ufo008/easy-translate

Use the state-of-the-art m2m100 to translate large data on CPU/GPU/TPU. Super Easy!

Easy-Translate is a script for translating large text files in your machine using the M2M100 models and NLLB200 models from Facebook/Meta AI. We also provide a script for Easy-Evaluation of your translations 🥳

Easy-Translate is built on top of 🤗HuggingFace's Transformers and 🤗HuggingFace's Accelerate library.

We currently 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).
• Greedy decoding / Beam Search decoding / Multinomial Sampling / Beam-Search Multinomial Sampling

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.

Supported Models

M2M100

M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation introduced in this paper and first released in this repository.

M2M100 can directly translate between 9,900 directions of 100 languages.

• 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

NLLB200

No Language Left Behind (NLLB) open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and more. It aims to help people communicate with anyone, anywhere, regardless of their language preferences. It was introduced in this paper and first released in this repository.

NLLB can directly translate between +40,000 of +200 languages.

• facebook/nllb-200-3.3B: https://huggingface.co/facebook/nllb-200-3.3B

• facebook/nllb-200-1.3B: https://huggingface.co/facebook/nllb-200-1.3B

• facebook/nllb-200-distilled-1.3B: https://huggingface.co/facebook/nllb-200-distilled-1.3B

• facebook/nllb-200-distilled-600M: https://huggingface.co/facebook/nllb-200-distilled-600M

Any other ModelForSeq2SeqLM from HuggingFace's Hub should work with this library: https://huggingface.co/models?pipeline_tag=text2text-generation

Citation

If you use this software please cite

@inproceedings{garcia-ferrero-etal-2022-model,
    title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
    author = "Garc{\'\i}a-Ferrero, Iker  and
      Agerri, Rodrigo  and
      Rigau, German",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.478",
    pages = "6403--6416",
}

Requirements

Pytorch >= 1.10.0
See: https://pytorch.org/get-started/locally/

Accelerate >= 0.12.0
pip install --upgrade accelerate

HuggingFace Transformers 
pip install --upgrade transformers

If you find errors using NLLB200, try installing transformers from source:
pip install git+https://github.com/huggingface/transformers.git

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.

# Use 2 GPUs
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 

Decoding/Sampling strategies

You can choose the decoding/sampling strategy to use and the number of candidate translation to output for each input sentence. By default we will use beam-search with 'num_beams' set to 5, and we will output the most likely candidate translation. But you can change this behavior:

Greedy decoding

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 \
--num_beams 1 

Multinomial Sampling

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 \
--num_beams 1 \
--do_sample \
--temperature 0.5 \
--top_k 100 \
--top_p 0.8 \
--num_return_sequences 1

Beam-Search decoding (DEFAULT)

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 \
--num_beams 5 \
--num_return_sequences 1 \ 

Beam-Search Multinomial Sampling

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 \
--num_beams 5 \
--num_return_sequences 1 \
--do_sample \
--temperature 0.5 \
--top_k 100 \
--top_p 0.8 

Evaluate translations

To run the evaluation script you need to install bert_score: pip install bert_score and 🤗HuggingFace's Datasets model: pip install datasets.

The evaluation script will calculate the following metrics:

• SacreBLEU
• BLEU
• ROUGE
• METEOR
• TER
• BertScore

Run the following command to evaluate the translations:

accelerate launch eval.py \
--pred_path sample_text/en2es.translation.m2m100_1.2B.txt 
--gold_path sample_text/es.txt \

If you want to save the results to a file use the --output_path flag.

See sample_text/en2es.m2m100_1.2B.json for a sample output.