| --- |
| library_name: transformers |
| datasets: |
| - NamCyan/tesoro-code |
| base_model: |
| - microsoft/codebert-base |
| --- |
| |
| # 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:** Encoder-based PLMs |
| - **Language(s):** Java |
| - **Finetuned from model:** [CodeBERT](https://huggingface.co/microsoft/codebert-base) |
|
|
| ### 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/codebert-base-technical-debt-code-tesoro") |
| model = AutoModelForSequenceClassification.from_pretrained("NamCyan/codebert-base-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. |
|
|
| ## 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} |
| } |
| ``` |
