class_docstr: Create a doc instance of text, obtain cleaned, readable text and
  metadata from this doc.
  
  Properties:
  raw: incoming, unedited text
  language: 2-letter code for the language of the text
  is_detected_language: is the language detected or specified beforehand
  is_reliable_language: is the language specified or was it reliably detected
  hint_language: language you expect your text to be
  _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
docstr: Extract a dict of categories and their probability in the text, with the possibility
  of using a custom model.
  
  >>> from textpipe.doc import Doc
  >>> doc = Doc('Sentence for testing text categorization')
  >>> doc.get_cats()
  {}
docstr_tok: ["Extract","a","dict","of","categories","and","their","probability","in","the","text",",","with","the","possibility","of","using","a","custom","model",".",">",">",">","from","textpipe.doc","import","Doc",">",">",">","doc","=","Doc","(","'Sentence","for","testing","text","categorization","'",")",">",">",">","doc.get_cats","(",")","{","}"]
code: def get_cats(self, model_name=None):
      """
      Extract a dict of categories and their probability in the text, with the possibility
      of using a custom model.
  
      >>> from textpipe.doc import Doc
      >>> doc = Doc('Sentence for testing text categorization')
      >>> doc.get_cats()
      {}
      """
      lang = (
          self.language
          if self.is_reliable_language
          else self.hint_language
      )
      return self._load_spacy_doc(lang, model_name).cats
  
imports: import functools
  import re
  import unicodedata
  from collections import Counter
  from urllib.parse import urlparse
  import cld2
  import numpy
  import spacy
  import spacy.matcher
  import textacy
  import textacy.ke
  import textacy.text_utils
  from bs4 import BeautifulSoup
  from datasketch import MinHash
  from gensim.models.keyedvectors import KeyedVectors
  from gensim.summarization.summarizer import summarize
  from textpipe.data.emoji import EMOJI_TO_UNICODE_NAME, EMOJI_TO_SENTIMENT
  from textpipe.wrappers import RedisKeyedVectors
  from textpipe.util import getattr_
label_desc: Use image node_id 36 for calling the Doc obj's underlying member method code with example usage: obj.get_cats(model_name) and returns: self