from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("NamCyan/phi-2-technical-debt-code-tesoro")
model = AutoModelForSequenceClassification.from_pretrained("NamCyan/phi-2-technical-debt-code-tesoro")

Training Details

Training Data: The model is finetuned using 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	125M	38.28	43.47
UniXCoder	125M	38.12	42.58
GraphCodeBERT	125M	39.38	44.21
RoBERTa	125M	35.37	38.22
ALBERT	11.8M	39.32	41.99
Encoder-Decoder-based PLMs
PLBART	140M	36.85	39.90
Codet5	220M	32.66	35.41
CodeT5+	220M	37.91	41.96
Decoder-based PLMs (LLMs)
TinyLlama	1.03B	37.05	40.05
DeepSeek-Coder	1.28B	42.52	46.19
OpenCodeInterpreter	1.35B	38.16	41.76
phi-2	2.78B	37.92	41.57
starcoder2	3.03B	35.37	41.77
CodeLlama	6.74B	34.14	38.16
Magicoder	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}
}