https://github.com/vidhi1290/llm---detect-ai-generated-text
AI-Generated Text Detection: A BERT-powered solution for accurately identifying AI-generated text. Seamlessly integrated, highly accurate, and user-friendly.🚀
https://github.com/vidhi1290/llm---detect-ai-generated-text
ai-generated bert bert-model detection-algorithm kaggle kaggle-competition llm machine-learning natural-language-processing nlp
Last synced: about 1 month ago
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AI-Generated Text Detection: A BERT-powered solution for accurately identifying AI-generated text. Seamlessly integrated, highly accurate, and user-friendly.🚀
- Host: GitHub
- URL: https://github.com/vidhi1290/llm---detect-ai-generated-text
- Owner: Vidhi1290
- Created: 2023-11-03T08:26:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-11T12:14:01.000Z (10 months ago)
- Last Synced: 2025-04-10T21:45:43.830Z (about 1 month ago)
- Topics: ai-generated, bert, bert-model, detection-algorithm, kaggle, kaggle-competition, llm, machine-learning, natural-language-processing, nlp
- Language: Jupyter Notebook
- Homepage:
- Size: 51.8 KB
- Stars: 57
- Watchers: 1
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI-Generated Text Detection using BERT
Welcome to our AI-Generated Text Detection project! In this repository, we present a robust solution for detecting AI-generated text using BERT, a cutting-edge natural language processing model. Whether you're a researcher, developer, or a curious enthusiast, this project empowers you to explore, understand, and combat AI-generated content effectively.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
- [How It Works](#how-it-works)
- [Contributing](#contributing)
- [License](#license)
## Introduction
AI-generated content is becoming increasingly sophisticated, making it challenging to distinguish between genuine and computer-generated text. Our project aims to tackle this issue by leveraging the power of BERT (Bidirectional Encoder Representations from Transformers) to identify and flag AI-generated text segments. Whether you're dealing with chatbots, articles, or social media posts, our solution offers accurate detection, ensuring the authenticity of digital content.
## Features
- **BERT-Powered Detection:** We utilize state-of-the-art BERT models to analyze the semantic context and linguistic nuances, enabling precise identification of AI-generated text.
- **Effortless Integration:** Seamlessly integrate our solution into your existing applications or workflows, ensuring hassle-free implementation for developers and researchers.
- **High Accuracy:** Our model is meticulously trained and fine-tuned to achieve high accuracy, minimizing false positives and false negatives for reliable results.
- **User-Friendly Interface:** With intuitive interfaces and clear instructions, users can easily navigate and utilize the detection tool without any technical expertise.
## Getting Started
Follow these simple steps to get started with our AI-Generated Text Detection tool:
1. **Clone the Repository:**
```bash
git clone https://github.com/your-username/ai-generated-text-detection.git
cd ai-generated-text-detection
```
- **Access the generated submission.csv file to explore the detected AI-generated text segments and their respective confidence scores.**
## How It Works
Our solution follows a comprehensive approach to AI-generated text detection:
**Data Preprocessing:** We clean and preprocess the textual data, removing noise and irrelevant information to enhance the accuracy of our model.
**BERT Tokenization:** Leveraging the BERT tokenizer, we encode the preprocessed text, preparing it for input into our detection model.
**Model Training:** Using a BERT-based sequence classification model, we train the system to distinguish between genuine and AI-generated text with a high degree of accuracy.
**Predictions:** Once trained, the model generates predictions for test data, highlighting potential AI-generated content segments.
**Result Analysis:** The results are saved in a CSV file, allowing users to review and analyze the detected segments along with their confidence scores.
## Contributing
We welcome contributions from the community! Whether you're a seasoned developer, a data science enthusiast, or a domain expert, your insights and expertise can enhance our project.
🚀 **Connect With Me:**
- LinkedIn: [LinkedIn Profile](https://www.linkedin.com/in/vidhi-waghela-434663198/)
- Kaggle: [Kaggle Profile](https://www.kaggle.com/vidhikishorwaghela)
- GitHub: [GitHub Profile](https://github.com/Vidhi1290)
If you find this project interesting or helpful, don't hesitate to follow me for more exciting updates and projects! Let's learn and grow together! 🌟