google.com/research/NaturalQuestions)
| [amazon-qa.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/amazon-qa.jsonl.gz) | (Question, Answer) pairs from Amazon | 1,095,290 | 57.48 | [AmazonQA](https://github.com/amazonqa/amazonqa)
| [S2ORC_title_abstract.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/S2ORC_title_abstract.jsonl.gz) | (Title, Abstract) pairs of scientific papers | 41,769,185 | 57.39 | [S2ORC](https://github.com/allenai/s2orc)
| [quora_duplicates.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates.jsonl.gz) | Duplicate question pairs from Quora | 103,663 | 57.36 | [QQP](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
| [WikiAnswers.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/WikiAnswers.jsonl.gz) | Sets of duplicates questions | 27,383,151 | 57.34 | [WikiAnswers Corpus](https://github.com/afader/oqa#wikianswers-corpus)
| [searchQA_top5_snippets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/searchQA_top5_snippets.jsonl.gz) | Question + Top5 text snippets from SearchQA dataset. Top5 | 117,220 | 57.34 | [search_qa](https://huggingface.co/datasets/search_qa)
| [stackexchange_duplicate_questions_title-body_title-body.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/stackexchange_duplicate_questions_title-body_title-body.jsonl.gz) | (Title+Body, Title+Body) pairs of duplicate questions from StackExchange | 250,460 | 57.30 | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963)
| [S2ORC_citations_titles.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/S2ORC_citations_titles.jsonl.gz) | Citation network (paper titles) | 51,030,086 | 57.28 | [S2ORC](https://github.com/allenai/s2orc)
| [stackexchange_duplicate_questions_body_body.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/stackexchange_duplicate_questions_body_body.jsonl.gz) | (Body, Body) pairs of duplicate questions from StackExchange | 250,519 | 57.26 | [Stack Exchange Data API](https://data.stackexchange.com/apple/query/fork/1456963)
| [agnews.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/agnews.jsonl.gz) | (Title, Description) pairs of news articles from the AG News dataset | 1,157,745 | 57.25 | [AG news corpus](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html)
| [quora_duplicates_triplets.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/quora_duplicates_triplets.jsonl.gz) | Duplicate question pairs from Quora with additional hard negatives (mined & denoised by cross-encoder) | 101,762 | 56.97 | [QQP](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
| [AllNLI.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/AllNLI.jsonl.gz) | Combination of SNLI + MultiNLI Triplets: (Anchor, Entailment_Text, Contradiction_Text) | 277,230 | 56.57 | [SNLI](https://huggingface.co/datasets/snli) and [MNLI](https://huggingface.co/datasets/multi_nli)
| [npr.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/npr.jsonl.gz) | (Title, Body) pairs from the npr.org website | 594,384 | 56.44 | [Pushshift](https://files.pushshift.io/news/)
| [specter_train_triples.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/specter_train_triples.jsonl.gz) | Triplets (Title, related_title, hard_negative) for Scientific Publications from Specter | 684,100 | 56.32 | [SPECTER](https://github.com/allenai/specter)
| [SimpleWiki.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz) | Matched pairs (English_Wikipedia, Simple_English_Wikipedia) | 102,225 | 56.15 | [SimpleWiki](https://cs.pomona.edu/~dkauchak/simplification/)
| [PAQ_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/PAQ_pairs.jsonl.gz) | Training pairs (query, answer_passage) from the PAQ dataset | 64,371,441 | 56.11 | [PAQ](https://github.com/facebookresearch/PAQ)
| [altlex.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/altlex.jsonl.gz) | Matched pairs (English_Wikipedia, Simple_English_Wikipedia) | 112,696 | 55.95 | [altlex](https://github.com/chridey/altlex/)
| [ccnews_title_text.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/ccnews_title_text.jsonl.gz) | (Title, article) pairs from the CC News dataset | 614,664 | 55.84 | [CC-News](https://huggingface.co/datasets/cc_news)
