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website/docs/api/spancategorizer.mdx
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@ -13,11 +13,11 @@ 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_exclusive`. When you
This component comes in two forms: `spancat` and `spancat_singlelabel`. 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_exclusive`. It uses a `Softmax` layer and
class for a span, then use `spancat_singlelabel`. It uses a `Softmax` layer and
treats the entities as a multi-class problem.
Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
@ -59,17 +59,17 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("spancat", config=config)
> ```
> #### Example (spancat_exclusive)
> #### Example (spancat_singlelabel)
>
> ```python
> from spacy.pipeline.spancat_exclusive import DEFAULT_EXCL_SPANCAT_MODEL
> from spacy.pipeline.spancat import DEFAULT_SPANCAT_SINGLELABEL_MODEL
> config = {
> "threshold": 0.5,
> "spans_key": "labeled_spans",
> "model": DEFAULT_EXCL_SPANCAT_MODEL,
> "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
> # Additional spancat_exclusive parameters
> "negative_weight": 1.0,
> "negative_weight": 0.8,
> "allow_overlap": True,
> }
> nlp.add_pipe("spancat_exclusive", config=config)
@ -80,11 +80,12 @@ architectures and their arguments and hyperparameters.
| `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~~ |
| `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. It is only available for the `spancat` component. ~~Optional[int]~~ |
| `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]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
| `negative_weight` | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many. It is only available for the `spancat_exclusive` component. Defaults to `1.0`. ~~float~~ |
| `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available for the `spancat_exclusive` component. 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~~ |
| `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~~ |
| `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~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/spancat.py
@ -122,6 +123,10 @@ shortcut for this and instantiate the component using its string name and
| `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~~ |
| `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~~ |
| `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"}