diff --git a/model/model.py b/model/model.py
new file mode 100644
index 0000000..ec24353
--- /dev/null
+++ b/model/model.py
@@ -0,0 +1,54 @@
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+def fine_tune(model_name, dataset_url=None, file=None, epochs=3, batch_size=8, learning_rate=5e-5):
+    try:
+        # Load dataset
+        if dataset_url:
+            dataset = load_dataset(dataset_url)
+        elif file:
+            dataset = load_dataset("csv", data_files={"train": file.name})
+        else:
+            raise ValueError("Please provide a dataset URL or upload a file.")
+
+        # Load model & tokenizer
+        model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+        def tokenize_function(examples):
+            return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+        dataset = dataset.map(tokenize_function, batched=True)
+
+        # Define training arguments
+        training_args = TrainingArguments(
+            output_dir="./results",
+            evaluation_strategy="epoch",
+            save_strategy="epoch",
+            logging_strategy="epoch",
+            learning_rate=learning_rate,
+            per_device_train_batch_size=batch_size,
+            per_device_eval_batch_size=batch_size,
+            num_train_epochs=epochs,
+            weight_decay=0.01,
+            push_to_hub=False,
+            report_to="all"
+        )
+
+        # Initialize Trainer
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=dataset["train"],
+            eval_dataset=dataset["test"],
+            tokenizer=tokenizer,
+        )
+
+        # Start training
+        trainer.train()
+
+        return "Fine-tuning complete."
+
+    except Exception as e:
+        return f"An error occurred: {e}"