Instructions to use zonn-ai/chatgpt-detector-roberta-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use zonn-ai/chatgpt-detector-roberta-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'zonn-ai/chatgpt-detector-roberta-ONNX');
chatgpt-detector-roberta (ONNX)
This is an ONNX version of Hello-SimpleAI/chatgpt-detector-roberta. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for text-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline
Model Card for Hello-SimpleAI/chatgpt-detector-roberta
This model is trained on the mix of full-text and splitted sentences of answers from Hello-SimpleAI/HC3.
More details refer to arxiv: 2301.07597 and Gtihub project Hello-SimpleAI/chatgpt-comparison-detection.
The base checkpoint is roberta-base. We train it with all Hello-SimpleAI/HC3 data (without held-out) for 1 epoch.
(1-epoch is consistent with the experiments in our paper.)
Citation
Checkout this papaer arxiv: 2301.07597
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
- Downloads last month
- 50
Model tree for zonn-ai/chatgpt-detector-roberta-ONNX
Base model
Hello-SimpleAI/chatgpt-detector-roberta