Update files from the datasets library (from 1.18.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.18.0

README.md

@@ -1,6 +1,6 @@
 ---
 annotations_creators:
-- found
+- no-annotation
 language_creators:
 - found
 languages:
@@ -167,7 +167,7 @@ pretty_name: CC100
 
 ## Dataset Description
 
-- **Homepage:** http://data.statmt.org/cc-100/
+- **Homepage:** https://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]
@@ -175,20 +175,19 @@ pretty_name: CC100
 
 ### 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")`
-
+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.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+CC-100 is mainly inteded to pretrain language models and word represantations.
 
 ### Languages
 
-[More Information Needed]
+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: https://data.statmt.org/cc-100/
+E.g.
+
+`dataset = load_dataset("cc100", lang="en")`
 
 ## Dataset Structure
 
@@ -200,6 +199,8 @@ An example from the `am` configuration:
 {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'}
 ```
 
+Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character.
+
 ### Data Fields
 
 The data fields are:
@@ -232,23 +233,23 @@ Sizes of some configurations:
 
 #### Who are the source language producers?
 
-[More Information Needed]
+The data comes from multiple web pages in a large variety of languages.
 
 ### Annotations
 
-[More Information Needed]
+The dataset does not contain any additional annotations.
 
 #### Annotation process
 
-[More Information Needed]
+[N/A]
 
 #### Who are the annotators?
 
-[More Information Needed]
+[N/A]
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+Being constructed from Common Crawl, personal and sensitive information might be present. This **must** be considered before training deep learning models with CC-100, specially in the case of text-generation models.
 
 ## Considerations for Using the Data
 
@@ -268,11 +269,11 @@ Sizes of some configurations:
 
 ### Dataset Curators
 
-[More Information Needed]
+This dataset was prepared by [Statistical Machine Translation at the University of Edinburgh](https://www.statmt.org/ued/) using the [CC-Net](https://github.com/facebookresearch/cc_net) toolkit by Facebook Research.
 
 ### Licensing Information
 
-[More Information Needed]
+Statistical Machine Translation at the University of Edinburgh makes no claims of intellectual property on the work of preparation of the corpus. By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset.
 
 ### Citation Information
 
@@ -326,4 +327,4 @@ Sizes of some configurations:
 
 ### Contributions
 
-Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

cc100.py

@@ -20,7 +20,7 @@ import datasets
 _DESCRIPTION = """\
 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.
 """
-_HOMEPAGE_URL = "http://data.statmt.org/cc-100/"
+_HOMEPAGE_URL = "https://data.statmt.org/cc-100/"
 _CITATION = """\
 @inproceedings{conneau-etal-2020-unsupervised,
     title = "Unsupervised Cross-lingual Representation Learning at Scale",
@@ -67,7 +67,7 @@ _CITATION = """\
 """
 
 _VERSION = "1.0.0"
-_BASE_URL = "http://data.statmt.org/cc-100/{}.txt.xz"
+_BASE_URL = "https://data.statmt.org/cc-100/{}.txt.xz"
 
 # Please note: due to the size of the data, only few examples are provided.
 # However, you can pass the lang parameter in config to fetch data of any language in the corpus

dataset_infos.json

@@ -1 +1 @@
-{"am": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "http://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "am", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 935440775, "num_examples": 3124561, "dataset_name": "cc100"}}, "download_checksums": {"http://data.statmt.org/cc-100/am.txt.xz": {"num_bytes": 138821056, "checksum": "97102e1118dee22103349ca38315aecee8cdcc62cb7d7f70d803d37c73525cf5"}}, "download_size": 138821056, "post_processing_size": null, "dataset_size": 935440775, "size_in_bytes": 1074261831}, "sr": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "http://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "sr", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10299427460, "num_examples": 35747957, "dataset_name": "cc100"}}, "download_checksums": {"http://data.statmt.org/cc-100/sr.txt.xz": {"num_bytes": 1578989320, "checksum": "e90b3df3955f7da69ccf2dd703aa89e54a4b05ee9a1e6f2bf9b34f11f11b4262"}}, "download_size": 1578989320, "post_processing_size": null, "dataset_size": 10299427460, "size_in_bytes": 11878416780}, "ka": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "http://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "ka", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10228918845, "num_examples": 31708119, "dataset_name": "cc100"}}, "download_checksums": {"http://data.statmt.org/cc-100/ka.txt.xz": {"num_bytes": 1100446372, "checksum": "e4d155ce56a203d819787b3d7e5c97383e6132b20f924680502a21eea216528f"}}, "download_size": 1100446372, "post_processing_size": null, "dataset_size": 10228918845, "size_in_bytes": 11329365217}}
+{"am": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "am", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 935440775, "num_examples": 3124561, "dataset_name": "cc100"}}, "download_checksums": {"https://data.statmt.org/cc-100/am.txt.xz": {"num_bytes": 138821056, "checksum": "97102e1118dee22103349ca38315aecee8cdcc62cb7d7f70d803d37c73525cf5"}}, "download_size": 138821056, "post_processing_size": null, "dataset_size": 935440775, "size_in_bytes": 1074261831}, "sr": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "sr", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10299427460, "num_examples": 35747957, "dataset_name": "cc100"}}, "download_checksums": {"https://data.statmt.org/cc-100/sr.txt.xz": {"num_bytes": 1578989320, "checksum": "e90b3df3955f7da69ccf2dd703aa89e54a4b05ee9a1e6f2bf9b34f11f11b4262"}}, "download_size": 1578989320, "post_processing_size": null, "dataset_size": 10299427460, "size_in_bytes": 11878416780}, "ka": {"description": "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.\n", "citation": "@inproceedings{conneau-etal-2020-unsupervised,\n    title = \"Unsupervised Cross-lingual Representation Learning at Scale\",\n    author = \"Conneau, Alexis  and\n      Khandelwal, Kartikay  and\n      Goyal, Naman  and\n      Chaudhary, Vishrav  and\n      Wenzek, Guillaume  and\n      Guzm{'a}n, Francisco  and\n      Grave, Edouard  and\n      Ott, Myle  and\n      Zettlemoyer, Luke  and\n      Stoyanov, Veselin\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.747\",\n    doi = \"10.18653/v1/2020.acl-main.747\",\n    pages = \"8440--8451\",\n    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.\",\n}\n@inproceedings{wenzek-etal-2020-ccnet,\n    title = \"{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data\",\n    author = \"Wenzek, Guillaume  and\n      Lachaux, Marie-Anne  and\n      Conneau, Alexis  and\n      Chaudhary, Vishrav  and\n      Guzm{'a}n, Francisco  and\n      Joulin, Armand  and\n      Grave, Edouard\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.494\",\n    pages = \"4003--4012\",\n    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.\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://data.statmt.org/cc-100/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cc100", "config_name": "ka", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10228918845, "num_examples": 31708119, "dataset_name": "cc100"}}, "download_checksums": {"https://data.statmt.org/cc-100/ka.txt.xz": {"num_bytes": 1100446372, "checksum": "e4d155ce56a203d819787b3d7e5c97383e6132b20f924680502a21eea216528f"}}, "download_size": 1100446372, "post_processing_size": null, "dataset_size": 10228918845, "size_in_bytes": 11329365217}}