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---
library_name: transformers
datasets:
- NamCyan/tesoro-code
base_model:
- codellama/CodeLlama-7b-hf
---
# Improving the detection of technical debt in Java source code with an enriched dataset
## Model Details
### Model Description
This model is the part of Tesoro project, used for detecting technical debt in source code. More information can be found at [Tesoro HomePage](https://github.com/NamCyan/tesoro.git).
- **Developed by:** [Nam Hai Le](https://github.com/NamCyan)
- **Model type:** Decoder-based PLMs
- **Language(s):** Java
- **Finetuned from model:** [Codellama](https://huggingface.co./codellama/CodeLlama-7b-hf)
### Model Sources
- **Repository:** [Tesoro](https://github.com/NamCyan/tesoro.git)
- **Paper:** [To be update]
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("NamCyan/CodeLlama-7b-technical-debt-code-tesoro")
model = AutoModelForSequenceClassification.from_pretrained("NamCyan/CodeLlama-7b-technical-debt-code-tesoro")
```
## Training Details
- Training Data: The model is finetuned using [tesoro-code](https://huggingface.co./datasets/NamCyan/tesoro-code)
- Infrastructure: Training process is conducted on two NVIDIA A100 GPUs with 80GB of VRAM. [LoRa](https://arxiv.org/abs/2106.09685) is adopted to train this model.
## Leaderboard
| Model | Model size | EM | F1 |
|:-------------|:-----------|:------------------|:------------------|
| **Encoder-based PLMs** |
| [CodeBERT](https://huggingface.co./microsoft/codebert-base) | 125M | 38.28 | 43.47 |
| [UniXCoder](https://huggingface.co./microsoft/unixcoder-base) | 125M | 38.12 | 42.58 |
| [GraphCodeBERT](https://huggingface.co./microsoft/graphcodebert-base)| 125M | *39.38* | *44.21* |
| [RoBERTa](https://huggingface.co./FacebookAI/roberta-base) | 125M | 35.37 | 38.22 |
| [ALBERT](https://huggingface.co./albert/albert-base-v2) | 11.8M | 39.32 | 41.99 |
| **Encoder-Decoder-based PLMs** |
| [PLBART](https://huggingface.co./uclanlp/plbart-base) | 140M | 36.85 | 39.90 |
| [Codet5](https://huggingface.co./Salesforce/codet5-base) | 220M | 32.66 | 35.41 |
| [CodeT5+](https://huggingface.co./Salesforce/codet5p-220m) | 220M | 37.91 | 41.96 |
| **Decoder-based PLMs (LLMs)** |
| [TinyLlama](https://huggingface.co./TinyLlama/TinyLlama_v1.1_math_code) | 1.03B | 37.05 | 40.05 |
| [DeepSeek-Coder](https://huggingface.co./deepseek-ai/deepseek-coder-1.3b-base) | 1.28B | **42.52** | **46.19** |
| [OpenCodeInterpreter](https://huggingface.co./m-a-p/OpenCodeInterpreter-DS-1.3B) | 1.35B | 38.16 | 41.76 |
| [phi-2](https://huggingface.co./microsoft/phi-2) | 2.78B | 37.92 | 41.57 |
| [starcoder2](https://huggingface.co./bigcode/starcoder2-3b) | 3.03B | 35.37 | 41.77 |
| [CodeLlama](https://huggingface.co./codellama/CodeLlama-7b-hf) | 6.74B | 34.14 | 38.16 |
| [Magicoder](https://huggingface.co./ise-uiuc/Magicoder-S-DS-6.7B) | 6.74B | 39.14 | 42.49 |
## Citing us
```bibtex
@article{nam2024tesoro,
title={Improving the detection of technical debt in Java source code with an enriched dataset},
author={Hai, Nam Le and Bui, Anh M. T. Bui and Nguyen, Phuong T. and Ruscio, Davide Di and Kazman, Rick},
journal={},
year={2024}
}
``` |