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* Store activations in Doc when `store_activations` is enabled This change adds the new `activations` attribute to `Doc`. This attribute can be used by trainable pipes to store their activations, probabilities, and guesses for downstream users. As an example, this change modifies the `tagger` and `senter` pipes to add an `store_activations` option. When this option is enabled, the probabilities and guesses are stored in `set_annotations`. * Change type of `store_activations` to `Union[bool, List[str]]` When the value is: - A bool: all activations are stored when set to `True`. - A List[str]: the activations named in the list are stored * Formatting fixes in Tagger * Support store_activations in spancat and morphologizer * Make Doc.activations type visible to MyPy * textcat/textcat_multilabel: add store_activations option * trainable_lemmatizer/entity_linker: add store_activations option * parser/ner: do not currently support returning activations * Extend tagger and senter tests So that they, like the other tests, also check that we get no activations if no activations were requested. * Document `Doc.activations` and `store_activations` in the relevant pipes * Start errors/warnings at higher numbers to avoid merge conflicts Between the master and v4 branches. * Add `store_activations` to docstrings. * Replace store_activations setter by set_store_activations method Setters that take a different type than what the getter returns are still problematic for MyPy. Replace the setter by a method, so that type inference works everywhere. * Use dict comprehension suggested by @svlandeg * Revert "Use dict comprehension suggested by @svlandeg" This reverts commit6e7b958f70. * EntityLinker: add type annotations to _add_activations * _store_activations: make kwarg-only, remove doc_scores_lens arg * set_annotations: add type annotations * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * TextCat.predict: return dict * Make the `TrainablePipe.store_activations` property a bool This means that we can also bring back `store_activations` setter. * Remove `TrainablePipe.activations` We do not need to enumerate the activations anymore since `store_activations` is `bool`. * Add type annotations for activations in predict/set_annotations * Rename `TrainablePipe.store_activations` to `save_activations` * Error E1400 is not used anymore This error was used when activations were still `Union[bool, List[str]]`. * Change wording in API docs after store -> save change * docs: tag (save_)activations as new in spaCy 4.0 * Fix copied line in morphologizer activations test * Don't train in any test_save_activations test * Rename activations - "probs" -> "probabilities" - "guesses" -> "label_ids", except in the edit tree lemmatizer, where "guesses" -> "tree_ids". * Remove unused W400 warning. This warning was used when we still allowed the user to specify which activations to save. * Formatting fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Replace "kb_ids" by a constant * spancat: replace a cast by an assertion * Fix EOF spacing * Fix comments in test_save_activations tests * Do not set RNG seed in activation saving tests * Revert "spancat: replace a cast by an assertion" This reverts commit0bd5730d16. Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
495 lines
27 KiB
Markdown
495 lines
27 KiB
Markdown
---
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title: SpanCategorizer
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tag: class,experimental
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source: spacy/pipeline/spancat.py
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new: 3.1
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teaser: 'Pipeline component for labeling potentially overlapping spans of text'
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api_base_class: /api/pipe
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api_string_name: spancat
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api_trainable: true
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---
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A span categorizer consists of two parts: a [suggester function](#suggesters)
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that proposes candidate spans, which may or may not overlap, and a labeler model
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that predicts zero or more labels for each candidate.
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Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
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Individual span scores can be found in `spangroup.attrs["scores"]`.
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## Assigned Attributes {#assigned-attributes}
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Predictions will be saved to `Doc.spans[spans_key]` as a
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[`SpanGroup`](/api/spangroup). The scores for the spans in the `SpanGroup` will
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be saved in `SpanGroup.attrs["scores"]`.
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`spans_key` defaults to `"sc"`, but can be passed as a parameter.
