Update dataset card

README.md

@@ -138,7 +138,7 @@ task_ids:
 - language-modeling
 - masked-language-modeling
 paperswithcode_id: cc100
-pretty_name: CC100
+pretty_name: CC-100
 dataset_info:
 - config_name: am
   features:
@@ -181,7 +181,7 @@ config_names:
 - sr
 ---
 
-# Dataset Card for CC100
+# Dataset Card for CC-100
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -210,8 +210,11 @@ config_names:
 ## Dataset Description
 
 - **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
+- **Repository:** [More Information Needed]
+- **Paper:** https://aclanthology.org/2020.acl-main.747/
+- **Paper:** https://aclanthology.org/2020.lrec-1.494/
+- **Paper:** https://arxiv.org/abs/1911.02116
+- **Paper:** https://arxiv.org/abs/1911.00359
 - **Leaderboard:** [More Information Needed]
 - **Point of Contact:** [More Information Needed]
 
@@ -221,7 +224,7 @@ This corpus is an attempt to recreate the dataset used for training XLM-R. This
 
 ### Supported Tasks and Leaderboards
 
-CC-100 is mainly inteded to pretrain language models and word represantations.
+CC-100 is mainly intended to pretrain language models and word representations.
 
 ### Languages
 
@@ -319,6 +322,8 @@ Statistical Machine Translation at the University of Edinburgh makes no claims o
 
 ### Citation Information
 
+Please cite the following if you found the resources in this corpus useful:
+
 ```bibtex
 @inproceedings{conneau-etal-2020-unsupervised,
     title = "Unsupervised Cross-lingual Representation Learning at Scale",
@@ -332,12 +337,16 @@ Statistical Machine Translation at the University of Edinburgh makes no claims o
       Ott, Myle  and
       Zettlemoyer, Luke  and
       Stoyanov, Veselin",
+    editor = "Jurafsky, Dan  and
+      Chai, Joyce  and
+      Schluter, Natalie  and
+      Tetreault, Joel",
     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",
+    url = "https://aclanthology.org/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.",
@@ -354,12 +363,26 @@ Statistical Machine Translation at the University of Edinburgh makes no claims o
       Guzm{\'a}n, Francisco  and
       Joulin, Armand  and
       Grave, Edouard",
-    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    editor = "Calzolari, Nicoletta  and
+      B{\'e}chet, Fr{\'e}d{\'e}ric  and
+      Blache, Philippe  and
+      Choukri, Khalid  and
+      Cieri, Christopher  and
+      Declerck, Thierry  and
+      Goggi, Sara  and
+      Isahara, Hitoshi  and
+      Maegaard, Bente  and
+      Mariani, Joseph  and
+      Mazo, H{\'e}l{\`e}ne  and
+      Moreno, Asuncion  and
+      Odijk, Jan  and
+      Piperidis, Stelios",
+    booktitle = "Proceedings of the Twelfth 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",
+    url = "https://aclanthology.org/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",

cc100.py

@@ -29,20 +29,23 @@ _CITATION = """\
       Goyal, Naman  and
       Chaudhary, Vishrav  and
       Wenzek, Guillaume  and
-      Guzm{'a}n, Francisco  and
+      Guzm{\\'a}n, Francisco  and
       Grave, Edouard  and
       Ott, Myle  and
       Zettlemoyer, Luke  and
       Stoyanov, Veselin",
+    editor = "Jurafsky, Dan  and
+      Chai, Joyce  and
+      Schluter, Natalie  and
+      Tetreault, Joel",
     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",
+    url = "https://aclanthology.org/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",
@@ -50,17 +53,30 @@ _CITATION = """\
       Lachaux, Marie-Anne  and
       Conneau, Alexis  and
       Chaudhary, Vishrav  and
-      Guzm{'a}n, Francisco  and
+      Guzm{\\'a}n, Francisco  and
       Joulin, Armand  and
       Grave, Edouard",
-    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    editor = "Calzolari, Nicoletta  and
+      B{\\'e}chet, Fr{\\'e}d{\\'e}ric  and
+      Blache, Philippe  and
+      Choukri, Khalid  and
+      Cieri, Christopher  and
+      Declerck, Thierry  and
+      Goggi, Sara  and
+      Isahara, Hitoshi  and
+      Maegaard, Bente  and
+      Mariani, Joseph  and
+      Mazo, H{\\'e}l{\\`e}ne  and
+      Moreno, Asuncion  and
+      Odijk, Jan  and
+      Piperidis, Stelios",
+    booktitle = "Proceedings of the Twelfth 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",
+    url = "https://aclanthology.org/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",
 }