| [codesearchnet.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/codesearchnet.jsonl.gz) | CodeSearchNet corpus is a dataset of (comment, code) pairs from opensource libraries hosted on GitHub. It contains code and documentation for several programming languages. | 1,151,414 | 55.80 | [CodeSearchNet](https://huggingface.co/datasets/code_search_net)
| [S2ORC_citations_abstracts.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/S2ORC_citations_abstracts.jsonl.gz) | Citation network (paper abstracts) | 39,567,485 | 55.74 | [S2ORC](https://github.com/allenai/s2orc)
| [sentence-compression.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/sentence-compression.jsonl.gz) | Pairs (long_text, short_text) about sentence-compression | 180,000 | 55.63 | [Sentence-Compression](https://github.com/google-research-datasets/sentence-compression)
| [TriviaQA_pairs.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/TriviaQA_pairs.jsonl.gz) | Pairs (query, answer) from TriviaQA dataset | 73,346 | 55.56 | [TriviaQA](https://huggingface.co/datasets/trivia_qa)
| [cnn_dailymail_splitted.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/cnn_dailymail_splitted.jsonl.gz) | (article, highlight sentence) with individual highlight sentences for each news article | 311,971 | 55.36 | [CNN Dailymail Dataset](https://huggingface.co/datasets/cnn_dailymail)
| [cnn_dailymail.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/cnn_dailymail.jsonl.gz) | (highlight sentences, article) with all highlight sentences as one text for each news article | 311,971 | 55.27 | [CNN Dailymail Dataset](https://huggingface.co/datasets/cnn_dailymail)
| [flickr30k_captions.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/flickr30k_captions.jsonl.gz) | Different captions for the same image from the Flickr30k dataset | 31,783 | 54.68 | [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/)
| [xsum.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/xsum.jsonl.gz) | (Summary, News Article) pairs from XSUM dataset | 226,711 | 53.86 | [xsum](https://huggingface.co/datasets/xsum)
| [coco_captions.jsonl.gz](https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/coco_captions.jsonl.gz) | Different captions for the same image | 82,783 | 53.77 | [COCO](https://cocodataset.org/)
**Disclaimer:** We only distribute these datasets in a specific format, but we do not vouch for their quality or fairness, or claim that you have license to use the dataset. It remains the user's responsibility to determine whether you as a user have permission to use the dataset under the dataset's license and to cite the right owner of the dataset. Please check the individual dataset webpages for the license agreements.
If you're a dataset owner and wish to update any part of it, or do not want your dataset to be included in this dataset collection, feel free to contact me.
| sentence-transformers/embedding-training-data | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-10-17T16:49:20+00:00 |
094614d3a0d3395455a4f5254033cab1110033a8 | # MS MARCO Passages Hard Negatives
[MS MARCO](https://microsoft.github.io/msmarco/) is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine.
This dataset repository contains files that are helpful to train bi-encoder models e.g. using [sentence-transformers](https://www.sbert.net).
## Training Code
You can find here an example how these files can be used to train bi-encoders: [SBERT.net - MS MARCO - MarginMSE](https://www.sbert.net/examples/training/ms_marco/README.html#marginmse)
## cross-encoder-ms-marco-MiniLM-L-6-v2-scores.pkl.gz
This is a pickled dictionary in the format: `scores[qid][pid] -> cross_encoder_score`
It contains 160 million cross-encoder scores for (query, paragraph) pairs using the [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) model.
## msmarco-hard-negatives.jsonl.gz
This is a jsonl file: Each line is a JSON object. It has the following format:
```
{"qid": 867436, "pos": [5238393], "neg": {"bm25": [...], ...}}
```
`qid` is the query-ID from MS MARCO, `pos` is a list with paragraph IDs for positive passages. `neg` is a dictionary where we mined hard negatives using different (mainly dense retrieval) systems.