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| Location | Value |
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| -------------------------------------- | -------------------------------------------------------- |
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| `Doc.spans[spans_key]` | The annotated spans. ~~SpanGroup~~ |
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| `Doc.spans[spans_key].attrs["scores"]` | The score for each span in the `SpanGroup`. ~~Floats1d~~ |
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config). See the
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[model architectures](/api/architectures) documentation for details on the
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architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy.pipeline.spancat import DEFAULT_SPANCAT_MODEL
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> config = {
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> "threshold": 0.5,
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> "spans_key": "labeled_spans",
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> "max_positive": None,
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> "model": DEFAULT_SPANCAT_MODEL,
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> "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
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> }
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> nlp.add_pipe("spancat", config=config)
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> ```
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| Setting | Description |
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| ----------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `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]~~ |
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| `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]~~ |
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| `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~~ |
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| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
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| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"indices"` and `"scores"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/spancat.py
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```
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## SpanCategorizer.\_\_init\_\_ {#init tag="method"}
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> spancat = nlp.add_pipe("spancat")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_spancat"}}
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> parser = nlp.add_pipe("spancat", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import SpanCategorizer
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> spancat = SpanCategorizer(nlp.vocab, model, suggester)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#create_pipe).
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `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]~~ |
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| `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]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `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~~ |
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| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
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| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
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## SpanCategorizer.\_\_call\_\_ {#call tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/spancategorizer#call) and [`pipe`](/api/spancategorizer#pipe)
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delegate to the [`predict`](/api/spancategorizer#predict) and
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[`set_annotations`](/api/spancategorizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> spancat = nlp.add_pipe("spancat")
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> # This usually happens under the hood
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> processed = spancat(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## SpanCategorizer.pipe {#pipe tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/spancategorizer#call) and
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[`pipe`](/api/spancategorizer#pipe) delegate to the
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[`predict`](/api/spancategorizer#predict) and
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[`set_annotations`](/api/spancategorizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> for doc in spancat.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## SpanCategorizer.initialize {#initialize tag="method"}
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Initialize the component for training. `get_examples` should be a function that
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returns an iterable of [`Example`](/api/example) objects. **At least one example
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should be supplied.** The data examples are used to **initialize the model** of
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the component and can either be the full training data or a representative
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sample. Initialization includes validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize) and lets you customize
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arguments it receives via the
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[`[initialize.components]`](/api/data-formats#config-initialize) block in the
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config.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat.initialize(lambda: examples, nlp=nlp)
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> ```
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>
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> ```ini
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> ### config.cfg
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> [initialize.components.spancat]
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>
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> [initialize.components.spancat.labels]
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> @readers = "spacy.read_labels.v1"
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> path = "corpus/labels/spancat.json
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> ```
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |
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## SpanCategorizer.predict {#predict tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects without
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modifying them.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> scores = spancat.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | The model's prediction for each document. |
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## SpanCategorizer.set_annotations {#set_annotations tag="method"}
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Modify a batch of [`Doc`](/api/doc) objects using pre-computed scores.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> scores = spancat.predict(docs)
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> spancat.set_annotations(docs, scores)
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> ```
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| Name | Description |
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| -------- | --------------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `scores` | The scores to set, produced by `SpanCategorizer.predict`. |
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## SpanCategorizer.update {#update tag="method"}
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Learn from a batch of [`Example`](/api/example) objects containing the
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predictions and gold-standard annotations, and update the component's model.
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Delegates to [`predict`](/api/spancategorizer#predict) and
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[`get_loss`](/api/spancategorizer#get_loss).
