
# 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",
}
```

{"language": ["en"], "tags": ["chatgpt"], "datasets": ["Hello-SimpleAI/HC3"], "pipeline_tag": "text-classification"}
transformers pytorch roberta text-classification chatgpt en dataset:Hello-SimpleAI/HC3 arxiv:2301.07597 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #chatgpt #en #dataset-Hello-SimpleAI/HC3 #arxiv-2301.07597 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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.

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.)

Checkout this papaer arxiv: 2301.07597



# 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.

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.)

Checkout this papaer arxiv: 2301.07597
TAGS
#transformers #pytorch #roberta #text-classification #chatgpt #en #dataset-Hello-SimpleAI/HC3 #arxiv-2301.07597 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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.

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.)

Checkout this papaer arxiv: 2301.07597
TAGS
#transformers #pytorch #roberta #text-classification #chatgpt #en #dataset-Hello-SimpleAI/HC3 #arxiv-2301.07597 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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.

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.)

Checkout this papaer arxiv: 2301.07597