Datasets:

Multilinguality:
multilingual
Size Categories:
10M<n<100M
1M<n<10M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
cc100 / README.md
system's picture
system HF staff
Update files from the datasets library (from 1.9.0)
a9db7bf
---
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
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## 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': 'α‰°αˆˆα‹‹α‹‹αŒ­ α‹¨αŒα‹΅αŒα‹³ αŠ αŠ•αŒαˆ αˆ™α‰… αŠ αŠ•α‰€αˆ³α‰…αˆ·αˆ ቲ-አሞሌ አαŒ₯α‰…αˆΌ ...\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
```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
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.