mbart-large-50-many-to-many-mmt
Maintainer: facebook
223
🖼️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
mbart-large-50-many-to-many-mmt is a multilingual machine translation model that can translate directly between any pair of 50 languages. It is a fine-tuned checkpoint of the mBART-large-50 model, introduced in the paper Multilingual Translation with Extensible Multilingual Pretraining and Finetuning. The model was developed by Facebook.
Similar multilingual translation models include mbart-large-50-many-to-one-mmt, which can translate to English from the same 50 languages, and Llama2-13b-Language-translate, which can translate from English to the 49 other languages.
Model inputs and outputs
Inputs
- Source text: The text to be translated, in one of the 50 supported languages.
- Target language: The language to translate the source text into, specified by the language code.
Outputs
- Translated text: The source text translated into the target language.
Capabilities
mbart-large-50-many-to-many-mmt can translate directly between any pair of the 50 supported languages, which include languages like Arabic, Chinese, Hindi, and Spanish. This allows for high-quality multilingual translation without the need for pivot languages.
What can I use it for?
You can use mbart-large-50-many-to-many-mmt for a variety of multilingual translation tasks, such as:
- Translating web content, documents, or other text between any of the 50 supported languages.
- Facilitating cross-lingual communication and collaboration in multinational organizations.
- Improving accessibility of information for speakers of different languages.
- Enhancing machine translation capabilities for commercial or research purposes.
See the model hub to explore more fine-tuned versions of the mBART-50 model.
Things to try
Try experimenting with different language combinations to see the model's performance across various language pairs. You can also fine-tune the model further on domain-specific data to improve its translation quality for your particular use case.
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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