def __init__(self, vocab_size, word_dim, n_sentiment_tag, tag_dim,
lstm_dim, mlp_dim, n_class, we):
super(BiLSTMWordTag, self).__init__()
self.vocab_size = vocab_size
self.word_dim = word_dim
self.n_sentiment_tag = n_sentiment_tag
self.tag_dim = tag_dim
self.lstm_dim = lstm_dim
assert self.lstm_dim == self.word_dim + self.tag_dim, "lstm_dim != word_dim + tag_dim"
self.mlp_dim = mlp_dim
self.n_class = n_class
self.drop = nn.Dropout(p=0.5)
self.word_embedding = TokenEmbedding(self.vocab_size,
self.word_dim,
pretrained_emb=we)
self.tag_embedding = nn.Embedding(self.n_sentiment_tag + 1,
self.tag_dim,
padding_idx=0)
self.bilstm = BiLstmEncoder(self.lstm_dim, self.lstm_dim)
self.interaction_layer = SelfAttention(2 * self.lstm_dim,
self.lstm_dim)
self.mlp_layer = nn.Linear(2 * self.lstm_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)
def __init__(self,
n_sentiment_tag=5,
tag_dim=50,
lstm_dim=50,
mlp_dim=100,
n_class=2):
super(BilstmClassifier, self).__init__()
self.n_sentiment_tag = n_sentiment_tag
self.tag_dim = tag_dim
self.lstm_tag_dim = lstm_dim
self.mlp_dim = mlp_dim
self.n_class = n_class
self.drop = nn.Dropout(p=0.5)
self.tag_embedding = TokenEmbedding(1 + self.n_sentiment_tag,
self.tag_dim)
self.tag_bilstm = BiLstmEncoder(self.tag_dim, self.lstm_tag_dim)
self.interaction_layer = SelfAttention(2 * self.lstm_tag_dim,
self.lstm_tag_dim)
self.mlp_layer = nn.Linear(2 * self.lstm_tag_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)
def __init__(self,
vocab_size,
word_dim,
lstm_dim,
mlp_dim,
n_class,
we=None):
super(BiLSTMAttention, self).__init__()
self.vocab_size = vocab_size
self.word_dim = word_dim
self.lstm_dim = lstm_dim
self.mlp_dim = mlp_dim
self.n_class = n_class
self.drop = nn.Dropout(p=0.5)
self.word_embedding = TokenEmbedding(self.vocab_size,
self.word_dim,
pretrained_emb=we)
self.bilstm = BiLstmEncoder(self.word_dim, self.lstm_dim)
self.interaction_layer = SelfAttention(2 * self.lstm_dim,
self.lstm_dim)
self.mlp_layer = nn.Linear(2 * self.lstm_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)
def __init__(self, vocab_size, word_dim, lstm_dim, n_sentiment_tag,
mlp_dim, n_class, we, interaction_type):
super(BilstmClassifier, self).__init__()
self.vocab_size = vocab_size
self.word_dim = word_dim
self.lstm_dim = lstm_dim
self.n_sentiment_tag = n_sentiment_tag
self.mlp_dim = mlp_dim
self.n_class = n_class
self.word_embedding = TokenEmbedding(self.vocab_size,
self.word_dim,
pretrained_emb=we)
self.bilstm = BiLstmEncoder(self.word_dim, self.lstm_dim)
if interaction_type == "max":
self.interaction_layer = MaxPooling()
elif interaction_type == "avg":
self.interaction_layer = AveragePooling()
else:
self.interaction_layer = SelfAttention(2 * self.lstm_dim,
self.lstm_dim)
self.mlp_layer = nn.Linear(2 * self.lstm_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)
self.word2sentiment_tag = nn.Linear(2 * self.lstm_dim,
self.n_sentiment_tag)
def __init__(self, vocab_size, word_dim, lstm_dim, mlp_dim, n_class, we=None):
super(BiLSTMLast, self).__init__()
self.vocab_size = vocab_size
self.word_dim = word_dim
self.lstm_dim = lstm_dim
self.mlp_dim = mlp_dim
self.n_class = n_class
self.word_embedding = TokenEmbedding(self.vocab_size, self.word_dim, pretrained_emb=we)
self.bilstm = nn.LSTM(self.word_dim, self.lstm_dim, batch_first=True, bidirectional=True)
self.mlp_layer = nn.Linear(2 * self.lstm_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)
def __init__(self, vocab_size, word_dim, lstm_dim, mlp_dim, n_class, we=None):
super(BiLSTMAverage, self).__init__()
self.vocab_size = vocab_size
self.word_dim = word_dim
self.lstm_dim = lstm_dim
self.mlp_dim = mlp_dim
self.n_class = n_class
self.word_embedding = TokenEmbedding(self.vocab_size, self.word_dim, pretrained_emb=we)
self.bilstm = BiLstmEncoder(self.word_dim, self.lstm_dim)
self.average_pooling_layer = AveragePooling()
self.mlp_layer = nn.Linear(2 * self.lstm_dim, self.mlp_dim)
self.classify_layer = nn.Linear(self.mlp_dim, self.n_class)