Openlid

🚀 OpenLID

OpenLID is a high-coverage, high-performance language identification model that covers 201 languages and encourages further research with openly available training data and per-language performance.

🚀 Quick Start

Here is how to use this model to detect the language of a given text:

>>> import fasttext
>>> from huggingface_hub import hf_hub_download

>>> model_path = hf_hub_download(repo_id="laurievb/OpenLID", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")

(('__label__eng_Latn',), array([0.81148803]))

>>> model.predict("Hello, world!", k=5)

(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'), 
 array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))

✨ Features

• High-coverage: Covers 201 languages.
• High-performance: A high-performance language identification model.
• Open availability: The training data and per-language performance are openly available to encourage further research.

📦 Installation

The model can be downloaded from the Hugging Face Hub. You can use the following code to download the model:

from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="laurievb/OpenLID", filename="model.bin")

💻 Usage Examples

Basic Usage

import fasttext
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="laurievb/OpenLID", filename="model.bin")
model = fasttext.load_model(model_path)
result = model.predict("Hello, world!")
print(result)

Advanced Usage

import fasttext
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="laurievb/OpenLID", filename="model.bin")
model = fasttext.load_model(model_path)
result = model.predict("Hello, world!", k=5)
print(result)

📚 Documentation

Model description

OpenLID is a high-coverage, high-performance language identification model. It is a fastText model which covers 201 languages. The training data and per-language performance are openly available to encourage further research.

The model and training data are described in Burchell et al. (2023) and the original fastText implementation is available through github.

Limitations and bias

The dataset and model only covers 201 languages: the ones we were able to test with the FLORES-200 Evaluation Benchmark. In addition, because our test set consists of sentences from a single domain (wiki articles), performance on this test set may not reflect how well our classifier works in other domains. Future work could create a LID test set representative of web data where these classifiers are often applied. Finally, most of the data was not audited by native speakers as would be ideal. Future versions of this dataset should have more languages verified by native speakers, with a focus on the least resourced languages.

Our work aims to broaden NLP coverage by allowing practitioners to identify relevant data in more languages. However, we note that LID is inherently a normative activity that risks excluding minority dialects, scripts, or entire microlanguages from a macrolanguage. Choosing which languages to cover may reinforce power imbalances, as only some groups gain access to NLP technologies. In addition, errors in LID can have a significant impact on downstream performance, particularly (as is often the case) when a system is used as a ‘black box’. The performance of our classifier is not equal across languages which could lead to worse downstream performance for particular groups. We mitigate this by providing metrics by class.

Training data

The model was trained on the OpenLID dataset which is available through the github repo.

Training procedure

The model was trained using fastText with the following hyperparameters set. All other hyperparameters were set to their default values.

Evaluation datasets

The model was evaluated using the FLORES-200 benchmark provided by Costa-jussà et al. (2022). Further information is available in the paper.

BibTeX entry and citation info

ACL citation (preferred)

@inproceedings{burchell-etal-2023-open,
    title = "An Open Dataset and Model for Language Identification",
    author = "Burchell, Laurie  and
      Birch, Alexandra  and
      Bogoychev, Nikolay  and
      Heafield, Kenneth",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.75",
    doi = "10.18653/v1/2023.acl-short.75",
    pages = "865--879",
    abstract = "Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033{\%} across 201 languages, outperforming previous work. We achieve this by training on a curated dataset of monolingual data, which we audit manually to ensure reliability. We make both the model and the dataset available to the research community. Finally, we carry out detailed analysis into our model{'}s performance, both in comparison to existing open models and by language class.",
}

ArXiv citation

@article{burchell2023open,
  title={An Open Dataset and Model for Language Identification},
  author={Burchell, Laurie and Birch, Alexandra and Bogoychev, Nikolay and Heafield, Kenneth},
  journal={arXiv preprint arXiv:2305.13820},
  year={2023}
}

📄 License

This model is licensed under the GPL-3.0 license.