def from_params(cls, vocab: Vocabulary, params: Params) -> 'DecomposableAttention':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
premise_encoder_params = params.pop("premise_encoder", None)
if premise_encoder_params is not None:
premise_encoder = Seq2SeqEncoder.from_params(premise_encoder_params)
else:
premise_encoder = None
hypothesis_encoder_params = params.pop("hypothesis_encoder", None)
if hypothesis_encoder_params is not None:
hypothesis_encoder = Seq2SeqEncoder.from_params(hypothesis_encoder_params)
else:
hypothesis_encoder = None
attend_feedforward = FeedForward.from_params(params.pop('attend_feedforward'))
similarity_function = SimilarityFunction.from_params(params.pop("similarity_function"))
compare_feedforward = FeedForward.from_params(params.pop('compare_feedforward'))
aggregate_feedforward = FeedForward.from_params(params.pop('aggregate_feedforward'))
initializer = InitializerApplicator.from_params(params.pop("initializer", []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
attend_feedforward=attend_feedforward,
similarity_function=similarity_function,
compare_feedforward=compare_feedforward,
aggregate_feedforward=aggregate_feedforward,
initializer=initializer,
premise_encoder=premise_encoder,
hypothesis_encoder=hypothesis_encoder)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'SemanticRoleLabeler':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
stacked_encoder = Seq2SeqEncoder.from_params(params.pop("stacked_encoder"))
binary_feature_dim = params.pop("binary_feature_dim")
initializer = InitializerApplicator.from_params(params.pop("initializer", []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
stacked_encoder=stacked_encoder,
binary_feature_dim=binary_feature_dim,
initializer=initializer)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'SemanticRoleLabeler':
"""
With an empty ``params`` argument, this will instantiate a SRL model with the same
configuration as published in the "Deep Semantic Role Labeling - What works and what's
next" paper, as long as you've set ``allennlp.common.constants.GLOVE_PATH`` to the
location of your gzipped 100-dimensional glove vectors.
If you want to change parameters, the keys in the ``params`` object must match the
constructor arguments above.
"""
default_embedder_params = {
'tokens': {
'type': 'embedding',
'pretrained_file': GLOVE_PATH,
'trainable': True
}
}
embedder_params = params.pop("text_field_embedder",
default_embedder_params)
text_field_embedder = TextFieldEmbedder.from_params(
vocab, embedder_params)
default_lstm_params = {
'type': 'alternating_lstm',
'input_size': 101, # Because of the verb_indicator feature.
'hidden_size': 300,
'num_layers': 8,
'recurrent_dropout_probability': 0.1,
'use_highway': True
}
encoder_params = params.pop("stacked_encoder", default_lstm_params)
stacked_encoder = Seq2SeqEncoder.from_params(encoder_params)
default_initializer_params = {
'bias': {
'type': 'normal',
'std': 0.1
},
'default': 'orthogonal'
}
initializer_params = params.pop('initializer',
default_initializer_params)
initializer = InitializerApplicator.from_params(initializer_params)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
stacked_encoder=stacked_encoder,
initializer=initializer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'SentenceClassifier':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
question_encoder = Seq2VecEncoder.from_params(params.pop("question_encoder"))
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
question_encoder=question_encoder,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'ToxicModel':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
encoder = Seq2VecEncoder.from_params(params.pop("encoder"))
classifier_feedforward = FeedForward.from_params(params.pop("classifier_feedforward"))
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
encoder=encoder,
classifier_feedforward=classifier_feedforward,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'DecAccSRL':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
premise_encoder_params = params.pop("premise_encoder", None)
if premise_encoder_params is not None:
premise_encoder = Seq2SeqEncoder.from_params(premise_encoder_params)
else:
premise_encoder = None
hypothesis_encoder_params = params.pop("hypothesis_encoder", None)
if hypothesis_encoder_params is not None:
hypothesis_encoder = Seq2SeqEncoder.from_params(hypothesis_encoder_params)
else:
hypothesis_encoder = None
srl_model_archive = params.pop('srl_model_archive', None)
if srl_model_archive is not None:
logger.info("Loaded pretrained SRL model from {}".format(srl_model_archive))
archive = load_archive(srl_model_archive)
srl_model = archive.model
else:
srl_model = None
attend_feedforward = FeedForward.from_params(params.pop('attend_feedforward'))
similarity_function = SimilarityFunction.from_params(params.pop("similarity_function"))
compare_feedforward = FeedForward.from_params(params.pop('compare_feedforward'))
aggregate_feedforward = FeedForward.from_params(params.pop('aggregate_feedforward'))
initializer = InitializerApplicator.from_params(params.pop("initializer", []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
attend_feedforward=attend_feedforward,
similarity_function=similarity_function,
compare_feedforward=compare_feedforward,
aggregate_feedforward=aggregate_feedforward,
initializer=initializer,
srl_model=srl_model,
premise_encoder=premise_encoder,
hypothesis_encoder=hypothesis_encoder)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'SentenceRepresentationModel':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab, embedder_params)
sentence_encoder_params = params.pop("sentence_encoder", None)
if sentence_encoder_params is not None:
sentence_encoder = Seq2SeqEncoder.from_params(
sentence_encoder_params)
else:
sentence_encoder = None
#hypothesis_encoder_params = params.pop("hypothesis_encoder", None)
#if hypothesis_encoder_params is not None:
# hypothesis_encoder = Seq2SeqEncoder.from_params(hypothesis_encoder_params)
#else:
# hypothesis_encoder = None
#srl_model_archive = params.pop('srl_model_archive', None)
#if srl_model_archive is not None:
# logger.info("Loaded pretrained SRL model from {}".format(srl_model_archive))
# archive = load_archive(srl_model_archive)
# srl_model = archive.model
#else:
srl_model = None
aggregate_feedforward = FeedForward.from_params(
params.pop('aggregate_feedforward'))
initializer = InitializerApplicator.from_params(
params.pop("initializer", []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
aggregate_feedforward=aggregate_feedforward,
initializer=initializer,
srl_model=srl_model,
sentence_encoder=sentence_encoder)