NLP Project - Patronising Language Classifier (SemEval 2022 Task 4)

Key Implementations:

1. Baseline Strategies:

• Naive Bayes Classifier
• Logistic Regression Classifier

2. RoBERTa-Based Model:

• Developed a RoBERTa-based model for text classification.

3. Top Performance:

• Achieved top 3rd place F1 score on the held-out test dataset (amongst the cohort of 120 teams at the university)

4. Hyperparameter Tuning:

• Used the Optuna library for hyperparameter tuning.

The project showcases my ability to build and evaluate NLP models, from simple baselines to more advanced state-of-the-art approaches. I developed this as part of my Master's module - "Natural Language Processing" group project.

If you have any questions or feedback, please don't hesitate to reach out.

A few Important links: Challenge website - https://competitions.codalab.org/competitions/34344
Link to GitHub repo - https://github.com/Perez-AlmendrosC/dontpatronizeme