license: mit
language:
- en
base_model:
- distilbert/distilbert-base-uncased
DistilBERT AI-vs-Human Text Classifier
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
Framework: PyTorch, Hugging Face Transformers
Training Details
Dataset size: 1367 samples
Batch size: 16
Epochs: 10
Learning rate: 5e-6
Performance
Best validation metrics:
Accuracy: 0.5693
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.
License MIT License