def build_generator(self, args):
from fairseq.iterative_refinement_generator import IterativeRefinementGenerator
return IterativeRefinementGenerator(
self.target_dictionary,
eos_penalty=getattr(args, 'iter_decode_eos_penalty', 0.0),
max_iter=getattr(args, 'iter_decode_max_iter', 10),
decoding_format=getattr(args, 'decoding_format', None),
adaptive=not getattr(args, 'iter_decode_force_max_iter', False))
def build_generator(self, models, args):
# add models input to match the API for SequenceGenerator
from fairseq.iterative_refinement_generator import IterativeRefinementGenerator
gen = IterativeRefinementGenerator(
self.target_dictionary,
eos_penalty=getattr(args, 'iter_decode_eos_penalty', 0.0),
max_iter=getattr(args, 'iter_decode_max_iter', 10),
beam_size=getattr(args, 'iter_decode_with_beam', 1),
reranking=getattr(args, 'iter_decode_with_external_reranker',
False),
decoding_format=getattr(args, 'decoding_format', None),
adaptive=not getattr(args, 'iter_decode_force_max_iter', False),
retain_history=getattr(args, 'retain_iter_history', False))
if self.args.use_lang_token:
gen.eos = self.target_dictionary.index('[{}]'.format(
self.args.target_lang))
return gen
def build_generator(self, models, args):
from fairseq.iterative_refinement_generator import IterativeRefinementGenerator
return IterativeRefinementGenerator(
self.target_dictionary,
eos_penalty=getattr(args, 'iter_decode_eos_penalty', 0.0),
max_iter=getattr(args, 'iter_decode_max_iter', 10),
beam_size=getattr(args, 'iter_decode_with_beam', 1),
reranking=getattr(args, 'iter_decode_with_external_reranker', False),
decoding_format=getattr(args, 'decoding_format', None),
adaptive=not getattr(args, 'iter_decode_force_max_iter', False),
retain_history=getattr(args, 'retain_iter_history', False))
def build_generator(self, models, args, **unused):
# add models input to match the API for SequenceGenerator
from fairseq.iterative_refinement_generator import IterativeRefinementGenerator
return IterativeRefinementGenerator(
self.target_dictionary,
eos_penalty=getattr(args, "iter_decode_eos_penalty", 0.0),
max_iter=getattr(args, "iter_decode_max_iter", 10),
beam_size=getattr(args, "iter_decode_with_beam", 1),
reranking=getattr(args, "iter_decode_with_external_reranker",
False),
decoding_format=getattr(args, "decoding_format", None),
adaptive=not getattr(args, "iter_decode_force_max_iter", False),
retain_history=getattr(args, "retain_iter_history", False),
)
def build_generator(self, models, args):
# add models input to match the API for SequenceGenerator
from fairseq.iterative_refinement_generator import IterativeRefinementGenerator
init_tokens = None if args.init_output == 'blank' else args.init_output
return IterativeRefinementGenerator(
self.target_dictionary,
eos_penalty=getattr(args, 'iter_decode_eos_penalty', 0.0),
max_iter=getattr(args, 'iter_decode_max_iter', 10),
beam_size=getattr(args, 'iter_decode_with_beam', 1),
reranking=getattr(args, 'iter_decode_with_external_reranker', False),
decoding_format=getattr(args, 'decoding_format', None),
adaptive=not getattr(args, 'iter_decode_force_max_iter', False),
retain_history=getattr(args, 'retain_iter_history', False),
init_tokens=init_tokens
)