def __init__(self, vocab, encoder, attention, g, out_dim):
super(LSTM, self).__init__(vocab)
self.text_encoder = encoder
self._classification_layer = torch.nn.Linear(
self.text_encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def __init__(self, vocab, model, out_features):
super(Classifier, self).__init__(vocab)
self.model = model
in_features = model.get_output_dim()
self._classification_layer = torch.nn.Linear(in_features, out_features)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def __init__(self, vocab, encoder, attention, g, out_dim):
super(Bert, self).__init__(vocab)
self.encoder = encoder
self.dense = torch.nn.Linear(self.encoder.get_output_dim(),
self.encoder.get_output_dim())
self.activation = torch.nn.Tanh()
self._classification_layer = torch.nn.Linear(
self.encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
class Classifier(Model):
def __init__(self, vocab, model, out_features):
super(Classifier, self).__init__(vocab)
self.model = model
in_features = model.get_output_dim()
self._classification_layer = torch.nn.Linear(in_features, out_features)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def forward(self, text, label, graph=None):
vec = self.model(text, graph)
logits = self._classification_layer(vec)
probs = torch.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
self._metric(predictions=logits, gold_labels=label)
output_dict['loss'] = self._loss(input=logits,
target=label.float())
return output_dict
def get_metrics(self, reset: bool = False):
metrics = {'f-score': self._metric.get_metric(reset)}
return metrics
class Bert(Model):
def __init__(self, vocab, encoder, attention, g, out_dim):
super(Bert, self).__init__(vocab)
self.encoder = encoder
self.dense = torch.nn.Linear(self.encoder.get_output_dim(),
self.encoder.get_output_dim())
self.activation = torch.nn.Tanh()
self._classification_layer = torch.nn.Linear(
self.encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def forward(self, text, label, graph):
hidden_states = self.encoder(text)
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
logits = self._classification_layer(pooled_output)
probs = torch.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
self._metric(predictions=logits, gold_labels=label)
output_dict['loss'] = self._loss(input=logits,
target=label.float())
return output_dict
def get_metrics(self, reset: bool = False):
metrics = {'f-score': self._metric.get_metric(reset)}
return metrics
class LSTM(Model):
def __init__(self, vocab, encoder, attention, g, out_dim):
super(LSTM, self).__init__(vocab)
self.text_encoder = encoder
self._classification_layer = torch.nn.Linear(
self.text_encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def forward(self, text, label, graph):
text_vec = self.text_encoder(text)
vec = text_vec[:, -1] # last hidden state
logits = self._classification_layer(vec)
probs = torch.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
self._metric(predictions=logits, gold_labels=label)
output_dict['loss'] = self._loss(input=logits,
target=label.float())
return output_dict
def get_metrics(self, reset: bool = False):
metrics = {'f-score': self._metric.get_metric(reset)}
return metrics
def __init__(self, vocab, encoder, attention, g, out_dim):
super(LabelEmbedModel, self).__init__(vocab)
_label_out_dim = 100
self.text_encoder = encoder
self.label_encoder = LabelEmbedding(g.num_features, _label_out_dim)
self.tl_attn = attention(self.text_encoder.get_output_dim(),
self.label_encoder.get_output_dim())
self.encoder = CnnEncoder(self.text_encoder.get_output_dim() +
self.label_encoder.get_output_dim(),
num_filters=100)
# self.encoder = CnnEncoder(self.text_encoder.get_output_dim(),
# num_filters=100)
self._classification_layer = torch.nn.Linear(
self.encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
class LabelEmbedModel(Model):
def __init__(self, vocab, encoder, attention, g, out_dim):
super(LabelEmbedModel, self).__init__(vocab)
_label_out_dim = 100
self.text_encoder = encoder
self.label_encoder = LabelEmbedding(g.num_features, _label_out_dim)
self.tl_attn = attention(self.text_encoder.get_output_dim(),
self.label_encoder.get_output_dim())
self.encoder = CnnEncoder(self.text_encoder.get_output_dim() +
self.label_encoder.get_output_dim(),
num_filters=100)
# self.encoder = CnnEncoder(self.text_encoder.get_output_dim(),
# num_filters=100)
self._classification_layer = torch.nn.Linear(
self.encoder.get_output_dim(), out_dim)
self._metric = MultiLabelMetric()
self._loss = torch.nn.BCEWithLogitsLoss()
def forward(self, text, label, graph):
text_vec = self.text_encoder(text)
label_vec = self.label_encoder(graph[0])
att_vec = self.tl_attn(text_vec, label_vec)
vec = torch.cat([text_vec, att_vec], dim=-1)
vec = self.encoder(vec, None)
logits = self._classification_layer(vec)
probs = torch.softmax(logits, dim=-1)
output_dict = {"logits": logits, "probs": probs}
if label is not None:
self._metric(predictions=logits, gold_labels=label)
output_dict['loss'] = self._loss(input=logits,
target=label.float())
return output_dict
def get_metrics(self, reset: bool = False):
metrics = {'f-score': self._metric.get_metric(reset)}
return metrics