Model Overview

This model can be used for translating text in source language (Fr) to a text in target language (En).

Model Architecture

The model is based on Transformer "Big" architecture originally presented in "Attention Is All You Need" paper [1]. In this particular instance, the model has 12 layers in the encoder and 2 layers in the decoder. It is using YouTokenToMe tokenizer [2].

Training

These models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 300k steps.

Datasets

While training this model, we used the following datasets:

• Europarl - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz
• Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
• Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz
• Giga French - http://statmt.org/wmt10/training-giga-fren.tar
• News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz
• Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip
• Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip
• Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip
• Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip
• WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.tsv.gz

Tokenizer Construction

We used the YouTokenToMe tokenizer [2] with shared encoder and decoder BPE tokenizers.

Performance

The accuracy of translation models are often measured using BLEU scores [3]. The model achieves the following sacreBLEU [4] scores on the WMT'13 and WMT'14 test sets

WMT13 - 35.5
WMT14 - 40.4

How to Use this Model

The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo
import nemo.collections.nlp as nemo_nlp
nmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name="nmt_fr_en_transformer12x2")

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_fr_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang fr

Input

This translate method of the NMT model accepts a list of de-tokenized strings.

Output

The translate method outputs a list of de-tokenized strings in the target language.

Limitations

No known limitations at this time.

References

[1] Vaswani, Ashish, et al. "Attention is all you need." arXiv preprint arXiv:1706.03762 (2017).

[2] https://github.com/VKCOM/YouTokenToMe

[3] https://en.wikipedia.org/wiki/BLEU

[4] https://github.com/mjpost/sacreBLEU

[5] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.