def from_params(cls, params: Params) -> 'Seq2Seq2VecEncoder':
seq2seq_encoder_params = params.pop("seq2seq_encoder")
seq2vec_encoder_params = params.pop("seq2vec_encoder")
seq2seq_encoder = Seq2SeqEncoder.from_params(seq2seq_encoder_params)
seq2vec_encoder = Seq2VecEncoder.from_params(seq2vec_encoder_params)
return cls(seq2seq_encoder=seq2seq_encoder,
seq2vec_encoder=seq2vec_encoder)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'TokenCharactersEncoder':
embedding_params: Params = params.pop("embedding")
# Embedding.from_params() uses "tokens" as the default namespace, but we need to change
# that to be "tokens" by default.
embedding_params.setdefault("vocab_namespace", "token_bpe")
embedding = Embedding.from_params(vocab, embedding_params)
encoder_params: Params = params.pop("encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
dropout = params.pop("dropout", 0.0)
params.assert_empty(cls.__name__)
return cls(embedding, encoder, dropout)
def from_params(cls, vocab, params): # type: ignore
# pylint: disable=arguments-differ
embedding_params = params.pop(u"embedding")
# Embedding.from_params() uses "tokens" as the default namespace, but we need to change
# that to be "token_characters" by default.
embedding_params.setdefault(u"vocab_namespace", u"token_characters")
embedding = Embedding.from_params(vocab, embedding_params)
encoder_params = params.pop(u"encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
dropout = params.pop_float(u"dropout", 0.0)
params.assert_empty(cls.__name__)
return cls(embedding, encoder, dropout)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'TokenCharactersEncoder': # type: ignore
# pylint: disable=arguments-differ
embedding_params: Params = params.pop("embedding")
# Embedding.from_params() uses "tokens" as the default namespace, but we need to change
# that to be "token_characters" by default.
embedding_params.setdefault("vocab_namespace", "token_characters")
embedding = Embedding.from_params(vocab, embedding_params)
encoder_params: Params = params.pop("encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
dropout = params.pop_float("dropout", 0.0)
params.assert_empty(cls.__name__)
return cls(embedding, encoder, dropout)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'GlyphEmbeddingWrapper':
# glyph_config
glyph_config = GlyphEmbeddingConfig()
glyph_config.output_size = params.pop_int("output_size", 300)
glyph_config.use_highway = True
glyph_config.dropout = params.pop_float("dropout", 0.0)
glyph_config.font_channels = params.pop_int("font_channels", 8)
glyph_config.glyph_embsize = params.pop_int("glyph_embsize", 256)
glyph_config.use_batch_norm = params.pop_bool("use_batch_norm", False)
# encoder_config
encoder_params: Params = params.pop("encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
params.assert_empty(cls.__name__)
return cls(vocab, glyph_config, encoder)
def from_params( # type: ignore
cls, vocab: Vocabulary,
params: Params) -> "TokenCharactersEncoder":
embedding_params: Params = params.pop("embedding")
# Embedding.from_params() uses "tokens" as the default namespace, but we need to change
# that to be "token_characters" by default. If num_embeddings is present, set default namespace
# to None so that extend_vocab call doesn't misinterpret that some namespace was originally used.
default_namespace = (None if embedding_params.get(
"num_embeddings", None) else "token_characters")
embedding_params.setdefault("vocab_namespace", default_namespace)
embedding = Embedding.from_params(vocab, embedding_params)
encoder_params: Params = params.pop("encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
dropout = params.pop_float("dropout", 0.0)
params.assert_empty(cls.__name__)
return cls(embedding, encoder, dropout)