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)
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, opt.hidden_dim)
self.cls_dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
self.dense_total = nn.Linear(opt.hidden_dim + opt.hidden_dim,
opt.polarities_dim)
self.conv = nn.Conv1d(300, 2 * opt.hidden_dim, 3)
def __init__(self, embedding_matrix, opt): super(ATAE_LSTM, self).__init__() self.opt = opt #Parser arguments assigned self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float)) #Initialized the word embedding matrix with the GloVe word embeddings for fine tuning self.squeeze_embedding = SqueezeEmbedding() #Instance of Squeeze Embedding Class from layers.squeeze_embedding created self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True) #Instance of DynamicLSTM Class from layers.dynamic_rnn created parameters passed, input_size=opt.embed_dim*2, hidden_size=opt.hidden_dim self.attention = NoQueryAttention(opt.hidden_dim+opt.embed_dim, score_function='bi_linear') #Instance of NoQueryAttention Class from layers.attention created parameters passes, embed_dim=opt.hidden_dim+opt.embed_dim and score function is 'bi_linear' self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim) #A linear layer is created with in_features=opt.hidden_dim, out_features=opt.polarities_dim
def __init__(self, embedding_matrix, opt):
super(Gated_CNN, self).__init__()
self.embed = nn.Embedding.from_pretrained(
torch.tensor(embedding_matrix, dtype=torch.float))
self.in_layer = GLU_Block(opt.kernel_size, opt.embed_dim,
opt.in_channels, opt.downbot)
self.squeeze_embedding = SqueezeEmbedding()
self.glu_layers = self.make_glu_layers(opt)
self.attention = NoQueryAttention(opt.in_channels,
score_function='bi_linear')
self.dense = nn.Linear(opt.in_channels, opt.polarities_dim)
def __init__(self, config, opt):
super(ATAE_LSTM, self).__init__()
self.opt = opt
# self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.bert = BertModel(config)
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.output_dim)
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,
config='a class with num_attention_heads, hidden_size, attention_probs_dropout_prob, output_attentions',
bert_dir='/mnt/sda1/bert/uncased_L-12_H-768_A-12',
drop=0.0,
head_type_vec=[1] * 3 + [0] * 9,
L=80,
win_size=5,
bert_dim=768,
num_class=3):
super(VH_BERT_TSA, self).__init__()
self.register_buffer('att_mask',
self.get_atten_mask(head_type_vec, L, win_size))
self.text_bert = VHBertModel.from_pretrained(bert_dir)
self.aspect_bert = BertModel.from_pretrained(bert_dir)
self.aspect_self_att_pooler = SelfAttention(config, L)
self.aspect_emphasize_att = NoQueryAttention(embed_dim=bert_dim * 2,
hidden_dim=bert_dim,
out_dim=bert_dim)
self.reduce2_bert_dim_linear = nn.Linear(bert_dim * 2, bert_dim)
self.reduce2_num_class_linear = nn.Linear(bert_dim, num_class)
self.drop = drop
self.L = L