README.md

metadata

title: Capstone Project
emoji: ⚡
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: false
short_description: AI vs Human text classifier

🤖 AI vs Human Text Classifier (RoBERTa)

This project fine-tunes RoBERTa to classify text as either:

• 🧑 Human-Written
• 🤖 AI-Generated

It was developed as a Capstone Project to explore the power of transformer-based models in detecting AI-generated content.

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📌 Project Overview

With the rapid rise of LLMs like GPT and other AI text generators, distinguishing between human-written and AI-generated text is becoming crucial in education, research, and online authenticity.
This project leverages RoBERTa, a transformer-based model, to build a binary text classifier.

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🛠️ Features

• Fine-tuned RoBERTa-base model
• Binary classification: Human (0) vs AI (1)
• Deployed with Gradio for easy interaction
• Model hosted on Hugging Face Model Hub

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📂 Dataset

The dataset used in training contains two columns:

• Text → the input text sample
• Generated → label (0 = Human, 1 = AI)

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🚀 Training

The model was fine-tuned on Google Colab using the Hugging Face transformers library.

Steps:

1. Load dataset (Text, Generated)
2. Preprocess using Hugging Face AutoTokenizer
3. Fine-tune RoBERTa with Trainer API
4. Evaluate using Accuracy, Precision, Recall, F1-score

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📊 Results

Validation accuracy achieved: ~99%

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference