Create model.py

model.py

@@ -0,0 +1,80 @@
+import torch
+from transformers import (
+    AutoTokenizer, 
+    AutoModelForSequenceClassification,
+    pipeline
+)
+import logging
+
+logger = logging.getLogger(__name__)
+
+class AcoliModel:
+    def __init__(self, model_path=None):
+        self.model_path = model_path
+        self.tokenizer = None
+        self.model = None
+        self.classifier = None
+        
+        if model_path:
+            self.load_model(model_path)
+    
+    def load_model(self, model_path):
+        """Load a trained model"""
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+            self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
+            self.classifier = pipeline(
+                "text-classification",
+                model=self.model,
+                tokenizer=self.tokenizer
+            )
+            logger.info(f"Model loaded successfully from {model_path}")
+        except Exception as e:
+            logger.error(f"Error loading model: {e}")
+            raise
+    
+    def predict(self, text):
+        """Make prediction on input text"""
+        if self.classifier is None:
+            raise ValueError("Model not loaded. Call load_model() first.")
+        
+        return self.classifier(text)
+    
+    def predict_batch(self, texts):
+        """Make predictions on multiple texts"""
+        if self.classifier is None:
+            raise ValueError("Model not loaded. Call load_model() first.")
+        
+        return [self.classifier(text) for text in texts]
+    
+    def get_model_info(self):
+        """Get model information"""
+        if self.model is None:
+            return "Model not loaded"
+        
+        return {
+            "model_type": type(self.model).__name__,
+            "num_parameters": sum(p.numel() for p in self.model.parameters()),
+            "model_path": self.model_path
+        }
+
+# Example usage and convenience functions
+def load_acoli_model(model_path="./acoli-model"):
+    """Convenience function to load the Acoli model"""
+    return AcoliModel(model_path)
+
+def create_training_instance():
+    """Create a training instance"""
+    from Train import AcoliTrainer
+    return AcoliTrainer()
+
+if __name__ == "__main__":
+    # Example usage
+    model = AcoliModel()
+    
+    # After training, you can load the model like this:
+    # model.load_model("./acoli-model")
+    # prediction = model.predict("Your Acoli text here")
+    # print(prediction)
+    
+    print("Acoli model class ready for use!")