Similarity Check: Using Clustering Algorithms with Hugging Face Models

Introduction

In the age of abundant data, analyzing and understanding text similarity has become essential for numerous applications, such as recommendation systems, plagiarism detection, and content categorization. Hugging Face, a pioneer in natural language processing (NLP), provides robust pre-trained models that can transform raw text into meaningful embeddings, enabling us to perform sophisticated similarity checks. When paired with clustering algorithms, these embeddings allow us to group similar texts effectively and uncover hidden patterns in datasets.

This blog will explore how to implement a similarity check using clustering algorithms, leveraging Hugging Face models to generate embeddings and applying techniques like K-Means and Hierarchical Clustering. By the end of this article, you will have a clear understanding of the process and a framework to implement it in your projects.

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