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: A list of the named entities with sensible defaults.
  
  >>> from textpipe.doc import Doc
  >>> doc = Doc('Sentence for testing Google text')
  >>> doc.ents
  [('Google', 'ORG')]
docstr_tok: ["A","list","of","the","named","entities","with","sensible","defaults",".",">",">",">","from","textpipe.doc","import","Doc",">",">",">","doc","=","Doc","(","'Sentence","for","testing","Google","text","'",")",">",">",">","doc.ents","[","(","'Google","'",",","'ORG","'",")","]"]
code: def ents(self):
      """
      A list of the named entities with sensible defaults.
  
      >>> from textpipe.doc import Doc
      >>> doc = Doc('Sentence for testing Google text')
      >>> doc.ents
      [('Google', 'ORG')]
      """
      return self.find_ents()
  
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 10 for calling the Doc obj's underlying member method code with example usage: obj.ents() and returns: self