def __init__(self, args, word_embeddings: TextFieldEmbedder,
vocab: Vocabulary) -> None:
super().__init__(vocab)
# parameters
self.args = args
self.word_embeddings = word_embeddings
# gate
self.x_linear = nn.Linear(self.args.embedding_size,
self.args.hidden_size,
bias=False)
self.W_z = nn.Linear(self.args.hidden_size, 1, bias=False)
self.U_z = nn.Linear(self.args.hidden_size, 1, bias=False)
self.W_r = nn.Linear(self.args.hidden_size, 1, bias=False)
self.U_r = nn.Linear(self.args.hidden_size, 1, bias=False)
self.W = nn.Linear(self.args.hidden_size, 1, bias=False)
self.U = nn.Linear(self.args.hidden_size, 1, bias=False)
# layers
self.event_embedding = EventEmbedding(args, self.word_embeddings)
self.lstm = DynamicLSTM(self.args.embedding_size,
self.args.hidden_size,
num_layers=1,
batch_first=True)
self.attention = Attention(self.args.hidden_size, score_function='mlp')
self.sigmoid = Sigmoid()
self.tanh = Tanh()
self.score = Score(self.args.hidden_size,
self.args.embedding_size,
threshold=self.args.threshold)
# metrics
self.accuracy = BooleanAccuracy()
self.f1_score = F1Measure(positive_label=1)
self.loss_function = BCELoss()