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Easy-translate is a script for translating large text files in your machine using the M2M100 models from Facebook/Meta AI. We also privide a script for Easy-Evaluation of your translations 🥳
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper and first released in this repository. The model that can directly translate between the 9,900 directions of 100 languages.
Easy-Translate is built on top of 🤗HuggingFace's Transformers and 🤗HuggingFace's Accelerate library. We support:
- CPU / multi-CPU / GPU / multi-GPU / TPU acceleration
- BF16 / FP16 / FP32 precision.
- Automatic batch size finder: Forget CUDA OOM errors. Set an initial batch size, if it doesn't fit, we will automatically adjust it.
- Sharded Data Parallel to load huge models sharded on multiple GPUs (See: https://huggingface.co/docs/accelerate/fsdp).
Test the 🔌 Online Demo here: https://huggingface.co/spaces/Iker/Translate-100-languages
Supported languages
See the Supported languages table for a table of the supported languages and their ids.
List of supported languages: Afrikaans, Amharic, Arabic, Asturian, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greeek, English, Spanish, Estonian, Persian, Fulah, Finnish, French, WesternFrisian, Irish, Gaelic, Galician, Gujarati, Hausa, Hebrew, Hindi, Croatian, Haitian, Hungarian, Armenian, Indonesian, Igbo, Iloko, Icelandic, Italian, Japanese, Javanese, Georgian, Kazakh, CentralKhmer, Kannada, Korean, Luxembourgish, Ganda, Lingala, Lao, Lithuanian, Latvian, Malagasy, Macedonian, Malayalam, Mongolian, Marathi, Malay, Burmese, Nepali, Dutch, Norwegian, NorthernSotho, Occitan, Oriya, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Albanian, Serbian, Swati, Sundanese, Swedish, Swahili, Tamil, Thai, Tagalog, Tswana, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Wolof, Xhosa, Yiddish, Yoruba, Chinese, Zulu
Supported Models
Facebook/m2m100_418M: https://huggingface.co/facebook/m2m100_418M
Facebook/m2m100_1.2B: https://huggingface.co/facebook/m2m100_1.2B
Facebook/m2m100_12B: https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt
Any other m2m100 model from HuggingFace's Hub: https://huggingface.co/models?search=m2m100
Requirements:
Pytorch >= 1.10.0
See: https://pytorch.org/get-started/locally/
Accelerate >= 0.7.1
pip install --upgrade accelerate
HuggingFace Transformers
pip install --upgrade transformers
Translate a file
Run python translate.py -h for more info.
Using a single CPU / GPU:
accelerate launch translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.m2m100_1.2B.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B
Multi-GPU:
See Accelerate documentation for more information (multi-node, TPU, Sharded model...): https://huggingface.co/docs/accelerate/index
You can use the Accelerate CLI to configure the Accelerate environment (Run accelerate config in your terminal) instead of using the --multi_gpu and --num_processes flags.
accelerate launch --multi_gpu --num_processes 2 --num_machines 1 translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.m2m100_1.2B.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B
Automatic batch size finder:
We will automatically find a batch size that fits in your GPU memory. The default initial batch size is 128 (You can set it with the --starting_batch_size 128 flag). If we find an Out Of Memory error, we will automatically decrease the batch size until we find a working one.
Choose precision:
Use the --precision flag to choose the precision of the model. You can choose between: bf16, fp16 and 32.
accelerate launch translate.py \
--sentences_path sample_text/en.txt \
--output_path sample_text/en2es.translation.m2m100_1.2B.txt \
--source_lang en \
--target_lang es \
--model_name facebook/m2m100_1.2B \
--precision fp16
Evaluate translations
To run the evaluation script you need to install bert_score: pip install bert_score
The evaluation script will calculate the following metrics:
Run the following command to evaluate the translations:
accelerate launch eval.py \
--pred_path sample_text/es.txt \
--gold_path sample_text/en2es.translation.m2m100_1.2B.txt
If you want to save the results to a file use the --output_path flag.