upload bert_func.py

bert_func.py

@@ -0,0 +1,37 @@
+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