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SEACrowd/cc100

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Unclear

The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at b9c955b
Licence files: LICENSE, licence not identifiedEvidence: LICENSE at b9c955b
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

self-supervised-pretraining

Citation text from the card

@inproceedings{conneau-etal-2020-unsupervised,
    title = "Unsupervised Cross-lingual Representation Learning at Scale",
    author = "Conneau, Alexis  and
      Khandelwal, Kartikay  and
      Goyal, Naman  and
      Chaudhary, Vishrav  and
      Wenzek, Guillaume  and
      Guzm{'a}n, Francisco  and
      Grave, Edouard  and
      Ott, Myle  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    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.747",
    doi = "10.18653/v1/2020.acl-main.747",
    pages = "8440--8451",
    abstract = "This paper shows that pretraining multilingual language models
    at scale leads to significant performance gains for a wide range of
    cross-lingual transfer tasks. We train a Transformer-based masked language
    model on one hundred languages, using more than two terabytes of filtered
    CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms
    multilingual BERT (mBERT) on a variety of cross-lingual benchmarks,
    including +14.6{%} average accuracy on XNLI, +13{%} average F1 score on
    MLQA, and +2.4{%} F1 score on NER. XLM-R performs particularly well on
    low-resource languages, improving 15.7{%} in XNLI accuracy for Swahili and
    11.4{%} for Urdu over previous XLM models. We

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dataset_licence({ "id": "SEACrowd/cc100" })
signal: "unclear"
stated_licence: null
sources: 0