---
dataset_info:
  features:
  - name: br
    dtype: string
  - name: fr
    dtype: string
  splits:
  - name: train
    num_bytes: 2708618
    num_examples: 30191
  download_size: 1805425
  dataset_size: 2708618
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- translation
language:
- br
- fr
multilinguality:
- multilingual
---


## Description
Paires breton/français du jeu de données MultiCCAligned disponible sur [OPUS](https://opus.nlpl.eu/results/br&fr/corpus-result-table).  

**⚠ Attention ⚠** :  il y a des problèmes d'alignement. Ce jeu de données n'est donc pas utilisbale tel quel et un post-processing serait à effectuer.

## Citations
#### MultiCCAligned
```
@inproceedings{el-kishky-etal-2020-ccaligned,
    title = "{CCA}ligned: A Massive Collection of Cross-Lingual Web-Document Pairs",
    author = "El-Kishky, Ahmed  and       Chaudhary, Vishrav  and       Guzm{\'a}n, Francisco  and      Koehn, Philipp",
    editor = "Webber, Bonnie  and       Cohn, Trevor  and       He, Yulan  and       Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.480",
    doi = "10.18653/v1/2020.emnlp-main.480",
    pages = "5960--5969",
    abstract = "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.",
}

```
#### OPUS
```
@inbook{4992de1b5fb34f3e9691772606b36edf,
title = "News from OPUS - A Collection of Multilingual Parallel Corpora with Tools and Interfaces",
author = "J{\"o}rg Tiedemann",
year = "2009",
language = "odefinierat/ok{\"a}nt",
volume = "V",
pages = "237--248",
editor = "N. Nicolov and K. Bontcheva and G. Angelova and R. Mitkov",
booktitle = "Recent Advances in Natural Language Processing",

}
```