NGC | Catalog
Welcome Guest
CatalogModelsNMT En Fr Transformer12x2

NMT En Fr Transformer12x2

For downloads and more information, please view on a desktop device.
Logo for NMT En Fr Transformer12x2


Neural Machine Translation (NMT) model to translate from English to French



Use Case



PyTorch with NeMo

Latest Version



June 30, 2021


892.17 MB

Model Overview

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

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].


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.


While training this model, we used the following datasets:

Tokenizer Construction

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


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 - 35.3
WMT14 - 41.3

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_en_fr_transformer12x2")

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/ --model=nmt_en_fr_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang fr --source_lang en


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


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


No known limitations at this time.


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




[5] NVIDIA NeMo Toolkit


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.