      |  to the binary data:
  
  +code("spacy.load under the hood").
      lang = 'en'
      pipeline = ['tensorizer', 'tagger', 'parser', 'ner']
      data_path = 'path/to/en_core_web_sm/en_core_web_sm-2.0.0'
  
      cls = spacy.util.get_lang_class(lang)   # 1. get Language instance, e.g. English()
      nlp = cls()                             # 2. initialise it
      for name in pipeline:
 
</s> remove     nlp = English().from_disk('/model', disable=['tensorizer', 'ner'])
 </s> add     nlp = English().from_disk('/model', disable=['ner']) </s> remove     | ... the model tells spaCy to use the language #[code "en"] and the pipeline
    |  #[code.u-break ["tensorizer", "tagger", "parser", "ner"]]. spaCy will
    |  then initialise #[code spacy.lang.en.English], and create each pipeline
 </s> add     | ... the model tells spaCy to use the language #[code "en"] and the
    |  pipeline #[code.u-break ["tagger", "parser", "ner"]]. spaCy will then
    |  initialise #[code spacy.lang.en.English], and create each pipeline </s> remove     |  required for the next component, the tagger, to make predictions of the
 </s> add     |  required for the first component, the tagger, to make predictions of the </s> remove     |  tensor is then
    |  summarized by concatenating max and mean pooling, and a multilayer
    |  perceptron is used to predict an output vector of length #[code nr_class],
    |  before a logistic activation is applied elementwise. The value of each
    |  output neuron is the probability that some class is present.
 </s> add     |  tensor is then summarized by concatenating max and mean pooling, and a
    |  multilayer perceptron is used to predict an output vector of length
    |  #[code nr_class], before a logistic activation is applied elementwise.
    |  The value of each output neuron is the probability that some class is
    |  present. </s> remove     "pipeline": ["tensorizer", "tagger", "parser", "ner"]
 </s> add     "pipeline": ["tagger", "parser", "ner"]
    pipeline = ['tagger', 'parser', 'ner']
      |  to the binary data:
  
  +code("spacy.load under the hood").
      lang = 'en'
    pipeline = ['tagger', 'parser', 'ner']
      data_path = 'path/to/en_core_web_sm/en_core_web_sm-2.0.0'
  
      cls = spacy.util.get_lang_class(lang)   # 1. get Language instance, e.g. English()
      nlp = cls()                             # 2. initialise it
      for name in pipeline:
keep keep keep keep replace keep keep keep keep keep
 Don't document the tensorizer for now 
website/usage/_processing-pipelines/_pipelines.jade