Update files from the datasets library (from 1.9.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.9.0

README.md

@@ -12,7 +12,7 @@ languages:
 - be
 - bg
 - bn
-- bn_rom
+- bn-Latn
 - br
 - bs
 - ca
@@ -39,7 +39,7 @@ languages:
 - ha
 - he
 - hi
-- hi_rom
+- hi-Latn
 - hr
 - ht
 - hu
@@ -71,7 +71,7 @@ languages:
 - mr
 - ms
 - my
-- my_zaw
+- my-x-zawgyi
 - ne
 - nl
 - 'no'
@@ -100,9 +100,9 @@ languages:
 - sv
 - sw
 - ta
-- ta_rom
+- ta-Latn
 - te
-- te_rom
+- te-Latn
 - th
 - tl
 - tn
@@ -110,7 +110,7 @@ languages:
 - ug
 - uk
 - ur
-- ur_rom
+- ur-Latn
 - uz
 - vi
 - wo
@@ -125,7 +125,10 @@ licenses:
 multilinguality:
 - multilingual
 size_categories:
-- n>1M
+  am:
+  - 1M<n<10M
+  sr:
+  - 10M<n<100M
 source_datasets:
 - original
 task_categories:
@@ -133,9 +136,10 @@ task_categories:
 task_ids:
 - language-modeling
 paperswithcode_id: cc100
+pretty_name: CC100
 ---
 
-# Dataset Card Creation Guide
+# Dataset Card for CC100
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -190,15 +194,27 @@ E.g.
 
 ### Data Instances
 
-[More Information Needed]
+An example from the `am` configuration:
+
+```
+{'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'}
+```
 
 ### Data Fields
 
-[More Information Needed]
+The data fields are:
+
+- id: id of the example
+- text: content as a string
 
 ### Data Splits
 
-[More Information Needed]
+Sizes of some configurations:
+
+|   name   |train|
+|----------|----:|
+|am|3124561|
+|sr|35747957|
 
 ## Dataset Creation
 
@@ -260,7 +276,53 @@ E.g.
 
 ### Citation Information
 
-[More Information Needed]
+```bibtex
+@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.",
+}
+```
+
+```bibtex
+@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