| # CodeT5-small Java Optimization Model |
|
|
| A fine-tuned Salesforce/codet5-small model for Java code optimization tasks. |
|
|
| ## Overview |
|
|
| This repository contains a fine-tuned CodeT5-small model specifically trained for Java code optimization. The model takes verbose or inefficient Java code and generates more optimal versions. |
|
|
| ## Model Information |
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| - **Base Model**: Salesforce/codet5-small |
| - **Training Dataset**: [nlpctx/java_optimisation](https://huggingface.co/datasets/nlpctx/java_optimisation) |
| - **Framework**: HuggingFace Transformers with Seq2SeqTrainer |
| - **Training Setup**: Dual-GPU DataParallel (Kaggle T4×2) |
| - **Dataset Size**: ~6K training / 680 validation Java optimization pairs |
| - **Optimization Focus**: Java code refactoring and performance improvements |
|
|
| ## Files |
|
|
| - `config.json` - Model configuration |
| - `generation_config.json` - Generation parameters |
| - `model.safetensors` - Model weights (safetensors format) |
| - `merges.txt` - BPE merges file |
| - `special_tokens_map.json` - Special tokens mapping |
| - `tokenizer_config.json` - Tokenizer configuration |
| - `vocab.json` - Vocabulary file |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import T5ForConditionalGeneration, T5Tokenizer |
| import torch |
| |
| # Load model and tokenizer |
| model = T5ForConditionalGeneration.from_pretrained("model_directory") |
| tokenizer = T5Tokenizer.from_pretrained("model_directory") |
| |
| # Prepare input Java code |
| java_code = "your Java code here" |
| input_ids = tokenizer(java_code, return_tensors="pt").input_ids |
| |
| # Generate optimized code |
| with torch.no_grad(): |
| outputs = model.generate( |
| input_ids, |
| max_length=512, |
| num_beams=4, |
| early_stopping=True |
| ) |
| |
| optimized_code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| print(optimized_code) |
| ``` |
|
|
| ## Example Optimizations |
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| The model has been trained to recognize and optimize common Java patterns: |
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| - **Switch Expressions**: Converting verbose switch statements to switch expressions |
| - **Collection Operations**: Replacing manual iterator removal with `removeIf()` |
| - **String Handling**: Optimizing string concatenation with `StringBuilder` |
| - **Loop Optimizations**: Improving iterative constructs |
| - **And more...** |
|
|
| ## Training Details |
|
|
| The model was fine-tuned using: |
| - **Base Model**: Salesforce/codet5-small |
| - **Dataset**: nlpctx/java_optimisation from Hugging Face |
| - **Training Framework**: Seq2SeqTrainer with DataParallel |
| - **Hardware**: Kaggle T4×2 (dual GPU) |
| - **Approach**: Standard supervised fine-tuning on Java optimization pairs |
| |
| ## License |
| |
| This model is provided for educational and demonstration purposes. |
| |
| ## Acknowledgements |
| |
| - Model based on Salesforce/codet5-small |
| - Training data from nlpctx/java_optimisation dataset |
| - Built with HuggingFace Transformers |
