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')) 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