Datasets:

allenai
/

wmt22_african

	# Dataset Card for allenai/wmt22_african

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [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)

	## Dataset Description

	- Homepage: https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html
	- Repository: [Needs More Information]
	- Paper: [Needs More Information]
	- Leaderboard: [Needs More Information]
	- Point of Contact: [Needs More Information]

	### Dataset Summary

	This dataset was created based on [metadata](https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african) for mined bitext released by Meta AI. It contains bitext for 248 pairs for the African languages that are part of the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).

	#### How to use the data

	There are two ways to access the data:

	* Via the Hugging Face Python datasets library

	```
	from datasets import load_dataset
	dataset = load_dataset("allenai/wmt22_african")
	```

	* Clone the git repo
	```
	git lfs install
	git clone https://huggingface.co/datasets/allenai/wmt22_african
	```


	### Supported Tasks and Leaderboards

	This dataset is one of resources allowed under the Constrained Track for the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).

	### Languages

	#### Focus languages

	\| Language \| Code \|
	\| -------- \| ---- \|
	\| Afrikaans \| afr \|
	\| Amharic \| amh \|
	\| Chichewa \| nya \|
	\| Nigerian Fulfulde \| fuv \|
	\| Hausa \| hau \|
	\| Igbo \| ibo \|
	\| Kamba \| kam \|
	\| Kinyarwanda \| kin \|
	\| Lingala \| lin \|
	\| Luganda \| lug \|
	\| Luo \| luo \|
	\| Northern Sotho \| nso \|
	\| Oroma \| orm \|
	\| Shona \| sna \|
	\| Somali \| som \|
	\| Swahili \| swh \|
	\| Swati \| ssw \|
	\| Tswana \| tsn \|
	\| Umbundu \| umb \|
	\| Wolof \| wol \|
	\| Xhosa \| xho \|
	\| Xitsonga \| tso \|
	\| Yoruba \| yor \|
	\| Zulu \| zul \|

	Colonial linguae francae: English - eng, French - fra

	## Dataset Structure

	The dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.

	### Data Instances

	The dataset contains 248 language pairs.

	Sentence counts for each pair can be found [here](https://huggingface.co/datasets/allenai/wmt22_african/blob/main/sentence_counts.txt).

	### Data Fields

	Every instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser_score', 'source_sentence_lid', 'target_sentence_lid', where 'lid' is language classification probability.

	Example:
	```
	{
	'translation':
	{
	'afr': 'In Mei 2007, in ooreenstemming met die spesifikasies van die Java Gemeenskapproses, het Sun Java tegnologie geherlisensieer onder die GNU General Public License.',
	'eng': 'As of May 2007, in compliance with the specifications of the Java Community Process, Sun relicensed most of its Java technologies under the GNU General Public License.'
	},
	'laser_score': 1.0717015266418457,
	'source_sentence_lid': 0.9996600151062012,
	'target_sentence_lid': 0.9972000122070312
	}
	```
	### Data Splits

	The data is not split into train, dev, and test.

	## Dataset Creation

	### Curation Rationale

	Parallel sentences from monolingual data in Common Crawl and ParaCrawl were identified via [Language-Agnostic Sentence Representation (LASER)](https://github.com/facebookresearch/LASER) encoders.

	### Source Data

	#### Initial Data Collection and Normalization

	Monolingual data was obtained from Common Crawl and ParaCrawl.

	#### Who are the source language producers?

	Contributors to web text in Common Crawl and ParaCrawl.

	### Annotations

	#### Annotation process

	The data was not human annotated. The metadata used to create the dataset can be found here: https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african

	#### Who are the annotators?

	The data was not human annotated. Parallel text from Common Crawl and Para Crawl monolingual data were identified automatically via [LASER](https://github.com/facebookresearch/LASER) encoders.

	### Personal and Sensitive Information

	[Needs More Information]

	## Considerations for Using the Data

	### Social Impact of Dataset

	This dataset provides data for training machine learning systems for many languages that have low resources available for NLP.

	### Discussion of Biases

	Biases in the data have not been studied.

	### Other Known Limitations

	[Needs More Information]

	## Additional Information

	### Dataset Curators

	[Needs More Information]

	### Licensing Information

	The dataset is released under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the Internet Archive [Terms of Use](https://archive.org/about/terms.php) in respect of the content contained in the dataset.

	### Citation Information

	NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022.

	### Contributions

	We thank the AllenNLP team at AI2 for hosting and releasing this data, including [Akshita Bhagia](https://akshitab.github.io/) (for engineering efforts to create the huggingface dataset), and [Jesse Dodge](https://jessedodge.github.io/) (for organizing the connection).