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README.md

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+---
+license: mit
+tags:
+- self-supervised-pretraining
+language:
+- ind
+- jav
+- sun
+---
+
+# cc100
+
+This corpus is an attempt to recreate the dataset used for training
+
+        XLM-R. This corpus comprises of monolingual data for 100+ languages and
+
+        also includes data for romanized languages (indicated by *_rom). This
+
+        was constructed using the urls and paragraph indices provided by the
+
+        CC-Net repository by processing January-December 2018 Commoncrawl
+
+        snapshots. Each file comprises of documents separated by
+
+        double-newlines and paragraphs within the same document separated by a
+
+        newline. The data is generated using the open source CC-Net repository.
+
+        No claims of intellectual property are made on the work of preparation
+
+        of the corpus.
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## Citation
+
+```
+@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",
+}
+```
+
+## License
+
+MIT
+
+## Homepage
+
+[https://data.statmt.org/cc-100/](https://data.statmt.org/cc-100/)
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)