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.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.attention = Attention(opt.hidden_dim * 2, score_function='mlp')
self.gru_cell = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(LSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(
torch.FloatTensor(embedding_matrix))
self.lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(PWCN_DEP, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.embed_dropout = nn.Dropout(opt.dropout)
self.txt_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.proximity = DependencyProximity(opt)
self.convs3 = nn.Conv1d(2*opt.hidden_dim, 2*opt.hidden_dim, 3, padding=1)
self.fc = nn.Linear(2*opt.hidden_dim, 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, embedding, opt):
super(AT_LSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(torch.FloatTensor(embedding))
self.lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.attention = Attention(opt.hidden_dim,
out_dim=opt.polarities_dim,
score_function='mlp')
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)
self.align_mat = None
self.col_align = None
self.row_align = None
def __init__(self, embedding_matrix, opt):
super(AELSTM, self).__init__()
self.opt = opt
self.n_head = 1
self.embed_dim = opt.embed_dim
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=False)
self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention = Attention(opt.hidden_dim, score_function='mlp')
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)
self.dense_total = nn.Linear(8*opt.hidden_dim*2, opt.polarities_dim)
self.cls_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.cls_asp_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.h_h_proj = nn.Linear(4*opt.hidden_dim,8*opt.hidden_dim)
self.cls_dense = nn.Linear(8*opt.hidden_dim,3)
self.conv = nn.Conv1d(300,2*opt.hidden_dim,3)
def __init__(self, embedding_matrix, opt):
super(LSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
bidirectional=True,
num_layers=1,
batch_first=True)
self.fc = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, bert, opt):
super(SDGCN, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.lstm_context = DynamicLSTM(opt.bert_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.lstm_aspect = DynamicLSTM(opt.bert_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.attention_aspect = Bilinear_Attention(opt.hidden_dim)
self.attention_context = Bilinear_Attention(opt.hidden_dim)
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(SELA, self).__init__()
self.opt = opt
self.fc_dim = 512
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.ctxR_lstm = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.inputdim = 3 * 2 * opt.hidden_dim
self.dense = nn.Sequential(nn.Linear(self.inputdim, self.fc_dim),
nn.Linear(self.fc_dim, self.fc_dim),
nn.Linear(self.fc_dim, opt.class_dim))
def __init__(self, embedding_matrix, opt):
super(SynGCN, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
# print('self.embed ->', type(self.embed), )
# self.embeddings = nn.Embedding(vocab_size, embedding_dim, padding_idx=padding_idx)
self.text_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.gc1 = GraphConvolution(2*opt.hidden_dim, 2*opt.hidden_dim)
self.gc2 = GraphConvolution(2*opt.hidden_dim, 2*opt.hidden_dim)
self.fc = nn.Linear(2*opt.hidden_dim, opt.polarities_dim)
self.text_embed_dropout = nn.Dropout(0.3)
def __init__(self, embedding_matrix_list, opt):
super(BILSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix_list[0], dtype=torch.float), freeze=True)
# self.input_drop = nn.Dropout(bilstm_config['input_drop'])
self.bilstm = DynamicLSTM(bilstm_config['embed_dim'],
bilstm_config['hidden_dim'],
num_layers=bilstm_config['num_layers'],
batch_first=True,
bidirectional=True
)
self.dense = nn.Linear(bilstm_config['hidden_dim'] * 2, opt.polarities_dim)
def __init__(self, config, opt):
super(MGAN, self).__init__()
self.opt = opt
self.bert = BertModel(config)
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.output_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),
freeze=False)
self.lstm_entity1 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim * 2,
num_layers=1,
batch_first=True,
bidirectional=True)
self.lstm_entity2 = DynamicLSTM(opt.embed_dim,
opt.hidden_dim * 2,
num_layers=1,
batch_first=True,
bidirectional=True)
self.lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim * 2,
num_layers=1,
batch_first=True,
bidirectional=True)
self.dense = nn.Linear(opt.hidden_dim * 6, opt.hidden_dim)
def __init__(self, embedding_matrix, opt):
super(BiLSTM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.dropout = nn.Dropout(0.5)
self.clause_cnn = ClauseCNN(opt)
self.bilstm = DynamicLSTM(opt.hidden_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.emotion_fc = nn.Linear(2 * opt.hidden_dim, 2 * opt.MLP_out_dim)
self.cause_fc = nn.Linear(2 * opt.hidden_dim, 2 * opt.MLP_out_dim)
self.pair_biaffine = Biaffine(opt, opt.MLP_out_dim, opt.MLP_out_dim, 1, bias=(True, False))
self.emotion_fc1 = nn.Linear(opt.MLP_out_dim, opt.polarities_dim)
self.cause_fc1 = nn.Linear(opt.MLP_out_dim, opt.polarities_dim)
def __init__(self, opt):
super(Bert_MGAN, self).__init__()
self.opt = opt
self.bert_embedding = bert_base.Bert_Base(opt)
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(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(MIMN, self).__init__()
self.opt = opt
self.img_extractor = nn.Sequential(
