fairseq_task_translation.py

import torch

# List available models
torch.hub.list('pytorch/fairseq')  # [..., 'transformer.wmt16.en-de', ... ]

# Load a transformer trained on WMT'16 En-De
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de', tokenizer='moses', bpe='subword_nmt')
en2de.eval()  # disable dropout

# The underlying model is available under the *models* attribute
assert isinstance(en2de.models[0], fairseq.models.transformer.TransformerModel)

# Move model to GPU for faster translation
en2de.cuda()

# Translate a sentence
en2de.translate('Hello world!')
# 'Hallo Welt!'

# Batched translation
en2de.translate(['Hello world!', 'The cat sat on the mat.'])
# ['Hallo Welt!', 'Die Katze saß auf der Matte.']