Embeddings — vector-based numerical representations of typically unstructured data like text — have been primarily popularized in the field of natural language processing (NLP).
This post is divided into three parts; they are: • Understanding Word Embeddings • Using Pretrained Word Embeddings • Training Word2Vec with Gensim • Training Word2Vec with PyTorch • Embeddings in Transformer Models Word embeddings represent words as dense vectors in a continuous space, where semantically similar words are positioned…
It would be difficult to argue that word embeddings — dense vector representations of words — have not dramatically revolutionized the field of natural language processing (NLP) by quantitatively capturing semantic relationships between words.