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> optimizer = nlp.initialize()
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> losses = spancat.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## SpanCategorizer.set_candidates {#set_candidates tag="method", new="3.3"}
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Use the suggester to add a list of [`Span`](/api/span) candidates to a list of
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[`Doc`](/api/doc) objects. This method is intended to be used for debugging
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purposes.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat.set_candidates(docs, "candidates")
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> ```
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| Name | Description |
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| ---------------- | -------------------------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `candidates_key` | Key of the Doc.spans dict to save the candidate spans under. ~~str~~ |
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## SpanCategorizer.get_loss {#get_loss tag="method"}
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Find the loss and gradient of loss for the batch of documents and their
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predicted scores.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> scores = spancat.predict([eg.predicted for eg in examples])
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> loss, d_loss = spancat.get_loss(examples, scores)
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> ```
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| Name | Description |
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| -------------- | --------------------------------------------------------------------------- |
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| `examples` | The batch of examples. ~~Iterable[Example]~~ |
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| `spans_scores` | Scores representing the model's predictions. ~~Tuple[Ragged, Floats2d]~~ |
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| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
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## SpanCategorizer.create_optimizer {#create_optimizer tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> optimizer = spancat.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## SpanCategorizer.use_params {#use_params tag="method, contextmanager"}
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Modify the pipe's model to use the given parameter values.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> with spancat.use_params(optimizer.averages):
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> spancat.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## SpanCategorizer.add_label {#add_label tag="method"}
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Add a new label to the pipe. Raises an error if the output dimension is already
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set, or if the model has already been fully [initialized](#initialize). Note
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that you don't have to call this method if you provide a **representative data
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sample** to the [`initialize`](#initialize) method. In this case, all labels
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found in the sample will be automatically added to the model, and the output
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dimension will be [inferred](/usage/layers-architectures#thinc-shape-inference)
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automatically.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat.add_label("MY_LABEL")
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------- |
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| `label` | The label to add. ~~str~~ |
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| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
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## SpanCategorizer.to_disk {#to_disk tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat.to_disk("/path/to/spancat")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## SpanCategorizer.from_disk {#from_disk tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat.from_disk("/path/to/spancat")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `SpanCategorizer` object. ~~SpanCategorizer~~ |
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## SpanCategorizer.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> spancat = nlp.add_pipe("spancat")
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> spancat_bytes = spancat.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `SpanCategorizer` object. ~~bytes~~ |
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## SpanCategorizer.from_bytes {#from_bytes tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> spancat_bytes = spancat.to_bytes()
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> spancat = nlp.add_pipe("spancat")
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> spancat.from_bytes(spancat_bytes)
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> ```
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|
| Name | Description |
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|
| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `SpanCategorizer` object. ~~SpanCategorizer~~ |
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## SpanCategorizer.labels {#labels tag="property"}
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The labels currently added to the component.
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|
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> #### Example
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>
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> ```python
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> spancat.add_label("MY_LABEL")
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> assert "MY_LABEL" in spancat.labels
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> ```
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|
| Name | Description |
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|
| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## SpanCategorizer.label_data {#label_data tag="property"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`SpanCategorizer.initialize`](/api/spancategorizer#initialize) to initialize
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the model with a pre-defined label set.
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|
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> #### Example
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>
|
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> ```python
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> labels = spancat.label_data
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> spancat.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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|
|
| Name | Description |
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|
| ----------- | ---------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Tuple[str, ...]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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|
serialization by passing in the string names via the `exclude` argument.
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|
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> #### Example
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|
>
|
|
> ```python
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> data = spancat.to_disk("/path", exclude=["vocab"])
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> ```
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|
|
| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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## Suggesters {#suggesters tag="registered functions" source="spacy/pipeline/spancat.py"}
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### spacy.ngram_suggester.v1 {#ngram_suggester}
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|
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> #### Example Config
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>
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> ```ini
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> [components.spancat.suggester]
|
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> @misc = "spacy.ngram_suggester.v1"
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> sizes = [1, 2, 3]
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> ```
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|
Suggest all spans of the given lengths. Spans are returned as a ragged array of
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|
integers. The array has two columns, indicating the start and end position.
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|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------------------------------------------------------- |
|
|
| `sizes` | The phrase lengths to suggest. For example, `[1, 2]` will suggest phrases consisting of 1 or 2 tokens. ~~List[int]~~ |
|
|
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
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|
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### spacy.ngram_range_suggester.v1 {#ngram_range_suggester}
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|
|
|
> #### Example Config
|
|
>
|
|
> ```ini
|
|
> [components.spancat.suggester]
|
|
> @misc = "spacy.ngram_range_suggester.v1"
|
|
> min_size = 2
|
|
> max_size = 4
|
|
> ```
|
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|
|
Suggest all spans of at least length `min_size` and at most length `max_size`
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|
(both inclusive). Spans are returned as a ragged array of integers. The array
|
|
has two columns, indicating the start and end position.
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------------------------------------------------------- |
|
|
| `min_size` | The minimal phrase lengths to suggest (inclusive). ~~[int]~~ |
|
|
| `max_size` | The maximal phrase lengths to suggest (exclusive). ~~[int]~~ |
|
|
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
|