def __init__(self,
vocab: Vocabulary,
embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder,
posclass_weight: Optional[float] = 1,
use_power: Optional[bool] = False,
dropout: Optional[float] = 0) -> None:
super().__init__(vocab)
self.embedder = embedder
self.encoder = encoder
if use_power:
self.classifier = torch.nn.Linear(
in_features=encoder.get_output_dim() + 1,
out_features=vocab.get_vocab_size('labels')
)
else:
self.classifier = torch.nn.Linear(
in_features=encoder.get_output_dim(),
out_features=vocab.get_vocab_size('labels')
)
self.use_power = use_power
self.f1_lie = F1Measure(vocab.get_token_index('False', 'labels'))
self.f1_truth = F1Measure(vocab.get_token_index('True', 'labels'))
self.micro_f1 = FBetaMeasure(average='micro')
self.macro_f1 = FBetaMeasure(average='macro')
weights = [1,1]
weights[vocab.get_token_index('False', 'labels')] = posclass_weight
self.loss = torch.nn.CrossEntropyLoss(weight = torch.Tensor(weights))
self.dropout = torch.nn.Dropout(dropout)
def __init__(
self,
# input_dim: int,
pooler: Seq2VecEncoder):
super().__init__()
self._input_dim = pooler.get_output_dim()
# we distribute the pooler across _spans_, not actual time
self._pooler = TimeDistributed(pooler)
def __init__(self,
word_embeddings: TextFieldEmbedder,
encoder: Seq2VecEncoder,
vocab: Vocabulary) -> None:
super().__init__(vocab)
self.word_embeddings = word_embeddings
self.encoder = encoder
self.hidden2tag = torch.nn.Linear(in_features=encoder.get_output_dim(),
out_features=vocab.get_vocab_size('labels'))
self.accuracy = CategoricalAccuracy()
self.loss_function = torch.nn.CrossEntropyLoss()
