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@ -17,8 +17,8 @@ This component comes in two forms: `spancat` and `spancat_singlelabel`. When you
need to perform multi-label classification on your spans, use `spancat`. The need to perform multi-label classification on your spans, use `spancat`. The
`spancat` component uses a `Logistic` layer where the output class probabilities `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 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 class for a span, then use `spancat_singlelabel` <Tag variant="new">3.5.1</Tag>.
treats the spans as a multi-class problem. It uses a `Softmax` layer and treats the spans as a multi-class problem.
Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc. Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
Individual span scores can be found in `spangroup.attrs["scores"]`. Individual span scores can be found in `spangroup.attrs["scores"]`.
@ -75,17 +75,17 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("spancat_exclusive", config=config) > nlp.add_pipe("spancat_exclusive", config=config)
> ``` > ```
| Setting | Description | | 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]~~ | | `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]~~ | | `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~~ | | `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~~ |
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Meant to be used in combination with the multi-class `spancat` component with a `Logistic` scoring layer. Defaults to `0.5`. ~~float~~ | | `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Meant to be used in combination with the multi-class `spancat` component with a `Logistic` scoring layer. Defaults to `0.5`. ~~float~~ |
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. Meant to be used together with the `spancat` component and defaults to 0 with `spancat_singlelabel`. ~~Optional[int]~~ | | `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. Meant to be used together with the `spancat` component and defaults to 0 with `spancat_singlelabel`. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ | | `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
| `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~~ | | `add_negative_label` | Whether to learn to predict a special negative label for each unannotated `Span` <Tag variant="new">3.5.1</Tag>. 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~~ | | `negative_weight` | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many <Tag variant="new">3.5.1</Tag>. It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~ |
| `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1. Defaults to `True`. ~~bool~~ | | `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1 <Tag variant="new">3.5.1</Tag>. Defaults to `True`. ~~bool~~ |
```python ```python
%%GITHUB_SPACY/spacy/pipeline/spancat.py %%GITHUB_SPACY/spacy/pipeline/spancat.py
@ -113,19 +113,19 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe). [`nlp.add_pipe`](/api/language#create_pipe).
| Name | Description | | Name | Description |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ | | `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]~~ | | `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]~~ | | `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]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ | | `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#sans) 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~~ | | `spans_key` | Key of the [`Doc.spans`](/api/doc#sans) 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~~ |
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ | | `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ | | `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
| `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1. Defaults to `True`. ~~bool~~ | | `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1 <Tag variant="new">3.5.1</Tag>. Defaults to `True`. ~~bool~~ |
| `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~~ | | `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` <Tag variant="new">3.5.1</Tag>. 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~~ | | `negative_weight` | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many <Tag variant="new">3.5.1</Tag>. It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~ |
## SpanCategorizer.\_\_call\_\_ {id="call",tag="method"} ## SpanCategorizer.\_\_call\_\_ {id="call",tag="method"}