---
license: mit
language:
- en
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
tags:
- bert
- text
- classification
library_name: transformers
---

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


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Training Details:

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

Batch size: 16

Epochs: 10

Learning rate: 5e-6


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Performance:

Best validation metrics:

Accuracy: 0.5730 (57.3%)

Precision: 0.6162

Recall: 0.9858 

F1-score: 0.6814

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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.

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Framework: PyTorch, Hugging Face Transformers

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License: MIT License

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NOTE: This model is experimental and not intended for production use