def from_params(cls, params: Params) -> "Elmo":
# Add files to archive
params.add_file_to_archive("options_file")
params.add_file_to_archive("weight_file")
options_file = params.pop("options_file")
weight_file = params.pop("weight_file")
requires_grad = params.pop("requires_grad", False)
num_output_representations = params.pop("num_output_representations")
do_layer_norm = params.pop_bool("do_layer_norm", False)
keep_sentence_boundaries = params.pop_bool("keep_sentence_boundaries",
False)
dropout = params.pop_float("dropout", 0.5)
scalar_mix_parameters = params.pop("scalar_mix_parameters", None)
params.assert_empty(cls.__name__)
return cls(
options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
requires_grad=requires_grad,
do_layer_norm=do_layer_norm,
keep_sentence_boundaries=keep_sentence_boundaries,
dropout=dropout,
scalar_mix_parameters=scalar_mix_parameters,
)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'ElmoTokenEmbedder': # type: ignore
# pylint: disable=arguments-differ
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float("dropout", 0.5)
namespace_to_cache = params.pop("namespace_to_cache", None)
if namespace_to_cache is not None:
vocab_to_cache = list(
vocab.get_token_to_index_vocabulary(namespace_to_cache).keys())
else:
vocab_to_cache = None
projection_dim = params.pop_int("projection_dim", None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
do_layer_norm=do_layer_norm,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim,
vocab_to_cache=vocab_to_cache)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'ElmoTokenEmbedder': # type: ignore
# pylint: disable=arguments-differ
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float("dropout", 0.5)
namespace_to_cache = params.pop("namespace_to_cache", None)
if namespace_to_cache is not None:
vocab_to_cache = list(vocab.get_token_to_index_vocabulary(namespace_to_cache).keys())
else:
vocab_to_cache = None
projection_dim = params.pop_int("projection_dim", None)
scalar_mix_parameters = params.pop('scalar_mix_parameters', None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
do_layer_norm=do_layer_norm,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim,
vocab_to_cache=vocab_to_cache,
scalar_mix_parameters=scalar_mix_parameters)
def from_params( # type: ignore
cls, vocab: Vocabulary, params: Params,
**extras) -> "ElmoTokenEmbedder":
params.add_file_to_archive("options_file")
params.add_file_to_archive("weight_file")
options_file = params.pop("options_file")
weight_file = params.pop("weight_file")
requires_grad = params.pop("requires_grad", False)
do_layer_norm = params.pop_bool("do_layer_norm", False)
dropout = params.pop_float("dropout", 0.5)
namespace_to_cache = params.pop("namespace_to_cache", None)
if namespace_to_cache is not None:
vocab_to_cache = list(
vocab.get_token_to_index_vocabulary(namespace_to_cache).keys())
else:
vocab_to_cache = None
projection_dim = params.pop_int("projection_dim", None)
scalar_mix_parameters = params.pop("scalar_mix_parameters", None)
params.assert_empty(cls.__name__)
return cls(
options_file=options_file,
weight_file=weight_file,
do_layer_norm=do_layer_norm,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim,
vocab_to_cache=vocab_to_cache,
scalar_mix_parameters=scalar_mix_parameters,
)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
char_map_file = params.pop('char_map_file', None)
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop_bool('do_layer_norm', False)
keep_sentence_boundaries = params.pop_bool('keep_sentence_boundaries', False)
dropout = params.pop_float('dropout', 0.5)
scalar_mix_parameters = params.pop('scalar_mix_parameters', None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
char_map_file=char_map_file,
requires_grad=requires_grad,
do_layer_norm=do_layer_norm,
keep_sentence_boundaries=keep_sentence_boundaries,
dropout=dropout,
scalar_mix_parameters=scalar_mix_parameters)
def from_params(cls, params: Params) -> 'B':
params.add_file_to_archive("filename")
filename = params.pop("filename")
c_params = params.pop("c")
c = C.from_params(c_params)
return cls(filename, c)
def from_params(cls, params: Params) -> 'B':
params.add_file_to_archive("filename")
filename = params.pop("filename")
c_params = params.pop("c")
c = C.from_params(c_params)
return cls(filename, c)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'ElmoTokenEmbedder':
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
do_layer_norm = params.pop('do_layer_norm', False)
dropout = params.pop("dropout", 0.5)
params.assert_empty(cls.__name__)
return cls(options_file, weight_file, do_layer_norm, dropout)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'ElmoTokenEmbedder':
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float("dropout", 0.5)
params.assert_empty(cls.__name__)
return cls(options_file, weight_file, do_layer_norm, dropout, requires_grad=requires_grad)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop('do_layer_norm', False)
params.assert_empty(cls.__name__)
return cls(options_file, weight_file, num_output_representations, do_layer_norm)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop_bool('do_layer_norm', False)
params.assert_empty(cls.__name__)
return cls(options_file, weight_file, num_output_representations,
requires_grad=requires_grad, do_layer_norm=do_layer_norm)
def from_params(cls, params: Params) -> 'PretrainedVAE':
# Add files to archive
params.add_file_to_archive('model_archive')
model_archive = params.pop('model_archive')
device = params.pop('device')
background_frequency = params.pop('background_frequency')
requires_grad = params.pop('requires_grad', False)
dropout = params.pop_float('dropout', None)
scalar_mix = params.pop('scalar_mix', None)
params.assert_empty(cls.__name__)
return cls(model_archive=model_archive,
device=device,
background_frequency=background_frequency,
requires_grad=requires_grad,
scalar_mix=scalar_mix,
dropout=dropout)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'ElmoTokenEmbedder':
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float("dropout", 0.5)
projection_dim = params.pop_int("projection_dim", None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
do_layer_norm=do_layer_norm,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float('dropout', 0.5)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
requires_grad=requires_grad,
do_layer_norm=do_layer_norm,
dropout=dropout)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop_bool('do_layer_norm', False)
keep_sentence_boundaries = params.pop_bool('keep_sentence_boundaries', False)
dropout = params.pop_float('dropout', 0.5)
scalar_mix_parameters = params.pop('scalar_mix_parameters', None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
requires_grad=requires_grad,
do_layer_norm=do_layer_norm,
keep_sentence_boundaries=keep_sentence_boundaries,
dropout=dropout,
scalar_mix_parameters=scalar_mix_parameters)
def from_params(
cls,
vocab: Vocabulary, # pylint: disable=unused-argument
params: Params
) -> 'VampireTokenEmbedder': # type: ignore
# pylint: disable=arguments-differ
params.add_file_to_archive('model_archive')
model_archive = params.pop('model_archive')
device = params.pop_int('device')
background_frequency = params.pop('background_frequency')
requires_grad = params.pop('requires_grad', False)
scalar_mix = params.pop("scalar_mix", None)
dropout = params.pop_float("dropout", None)
expand_dim = params.pop_float("expand_dim", False)
projection_dim = params.pop_int("projection_dim", None)
params.assert_empty(cls.__name__)
return cls(expand_dim=expand_dim,
scalar_mix=scalar_mix,
background_frequency=background_frequency,
device=device,
model_archive=model_archive,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim)
def from_params(cls, params: Params) -> 'C':
params.add_file_to_archive("c_file")
c_file = params.pop("c_file")
return cls(c_file)
def from_params(cls, params: Params) -> 'C':
params.add_file_to_archive("c_file")
c_file = params.pop("c_file")
return cls(c_file)
