//- 💫 DOCS > API > TEXTCATEGORIZER

include ../_includes/_mixins

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

//- This class inherits from Pipe, so this page uses the template in pipe.jade.
!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })