OpenLID

Fast natural language identification for 200+ languages, plus all the data to train the model.

Features

Supports 201 languages
High performance
Fast and easy to use
Fully transparent: training data and per-language performance openly available

Get started

OpenLID is a fastText model.

To download and decompress:

wget https://data.statmt.org/lid/lid201-model.bin.gz
pigz -d lid201-model.bin.gz

Example to get most likely labels for $DATA:

fasttext predict lid201-model.bin $DATA > output.fasttext

Dataset

Download the dataset (c. 21GB) and convert it to fastText training format:

wget https://data.statmt.org/lid/lid201-data.tsv.gz
pigz -dc lid201-data.tsv.gz | awk -F"\t" '{print"__label__"$2" "$1}' > lid201-data.fasttext.tsv

Each tab-separated line consists of a sentence in one of the 201 languages, a code for the language, and script (e.g. wol_Latn = Wolof in Latin script), and the source of that line of data.

The classes in the training dataset have been sampled to help ameliorate class skew. This means that the larger classes have been subsampled and the smaller classes have been upsampled. If you would like the unsampled datset, you can download it from https://data.statmt.org/lid/lid201-data-unsampled.tsv.gz.

Citations

If you use our model, please cite us. If you use the dataset, please cite us plus all the articles in the citations.bib file. Thank you to everyone who put in so much work creating these datasets!

Licenses

The model is licensed under the GNU General Public License v3.0. The individual datasets that make up the training dataset have different licenses but all allow (at minimum) free use for research - a full list is available in this repo.