nllb-200-distilled-600M

Maintainer: facebook

Total Score

378

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

nllb-200-distilled-600M is a machine translation model developed by Facebook that can translate between 200 languages. It is a distilled version of the larger nllb-200 model, with 600 million parameters. Like its larger counterpart, nllb-200-distilled-600M was trained on a diverse dataset spanning many low-resource languages, with the goal of providing high-quality translation capabilities across a broad range of languages. This model outperforms previous open-source translation models, especially for low-resource language pairs.

The nllb-200-distilled-600M model is part of the NLLB family of models, which also includes the larger nllb-200-3.3B variant. Both models were developed by the Facebook AI Research team and aim to push the boundaries of machine translation, particularly for underserved languages. The distilled 600M version offers a more compact and efficient model for applications where smaller size is important.

Model inputs and outputs

Inputs

  • Text: The nllb-200-distilled-600M model takes single sentences as input and translates them between 200 supported languages.

Outputs

  • Translated text: The output of the model is the translated text in the target language. The model supports translation in both directions between any of the 200 languages.

Capabilities

nllb-200-distilled-600M is a powerful multilingual translation model that can handle a wide variety of languages, including low-resource ones. It has been shown to outperform previous open-source models, especially on language pairs involving African and other underrepresented languages. The model can be used to enable communication and information access for communities that have historically had limited options for high-quality machine translation.

What can I use it for?

The primary intended use of nllb-200-distilled-600M is for research in machine translation, with a focus on low-resource languages. Researchers can use the model to explore techniques for improving translation quality, especially for language pairs that have been underserved by previous translation systems.

While the model is not intended for production deployment, it could potentially be fine-tuned or adapted for certain real-world applications that require multilingual translation, such as supporting communication in international organizations, facilitating access to information for speakers of minority languages, or aiding in the localization of content and software. However, users should carefully evaluate the model's performance and limitations before deploying it in any mission-critical or high-stakes scenarios.

Things to try

One interesting aspect of nllb-200-distilled-600M is its ability to translate between a wide range of language pairs, including many low-resource languages. Researchers could experiment with using the model as a starting point for fine-tuning on specific domains or tasks, to see if the model's broad capabilities can be leveraged to improve translation quality in targeted applications.

Additionally, the model's performance could be analyzed in depth to better understand its strengths and weaknesses across different language pairs and domains. This could inform future research directions and model development efforts to further advance the state of the art in multilingual machine translation.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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