  +code.
      nlp = spacy.load('en')
  
  p
      | ... 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
      |  component and add it to the processing pipeline. It'll then load in the
      |  model's data from its data ditectory and return the modified
      |  #[code Language] class for you to use as the #[code nlp] object.
  
  p
 
</s> remove     "pipeline": ["tensorizer", "tagger", "parser", "ner"]
 </s> add     "pipeline": ["tagger", "parser", "ner"] </s> remove     nlp = English().from_disk('/model', disable=['tensorizer', 'ner'])
 </s> add     nlp = English().from_disk('/model', disable=['ner']) </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     |  document. This step is similar to the #[+api("tensorizer") #[code Tensorizer]]
    |  component, but the #[code TextCategorizer] uses its own CNN model, to
 </s> add     |  document. The #[code TextCategorizer] uses its own CNN model, to
    | ... 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
  +code.
      nlp = spacy.load('en')
  
  p
    | ... 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
    | ... 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
    | ... 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
      |  component and add it to the processing pipeline. It'll then load in the
      |  model's data from its data ditectory and return the modified
      |  #[code Language] class for you to use as the #[code nlp] object.
  
  p
keep keep keep keep replace replace replace keep keep keep keep keep
 Don't document the tensorizer for now 
website/usage/_processing-pipelines/_pipelines.jade