This model can be used for translating text in source language (De/Es/Fr) to a text in target language (En).
The model is based on Transformer "Big" architecture originally presented in "Attention Is All You Need" paper . In this particular instance, the model has 24 layers in the encoder and 6 layers in the decoder. It is using SentencePiece tokenizer .
These models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences. The NeMo toolkit  was used for training this model over roughly 500k steps.
While training this model, we used the following datasets:
We used the SentencePiece tokenizer  with shared encoder and decoder BPE tokenizers.
The accuracy of translation models are often measured using BLEU scores . The model achieves the following sacreBLEU  scores on WMT test sets
De WMT13 - 35.3 WMT14 - 37.9 Es WMT12 - 40.5 WMT13 - 37.5 Fr WMT13 - 36.3 WMT14 - 40.9
The model is available for use in the NeMo toolkit , and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
import nemo import nemo.collections.nlp as nemo_nlp nmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name="mnmt_deesfr_en_transformer24x6")
python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=mnmt_deesfr_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang [SOURCE_LANGUAGE]
where [SOURCE_LANGUAGE] can be 'de' or 'es' or 'fr'
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.
 Vaswani, Ashish, et al. "Attention is all you need." arXiv preprint arXiv:1706.03762 (2017).