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20 lines
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20 lines
1.0 KiB
Plaintext
//- 💫 DOCS > API > TEXTCATEGORIZER
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include ../_includes/_mixins
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p
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| The model supports classification with multiple, non-mutually exclusive
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| labels. You can change the model architecture rather easily, but by
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| default, the #[code TextCategorizer] class uses a convolutional
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| neural network to assign position-sensitive vectors to each word in the
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| document. This step is similar to the #[+api("tensorizer") #[code Tensorizer]]
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| component, but the #[code TextCategorizer] uses its own CNN model, to
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| avoid sharing weights with the other pipeline components. The document
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| tensor is then
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| summarized by concatenating max and mean pooling, and a multilayer
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| perceptron is used to predict an output vector of length #[code nr_class],
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| before a logistic activation is applied elementwise. The value of each
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| output neuron is the probability that some class is present.
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!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })
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