LoRA Fine-Tuned AI-generated Detector

Disclaimer

This ONNX model was converted from the original model available in safetensors format. The conversion was performed to enable compatibility with frameworks or tools that utilize ONNX models.

Please note that this repository is not affiliated with the creators of the original model. All credit for the model’s development belongs to the original authors. To access the original model, please visit: Original Model Link.

If you have any questions about the original model, its licensing, or usage, please refer to the source link provided above.

• Label_0: Represents human-written content.
• Label_1: Represents AI-generated content.

Model Details

• Base Model: intfloat/e5-small
• Fine-Tuning Technique: LoRA (Low-Rank Adaptation)
• Task: Sequence Classification
• Use Cases: Text classification for AI-generated detection.
• Hyperparameters:
  • Learning rate: 5e-5
  • Epochs: 3
  • LoRA rank: 8
  • LoRA alpha: 16

Training Details

• Dataset:
  • 10,000 twitters and 10,000 rewritten twitters with GPT-4o-mini.
  • 80,000 human-written text from RAID-train.
  • 128,000 AI-generated text from RAID-train.
• Hardware: Fine-tuned on a single NVIDIA A100 GPU.
• Training Time: Approximately 2 hours.
• Evaluation Metrics:

  Metric	(Raw) E5-small	Fine-tuned
  Accuracy	65.2%	89.0%
  F1 Score	0.653	0.887
  AUC	0.697	0.976

Collaborators

• Menglin Zhou
• Jiaping Liu
• Xiaotian Zhan

Citation

@inproceedings{dugan-etal-2024-raid,
    title = "{RAID}: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors",
    author = "Dugan, Liam  and
      Hwang, Alyssa  and
      Trhl{\'\i}k, Filip  and
      Zhu, Andrew  and
      Ludan, Josh Magnus  and
      Xu, Hainiu  and
      Ippolito, Daphne  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.674",
    pages = "12463--12492",
}