DistilBERT AI Text Detector (updated)

This is a binary text classification model built on top of distilbert-base-uncased. It has been fine-tuned to distinguish between AI-generated and human-written text.

Base model:

DistilBERT (uncased)

Task: Sequence classification

Labels:

0 → Human-written text

1 → AI-generated text

Training Details:

Model is fine-tuned on a small custom dataset of ~1.4k samples

Batch size: 16

Epochs: 10

Learning rate: 5e-6

Performance:

Accuracy: 0.5693 (~57%)

Precision: 0.6162

Recall: 0.9858

F1-score: 0.6814

Usage

Load the model and tokenizer with the Hugging Face Transformers library, provide a text input and the model will output a label indicating whether the text is AI-generated or human-written.

Framework:

PyTorch, Hugging Face Transformers

License: MIT License

NOTE:

This model is experimental and not intended for production use