*list(Models.resnet18(pretrained=True).children())[:-1])
self.embed = nn.Embedding.from_pretrained(torch.tensor(
embedding_matrix, dtype=torch.float),
freeze=False)
self.bi_lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_img = DynamicLSTM(opt.embed_dim_img,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.attention_text = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.attention_img = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.attention_text2img = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.attention_img2text = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.gru_cell_text = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.gru_cell_img = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.bn = nn.BatchNorm1d(opt.hidden_dim * 2, affine=False)
self.fc = nn.Linear(opt.hidden_dim * 4, 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)
self.cls_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.cls_asp_lstm = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.dense_total = nn.Linear(3*opt.hidden_dim*2, opt.polarities_dim)
self.h_h_proj = nn.Linear(4*opt.hidden_dim,3*opt.hidden_dim)
self.cls_dense = nn.Linear(3*opt.hidden_dim,3)
self.conv = nn.Conv1d(300,2*opt.hidden_dim,3)
def __init__(self, embeddings, opt):
super().__init__()
self.opt = opt
self.in_dim = opt.embed_dim
self.emb = nn.Embedding.from_pretrained(
torch.tensor(embeddings, dtype=torch.float))
self.mem_dim = 2 * opt.hidden_dim # 200
self.input_dropout = nn.Dropout(opt.input_dropout)
# rnn layer
if self.opt.no_word_rnn == False:
self.input_W_R = nn.Linear(self.in_dim, opt.rnn_hidden)
self.w_rnn = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=opt.rnn_layer,
batch_first=True,
bidirectional=True) # (32,75,45,200)
self.in_dim = opt.hidden_dim * 2
self.w_rnn_drop = nn.Dropout(
opt.rnn_word_dropout) # use on last layer output
if self.opt.no_clause_rnn == False:
self.input_W_R = nn.Linear(self.in_dim, opt.rnn_hidden)
self.c_rnn = DynamicLSTM(self.in_dim,
opt.hidden_dim,
num_layers=opt.rnn_layer,
batch_first=True,
bidirectional=True) # (32,75,45,200)
self.in_dim = opt.hidden_dim * 2
self.c_rnn_drop = nn.Dropout(
opt.rnn_clause_dropout) # use on last layer output
self.in_drop = nn.Dropout(opt.input_dropout)
# self.emo_trans = nn.Linear(self.in_dim, 100)
self.clause_encode = Attention(2 * opt.hidden_dim, 1, opt.max_sen_len,
opt) # (32,75,200)
def __init__(self, embedding_matrix, opt):
super(CAER, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.text_lstm = DynamicLSTM(opt.embed_dim,
opt.embed_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.text_dfc = nn.Linear(2 * opt.embed_dim, 1)
self.text_fc = nn.Linear(opt.embed_dim, 1)
self.text_embed_dropout = nn.Dropout(0.3)
self.fc_dropout = nn.Dropout(0.3)
def __init__(self, config, opt):
super(TNet_LF, self).__init__()
print("this is TNet_LF model")
self.bert = BertModel(config)
self.position = Absolute_Position_Embedding(opt)
self.opt = opt
D = opt.embed_dim # 模型词向量维度
C = opt.output_dim # 分类数目
L = opt.max_seq_length
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)
def __init__(self, config, opt):
super(RAM, self).__init__()
self.opt = opt
# self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.bert = BertModel(config)
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.output_dim)
def __init__(self, embedding_matrix, opt):
super(LSTM_Attention, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(
embedding_matrix, dtype=torch.float),
freeze=False)
self.lstm_context = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.attention_context = SelfAttention(opt.hidden_dim,
score_function='bi_linear')
# self.pooling = nn.AvgPool1d(kernel_size=5)
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, config, opt, _type='c'):
super(Cabasc, self).__init__()
self.opt = opt
self.type = _type
self.bert = BertModel(config)
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.output_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, bert, opt):
super(ATAE_LSTM, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.squeeze_embedding = SqueezeEmbedding()
self.lstm = DynamicLSTM(opt.bert_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True)
self.attention = NoQueryAttention(opt.hidden_dim + opt.bert_dim,
score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, embedding, opt):
super(LRG, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.FloatTensor(embedding))
self.bi_all_ctx = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_left_ctx = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_right_ctx = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.bi_lstm_aspect = DynamicLSTM(opt.embed_dim,
opt.hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True)
self.attention = Attention(opt.hidden_dim * 2, score_function='mlp')
self.left_attention = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.right_attention = Attention(opt.hidden_dim * 2,
score_function='mlp')
self.left_gru_cell = nn.GRUCell(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.right_gru_cell = nn.GRUCell(opt.hidden_dim * 2,
opt.hidden_dim * 2)
self.w1 = nn.Linear(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.w2 = nn.Linear(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.w3 = nn.Linear(opt.hidden_dim * 2, opt.hidden_dim * 2)
self.dense = nn.Linear(opt.hidden_dim * 2, opt.polarities_dim)
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)
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)