small100
Maintainer: alirezamsh
54
🗣️
| 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
small100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, introduced in this paper. It achieves competitive results with the larger M2M-100 model while being much smaller and faster. The model architecture and config are the same as M2M-100, but the tokenizer is modified to adjust language codes.
Similar models include the M2M-100 418M and M2M-100 1.2B models, which are also multilingual encoder-decoder models trained for Many-to-Many translation. The YaLM 100B and Multilingual-MiniLM-L12-H384 models are also large-scale multilingual language models, but are not focused specifically on translation.
Model inputs and outputs
small100 is a seq-to-seq model for the translation task. The input to the model is source:[tgt_lang_code] + src_tokens + [EOS] and the target is tgt_tokens + [EOS]. This allows the model to translate between any of the over 10,000 supported language pairs.
Inputs
- Source text: The text to be translated, with the target language code prepended.
- Target text: The expected translation, used for supervised training.
Outputs
- Translated text: The model's translation of the input text into the target language.
Capabilities
small100 can directly translate between over 10,000 language pairs, covering a wide range of languages including major world languages as well as many low-resource languages. It achieves strong translation quality while being significantly smaller and faster than the larger M2M-100 models.
What can I use it for?
small100 can be used for a variety of multilingual translation tasks, such as:
- Translating content between any of the supported language pairs, such as translating a web page or document from one language to another.
- Enabling cross-lingual communication and collaboration, by allowing users to seamlessly communicate in their preferred languages.
- Localizing and internationalizing software, websites, or other digital content for global audiences.
- Aiding language learning by providing translations between languages.
The small size and fast inference speed of small100 also make it suitable for deployment in resource-constrained environments, such as edge devices or mobile applications.
Things to try
One interesting aspect of small100 is its ability to translate between a wide range of language pairs, including many low-resource languages. You could experiment with translating between less common language pairs to see the model's capabilities. Additionally, you could fine-tune the model on domain-specific data to improve its performance for particular use cases, such as legal, medical, or technical 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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