Update model card

README.md

@@ -1,6 +1,3 @@
----
-{}
----
 # HP BERT Intent Classification Model
 
 This model is fine-tuned BERT for classifying different types of queries in the HP documentation context.
@@ -22,8 +19,7 @@ class BertInference:
     def __init__(self, model_path):
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.model = AutoModelForSequenceClassification.from_pretrained(model_path).to(self.device)
-        self.tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
-        self.template = "Question: {} Response: "
+        self.tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
         self.label_map = {
             0: "query_with_pdf",
             1: "summarize_pdf",
@@ -31,12 +27,10 @@ class BertInference:
         }
 
     def predict(self, text):
-        # Format the input text
-        formatted_text = self.template.format(text)
-        
+        # Format the input text        
         # Tokenize
         inputs = self.tokenizer(
-            formatted_text,
+            text,
             truncation=True,
             max_length=512,
             padding='max_length',
@@ -61,7 +55,7 @@ class BertInference:
 
 def main():
     # Initialize the model
-    model_path = "output_dir_decision"  # Path to your saved model
+    model_path = "nexaai2b/Octopus-xlm-roberta-BERT-intent-classification"  # Path to your saved model
     inferencer = BertInference(model_path)
 
     # Example usage