Welcome to this NLP tutorial! In this tutorial, we aim to provide you with a clear understanding of how text classification works, leveraging some of the most influential techniques in the field. We will explore three distinct tasks, each designed to build upon the previous, enhancing your knowledge and skills in natural language processing (NLP).
This tutorial is structured around three key topics:
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Understanding Word Embeddings: Begin your journey by exploring what word embeddings are and how they represent words in a way that computers can understand.
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Introduction to Word2Vec: Although Word2Vec is an older model and not commonly used in modern NLP tasks, understanding its fundamentals, such as the concept of window sizing for context, is crucial. For those interested in exploring further, a link to the complete tutorial is provided. Word2Vec Tutorial
Note: We limit our exploration of Word2Vec because it's an older embedding model. However, the foundational concepts remain relevant for understanding more advanced models.
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Text Classification with BERT: Advance to learning about BERT (Bidirectional Encoder Representations from Transformers), a model that can be thought of as a bigger and better version of Word2Vec. This section will guide you through using BERT for text classification tasks, showcasing its effectiveness in handling such challenges.
Understanding these topics will not only enhance your knowledge of NLP but also equip you with the skills to apply advanced models like BERT in practical scenarios. By the end of this tutorial, you should have a solid foundation in text classification, word embeddings, and the evolution of NLP models over time.
Before diving into the tutorials, ensure you have a suitable development environment set up. Each tutorial provides specific setup instructions, so follow those carefully to avoid any issues.