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Update overwrite and scorer in API docs (#9384)
* Update overwrite and scorer in API docs * Rephrase morphologizer extend + example
This commit is contained in:
Adriane Boyd
GitHub
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@ -51,16 +51,17 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("entity_linker", config=config)
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> ```
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| Setting | Description |
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| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
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| Setting | Description |
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| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
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@ -93,18 +94,20 @@ custom knowledge base, you should either call
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[`set_kb`](/api/entitylinker#set_kb) or provide a `kb_loader` in the
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[`initialize`](/api/entitylinker#initialize) call.
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| Name | Description |
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| ---------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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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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| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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| Name | Description |
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| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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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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| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
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## EntityLinker.\_\_call\_\_ {#call tag="method"}
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@ -42,9 +42,12 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("morphologizer", config=config)
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> ```
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| Setting | Description |
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| ------- | ------------------------------------------------------------------------------------------------------- |
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| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| Setting | Description |
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| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
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| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
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@ -56,6 +59,19 @@ 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#add_pipe).
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The `overwrite` and `extend` settings determine how existing annotation is
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handled (with the example for existing annotation `A=B|C=D` + predicted
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annotation `C=E|X=Y`):
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- `overwrite=True, extend=True`: overwrite values of existing features, add any
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new features (`A=B|C=D` + `C=E|X=Y` → `A=B|C=E|X=Y`)
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- `overwrite=True, extend=False`: overwrite completely, removing any existing
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features (`A=B|C=D` + `C=E|X=Y` → `C=E|X=Y`)
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- `overwrite=False, extend=True`: keep values of existing features, add any new
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features (`A=B|C=D` + `C=E|X=Y` → `A=B|C=D|X=Y`)
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- `overwrite=False, extend=False`: do not modify the existing annotation if set
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(`A=B|C=D` + `C=E|X=Y` → `A=B|C=D`)
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> #### Example
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>
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> ```python
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@ -71,13 +87,15 @@ shortcut for this and instantiate the component using its string name and
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> morphologizer = Morphologizer(nlp.vocab, model)
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> ```
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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| Name | Description |
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| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
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| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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## Morphologizer.\_\_call\_\_ {#call tag="method"}
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@ -39,9 +39,11 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("senter", config=config)
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> ```
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| Setting | Description |
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| ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| Setting | Description |
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| ---------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"`. ~~Optional[Callable]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/senter.pyx
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@ -70,13 +72,14 @@ 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#add_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` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"`. ~~Optional[Callable]~~ |
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| Name | Description |
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| ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"`. ~~Optional[Callable]~~ |
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## SentenceRecognizer.\_\_call\_\_ {#call tag="method"}
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@ -37,9 +37,11 @@ how the component should be configured. You can override its settings via the
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> nlp.add_pipe("sentencizer", config=config)
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> ```
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| Setting | Description |
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| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. Defaults to `None`. ~~Optional[List[str]]~~ | `None` |
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| Setting | Description |
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| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. Defaults to `None`. ~~Optional[List[str]]~~ | `None` |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"` ~~Optional[Callable]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/sentencizer.pyx
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@ -60,11 +62,12 @@ Initialize the sentencizer.
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> sentencizer = Sentencizer()
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> ```
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. ~~Optional[List[str]]~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"` ~~Optional[Callable]~~ |
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| Name | Description |
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| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `punct_chars` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. ~~Optional[List[str]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for the attribute `"sents"` ~~Optional[Callable]~~ |
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```python
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### punct_chars defaults
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@ -40,9 +40,11 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("tagger", config=config)
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> ```
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| Setting | Description |
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| ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| Setting | Description |
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| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/tagger.pyx
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@ -69,13 +71,14 @@ 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#add_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 predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~ |
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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 predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). ~~Model[List[Doc], List[Floats2d]]~~ |
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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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| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~ |
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## Tagger.\_\_call\_\_ {#call tag="method"}
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