It contains hard negatives mined from BM25 (using ElasticSearch) and the following dense models:
```
msmarco-distilbert-base-tas-b
msmarco-distilbert-base-v3
msmarco-MiniLM-L-6-v3
distilbert-margin_mse-cls-dot-v2
distilbert-margin_mse-cls-dot-v1
distilbert-margin_mse-mean-dot-v1
mpnet-margin_mse-mean-v1
co-condenser-margin_mse-cls-v1
distilbert-margin_mse-mnrl-mean-v1
distilbert-margin_mse-sym_mnrl-mean-v1
distilbert-margin_mse-sym_mnrl-mean-v2
co-condenser-margin_mse-sym_mnrl-mean-v1
```
From each system, 50 most similar paragraphs were mined for a given query.
| sentence-transformers/msmarco-hard-negatives | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-08-18T15:04:34+00:00 |
641817dd485daeeb871a83fc4cbdbeb3c4b05a35 | # Parallel Sentences for 50+ languages
This repository contains parallel sentences (i.e. English + same sentences in other language) for 50+ different languages in a simple tsv.gz format:
```
english_sentences\tsentence_in_other_language
```
Sentences stem from the [OPUS website](https://opus.nlpl.eu/).
The following datasets are included:
- [Europarl](https://opus.nlpl.eu/Europarl.php)
- [GlobalVoices](https://opus.nlpl.eu/GlobalVoices.php)
- [JW300](https://opus.nlpl.eu/JW300.php)
- [MUSE](https://github.com/facebookresearch/MUSE)
- [News-Commentary](https://opus.nlpl.eu/News-Commentary.php)
- [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles.php)
- [Tatoeba](https://tatoeba.org/)
- Talks - Custom translated transcripts of talks
- [WikiMatrix](https://opus.nlpl.eu/WikiMatrix.php)
- WikiTitles - Custom dataset with parallel Wikipedia titles
## Usage
These sentences can be used to train multi-lingual sentence embedding models. For more details, see [SBERT.net - Multilingual-Model](https://www.sbert.net/examples/training/multilingual/README.html)
**This dataset can not yet be used with Hugging Face dataset library. You must download the individual TSV files.**
| sentence-transformers/parallel-sentences | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-10-19T18:59:59+00:00 |
60f9fef73b56b73dfaefeab3729410f3cf9f846d | # Reddit (Title, Body)-Pairs
This dataset contains jsonl-Files about (title, body) pairs from Reddit. Each line is a JSON object of the following format:
```
{'title': 'The title of a thread', 'body': 'The longer body of the thread', 'subreddit': 'subreddit_name'}
```
The 2021 file contains submissions up until including 2021-06. Entries in the respective files are shuffled on a monthly basis.
The data has been filtered for:
- Remove threads with an upvote_ratio < 0.5
- Only include threads with a title more than 25 characters and bodies with `len(title)+25 < len(body) < 4096`
- Only keep threads with at least 3 comments or at least 3 upvotes.
## Overview
| File | Lines |
| --- | :---: |
| reddit_title_text_2010.jsonl.gz | 431,782
| reddit_title_text_2011.jsonl.gz | 1,673,264
| reddit_title_text_2012.jsonl.gz | 3,727,526
| reddit_title_text_2013.jsonl.gz | 5,713,956
| reddit_title_text_2014.jsonl.gz | 8,538,976
| reddit_title_text_2015.jsonl.gz | 11,064,453
| reddit_title_text_2016.jsonl.gz | 12,224,789
| reddit_title_text_2017.jsonl.gz | 13,558,139
| reddit_title_text_2018.jsonl.gz | 15,552,110
| reddit_title_text_2019.jsonl.gz | 19,224,970
| reddit_title_text_2020.jsonl.gz | 23,030,988
| reddit_title_text_2021.jsonl.gz | 12,704,958
Note: The data comes from [Pushshift](https://files.pushshift.io/reddit/). Please have a look at the respective license of Reddit and Pushshift before using the data.
