Datasets:
metadata
annotations_creators:
- no-annotation
language_creators:
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language:
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license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- extended
task_categories:
- translation
task_ids: []
paperswithcode_id: opus-100
pretty_name: Opus100
config_names:
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path: bg-en/train-*
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Dataset Card for Opus100
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). Selected the languages based on the volume of parallel data available in OPUS.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
Dataset Structure
Data Instances
{
"ca": "El departament de bombers té el seu propi equip d'investigació.",
"en": "Well, the fire department has its own investigative unit."
}
Data Fields
src_tag:stringtext in source languagetgt_tag:stringtranslation of source language in target language
Data Splits
The dataset is split into training, development, and test portions. Data was prepared by randomly sampled up to 1M sentence pairs per language pair for training and up to 2000 each for development and test. To ensure that there was no overlap (at the monolingual sentence level) between the training and development/test data, they applied a filter during sampling to exclude sentences that had already been sampled. Note that this was done cross-lingually so that, for instance, an English sentence in the Portuguese-English portion of the training data could not occur in the Hindi-English test set.
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
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contributions
Thanks to @vasudevgupta7 for adding this dataset.