LLM---Detect-AI-Generated-Text and LLM-Text-Detection

Both are functionally equivalent BERT-based classifiers for detecting AI-generated text, making them direct competitors rather than complementary or related tools in an ecosystem.

Maintenance 6/25
Adoption 9/25
Maturity 16/25
Community 14/25
Maintenance 0/25
Adoption 8/25
Maturity 8/25
Community 18/25
Stars: 76
Forks: 11
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 59
Forks: 12
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
No Package No Dependents
No License Stale 6m No Package No Dependents

About LLM---Detect-AI-Generated-Text

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.🚀

This tool helps you check if text from sources like chatbots, articles, or social media posts was written by a human or an AI. You provide the text, and it tells you which parts are likely AI-generated, complete with a confidence score. This is useful for anyone who needs to ensure the authenticity and originality of digital content, such as content moderators, educators, or journalists.

content-verification plagiarism-detection digital-authenticity academic-integrity information-vetting

About LLM-Text-Detection

beingamanforever/LLM-Text-Detection

A complete overview and insights into AI-Text detection :seedling: using the powerful BERT(Bi-directional encoder representation transformer) to predict if a text is AI-generated :sunflower: or Human-authored :rocket:

This project helps identify whether a piece of text was written by a human or generated by an AI. You provide text content, and it tells you if it's likely AI-generated or human-authored. This is useful for content moderators, educators, journalists, or anyone needing to verify the authenticity of written material.

content-verification plagiarism-detection fraud-prevention text-authenticity digital-forensics

Scores updated daily from GitHub, PyPI, and npm data. How scores work