  p
      |  Aside from spaCy's built-in word vectors, which were trained on a lot of
      |  text with a wide vocabulary, the parsing, tagging and NER models also
      |  rely on vector representations of the #[strong meanings of words in context].
      |  As the first component of the
      |  #[+a("/usage/processing-pipelines") processing pipeline], the
      |  tensorizer encodes a document's internal meaning representations as an
      |  array of floats, also called a tensor. This allows spaCy to make a
      |  reasonable guess at a word's meaning, based on its surrounding words.
      |  Even if a word hasn't been seen before, spaCy will know #[em something]
      |  about it. Because spaCy uses a 4-layer convolutional network, the
      |  tensors are sensitive to up to #[strong four words on either side] of a
 
</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     |  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     |  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.
    |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an
  p
      |  Aside from spaCy's built-in word vectors, which were trained on a lot of
      |  text with a wide vocabulary, the parsing, tagging and NER models also
      |  rely on vector representations of the #[strong meanings of words in context].
    |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an
    |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an
    |  As the  #[+a("/usage/processing-pipelines") processing pipeline] is
    |  applied spaCy encodes a document's internal meaning representations as an
      |  array of floats, also called a tensor. This allows spaCy to make a
      |  reasonable guess at a word's meaning, based on its surrounding words.
      |  Even if a word hasn't been seen before, spaCy will know #[em something]
      |  about it. Because spaCy uses a 4-layer convolutional network, the
      |  tensors are sensitive to up to #[strong four words on either side] of a
keep keep keep keep replace replace replace keep keep keep keep keep
 Don't document the tensorizer for now 
website/usage/_vectors-similarity/_basics.jade