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: Return textranked keyterms for the document.
  
  >>> from pprint import pprint
  >>> from textpipe.doc import Doc
  >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
  >>> pprint(doc.extract_keyterms(n_terms=3))
  [('awesome capital', 0.2384595585785324),
   ('Netherlands', 0.1312376837867799),
   ('Amsterdam', 0.07452637881734389)]
docstr_tok: ["Return","textranked","keyterms","for","the","document",".",">",">",">","from","pprint","import","pprint",">",">",">","from","textpipe.doc","import","Doc",">",">",">","doc","=","Doc","(","'Amsterdam","is","the","awesome","capital","of","the","Netherlands",".","'",")",">",">",">","pprint","(","doc.extract_keyterms","(","n_terms=3",")",")","[","(","'awesome","capital","'",",","0.2384595585785324",")",",","(","'Netherlands","'",",","0.1312376837867799",")",",","(","'Amsterdam","'",",","0.07452637881734389",")","]"]
code: def keyterms(self):
      """
      Return textranked keyterms for the document.
  
      >>> from pprint import pprint
      >>> from textpipe.doc import Doc
      >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
      >>> pprint(doc.extract_keyterms(n_terms=3))
      [('awesome capital', 0.2384595585785324),
       ('Netherlands', 0.1312376837867799),
       ('Amsterdam', 0.07452637881734389)]
      """
      return self.extract_keyterms()
  
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 22 for calling the Doc obj's underlying member method code with example usage: obj.keyterms() and returns: self