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website/docs/api/spancategorizer.mdx
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@ -76,7 +76,7 @@ architectures and their arguments and hyperparameters.
> ```
| Setting | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
| `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
@ -114,7 +114,7 @@ shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
@ -127,7 +127,6 @@ shortcut for this and instantiate the component using its string name and
| `add_negative_label` | Whether to learn to predict a special negative label for each unannotated `Span`. This should be `True` when using a `Softmax` classifier layer and so its `True` by default for `spancat_singlelabel`. Spans with negative labels and their scores are not stored as annotations. ~~bool~~ |
| `negative_weight` | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many. It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~ |
## SpanCategorizer.\_\_call\_\_ {id="call",tag="method"}
Apply the pipe to one document. The document is modified in place, and returned.

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website/docs/api/spangroup.mdx
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@ -219,7 +219,6 @@ Iterate over the spans in this span group.
| ---------- | ----------------------------------- |
| **YIELDS** | A span in this span group. ~~Span~~ |
## SpanGroup.append {id="append",tag="method"}
Add a [`Span`](/api/span) object to the group. The span must refer to the same