How to Visualize Text Embeddings with TensorBoard

A word embedding is any method which converts words into numbers, and it is the primary task of any Machine Learning (ML) workflow involving text data. Independently from the problem being faced (classification, clustering, …), leveraging an effective numeric representation of the input text is paramount to the success of the ML model. But what is an effective numeric representation of text? Basically, we want to embed a word in a number or a vector able to convey information about the meaning of a word. One way to intuitively appreciate this concept is provided by word analogies¹, i.e. relationships of the form: "word₁ is to word₂ as word₃ is to word₄".