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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 |