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import numpy as np | |
import torch | |
import transformers | |
import joblib | |
def load_bert_lr_model(path): | |
model_class = transformers.BertModel | |
tokenizer_class = transformers.BertTokenizer | |
pretrained_weights = 'bert-base-uncased' | |
model = model_class.from_pretrained(pretrained_weights) | |
tokenizer = tokenizer_class.from_pretrained(pretrained_weights) | |
lr = joblib.load(path) | |
return model, tokenizer, lr | |
def prediction(text, model, tokenizer, lr, max_len=256): | |
input = tokenizer.encode(text, | |
add_special_tokens=True, | |
padding='max_length', | |
truncation=True, | |
return_tensors='np', | |
max_length=max_len) | |
att_mask = np.where(input != 0, 1, 0) | |
input = torch.tensor(input) | |
att_mask = torch.tensor(att_mask) | |
last_hidden_states = model(input, attention_mask=att_mask) | |
vector = last_hidden_states[0][:,0,:].detach().numpy() | |
pred = lr.predict(vector)[0] | |
if pred == 1: | |
result = 'Positive review' | |
else: | |
result = 'Negative review' | |
return result |