# SeqXGPT - Sentence-Level AI-Generated Text Detector

A trained SeqXGPT model for detecting AI-generated text at the sentence level.

## Model Description

This model implements the SeqXGPT architecture from the paper ["SeqXGPT: Sentence-Level AI-Generated Text Detection"](https://arxiv.org/abs/2310.08903).

## Training Results

| Metric | Score |
|--------|-------|
| Sentence Accuracy | 95.6% |
| Macro F1 Score | 95.6% |
| Word Accuracy | 95.5% |

## Supported Labels

- `gpt2`: GPT-2 generated
- `gptneo`: GPT-Neo generated
- `gptj`: GPT-J generated
- `llama`: LLaMA generated
- `gpt3re`: GPT-3 rewritten
- `human`: Human written

## Usage

```python
import torch
from model import ModelWiseTransformerClassifier

# Load model
model = torch.load("seqxgpt_transformer.pt")
model.eval()

# Inference requires feature extraction from 4 LLMs (GPT-2, GPT-Neo, GPT-J, LLaMA)
# See the original SeqXGPT repo for full pipeline
```

## Citation

```bibtex
@misc{wang2023seqxgpt,
    title={SeqXGPT: Sentence-Level AI-Generated Text Detection}, 
    author={Pengyu Wang and Linyang Li and Ke Ren and Botian Jiang and Dong Zhang and Xipeng Qiu},
    year={2023},
    eprint={2310.08903},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```