Evaluating M-BERT on Natural Language Inference Tasks

Multilingual NLP models have revolutionized how we process multiple languages, but understanding how well they transfer knowledge across languages requires systematic evaluation. This text-based course guides you through the core concepts of Natural Language Inference (NLI) and teaches you how to rigorously evaluate M-BERT's performance. You will transition from understanding basic transformer architectures to actively fine-tuning and evaluating M-BERT on NLI datasets. Through clear written explanations and step-by-step code walkthroughs, you will learn how to measure cross-lingual generalization, interpret evaluation metrics, and identify model strengths and weaknesses across different languages. What you'll learn: - Understand the foundational concepts of Natural Language Inference (NLI) and multilingual language models. - Prepare and preprocess cross-lingual datasets using modern dataset libraries. - Configure M-BERT for sequence classification tasks. - Fine-tune the model using structured, reproducible training workflows. - Evaluate model performance using key metrics such as accuracy, precision, recall, and F1-score. - Analyze cross-lingual transfer capabilities and error patterns in model predictions. The course begins with essential terminology, outlining the mechanics of M-BERT and the structure of premise-hypothesis pairs in NLI tasks. You will then progress through data preparation, model training configuration, and detailed evaluation strategies using clean, written code examples. This course is designed for aspiring NLP practitioners, data scientists, and software engineers who want a clear introduction to evaluating multilingual models. No prior experience with M-BERT is required, though a basic familiarity with Python and machine learning concepts will help you get the most out of the material. Start reading today to build a solid foundation in evaluating multilingual transformer models.