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import torch
from torch import nn
from models.preprocess_stage.bert_model import model
from models.preprocess_stage.bert_model import preprocess_bert
MAX_LEN = 100
# DEVICE='cpu'
class BertTunnig(nn.Module):
def __init__(self, bert_model):
super().__init__()
self.bert = bert_model
for weights in self.bert.parameters():
weights.requires_grad = False
self.fc1 = nn.Linear(768, 256)
self.drop1 = nn.Dropout(p=0.5)
self.fc2 = nn.Linear(256, 32)
self.fc_out = nn.Linear(32, 1)
def forward(self, x, attention_mask):
output = self.bert(x, attention_mask=attention_mask)[0][:, 0, :]
output = self.fc1(output)
output_drop = self.drop1(output)
output = self.fc2(output_drop)
output = self.fc_out(output)
return torch.sigmoid(output)
model_tunning = BertTunnig(bert_model=model)
model_tunning.load_state_dict(torch.load('models/weights/BertTunnigWeights.pt', map_location=torch.device('cpu')))
def predict_2(text):
preprocessed_text, attention_mask = preprocess_bert(text, MAX_LEN=MAX_LEN)
preprocessed_text, attention_mask = torch.tensor(preprocessed_text).unsqueeze(0), torch.tensor([attention_mask])
# model_tunning.to(DEVICE)
with torch.inference_mode():
predict = round(model_tunning(preprocessed_text, attention_mask=attention_mask).item())
return predict |