metadata
language: en
tags:
- text-classification
- ai-text-detection
- pytorch
license: mit
Moodlerz/bert-detector-eli5
What is this?
This model was fine-tuned as part of a research project comparing transformer-based AI-text detectors across two benchmark datasets: HC3 and ELI5.
The task is binary classification:
- Label 0 → Human-written text
- Label 1 → LLM-generated text
Model details
BERT (bert-base-uncased) fine-tuned on ELI5 for AI-text detection. Binary classifier: human (0) vs LLM-generated (1). Trained with 1 epoch, dropout=0.2, early stopping on ROC-AUC.
Training setup
| Setting | Value |
|---|---|
| Epochs | 1 |
| Batch size (train) | 16 |
| Learning rate | 2e-5 |
| Warmup steps | 500 |
| Weight decay | 0.01 |
| Dropout | 0.2 |
| Max seq length | 512 |
| Validation split | 10% |
| Best model metric | ROC-AUC |
Datasets
- HC3 — Human ChatGPT Comparison Corpus
- ELI5 — Explain Like I'm 5 (Reddit QA dataset)
Cross-dataset evaluation (e.g. trained on HC3, tested on ELI5) was used to measure generalisability of each detector.
How to load
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("Moodlerz/bert-detector-eli5")
tokenizer = AutoTokenizer.from_pretrained("Moodlerz/bert-detector-eli5")
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
logits = model(**inputs).logits
prob_llm = torch.softmax(logits, dim=-1)[0][1].item()
print(f"P(LLM-generated): {prob_llm:.4f}")
Notes
- Local training dir:
./models/BERT_eli5 - All models in this series are private repos under
Moodlerz. - Part of a larger study — do not use for production content moderation without further evaluation.