What is spaCy used for in vector generation?

spaCy is used for generating vectors of words and texts, which allows transforming textual information into a numerical format suitable for machine learning and analysis. Vectors reflect the semantic meaning of words or sentences, enabling comparison by meaning, clustering, classification, and other NLP tasks.

Main applications of vector generation in spaCy:

• Word embeddings: spaCy provides pre-trained models that assign each word a fixed-dimensional vector.
• Sentence and document vectors: aggregating word vectors to obtain a vector for the entire text.
• Finding similar words and texts: comparing vectors using cosine similarity.

Example of usage:

import spacy

nlp = spacy.load("en_core_web_md")  # model with vector representations

doc = nlp("Apple is looking at buying U.K. startup")

print(doc.vector)  # vector of the entire sentence
print(doc[0].vector)  # vector of the first word "Apple"