Welcome to the Toxicity Detection Model repository! This notebook contains code for training and evaluating a toxicity detection model using natural language processing techniques.
##Overview Toxicity in online discussions and social media platforms is a significant issue. This notebook aims to provide a solution by demonstrating how to build and evaluate a toxicity detection model using machine learning.
Data Preprocessing: The notebook demonstrates how to preprocess text data for toxicity detection.
Model Training: Shows how to train a toxicity detection model using a machine learning algorithm.
Model Evaluation: Provides methods for evaluating the performance of the trained model.
Clone the Repository: Clone the repository to your local machine using the following command
git clone https://github.com/Saarthakm4/Toxicity_detection_model.git Open the Notebook: Open the toxicity_detection.ipynb notebook in a Jupyter environment or any compatible notebook viewer.
Run the Notebook Cells: Execute each cell in the notebook sequentially to understand the code and its functionality.
toxicity_detection.ipynb: Jupyter notebook containing code for toxicity detection. LICENSE: MIT License file. README.md: This file. Requirements Python 3.x Jupyter Notebook or JupyterLab Pandas NumPy Scikit-learn NLTK (Natural Language Toolkit)
This project is licensed under the MIT License - see the LICENSE file for details.
This notebook is based on research in natural language processing and machine learning. We acknowledge the contributions of the open-source community and the developers of the libraries used in this notebook.
Feedback, bug reports, and contributions are welcome! Please feel free to submit issues or contact us directly with any questions or suggestions.