This project aims to detect any possible plagiarism in documents using Natural Language Processing (NLP) techniques and the BERT model. The BERT model is a state-of-the-art deep learning model that has shown promising results in various NLP tasks, including plagiarism detection.
Python 3.x PyTorch Transformers library Pandas library Scikit-learn library
Clone the repository to your local machine. Install the required libraries using pip install -r requirements.txt. Run the plagiarism_detection.py script to train and test the model.
The input documents are preprocessed using NLP techniques such as tokenization, stop word removal, and stemming. The preprocessed documents are then fed into the BERT model, which extracts features from the text. The features are then used to classify the documents as plagiarised or non-plagiarised based on a benchmark score. The benchmark score can be adjusted to suit different use cases and requirements.
Explore other NLP techniques and models for plagiarism detection. Improve the model's accuracy and efficiency by fine-tuning hyperparameters. Develop a web-based application to provide a user-friendly interface for plagiarism detection.