Alirezamsh

small100

alirezamsh

54

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.

Read more