Dataset Card for "flores"

Dataset Summary

Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

neen

  • Size of downloaded dataset files: 1.47 MB
  • Size of the generated dataset: 1.77 MB
  • Total amount of disk used: 3.24 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "translation": "{\"en\": \"This is the wrong translation!\", \"ne\": \"यस वाहेक आगम पूजा, तारा पूजा, व्रत आदि पनि घरभित्र र वाहिर दुवै स्थानमा गरेको पा..."
}

sien

  • Size of downloaded dataset files: 1.47 MB
  • Size of the generated dataset: 1.92 MB
  • Total amount of disk used: 3.40 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "translation": "{\"en\": \"This is the wrong translation!\", \"si\": \"එවැනි ආවරණයක් ලබාදීමට රක්ෂණ සපයන්නෙකු කැමති වුවත් ඒ සාමාන් යයෙන් බොහෝ රටවල පොදු ..."
}

Data Fields

The data fields are the same among all splits.

neen

  • translation: a multilingual string variable, with possible languages including ne, en.

sien

  • translation: a multilingual string variable, with possible languages including si, en.

Data Splits

name validation test
neen 2560 2836
sien 2899 2767

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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

@misc{guzmn2019new,
    title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
    author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
    year={2019},
    eprint={1902.01382},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.

Models trained or fine-tuned on flores

None yet