Summary of Changes

Implemented a custom Logistic Regression model for sentiment analysis using spaCy without relying on scikit-learn. The model processes datasets to classify text as positive or negative. Additionally, a comprehensive README.md file was created to guide users through installation, usage, and testing of the model.

Description

This pull request introduces a new feature that includes a custom Logistic Regression sentiment analysis model. The implementation is based on spaCy and avoids using scikit-learn, thereby providing a more integrated solution within the spaCy ecosystem.

The model processes the dataset, trains the logistic regression classifier, and outputs classification results. The README file has been updated to include detailed instructions for running the model, testing its performance, and using it within user projects.

Types of change

• New Feature: Introduced a custom Logistic Regression model for sentiment analysis only using spaCy.
• Documentation: Updated the README.md file to include usage instructions, testing guidelines, and examples.

Checklist

• I confirm that I have the right to submit this contribution under the project's MIT license.
• I ran the tests, and all new and existing tests passed.
• My changes don't require a change to the documentation, or if they do, I've added all required information.