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, embedding_matrix, opt):
super(TD_LSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm_l = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.lstm_r = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(AOA, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.ctx_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.asp_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.dense = nn.Linear(2 * opt.hidden_dim, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(ATAE_LSTM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention = NoQueryAttention(opt.hidden_dim+opt.embed_dim, score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(MGAN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.ctx_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.asp_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.location = LocationEncoding(opt)
self.w_a2c = nn.Parameter(
torch.Tensor(2 * opt.hidden_dim, 2 * opt.hidden_dim))
self.w_c2a = nn.Parameter(
torch.Tensor(2 * opt.hidden_dim, 2 * opt.hidden_dim))
self.alignment = AlignmentMatrix(opt)
self.dense = nn.Linear(8 * opt.hidden_dim, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(LCA_LSTM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm = DynamicLSTM(opt.embed_dim,
opt.embed_dim,
num_layers=1,
batch_first=True)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
self.lc_embed = nn.Embedding(2, opt.embed_dim)
self.linear = nn.Linear(opt.embed_dim * 2, opt.embed_dim)
self.classifier = nn.Linear(opt.embed_dim, 2)
def __init__(self, embedding_matrix, opt):
super(RAM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.bi_lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.att_linear = nn.Linear(opt.hidden_dim * 2 + 1 + opt.embed_dim * 2,
1)
self.gru_cell = nn.GRUCell(opt.hidden_dim * 2 + 1, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
def __init__(self, embedding_matrix, opt, _type='c'):
super(Cabasc, self).__init__()
self.opt = opt
self.type = _type
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding(batch_first=True)
self.linear1 = nn.Linear(3 * opt.embed_dim, opt.embed_dim)
self.linear2 = nn.Linear(opt.embed_dim, 1, bias=False)
self.mlp = nn.Linear(opt.embed_dim, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
# context attention layer
self.rnn_l = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='GRU')
self.rnn_r = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
rnn_type='GRU')
self.mlp_l = nn.Linear(opt.hidden_dim, 1)
self.mlp_r = nn.Linear(opt.hidden_dim, 1)
def __init__(self, embedding_matrix, opt):
super(TNet_LF, self).__init__()
print("this is TNet_LF model")
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.position = Absolute_Position_Embedding(opt)
self.opt = opt
D = opt.embed_dim # 模型词向量维度
C = opt.polarities_dim # 分类数目
L = opt.max_seq_len
HD = opt.hidden_dim
self.lstm1 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.lstm2 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.convs3 = nn.Conv1d(2 * HD, 50, 3, padding=1)
self.fc1 = nn.Linear(4 * HD, 2 * HD)
self.fc = nn.Linear(50, C)