Text Classification
Transformers
Safetensors
roberta
code-defect-detection
c
text-embeddings-inference
Instructions to use lafarizo/code_defect_detection_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lafarizo/code_defect_detection_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lafarizo/code_defect_detection_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lafarizo/code_defect_detection_v1") model = AutoModelForSequenceClassification.from_pretrained("lafarizo/code_defect_detection_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 893 Bytes
4cd45a0 3bab705 1c96033 | 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 | ---
title: "Code Defect Detection v1"
tags:
- code-defect-detection
- c
library_name: "transformers"
datasets:
- semeru/code-code-DefectDetection
---
# Code Defect Detection v1
Code Defect Detection for C language
### Model Sources
- **Repository:** [mrm8488/codebert2codebert-finetuned-code-defect-detection](https://huggingface.co/mrm8488/codebert2codebert-finetuned-code-defect-detection)
### Dataset
- **Repository:** [semeru/code-code-DefectDetection](https://huggingface.co/datasets/semeru/code-code-DefectDetection)
| Results | Value |
|---------------------------|--------------|
| **Evaluation Loss** | 0.7605 |
| **Accuracy** | 66.76% |
| **Precision** | 65.64% |
| **Recall** | 58.01% |
| **F1 Score** | 61.59% |
| **AUC** | 73.52% |
|