def __init__(self, bert, opt):
super(AEN_BERT, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.attn_k = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(AEN_GloVe, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
self.attn_k = Attention(opt.embed_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.attn_q = Attention(opt.embed_dim,
out_dim=opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(IAN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention_aspect = Attention(opt.hidden_dim, score_function='bi_linear')
self.attention_context = Attention(opt.hidden_dim, score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim*2, opt.polarities_dim)
def __init__(self,
args,
word_embeddings: TextFieldEmbedder,
vocab: Vocabulary,
domain_info: bool = True) -> None:
super().__init__(vocab)
# parameters
self.args = args
self.word_embeddings = word_embeddings
self.domain = domain_info
# layers
self.event_embedding = EventEmbedding(args, self.word_embeddings)
self.event_type_embedding = EventTypeEmbedding(args,
self.word_embeddings)
self.lstm = LSTM(input_size=self.args.embedding_size,
hidden_size=self.args.hidden_size)
self.W_c = Linear(self.args.embedding_size,
self.args.hidden_size,
bias=False)
self.W_e = Linear(self.args.hidden_size,
self.args.hidden_size,
bias=False)
self.relu = ReLU()
self.linear = Linear(self.args.hidden_size, self.args.embedding_size)
self.attention = Attention(self.args.hidden_size, score_function='mlp')
self.score = Score(self.args.embedding_size,
self.args.embedding_size,
threshold=self.args.threshold)
# metrics
self.accuracy = BooleanAccuracy()
self.f1_score = F1Measure(positive_label=1)
self.loss_function = BCELoss()
def __init__(self, args, word_embeddings: TextFieldEmbedder,
vocab: Vocabulary) -> None:
super().__init__(vocab)
# parameters
self.args = args
self.word_embeddings = word_embeddings
# gate
self.W_z = nn.Linear(self.args.embedding_size, 1, bias=False)
self.U_z = nn.Linear(self.args.embedding_size, 1, bias=False)
self.W_r = nn.Linear(self.args.embedding_size, 1, bias=False)
self.U_r = nn.Linear(self.args.embedding_size, 1, bias=False)
self.W = nn.Linear(self.args.embedding_size, 1, bias=False)
self.U = nn.Linear(self.args.embedding_size, 1, bias=False)
# layers
self.event_embedding = EventEmbedding(args, self.word_embeddings)
self.attention = Attention(self.args.embedding_size,
score_function='mlp')
self.sigmoid = Sigmoid()
self.tanh = Tanh()
self.score = Score(self.args.embedding_size,
self.args.embedding_size,
threshold=self.args.threshold)
# metrics
self.accuracy = BooleanAccuracy()
self.f1_score = F1Measure(positive_label=1)
self.loss_function = BCELoss()
def __init__(self, embedding_matrix, opt):
super(MemNet, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding(batch_first=True)
self.attention = Attention(opt.embed_dim, score_function='mlp')
self.x_linear = nn.Linear(opt.embed_dim, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)