| ---
|
| language:
|
| - code
|
|
|
| license: mit
|
| tags:
|
| - code-generation
|
| - bug-fixing
|
| - code-repair
|
| - codet5
|
| - debugging
|
| datasets:
|
| - custom
|
| metrics:
|
| - accuracy
|
| - exact-match
|
| library_name: transformers
|
| pipeline_tag: text2text-generation
|
| ---
|
|
|
| # brainbug
|
|
|
| ## Model Description
|
|
|
| This is a fine-tuned **CodeT5** model for automatic bug detection and code repair. The model has been trained to identify and fix various types of programming errors in Python code.
|
|
|
| ## Supported Error Types
|
|
|
| - **WVAV**: Wrong Variable Used in Variable Assignment
|
| - **MLAC**: Missing Line After Call
|
| - **WPFV**: Wrong Parameter in Function/Method Call
|
| - And more...
|
|
|
| ## Model Details
|
|
|
| - **Base Model**: `Salesforce/codet5-base`
|
| - **Fine-tuned on**: Custom bug-fix dataset
|
| - **Task**: Code-to-Code generation (bug fixing)
|
| - **Language**: Python
|
| - **Model Size**: 220M parameters
|
|
|
| ## Usage
|
|
|
| ```python
|
| from transformers import T5ForConditionalGeneration, RobertaTokenizer
|
|
|
| # Load model and tokenizer
|
| model = T5ForConditionalGeneration.from_pretrained("Sagar123x/brainbug")
|
| tokenizer = RobertaTokenizer.from_pretrained("Sagar123x/brainbug")
|
|
|
| # Example: Fix buggy code
|
| faulty_code = """
|
| def check_for_file(self, file_path):
|
| files = self.connection.glob(file_path)
|
| return len(files) == 1
|
| """
|
|
|
| # Prepare input
|
| input_text = f"Fix WVAV: {faulty_code}"
|
| inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)
|
|
|
| # Generate fix
|
| outputs = model.generate(**inputs, max_length=256, num_beams=5)
|
| fixed_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
|
| print(fixed_code)
|
| ```
|
|
|
| ## Training Details
|
|
|
| - **Training Epochs**: 10
|
| - **Batch Size**: 1 (with gradient accumulation)
|
| - **Learning Rate**: 3e-5
|
| - **Optimizer**: AdamW
|
| - **Hardware**: NVIDIA RTX 4050 (6GB)
|
|
|
| ## Performance Metrics
|
|
|
| - **Exact Match Accuracy**: 2.60%
|
| - **Token-Level Accuracy**: 28.52%
|
| - **Average Similarity**: 76.75%
|
|
|
|
|
| ## Limitations
|
|
|
| - Trained primarily on Python code
|
| - Best performance on error types seen during training
|
| - May not handle very long code snippets (>256 tokens)
|
| - Requires error type specification for optimal results
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @misc{brainbug-codet5,
|
| author = {Your Name},
|
| title = {BrainBug: CodeT5 for Automatic Bug Repair},
|
| year = {2025},
|
| publisher = {HuggingFace},
|
| howpublished = {\url{https://huggingface.co/Sagar123x/brainbug}}
|
| }
|
| ```
|
|
|
| ## License
|
|
|
| MIT License
|
|
|
| ## Contact
|
|
|
| For questions or issues, please open an issue on the model repository.
|
|
|
