def build_model_multilingual(cls, args, task):
"""Build a new multilingual model instance."""
encoders = []
for lang in args.multiling_encoder_lang:
d = task.source_dictionaries.get(lang, None)
if d is not None:
encoders.append(RNNModel.build_encoder(args, d))
else:
encoders.append(None)
encoder = MultilingualEncoder(
task.source_dictionary,
encoders,
hidden_dim=args.encoder_hidden_dim,
num_layers=args.encoder_layers,
rescale_grads=args.multiling_rescale_grads,
)
decoders = []
for lang in args.multiling_decoder_lang:
d = task.target_dictionaries.get(lang, None)
if d is not None:
decoders.append(RNNModel.build_decoder(args, None, d))
else:
decoders.append(None)
decoder = MultilingualDecoder(
task.target_dictionary,
decoders,
hidden_dim=args.encoder_hidden_dim,
rescale_grads=args.multiling_rescale_grads,
)
return cls(task, encoder, decoder)
def build_model_multilingual(cls, args, src_dict, dst_dict):
"""Build a new multilingual model instance."""
encoders = []
src_dict = pytorch_translate_dictionary.MaxVocabDictionary()
for dict_path in args.multiling_source_vocab_file:
d = pytorch_translate_dictionary.Dictionary.load(dict_path)
src_dict.push(d)
encoders.append(RNNModel.build_encoder(args, d))
encoder = MultilingualEncoder(
src_dict,
encoders,
hidden_dim=args.encoder_hidden_dim,
num_layers=args.encoder_layers,
rescale_grads=args.multiling_rescale_grads,
)
decoders = []
dst_dict = pytorch_translate_dictionary.MaxVocabDictionary()
for dict_path in args.multiling_target_vocab_file:
d = pytorch_translate_dictionary.Dictionary.load(dict_path)
dst_dict.push(d)
decoders.append(RNNModel.build_decoder(args, None, d))
decoder = MultilingualDecoder(
dst_dict,
decoders,
hidden_dim=args.encoder_hidden_dim,
rescale_grads=args.multiling_rescale_grads,
)
return cls(encoder, decoder)