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import torch
import torch.nn as nn
from .bert import BERT
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import torch.nn as nn
from bert import BERT
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class BERTForClassification(nn.Module):
"""
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Fine-tune Task Classifier Model
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Progress Classifier Model
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"""
def __init__(self, bert: BERT, vocab_size, n_labels):
"""
:param bert: BERT model which should be trained
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:param vocab_size: total vocab size
:param n_labels: number of labels for the task
"""
super().__init__()
self.bert = bert
self.linear = nn.Linear(self.bert.hidden, n_labels)
def forward(self, x, segment_label):
x = self.bert(x, segment_label)
return self.linear(x[:, 0])
class BERTForClassificationWithFeats(nn.Module):
"""
Fine-tune Task Classifier Model
BERT embeddings concatenated with features
"""
def __init__(self, bert: BERT, n_labels, feat_size=9):
"""
:param bert: BERT model which should be trained
:param vocab_size: total vocab size
:param n_labels: number of labels for the task
"""
super().__init__()
self.bert = bert
# self.linear1 = nn.Linear(self.bert.hidden+feat_size, 128)
self.linear = nn.Linear(self.bert.hidden+feat_size, n_labels)
# self.RELU = nn.ReLU()
# self.linear2 = nn.Linear(128, n_labels)
def forward(self, x, segment_label, feat):
x = self.bert(x, segment_label)
x = torch.cat((x[:, 0], feat), dim=-1)
# x = self.linear1(x)
# x = self.RELU(x)
# return self.linear2(x)
return self.linear(x)
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:param vocab_size: total vocab size for masked_lm
"""
super().__init__()
self.bert = bert
self.linear = nn.Linear(self.bert.hidden, n_labels)
# self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, x, segment_label):
x = self.bert(x, segment_label)
return x, self.linear(x[:, 0])
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