infix_finditer is the function that is used to handle non-whitespace separators, such as hyphens.prefix_search is the function that is used to handle preceding punctuation, such as opening parentheses.nlp.vocab is a storage container for special cases and is used to handle cases like contractions and emoticons.In order for you to customize, you can pass various parameters to the Tokenizer class: tokenizer = customize_tokenizer ( custom_nlp ) > custom_tokenizer_about_doc = custom_nlp ( about_text ) > print () # Adds support to use `-` as the delimiter for tokenization. compile ( r '''''' ) > def customize_tokenizer ( nlp ). load ( 'en_core_web_sm' ) > prefix_re = spacy. > import re > import spacy > from spacy.tokenizer import Tokenizer > custom_nlp = spacy. is_stop detects if the token is a stop word or not.shape_ prints out the shape of the word.is_space detects if the token is a space or not.is_punct detects if the token is a punctuation symbol or not.is_alpha detects if the token consists of alphabetic characters or not.text_with_ws prints token text with trailing space (if present).In this example, some of the commonly required attributes are accessed: False He 86 He True False False Xx True is 89 is True False False xx True interested 92 interested True False False xxxx False in 103 in True False False xx True learning 106 learning True False False xxxx False Natural 115 Natural True False False Xxxxx False Language 123 Language True False False Xxxxx False Processing 132 Processing True False False Xxxxx False. Gus 0 Gus True False False Xxx False Proto 4 Proto True False False Xxxxx False is 10 is True False False xx True a 13 a True False False x True Python 15 Python True False False Xxxxx False developer 22 developer True False False xxxx False currently 32 currently True False False xxxx False working 42 working True False False xxxx False for 50 for True False False xxx True a 54 a True False False x True London 56 London True False False Xxxxx False - 62 - False True False - False based 63 based True False False xxxx False Fintech 69 Fintech True False False Xxxxx False company 77 company True False False xxxx False. In spaCy, you can print tokens by iterating on the Doc object: These units are used for further analysis, like part of speech tagging. Tokenization is useful because it breaks a text into meaningful units. It allows you to identify the basic units in your text. Tokenization is the next step after sentence detection. These sentences are still obtained via the sents attribute, as you saw before. Note that custom_ellipsis_sentences contain three sentences, whereas ellipsis_sentences contains two sentences. sents ) > for sentence in ellipsis_sentences. > # Sentence Detection with no customization > ellipsis_doc = nlp ( ellipsis_text ) > ellipsis_sentences = list ( ellipsis_doc. sents ) > for sentence in custom_ellipsis_sentences. add_pipe ( set_custom_boundaries, before = 'parser' ) > custom_ellipsis_doc = custom_nlp ( ellipsis_text ) > custom_ellipsis_sentences = list ( custom_ellipsis_doc. ' ) > # Load a new model instance > custom_nlp = spacy. # Adds support to use `.` as the delimiter for sentence detection.
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