Dataset Card Creation Guide

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

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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

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Data Fields

  • src_tag: string text in source language
  • tgt_tag: string translation 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

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Source Data

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Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

      title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation}, 
      author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},


Thanks to @vasudevgupta7 for adding this dataset.

Models trained or fine-tuned on opus100