Update README.md

README.md

@@ -9,6 +9,7 @@ A Transformer-based code fixing model trained on diverse buggy → fixed code pa
 - **Languages**: Python (initially), can be expanded to JS, Go, etc.
 
 ## 🔧 Intended Use
+
 Input a buggy function or script and receive a syntactically and semantically corrected version.
 
 **Example**:
@@ -20,35 +21,3 @@ def add(x, y)
 # Output:
 def add(x, y):
     return x + y
-```
-
-## 🧠 How it Works
-The model learns from training examples that map erroneous code to corrected code. It uses token-level sequence generation to predict patches.
-
-## 🚀 Inference
-Use `transformers` pipeline or run via CLI:
-```python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-model = AutoModelForSeq2SeqLM.from_pretrained("YOUR_USERNAME/aifixcode-model")
-tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/aifixcode-model")
-input_code = "def foo(x):\n print(x"
-inputs = tokenizer(input_code, return_tensors="pt")
-out = model.generate(**inputs, max_length=512)
-print(tokenizer.decode(out[0], skip_special_tokens=True))
-```
-
-## 📂 Dataset Format
-```json
-[
-  {
-    "input": "def add(x, y)\n return x + y",
-    "output": "def add(x, y):\n    return x + y"
-  }
-]
-```
-
-## 🛡️ License
-MIT License
-
-## 🙏 Acknowledgements
-Built using 🤗 HuggingFace Transformers + Salesforce CodeT5.