mbart-large-50-many-to-one-mmt

Maintainer: facebook

Total Score

52

Last updated 5/19/2024

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

mbart-large-50-many-to-one-mmt is a fine-tuned checkpoint of the mBART-large-50 model. It was introduced in the paper "Multilingual Translation with Extensible Multilingual Pretraining and Finetuning" and is a multilingual machine translation model that can translate directly between any pair of 50 languages. This model is an extension of the original mBART model, adding support for an additional 25 languages to create a 50-language multilingual translation system.

The mBART-50 model was pre-trained using a "Multilingual Denoising Pretraining" objective, where the model is tasked with reconstructing the original text from a noised version. This allows the model to learn a multilingual representation that can be effectively fine-tuned for translation tasks.

Some similar models include the Llama2-13b-Language-translate model, which is also a fine-tuned multilingual translation model, and the M2M100-1.2B model, which can directly translate between 9,900 language directions across 100 languages.

Model inputs and outputs

Inputs

  • Source text in any of the 50 supported languages

Outputs

  • Translated text in the target language

Capabilities

The mbart-large-50-many-to-one-mmt model can translate directly between any pair of the 50 supported languages, which include a diverse set of languages such as Arabic, Chinese, Hindi, Russian, and more. This makes it a powerful tool for multilingual translation tasks.

What can I use it for?

The mbart-large-50-many-to-one-mmt model can be used for a variety of multilingual translation tasks, such as:

  • Translating content (e.g. articles, documents, websites) between different languages
  • Enabling cross-lingual communication and collaboration
  • Providing language support for global businesses or organizations
  • Assisting with language learning and education

See the model hub to explore other fine-tuned versions of the mBART-50 model that may be better suited for your specific use case.

Things to try

One interesting thing to try with this model is to explore how it handles translations between more linguistically distant languages, such as translating from a European language to an Asian language. The model's multilingual pre-training should allow it to capture cross-lingual relationships, but the quality of the translations may vary depending on the language pair. Additionally, you could experiment with translating between low-resource languages, where the model's performance may provide insight into its generalization capabilities.



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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