metadata

annotations_creators:
  - found
language_creators:
  - found
languages:
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - bn-Latn
  - br
  - bs
  - ca
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - ff
  - fi
  - fr
  - fy
  - ga
  - gd
  - gl
  - gn
  - gu
  - ha
  - he
  - hi
  - hi-Latn
  - hr
  - ht
  - hu
  - hy
  - id
  - ig
  - is
  - it
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lg
  - li
  - ln
  - lo
  - lt
  - lv
  - mg
  - mk
  - ml
  - mn
  - mr
  - ms
  - my
  - my-x-zawgyi
  - ne
  - nl
  - 'no'
  - ns
  - om
  - or
  - pa
  - pl
  - ps
  - pt
  - qu
  - rm
  - ro
  - ru
  - sa
  - si
  - sc
  - sd
  - sk
  - sl
  - so
  - sq
  - sr
  - ss
  - su
  - sv
  - sw
  - ta
  - ta-Latn
  - te
  - te-Latn
  - th
  - tl
  - tn
  - tr
  - ug
  - uk
  - ur
  - ur-Latn
  - uz
  - vi
  - wo
  - xh
  - yi
  - yo
  - zh-Hans
  - zh-Hant
  - zu
licenses:
  - unknown
multilinguality:
  - multilingual
size_categories:
  am:
    - 1M<n<10M
  sr:
    - 10M<n<100M
source_datasets:
  - original
task_categories:
  - sequence-modeling
task_ids:
  - language-modeling
paperswithcode_id: cc100
pretty_name: CC100

Dataset Card for CC100

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: http://data.statmt.org/cc-100/
Repository: None
Paper: https://www.aclweb.org/anthology/2020.acl-main.747.pdf, https://www.aclweb.org/anthology/2020.lrec-1.494.pdf
Leaderboard: [More Information Needed]
Point of Contact: [More Information Needed]

Dataset Summary

To load a language which isn't part of the config, all you need to do is specify the language code in the config. You can find the valid languages in Homepage section of Dataset Description: http://data.statmt.org/cc-100/ E.g.

dataset = load_dataset("cc100", lang="en")

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

An example from the am configuration:

{'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'}

Data Fields

The data fields are:

id: id of the example
text: content as a string

Data Splits

Sizes of some configurations:

name	train
am	3124561
sr	35747957

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

@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 also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.",
}

@inproceedings{wenzek-etal-2020-ccnet,
    title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
    author = "Wenzek, Guillaume  and
      Lachaux, Marie-Anne  and
      Conneau, Alexis  and
      Chaudhary, Vishrav  and
      Guzm{\'a}n, Francisco  and
      Joulin, Armand  and
      Grave, Edouard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.494",
    pages = "4003--4012",
    abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

Contributions

Thanks to @abhishekkrthakur for adding this dataset.

Datasets:
cc100

Dataset Card for CC100

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions