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 words with their counts
  
  >>> from pprint import pprint
  >>> from textpipe.doc import Doc
  >>> pprint(Doc('Test sentence for testing vectorisation of a sentence.').word_counts)
  {'.': 1,
   'Test': 1,
   'a': 1,
   'for': 1,
   'of': 1,
   'sentence': 2,
   'testing': 1,
   'vectorisation': 1}
docstr_tok: ["Extract","words","with","their","counts",">",">",">","from","pprint","import","pprint",">",">",">","from","textpipe.doc","import","Doc",">",">",">","pprint","(","Doc","(","'Test","sentence","for","testing","vectorisation","of","a","sentence",".","'",")",".word_counts",")","{","'",".","'",":","1",",","'Test","'",":","1",",","'","a","'",":","1",",","'for","'",":","1",",","'of","'",":","1",",","'sentence","'",":","2",",","'testing","'",":","1",",","'vectorisation","'",":","1","}"]
code: def word_counts(self):
      """
      Extract words with their counts
  
      >>> from pprint import pprint
      >>> from textpipe.doc import Doc
      >>> pprint(Doc('Test sentence for testing vectorisation of a sentence.').word_counts)
      {'.': 1,
       'Test': 1,
       'a': 1,
       'for': 1,
       'of': 1,
       'sentence': 2,
       'testing': 1,
       'vectorisation': 1}
      """
  
      return dict(Counter(word for word, _ in self.words))
  
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 18 for calling the Doc obj's underlying member method code with example usage: obj.word_counts() and returns: dict