def __init__(self, embedding_matrix, opt):
super(AEN, 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(LCF_GLOVE, self).__init__()
self.config = BertConfig.from_json_file("config.json")
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.mha_global = SelfAttention(self.config, opt)
self.mha_local = SelfAttention(self.config, opt)
self.ffn_global = PositionwiseFeedForward(self.opt.hidden_dim,
dropout=self.opt.dropout)
self.ffn_local = PositionwiseFeedForward(self.opt.hidden_dim,
dropout=self.opt.dropout)
self.mha_local_SA = SelfAttention(self.config, opt)
self.mha_global_SA = SelfAttention(self.config, opt)
self.mha_SA_single = SelfAttention(self.config, opt)
self.bert_pooler = BertPooler(self.config)
self.bert_pooler1 = BertPooler(self.config)
self.bert_pooler2 = BertPooler(self.config)
self.dense1 = nn.Linear(opt.hidden_dim, opt.polarities_dim)
self.dense2 = nn.Linear(opt.hidden_dim, opt.polarities_dim)
self.sentiment_pool = nn.Linear(6, 3)
self.dropout = nn.Dropout(opt.dropout)
self.mean_pooling_double = nn.Linear(opt.embed_dim * 2, opt.hidden_dim)
self.mean_pooling_single = nn.Linear(opt.embed_dim, opt.hidden_dim)
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def __init__(self, bert, opt):
super(AEN_BERT, self).__init__()
#print(" 1 In AEN_BERT ")
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
#print(" 2 In AEN_BERT ")
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)
#print(" 3 In AEN_BERT ")
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
self.hat = False
self.last = torch.nn.ModuleList()
for t in range(self.opt.taskcla):
self.last.append(nn.Linear(opt.hidden_dim * 3, opt.polarities_dim))
def __init__(self, bert, opt):
"""
注意力编码器网络, Attentional Encoder Network for Targeted Sentiment Classification
:param bert:
:param opt:
"""
super(AEN_BERT, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
# attn_k和 attn_q的初始化
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)
# 初始化ffn_c, PCT层
self.ffn_c = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
# 目标特定的注意力层初始化
self.attn_s1 = Attention(opt.hidden_dim,
n_head=8,
score_function='mlp',
dropout=opt.dropout)
# 最终输出层定义
self.dense = nn.Linear(opt.hidden_dim * 3, opt.polarities_dim)
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(LCF_GLOVE, self).__init__()
self.config = BertConfig.from_json_file("utils/bert_config.json")
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.mha_global = SelfAttention(self.config, opt)
self.mha_local = SelfAttention(self.config, opt)
self.ffn_global = PositionwiseFeedForward(self.opt.embed_dim, dropout=self.opt.dropout)
self.ffn_local = PositionwiseFeedForward(self.opt.embed_dim, dropout=self.opt.dropout)
self.mha_local_SA = SelfAttention(self.config, opt)
self.mha_global_SA = SelfAttention(self.config, opt)
self.pool = BertPooler(self.config)
self.dropout = nn.Dropout(opt.dropout)
self.linear = nn.Linear(opt.embed_dim * 2, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
def __init__(self, bert, opt):
super(AEN_BERT_HAT, 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.gate = torch.nn.Sigmoid()
self.ec0 = torch.nn.Embedding(self.opt.taskcla, opt.hidden_dim)
self.ec1 = torch.nn.Embedding(self.opt.taskcla, opt.hidden_dim)
self.ec2 = torch.nn.Embedding(self.opt.taskcla, opt.hidden_dim)
self.ec3 = torch.nn.Embedding(self.opt.taskcla, opt.hidden_dim)
self.ec4 = torch.nn.Embedding(self.opt.taskcla, opt.hidden_dim)
self.hat = True
self.last = torch.nn.ModuleList()
for t in range(self.opt.taskcla):
self.last.append(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)
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, bert, opt):
super(AEN_SIMPLE, self).__init__()
self.opt = opt
self.bert = bert
self.squeeze_embedding = SqueezeEmbedding()
self.dropout = nn.Dropout(opt.dropout)
self.attn_q = Attention(opt.bert_dim,
out_dim=opt.hidden_dim,
n_head=opt.mha_heads,
score_function='mlp',
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.lstm = nn.LSTM(opt.hidden_dim,
opt.lstm_hid,
opt.lstm_layer,
batch_first=True,
bidirectional=opt.lstm_bidir)
self.dense = nn.Linear(opt.lstm_hid * (2 if opt.lstm_bidir else 1),
opt.polarities_dim)
def __init__(self, embedding_matrix, opt):
super(TargetedTransformer, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
# self.position_enc = nn.Embedding.from_pretrained(
# get_sinusoid_encoding_table(n_position, d_word_vec, padding_idx=0),
# freeze=True)
self.attn_text = BearAttention(opt.embed_dim,
hidden_dim=opt.att_dim,
out_dim=opt.hidden_dim,
n_head=opt.heads,
score_function='dot_product',
dropout=opt.dropout)
# self.attn_text = Attention(
# opt.embed_dim,
# out_dim=opt.hidden_dim,
# n_head=8,
# score_function='dot_product',
# dropout=opt.dropout)
self.attn_text2 = Attention(opt.att_dim,
hidden_dim=opt.att_dim,
out_dim=opt.hidden_dim,
n_head=opt.heads,
score_function='dot_product',
dropout=opt.dropout)
self.attn_aspect = BearAttention(opt.embed_dim,
hidden_dim=opt.att_dim,
out_dim=opt.hidden_dim,
n_head=opt.heads,
score_function='dot_product',
dropout=opt.dropout)
self.attn_aspect2 = Attention(opt.att_dim,
hidden_dim=opt.att_dim,
out_dim=opt.hidden_dim,
n_head=opt.heads,
score_function='dot_product',
dropout=opt.dropout)
self.ffn_c = PositionwiseFeedForward(opt.att_dim, dropout=opt.dropout)
self.ffn_c2 = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.ffn_t = PositionwiseFeedForward(opt.att_dim, dropout=opt.dropout)
self.ffn_t2 = PositionwiseFeedForward(opt.hidden_dim,
dropout=opt.dropout)
self.attn_s1 = Attention(opt.hidden_dim,
n_head=opt.heads,
score_function='dot_product',
dropout=opt.dropout)
# self.layer_norm1 = nn.LayerNorm(opt.hidden_dim)
# self.layer_norm2 = nn.LayerNorm(opt.hidden_dim)
# self.lstm = DynamicLSTM(
# opt.hidden_dim,
# opt.hidden_dim,
# num_layers=1,
# only_use_last_hidden_state=True,
# batch_first=True)
# self.lstm = nn.LSTM(
# opt.hidden_dim, opt.hidden_dim, num_layers=1,
# bias=True, batch_first=True, dropout=opt.dropout, bidirectional=False)
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)