Be aware that this dataset is not filtered for biases, hate-speech, spam, racial slurm etc. It depicts the content as it is posted on Reddit. | sentence-transformers/reddit-title-body | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-10-19T08:20:35+00:00 |
bd7fbf9eaedeba71e8e3749694de5cae722c1f51 | seregadgl/test_set | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-09-13T17:48:12+00:00 |
|
98e9d50791bbbd493438122d379c16dc51a74bf8 |
# Dataset Card for severo/embellishments
## Dataset Description
- **Homepage:** [Digitised Books - Images identified as Embellishments - Homepage](https://bl.iro.bl.uk/concern/datasets/59d1aa35-c2d7-46e5-9475-9d0cd8df721e)
- **Point of Contact:** [Sylvain Lesage](mailto:[email protected])
### Dataset Summary
This small dataset contains the thumbnails of the first 100 entries of [Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG](https://bl.iro.bl.uk/concern/datasets/59d1aa35-c2d7-46e5-9475-9d0cd8df721e). It has been uploaded to the Hub to reproduce the tutorial by Daniel van Strien: [Using 🤗 datasets for image search](https://danielvanstrien.xyz/metadata/deployment/huggingface/ethics/huggingface-datasets/faiss/2022/01/13/image_search.html).
## Dataset Structure
### Data Instances
A typical row contains an image thumbnail, its filename, and the year of publication of the book it was extracted from.
An example looks as follows:
```
{
'fname': '000811462_05_000205_1_The Pictorial History of England being a history of the people as well as a hi_1855.jpg',
'year': '1855',
'path': 'embellishments/1855/000811462_05_000205_1_The Pictorial History of England being a history of the people as well as a hi_1855.jpg',
'img': ...
}
```
### Data Fields
- `fname`: the image filename.
- `year`: a string with the year of publication of the book from which the image has been extracted
- `path`: local path to the image
- `img`: a thumbnail of the image with a max height and width of 224 pixels
### Data Splits
The dataset only contains 100 rows, in a single 'train' split.
## Dataset Creation
### Curation Rationale
This dataset was chosen by Daniel van Strien for his tutorial [Using 🤗 datasets for image search](https://danielvanstrien.xyz/metadata/deployment/huggingface/ethics/huggingface-datasets/faiss/2022/01/13/image_search.html), which includes the code in Python to do it.
### Source Data
#### Initial Data Collection and Normalization
As stated on the British Library webpage:
> The images were algorithmically gathered from 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. The images are in .JPEG format.d BCP-47 code is `en`.
#### Who are the source data producers?
British Library, British Library Labs, Adrian Edwards (Curator), Neil Fitzgerald (Contributor ORCID)
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
This is a toy dataset that aims at:
- validating the process described in the tutorial [Using 🤗 datasets for image search](https://danielvanstrien.xyz/metadata/deployment/huggingface/ethics/huggingface-datasets/faiss/2022/01/13/image_search.html) by Daniel van Strien,
- showing the [dataset viewer](https://huggingface.co/datasets/severo/embellishments/viewer/severo--embellishments/train) on an image dataset.
## Additional Information
### Dataset Curators
The dataset was created by Sylvain Lesage at Hugging Face, to replicate the tutorial [Using 🤗 datasets for image search](https://danielvanstrien.xyz/metadata/deployment/huggingface/ethics/huggingface-datasets/faiss/2022/01/13/image_search.html) by Daniel van Strien.
### Licensing Information
CC0 1.0 Universal Public Domain
| severo/embellishments | [
"annotations_creators:no-annotation",
"size_categories:n<1K",
"source_datasets:original",
"license:cc0-1.0",
"region:us"
] | 2022-03-02T23:29:22+00:00 | {"annotations_creators": ["no-annotation"], "license": ["cc0-1.0"], "size_categories": ["n<1K"], "source_datasets": ["original"], "pretty_name": "Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG"} | 2022-10-25T08:56:40+00:00 |
95a13b195e2d2a859bdf1c70e0906c8b5259e35a | shaina/covid19 | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2022-01-28T21:59:24+00:00 |
|
4dd08ebac7cf221d87d3175fca5d5562d3923c34 | A smaller version (100 samples) of https://huggingface.co/datasets/bs-modeling-metadata/website_metadata_c4 | shanya/website_metadata_c4_toy | [
"region:us"
] | 2022-03-02T23:29:22+00:00 | {} | 2021-10-04T15:55:11+00:00 |
Subsets and Splits