---
license: mit
language: en
library_name: transformers
pipeline_tag: text-classification
tags:
  - ai-text-detection
  - academic-integrity
  - paperguard
  - distilbert
datasets:
  - Rajarshi-Roy-research/Defactify_Text_Dataset
  - Ateeqq/AI-and-Human-Generated-Text
  - artem9k/ai-text-detection-pile
---

# PaperGuard AI Detector (v2.0)

Fine-tuned **DistilBERT** (`distilbert-base-cased`) sequence classifier that
detects AI-generated text in academic papers, essays, and reports. It is the
core AI-detection engine of the [PaperGuard](https://github.com/sameerreddy789/PaperGuard)
multi-agent academic-integrity system.

## Labels
`0 = ai`, `1 = human` (see `config.json` `id2label`).

## Training data
v2.0 ("mega") continues from the v1.5 checkpoint and adds a larger, more diverse
mix so the model sees many modern LLM writing styles:

- **Claude Opus distillation** — recent Anthropic-style generations
- **Ateeqq/AI-and-Human-Generated-Text** — academic abstracts (human + AI)
- **artem9k/ai-text-detection-pile** — large open human-vs-AI corpus
- (v1.5 lineage) **Defactify** + **Ateeqq**, spanning 30+ frontier LLMs
  (GPT-4o, LLaMA-3, Claude 3, Gemini, Mistral, Qwen, ChatGPT, …)

## Important: use the logit margin, not the raw softmax
On easy/separable data the model becomes **overconfident** — its softmax
saturates (it can report ~0% AI even on genuine AI text). The discriminative
signal lives in the **logit margin** (`human_logit − ai_logit`). PaperGuard
therefore scores AI-likelihood from a logistic **calibration of the margin**,
not the raw softmax. After calibration it flags clean/academic AI at ~70–90%
while keeping human text low (~10%).

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tok = AutoTokenizer.from_pretrained("vediumsameer/paperguard-ai-detector")
model = AutoModelForSequenceClassification.from_pretrained("vediumsameer/paperguard-ai-detector")

text = "The rapid advancement of artificial intelligence has transformed modern education..."
inputs = tok(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**inputs).logits[0]

# Recommended: score off the margin (softmax is saturated)
margin = float(logits[model.config.label2id["human"]] - logits[model.config.label2id["ai"]])
# lower margin  -> more AI-like ; higher margin -> more human-like
print("logit margin (human - ai):", margin)
```

## Limitations
- **Blind spot:** slang / style-masked AI (an LLM told to write casually) can
  still read as human. PaperGuard mitigates this with embedding-based
  stylometric "patchwork" detection, and a v2.1 trained on adversarial /
  multi-model / reasoning data is planned to close the gap.
- AI detection is a **probabilistic indicator**, not proof of authorship.

## Part of PaperGuard
This model powers the AI-detection layer of PaperGuard, which also does citation
claim verification, plagiarism, and writing-quality analysis. See the
[project repo](https://github.com/sameerreddy789/PaperGuard).