Add store_activations to docstrings.

spacy/pipeline/edit_tree_lemmatizer.py

@@ -109,6 +109,8 @@ class EditTreeLemmatizer(TrainablePipe):
             frequency in the training data.
         overwrite (bool): overwrite existing lemma annotations.
         top_k (int): try to apply at most the k most probable edit trees.
+        store_activations (Union[bool, List[str]]): Model activations to store in
+            Doc when annotating. supported activations are: "probs" and "guesses".
         """
         self.vocab = vocab
         self.model = model

spacy/pipeline/entity_linker.py

@@ -98,6 +98,8 @@ def make_entity_linker(
     get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
         produces a list of candidates, given a certain knowledge base and a textual mention.
     scorer (Optional[Callable]): The scoring method.
+    store_activations (Union[bool, List[str]]): Model activations to store in
+        Doc when annotating. supported activations are: "ents" and "scores".
     """
 
     if not model.attrs.get("include_span_maker", False):

spacy/pipeline/morphologizer.pyx

@@ -114,6 +114,8 @@ class Morphologizer(Tagger):
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_token_attr for the attributes "pos" and "morph" and
             Scorer.score_token_attr_per_feat for the attribute "morph".
+        store_activations (Union[bool, List[str]]): Model activations to store in
+            Doc when annotating. supported activations are: "probs" and "guesses".
 
         DOCS: https://spacy.io/api/morphologizer#init
         """

spacy/pipeline/senter.pyx

@@ -92,6 +92,8 @@ class SentenceRecognizer(Tagger):
             losses during training.
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_spans for the attribute "sents".
+        store_activations (Union[bool, List[str]]): Model activations to store in
+            Doc when annotating. supported activations are: "probs" and "guesses".
 
         DOCS: https://spacy.io/api/sentencerecognizer#init
         """

spacy/pipeline/spancat.py

@@ -141,6 +141,8 @@ def make_spancat(
         0.5.
     max_positive (Optional[int]): Maximum number of labels to consider positive
         per span. Defaults to None, indicating no limit.
+    store_activations (Union[bool, List[str]]): Model activations to store in
+        Doc when annotating. supported activations are: "indices" and "scores".
     """
     return SpanCategorizer(
         nlp.vocab,

spacy/pipeline/tagger.pyx

@@ -107,6 +107,8 @@ class Tagger(TrainablePipe):
             losses during training.
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_token_attr for the attribute "tag".
+        store_activations (Union[bool, List[str]]): Model activations to store in
+            Doc when annotating. supported activations are: "probs" and "guesses".
 
         DOCS: https://spacy.io/api/tagger#init
         """

spacy/pipeline/textcat.py

@@ -107,6 +107,8 @@ def make_textcat(
         scores for each category.
     threshold (float): Cutoff to consider a prediction "positive".
     scorer (Optional[Callable]): The scoring method.
+    store_activations (Union[bool, List[str]]): Model activations to store in
+        Doc when annotating. supported activations is: "probs".
     """
     return TextCategorizer(
         nlp.vocab,

spacy/pipeline/textcat_multilabel.py

@@ -155,6 +155,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
         name (str): The component instance name, used to add entries to the
             losses during training.
         threshold (float): Cutoff to consider a prediction "positive".
+        store_activations (Union[bool, List[str]]): Model activations to store in
+            Doc when annotating. supported activations is: "probs".
 
         DOCS: https://spacy.io/api/textcategorizer#init
         """