Create model.py

model.py

@@ -0,0 +1,34 @@
+import torch
+
+from transformers import BertForSequenceClassification, BertTokenizer, DataCollatorForTokenClassification
+import numpy as np
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+class BERT():
+    def __init__(self):
+        self.num_classes = 13
+        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+
+
+# Load pre-trained model and tokenizer
+        self.model = BertForSequenceClassification.from_pretrained("bert-base-uncased").to(self.device)
+        self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Modify the output layer to match the number of classes
+        self.model.classifier = torch.nn.Linear(in_features = 768, out_features= self.num_classes)
+        self.data_collator = DataCollatorForTokenClassification(self.tokenizer)
+        
+    def getModel(self):
+        return self.model
+    
+    
+    def get_tokenizer(self):
+        return self.tokenizer
+    
+    def tokenize(self, txt):
+        return self.tokenizer(txt, return_tensors='pt')
+    
+
+       
+