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91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 a2c3f1f 91c1980 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | # 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
- **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
The model has been trained to recognize and optimize common Java patterns:
- **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 |