Spaces:
Running
Running
File size: 1,552 Bytes
5c72fe4 6a34fd4 5c72fe4 6a34fd4 5c72fe4 1922da0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import torch
import torch.nn as nn
from .bert import BERT
class BERTForClassification(nn.Module):
"""
Fine-tune Task Classifier Model
"""
def __init__(self, bert: BERT, vocab_size, n_labels):
"""
: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.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) |