explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-04-25 03:13:41 +03:00
Code Issues Packages Projects Releases Wiki Activity

Update documentation for spancat

Browse Source
This commit is contained in:
Lj Miranda 2022-11-24 11:20:16 +08:00
parent 3315540896
commit 4b5c5f197b
1 changed files with 38 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
website/docs/api/spancategorizer.md
View File

@ -11,11 +11,19 @@ api_trainable: true
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.
that predicts zero or more labels for each candidate.
This component comes in two forms: `spancat` and `spancat_exclusive`. 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 exactly one true
class for a span, then use `spancat_exclusive`. 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.
Individual span scores can be found in `spangroup.attrs["scores"]`.
## Assigned Attributes {#assigned-attributes}
Predictions will be saved to `Doc.spans[spans_key]` as a
@ -38,7 +46,7 @@ how the component should be configured. You can override its settings via the
[model architectures](/api/architectures) documentation for details on the
architectures and their arguments and hyperparameters.
> #### Example
> #### Example (spancat)
>
> ```python
> from spacy.pipeline.spancat import DEFAULT_SPANCAT_MODEL
@ -52,6 +60,25 @@ architectures and their arguments and hyperparameters.
> nlp.add_pipe("spancat", config=config)
> ```
> #### Example (spancat_exclusive)
>
> ```python
> from spacy.pipeline.spancat import DEFAULT_SPANCAT_MODEL
> config = {
> "threshold": 0.5,
> "spans_key": "labeled_spans",
> "max_positive": None,
> "model": DEFAULT_SPANCAT_MODEL,
> "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
> # Additional spancat_exclusive parameters
> "negative_weight": 1.0,
> "allow_overlap": True,
> }
> nlp.add_pipe("spancat_exclusive", config=config)
> ```
| 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]~~ |
@ -60,17 +87,26 @@ architectures and their arguments and hyperparameters.
| `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]~~ |
| `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 down weigh the negative samples if there are too many. It is only available when using the `spancat_exclusive` component. ~~float~~ |
| `allow_overlap` | If `True`, the data is assumed to contain overlapping spans. It is only available when using the `spancat_exclusive` component. ~~bool~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/spancat.py
```
```python
%%GITHUB_SPACY/spacy/pipeline/spancat_exclusive.py
```
## SpanCategorizer.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via add_pipe with default model
> # Use 'spancat_exclusive' for exclusive clases
> spancat = nlp.add_pipe("spancat")
>
> # Construction via add_pipe with custom model