Easy-Translate is a script for translating large text files in your machine using the [M2M100 models](https://arxiv.org/pdf/2010.11125.pdf) and [NLLB200 models](https://research.facebook.com/publications/no-language-left-behind/) from Facebook/Meta AI. We also provide a [script](#evaluate-translations) for Easy-Evaluation of your translations 🥳 Easy-Translate is built on top of 🤗HuggingFace's [Transformers](https://huggingface.co/docs/transformers/index) and 🤗HuggingFace's [Accelerate](https://huggingface.co/docs/accelerate/index) 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: ). - Greedy decoding / Beam Search decoding / Multinomial Sampling / Beam-Search Multinomial Sampling >Test the 🔌 Online Demo here: ## Supported languages See the [Supported languages table](supported_languages.md) 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](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. >M2M100 can directly translate between 9,900 directions of 100 languages. - **Facebook/m2m100_418M**: - **Facebook/m2m100_1.2B**: - **Facebook/m2m100_12B**: ### 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](https://research.facebook.com/publications/no-language-left-behind/) and first released in [this](https://github.com/facebookresearch/fairseq/tree/nllb) repository. >NLLB can directly translate between +40,000 of +200 languages. - **facebook/nllb-200-3.3B**: - **facebook/nllb-200-1.3B**: - **facebook/nllb-200-distilled-1.3B**: - **facebook/nllb-200-distilled-600M**: Any other ModelForSeq2SeqLM from HuggingFace's Hub should work with this library: ## 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 ```bash 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...): 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. ```bash # 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. ```bash 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 ```bash 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 ```bash 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)** ```bash 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 ```bash 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](https://github.com/Tiiiger/bert_score): `pip install bert_score` and 🤗HuggingFace's [Datasets](https://huggingface.co/docs/datasets/index) model: `pip install datasets`. The evaluation script will calculate the following metrics: - [SacreBLEU](https://github.com/huggingface/datasets/tree/master/metrics/sacrebleu) - [BLEU](https://github.com/huggingface/datasets/tree/master/metrics/bleu) - [ROUGE](https://github.com/huggingface/datasets/tree/master/metrics/rouge) - [METEOR](https://github.com/huggingface/datasets/tree/master/metrics/meteor) - [TER](https://github.com/huggingface/datasets/tree/master/metrics/ter) - [BertScore](https://github.com/huggingface/datasets/tree/master/metrics/bertscore) Run the following command to evaluate the translations: ```bash 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. See [sample_text/en2es.m2m100_1.2B.json](sample_text/en2es.m2m100_1.2B.json) for a sample output.