//- 💫 DOCS > API > TEXTCATEGORIZER

include ../../_includes/_mixins

p
    |  Add text categorization models to spaCy pipelines. The model supports
    |  classification with multiple, non-mutually exclusive labels.

p
    |  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
    |  output neuron is the probability that some class is present.

+under-construction