Nllb

🚀 NLLB-200

This is a machine translation model that supports translation among 200 languages, mainly for machine - translation research, especially for low - resource languages.

Language Support

Tags

• nllb
• translation

License

• cc - by - nc - 4.0

Datasets

• flores - 200

Metrics

• bleu
• spbleu
• chrf++

Duplicated From

facebook/nllb - 200 - distilled - 600M

Widget Example

• Source Language: fr
• Target Language: en
• Text: "Ceci est un example"
• Example Title: FR->EN

🚀 Quick Start

This is the model card of NLLB - 200's distilled 600M variant. Here are the metrics for that particular checkpoint.

✨ Features

• Multilingual Support: Supports translation among 200 languages.
• Research - Oriented: Primarily designed for machine - translation research, especially for low - resource languages.

📚 Documentation

Intended Use

• Primary Intended Uses: NLLB - 200 is a machine translation model primarily intended for research in machine translation, especially for low - resource languages. It allows for single - sentence translation among 200 languages. Information on how to use the model can be found in the Fairseq code repository along with the training code and references to evaluation and training data.
• Primary Intended Users: Primary users are researchers and the machine - translation research community.
• Out - of - Scope Use Cases: NLLB - 200 is a research model and is not released for production deployment. It is trained on general - domain text data and is not intended to be used with domain - specific texts, such as medical or legal domains. The model is not intended for document translation. Since the model was trained with input lengths not exceeding 512 tokens, translating longer sequences might result in quality degradation. NLLB - 200 translations cannot be used as certified translations.

Metrics

• Model Performance Measures: The NLLB - 200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by the machine - translation community. Additionally, human evaluation with the XSTS protocol was performed, and the toxicity of the generated translations was measured.

Evaluation Data

• Datasets: The Flores - 200 dataset is described in Section 4.
• Motivation: Flores - 200 was used as it provides full evaluation coverage of the languages in NLLB - 200.
• Preprocessing: Sentence - split raw text data was preprocessed using SentencePiece. The SentencePiece model is released along with NLLB - 200.

Training Data

• Parallel multilingual data from a variety of sources was used to train the model. A detailed report on data selection and construction process is provided in Section 5 of the paper. Monolingual data constructed from Common Crawl was also used, with more details in Section 5.2.

Ethical Considerations

• In this work, a reflexive approach was taken in technological development to prioritize human users and minimize risks transferred to them. Many languages chosen for this study are low - resource languages, especially African languages. While quality translation can improve education and information access in these communities, bad actors may misappropriate the work for nefarious activities, leading to unintended use. Regarding data acquisition, the training data was mined from various publicly available web sources. Although much effort was put into data cleaning, personally identifiable information may not be entirely eliminated. Finally, despite efforts to optimize translation quality, mistranslations may still occur, which could have adverse impacts on decision - makers relying on these translations, especially in health and safety - related matters.

Caveats and Recommendations

• The model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB - MD. Also, the supported languages may have variations that the model does not capture. Users should make appropriate assessments.

Carbon Footprint Details

• The carbon dioxide (CO2e) estimate is reported in Section 8.8.

Additional Information

• Paper or Other Resources: NLLB Team et al, No Language Left Behind: Scaling Human - Centered Machine Translation, Arxiv, 2022
• License: CC - BY - NC
• Contact for Questions or Comments: https://github.com/facebookresearch/fairseq/issues