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: Loads the spacy default language module for the Doc's language
docstr_tok: ["Loads","the","spacy","default","language","module","for","the","Doc","'s","language"]
code: def _get_default_nlp(lang):
      """
      Loads the spacy default language module for the Doc's language
      """
      try:
          return spacy.load(
              "{}_core_{}_sm".format(
                  lang, "web" if lang == "en" else "news"
              )
          )
      except IOError:
          # pylint: disable=raise-missing-from
          raise TextpipeMissingModelException(
              f'Default model for language "{lang}" '
              f"is not available."
          )
  
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 7 for calling the Doc obj's underlying member method code with example usage: obj._get_default_nlp(lang) and returns: spacy