README.md

---
title: FineTextTector - AI Text Detector
emoji: 🤖
colorFrom: indigo
colorTo: blue
sdk: streamlit
sdk_version: 1.30.0
app_file: app.py
pinned: false
license: mit
---

## About the Project

This project aims to develop a robust machine learning model capable of distinguishing between human-written and AI-generated text. With the rapid advancement of large language models (LLMs) such as ChatGPT and Gemini, the ability to identify the origin of a text has become crucial in various domains, including academic integrity, content moderation, misinformation detection, and authorship verification. The project leverages state-of-the-art natural language processing (NLP) techniques and transfer learning to build a binary classifier that can accurately predict whether a given text was authored by a human or generated by an AI.

The workflow encompasses comprehensive exploratory data analysis (EDA), advanced text preprocessing, model selection and fine-tuning, and thorough evaluation. The final model is designed to be easily deployable and accessible for real-world applications.

## About the Dataset

The dataset used in this project is sourced from Kaggle: [AI vs Human Text Dataset](https://www.kaggle.com/datasets/shanegerami/ai-vs-human-text). It contains a large collection of text samples, each labeled as either human-written or AI-generated. The dataset is well-suited for binary classification tasks and provides a diverse range of topics and writing styles, making it ideal for training and evaluating models that need to generalize across different types of content.

- **Features:**
  - `text`: The actual text sample.
  - `generated`: Label indicating the source (0 for human, 1 for AI).

The dataset is split into training, validation, and test sets to ensure unbiased evaluation and robust model performance.

## Notebook Summary

The main notebook, `ai_vs_human_text_fine_tuned_classifier.ipynb`, guides users through the entire process of building the classifier:

1. **Problem Definition:** Outlines the motivation and objectives.
2. **Exploratory Data Analysis (EDA):** Visualizes class distributions, text lengths, lexical richness, punctuation usage, and stopword ratios to uncover patterns and differences between human and AI texts.
3. **Text Preprocessing:** Applies normalization, stopword removal, noise filtering (removing URLs, emails, hashtags, mentions, numbers), and filters out outlier texts based on length.
4. **Model Selection:** Utilizes transfer learning with the `distilbert/distilroberta-base` model, enhanced with LoRA (Low-Rank Adaptation) for efficient fine-tuning.
5. **Training:** Fine-tunes the model on a subset of the data, using stratified splits and advanced training arguments for optimal performance.
6. **Evaluation:** Assesses the model using accuracy, precision, recall, and F1-score on a held-out test set.
7. **Deployment:** Demonstrates how to push the trained model and tokenizer to Hugging Face Hub for sharing and reuse.

## Model Results

### Preprocessing

- **Lowercasing and Stripping:** All text is converted to lowercase and stripped of extra whitespace.
- **Punctuation and Stopword Removal:** Punctuation is removed, and stopwords are filtered out to focus on meaningful content.
- **Noise Filtering:** Regular expressions are used to remove URLs, emails, hashtags, mentions, and numbers.
- **Outlier Filtering:** Texts that are extremely short or long (based on quantiles) are removed to ensure consistent input lengths for the model.
- **Deduplication:** Duplicate texts are dropped to prevent data leakage.

### Training

- **Model Architecture:** The project uses `distilbert/distilroberta-base`, a distilled version of RoBERTa, known for its efficiency and strong performance on text classification tasks.
- **LoRA Fine-Tuning:** LoRA (Low-Rank Adaptation) is applied to reduce the number of trainable parameters, making the fine-tuning process more memory- and compute-efficient without sacrificing accuracy.
- **Training Arguments:** The model is trained for 2 epochs with early stopping, regular evaluation, and checkpointing. Batch sizes and learning rates are carefully chosen for stability and speed.

### Evaluation

- **Metrics:** The model is evaluated using accuracy, precision, recall, and F1-score. These metrics provide a comprehensive view of the classifier's performance, especially in distinguishing between the two classes.
- **Results:** The fine-tuned model demonstrates strong performance, with high accuracy and balanced precision/recall, indicating its effectiveness in real-world scenarios.

## How to Install

Follow these steps to set up the environment using Python's built-in `venv`:

```bash
# Clone the repository
git clone https://github.com/DeepActionPotential/FineTextTector
cd FineTextTector

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate


# Install required packages
pip install -r requirements.txt
```



## How to Use the Software

 - ## [Demo-video](images/FineTextTector.mp4)
 - ![Demo-image](images/1.png)
 - ![Demo-image](images/2.png)

## Technologies Used

- **Transformers (Hugging Face):** Core library for model loading, tokenization, and training. Enables transfer learning with state-of-the-art NLP models.
- **Datasets (Hugging Face):** Efficient data handling, splitting, and preprocessing.
- **PEFT (Parameter-Efficient Fine-Tuning):** Implements LoRA for memory- and compute-efficient model adaptation.
- **Optuna:** Automated hyperparameter optimization to fine-tune model performance.
- **Scikit-learn:** Data splitting, metrics calculation, and utility functions.
- **Seaborn & Matplotlib:** Data visualization for EDA and result interpretation.
- **NLTK:** Stopword lists and basic NLP utilities.
- **Python venv:** Isolated environment management for reproducible installations.

These technologies collectively enable efficient, scalable, and reproducible development of advanced NLP models.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---