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kadarakos 2023-03-07 16:15:15 +00:00
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website/docs/api/spancategorizer.mdx
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@ -13,12 +13,12 @@ A span categorizer consists of two parts: a [suggester function](#suggesters)
that proposes candidate spans, which may or may not overlap, and a labeler model
that predicts zero or more labels for each candidate.
This component comes in two forms: `spancat` and `spancat_singlelabel` (added in spaCy v3.5.1). When you
need to perform multi-label classification on your spans, use `spancat`. The
`spancat` component uses a `Logistic` layer where the output class probabilities
are independent for each class. However, if you need to predict at most one true
class for a span, then use `spancat_singlelabel`.
It uses a `Softmax` layer and treats the task as a multi-class problem.
This component comes in two forms: `spancat` and `spancat_singlelabel` (added in
spaCy v3.5.1). When you need to perform multi-label classification on your
spans, use `spancat`. The `spancat` component uses a `Logistic` layer where the
output class probabilities are independent for each class. However, if you need
to predict at most one true class for a span, then use `spancat_singlelabel`. It
uses a `Softmax` layer and treats the task as a multi-class problem.
Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
Individual span scores can be found in `spangroup.attrs["scores"]`.