def __init__(self, token_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
span_decoder: Topdown_Span_Parser_Factory,
span_extractor: SpanExtractor,
remote_parser: Remote_Parser_Factory, evaluator: any,
vocab: Vocabulary) -> None:
super().__init__(vocab)
self.token_embedder = token_embedder
self.encoder = encoder
self.span_decoder = span_decoder(span_extractor.get_output_dim(),
vocab)
self.span_extractor = span_extractor
self.remote_parser = remote_parser(span_extractor.get_output_dim(),
vocab)
self.evaluator = evaluator
def __init__(self,
subword_embeddings: TextFieldEmbedder,
subword_aggregator: SpanExtractor,
vocab: Vocabulary = None,
freeze_encoder: bool = True):
super().__init__(vocab)
self.subword_embeddings = subword_embeddings
self._freeze_encoder = freeze_encoder
# turn off gradients if don't want to fine tune encoder
for parameter in self.subword_embeddings.parameters():
parameter.requires_grad = not self._freeze_encoder
self.subword_aggregator = subword_aggregator
self.classifier = TimeDistributed(
torch.nn.Linear(in_features=subword_aggregator.get_output_dim(),
out_features=vocab.get_vocab_size("labels")))
self.accuracy = CategoricalAccuracy()