Instructions to use Hello-SimpleAI/chatgpt-detector-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hello-SimpleAI/chatgpt-detector-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta") model = AutoModelForSequenceClassification.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta") - Inference
- Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - Hello-SimpleAI/HC3 | |
| language: | |
| - en | |
| pipeline_tag: text-classification | |
| tags: | |
| - chatgpt | |
| # Model Card for `Hello-SimpleAI/chatgpt-detector-roberta` | |
| This model is trained on **the mix of full-text and splitted sentences** of `answer`s from [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3). | |
| More details refer to [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Gtihub project [Hello-SimpleAI/chatgpt-comparison-detection](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection). | |
| The base checkpoint is [roberta-base](https://huggingface.co/roberta-base). | |
| We train it with all [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) data (without held-out) for 1 epoch. | |
| (1-epoch is consistent with the experiments in [our paper](https://arxiv.org/abs/2301.07597).) | |
| ## Citation | |
| Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/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", | |
| } | |
| ``` | |