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import config
import transformers
import torch.nn as nn
class BERTBaseUncased(nn.Module):
def __init__(self):
super(BERTBaseUncased, self).__init__()
self.bert = transformers.BertModel.from_pretrained(config.BERT_PATH)
self.bert_drop = nn.Dropout(0.3)
self.out = nn.Linear(768, 3)
# self.out = nn.Linear(256, 3)
nn.init.xavier_uniform_(self.out.weight)
def forward(self, ids, mask, token_type_ids):
_, o2 = self.bert(
ids,
attention_mask=mask,
token_type_ids=token_type_ids
)
bo = self.bert_drop(o2)
# bo = self.tanh(self.fc(bo)) # to be commented if original
output = self.out(bo)
return output
def extract_features(self, ids, mask, token_type_ids):
_, o2 = self.bert(
ids,
attention_mask=mask,
token_type_ids=token_type_ids
)
bo = self.bert_drop(o2)
return bo |