Spaces:
Runtime error
Runtime error
File size: 1,626 Bytes
6f2ff70 | 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 52 53 54 55 56 57 58 59 60 61 62 | import torch.nn as nn
from .bert import BERT
class BERTLM(nn.Module):
"""
BERT Language Model
Next Sentence Prediction Model + Masked Language Model
"""
def __init__(self, bert: BERT, vocab_size):
"""
:param bert: BERT model which should be trained
:param vocab_size: total vocab size for masked_lm
"""
super().__init__()
self.bert = bert
self.next_sentence = NextSentencePrediction(self.bert.hidden)
self.mask_lm = MaskedLanguageModel(self.bert.hidden, vocab_size)
def forward(self, x, segment_label):
x = self.bert(x, segment_label)
return self.next_sentence(x), self.mask_lm(x)
class NextSentencePrediction(nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output size
"""
super().__init__()
self.linear = nn.Linear(hidden, 2)
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, x):
return self.softmax(self.linear(x[:, 0]))
class MaskedLanguageModel(nn.Module):
"""
predicting origin token from masked input sequence
n-class classification problem, n-class = vocab_size
"""
def __init__(self, hidden, vocab_size):
"""
:param hidden: output size of BERT model
:param vocab_size: total vocab size
"""
super().__init__()
self.linear = nn.Linear(hidden, vocab_size)
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, x):
return self.softmax(self.linear(x))
|