      |  The model supports classification with multiple, non-mutually exclusive
      |  labels. You can change the model architecture rather easily, but by
      |  default, the #[code TextCategorizer] class uses a convolutional
      |  neural network to assign position-sensitive vectors to each word in the
      |  document. This step is similar to the #[+api("tensorizer") #[code Tensorizer]]
      |  component, but the #[code TextCategorizer] uses its own CNN model, to
      |  avoid sharing weights with the other pipeline components. The document
      |  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
 
</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     |  As the first component of the
    |  #[+a("/usage/processing-pipelines") processing pipeline], the
    |  tensorizer encodes a document's internal meaning representations as an
 </s> add     |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an </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     | ... 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
    |  document. The #[code TextCategorizer] uses its own CNN model, to
      |  The model supports classification with multiple, non-mutually exclusive
      |  labels. You can change the model architecture rather easily, but by
      |  default, the #[code TextCategorizer] class uses a convolutional
      |  neural network to assign position-sensitive vectors to each word in the
    |  document. The #[code TextCategorizer] uses its own CNN model, to
    |  document. The #[code TextCategorizer] uses its own CNN model, to
      |  avoid sharing weights with the other pipeline components. The document
      |  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
keep keep keep keep replace replace keep keep keep keep keep
 Don't document the tensorizer for now 
website/api/textcategorizer.jade