spaCy/website/api/textcategorizer.jade
2017-10-03 14:27:22 +02:00

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//- 💫 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.
!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })