This Google Colab notebook provides a step-by-step guide for training a BERT model for spam and scam detection on Instagram comments. Follow the instructions in the notebook to train and fine-tune the BERT model using the provided dataset. Additionally, you can compare the results with three other models (Decision Tree, Linear Regression, Random Forest).
- https://github.com/ScamSpot/scamspot_ml-models/
- https://github.com/ScamSpot/scamspot_ig-comment-scraper
- https://github.com/ScamSpot/scamspot_api/
- https://github.com/ScamSpot/scamspot_chrome-extension/
Utilizes the powerful BERT (Bidirectional Encoder Representations from Transformers) model for natural language processing Easy-to-follow code and instructions for training the model Customizable parameters and options for fine-tuning Preprocessed dataset for spam and scam detection on Instagram comments
- Open the notebook in Google Colab.
- Follow the instructions in the notebook to run each code cell and fine-tune the BERT model.
- Customize the parameters and settings according to your requirements.
- Monitor the training progress and evaluate the model's performance.
- Save the trained model for further use or deployment.
- Compare the results with Decision Tree, Linear Regression, and Random Forest models.
Google Colab account Python 3.x
This project is licensed under the MIT License. See LICENSE for more information.
If you have any questions, feedback, or inquiries, please contact me at stefan@erben.eu.
README.md was generated automatically