![]() Tokens are an important container type in spaCy and have a very rich set of features. As you may have noticed, “word tokenization” is a slightly misleading term, as captured tokens include punctuation and other nonword strings. This model includes a default processing pipeline that you can customize, as you’ll see later in the project section.Īfter that, you generate a list of tokens and print it. In this code, you set up some example text to tokenize, load spaCy’s English model, and then tokenize the text by passing it into the nlp constructor. load ( "en_core_web_sm" ) > doc = nlp ( text ) > token_list = > token_list "Where could she be?" he wondered as he continued to wait for Marta to appear with the pets. The car had been hastily packed and Marta was inside trying to round up the last of the pets. > import spacy > text = """ Dave watched as the forest burned up on the hill, only a few miles from his house. There are lots of great tools to help with this, such as the Natural Language Toolkit, TextBlob, and spaCy. Vectorizing text by turning the text into a numerical representation for consumption by your classifierĪll these steps serve to reduce the noise inherent in any human-readable text and improve the accuracy of your classifier’s results.Normalizing words by condensing all forms of a word into a single form. ![]()
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