SMALL 100 Model SMaLL 100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M 100 while being much smaller and faster. It is introduced in this paper(accepted to EMNLP2022), and initially released in this repository. The model architecture and config are the same as M2M 100 implementation, but the tokenizer is modified to adjust language codes. So, you should load the tokenizer locally from tokenization small100.py file for the moment. Demo : https://huggingface.co/spaces/alirezamsh/small100 Note : SMALL100Tokenizer requires sentencepiece, so make sure to install it by: Supervised Training SMaLL 100 is a seq to seq model for the translation task. The input to the model is and . An example of supervised training is shown below: Training data can be provided upon request. Generation Beam size of 5, and maximum target length of 256 is used for the generation. Evaluation Please refer to original repository for spBLEU computation. Languages Covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Bre…
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