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docs: tag (save_)activations as new in spaCy 4.0
This commit is contained in:
Daniël de Kok
parent
51c87c5f98
commit
d245da08e1
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@ -751,23 +751,23 @@ The L2 norm of the document's vector representation.
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## Attributes {#attributes}
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| Name | Description |
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| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| `text` | A string representation of the document text. ~~str~~ |
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| `text_with_ws` | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~ |
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| `mem` | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~ |
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| `vocab` | The store of lexical types. ~~Vocab~~ |
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| `tensor` <Tag variant="new">2</Tag> | Container for dense vector representations. ~~numpy.ndarray~~ |
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| `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ |
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| `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ |
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| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ |
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| `sentiment` | The document's positivity/negativity score, if available. ~~float~~ |
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| `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ |
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| `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ |
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| `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ |
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| `has_unknown_spaces` | Whether the document was constructed without known spacing between tokens (typically when created from gold tokenization). ~~bool~~ |
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| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |
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| `activations` | A dictionary of activations per trainable pipe (available when the `save_activations` option of a pipe is enabled). ~~Dict[str, Option[Any]]~~ |
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| Name | Description |
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| ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| `text` | A string representation of the document text. ~~str~~ |
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| `text_with_ws` | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~ |
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| `mem` | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~ |
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| `vocab` | The store of lexical types. ~~Vocab~~ |
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| `tensor` <Tag variant="new">2</Tag> | Container for dense vector representations. ~~numpy.ndarray~~ |
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| `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ |
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| `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ |
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| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ |
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| `sentiment` | The document's positivity/negativity score, if available. ~~float~~ |
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| `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ |
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| `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ |
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| `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ |
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| `has_unknown_spaces` | Whether the document was constructed without known spacing between tokens (typically when created from gold tokenization). ~~bool~~ |
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| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |
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| `activations` <Tag variant="new">4.0</Tag> | A dictionary of activations per trainable pipe (available when the `save_activations` option of a pipe is enabled). ~~Dict[str, Option[Any]]~~ |
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## Serialization fields {#serialization-fields}
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@ -44,15 +44,15 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("trainable_lemmatizer", config=config, name="lemmatizer")
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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 edit tree probabilities. The output vectors should match the number of edit trees 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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| `backoff` | ~~Token~~ attribute to use when no applicable edit tree is found. Defaults to `orth`. ~~str~~ |
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| `min_tree_freq` | Minimum frequency of an edit tree in the training set to be used. Defaults to `3`. ~~int~~ |
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| `overwrite` | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `top_k` | The number of most probable edit trees to try before resorting to `backoff`. Defaults to `1`. ~~int~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"lemma"`. ~~Optional[Callable]~~ |
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| `save_activations` | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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| Setting | Description |
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| ----------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `model` | A model instance that predicts the edit tree probabilities. The output vectors should match the number of edit trees 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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| `backoff` | ~~Token~~ attribute to use when no applicable edit tree is found. Defaults to `orth`. ~~str~~ |
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| `min_tree_freq` | Minimum frequency of an edit tree in the training set to be used. Defaults to `3`. ~~int~~ |
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| `overwrite` | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
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| `top_k` | The number of most probable edit trees to try before resorting to `backoff`. Defaults to `1`. ~~int~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"lemma"`. ~~Optional[Callable]~~ |
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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/edit_tree_lemmatizer.py
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@ -52,20 +52,20 @@ 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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| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~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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| `save_activations` | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
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| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
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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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| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~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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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
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| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
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@ -42,13 +42,13 @@ 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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| `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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| `save_activations` | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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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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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
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@ -39,12 +39,12 @@ 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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| `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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| `save_activations` | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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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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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/senter.pyx
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@ -52,15 +52,15 @@ architectures and their arguments and hyperparameters.
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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` | Save activations in `Doc` when annotating. Saved activations are `"indices"` and `"scores"`. ~~Union[bool, list[str]]~~ |
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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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@ -40,13 +40,13 @@ 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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| `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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| `neg_prefix` <Tag variant="new">3.2.1</Tag> | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~ |
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| `save_activations` | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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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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| `neg_prefix` <Tag variant="new">3.2.1</Tag> | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~ |
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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probs"` and `"guesses"`. ~~Union[bool, list[str]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/tagger.pyx
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@ -117,15 +117,15 @@ 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` | The Thinc [`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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| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
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| `save_activations` | Save activations in `Doc` when annotating. The supported activations is `"probs"`. ~~Union[bool, list[str]]~~ |
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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 Thinc [`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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| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
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| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. The supported activations is `"probs"`. ~~Union[bool, list[str]]~~ |
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## TextCategorizer.\_\_call\_\_ {#call tag="method"}
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Reference in New Issue
Block a user