def from_params(cls, vocab: Vocabulary, params: Params) -> 'TreeAttention':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
premise_encoder_params = params.pop("premise_encoder", None)
premise_encoder = Seq2SeqEncoder.from_params(premise_encoder_params)
attention_similarity = SimilarityFunction.from_params(params.pop('attention_similarity'))
phrase_probability = FeedForward.from_params(params.pop('phrase_probability'))
edge_probability = FeedForward.from_params(params.pop('edge_probability'))
edge_embedding = Embedding.from_params(vocab, params.pop('edge_embedding'))
use_encoding_for_node = params.pop('use_encoding_for_node')
ignore_edges = params.pop('ignore_edges', False)
init_params = params.pop('initializer', None)
initializer = (InitializerApplicator.from_params(init_params)
if init_params is not None
else InitializerApplicator())
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
phrase_probability=phrase_probability,
edge_probability=edge_probability,
premise_encoder=premise_encoder,
edge_embedding=edge_embedding,
use_encoding_for_node=use_encoding_for_node,
attention_similarity=attention_similarity,
ignore_edges=ignore_edges,
initializer=initializer)
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)