def from_params(cls, vocab: Vocabulary, params: Params) -> 'EtdHANLinAtt':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab=vocab, params=embedder_params)
word_encoder = Seq2SeqEncoder.from_params(params.pop("word_encoder"))
sentence_encoder = Seq2SeqEncoder.from_params(
params.pop("sentence_encoder"))
classifier_feedforward = params.pop("classifier_feedforward")
if classifier_feedforward.pop('type') == 'feedforward':
classifier_feedforward = FeedForward.from_params(
classifier_feedforward)
else:
classifier_feedforward = Maxout.from_params(classifier_feedforward)
use_positional_encoding = params.pop("use_positional_encoding", False)
bce_pos_weight = params.pop_int("bce_pos_weight", 10)
attended_text_dropout = params.pop_float("attended_text_dropout", 0.0)
initializer = InitializerApplicator.from_params(
params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(
params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
word_encoder=word_encoder,
sentence_encoder=sentence_encoder,
classifier_feedforward=classifier_feedforward,
attended_text_dropout=attended_text_dropout,
bce_pos_weight=bce_pos_weight,
use_positional_encoding=use_positional_encoding,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'EtdDebugModel':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab=vocab, params=embedder_params)
abstract_text_encoder = Seq2SeqEncoder.from_params(
params.pop("abstract_text_encoder"))
attention_encoder = AttentionEncoder.from_params(
params.pop("attention_encoder"))
classifier_feedforward = params.pop("classifier_feedforward")
if classifier_feedforward.pop('type') == 'feedforward':
classifier_feedforward = FeedForward.from_params(
classifier_feedforward)
else:
classifier_feedforward = Maxout.from_params(classifier_feedforward)
use_positional_encoding = params.pop("use_positional_encoding", False)
initializer = InitializerApplicator.from_params(
params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(
params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
abstract_text_encoder=abstract_text_encoder,
attention_encoder=attention_encoder,
classifier_feedforward=classifier_feedforward,
use_positional_encoding=use_positional_encoding,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'EtdRNN':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab=vocab, params=embedder_params)
abstract_text_encoder = Seq2SeqEncoder.from_params(params.pop("abstract_text_encoder"))
attention_encoder = params.pop("attention_encoder")
attention_type = attention_encoder.pop('type')
if attention_type == 'linear_attention':
attention_encoder = AttentionEncoder.from_params(attention_encoder)
elif attention_type == 'self_attention':
attention_encoder = SelfAttentionEncoder.from_params(attention_encoder)
elif attention_type == 'multi_head':
attention_encoder = MultiHeadAttentionEncoder.from_params(attention_encoder)
else:
attention_encoder = Pooling.from_params(attention_encoder)
classifier_feedforward = params.pop("classifier_feedforward")
if classifier_feedforward.pop('type') == 'feedforward':
classifier_feedforward = FeedForward.from_params(classifier_feedforward)
else:
classifier_feedforward = Maxout.from_params(classifier_feedforward)
use_positional_encoding = params.pop("use_positional_encoding", False)
bce_pos_weight = params.pop_int("bce_pos_weight", 10)
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
abstract_text_encoder=abstract_text_encoder,
attention_encoder=attention_encoder,
classifier_feedforward=classifier_feedforward,
bce_pos_weight=bce_pos_weight,
use_positional_encoding=use_positional_encoding,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'EtdBCN':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab=vocab, params=embedder_params)
title_text_encoder = Seq2SeqEncoder.from_params(
params.pop("title_text_encoder"))
abstract_text_encoder = Seq2SeqEncoder.from_params(
params.pop("abstract_text_encoder"))
title_text_projection = FeedForward.from_params(
params.pop("title_text_projection"))
abstract_text_projection = FeedForward.from_params(
params.pop("abstract_text_projection"))
bi_attention_encoder = BiAttentionEncoder.from_params(
params.pop("attention_encoder"))
classifier_feedforward = params.pop("classifier_feedforward")
if classifier_feedforward.pop('type') == 'feedforward':
classifier_feedforward = FeedForward.from_params(
classifier_feedforward)
else:
classifier_feedforward = Maxout.from_params(classifier_feedforward)
use_positional_encoding = params.pop("use_positional_encoding", False)
bce_pos_weight = params.pop_int("bce_pos_weight", 10)
initializer = InitializerApplicator.from_params(
params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(
params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
title_text_encoder=title_text_encoder,
abstract_text_encoder=abstract_text_encoder,
title_text_projection=title_text_projection,
abstract_text_projection=abstract_text_projection,
bi_attention_encoder=bi_attention_encoder,
classifier_feedforward=classifier_feedforward,
bce_pos_weight=bce_pos_weight,
use_positional_encoding=use_positional_encoding,
initializer=initializer,
regularizer=regularizer)