      |  example, the default pipeline first applies the tensorizer, which
      |  pre-processes the doc and encodes its internal
      |  #[strong meaning representations] as an array of floats, also called a
      |  #[strong tensor]. This includes the tokens and their context, which is
      |  required for the next component, the tagger, to make predictions of the
      |  part-of-speech tags. Because spaCy's models are neural network models,
      |  they only "speak" tensors and expect the input #[code Doc] to have
      |  a #[code tensor].
 
</s> remove     |  require certain modifications on the #[code Doc] to process it. For
    |  example, the default pipeline first applies the tensorizer, which
    |  pre-processes the doc and encodes its internal
 </s> add     |  require certain modifications on the #[code Doc] to process it. As the
    |  processing pipeline is applied, spaCy encodes the document's internal </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     |  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 </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.
    |  required for the first component, the tagger, to make predictions of the
      |  example, the default pipeline first applies the tensorizer, which
      |  pre-processes the doc and encodes its internal
      |  #[strong meaning representations] as an array of floats, also called a
      |  #[strong tensor]. This includes the tokens and their context, which is
    |  required for the first component, the tagger, to make predictions of the
      |  part-of-speech tags. Because spaCy's models are neural network models,
      |  they only "speak" tensors and expect the input #[code Doc] to have
      |  a #[code tensor].
keep keep keep keep replace keep keep keep
 Don't document the tensorizer for now 
website/usage/_spacy-101/_pipelines.jade