Cleanup/remove backwards compat overwrite settings (#11888)

* Remove backwards-compatible overwrite from Entity Linker

This also adds a docstring about overwrite, since it wasn't present.

* Fix docstring

* Remove backward compat settings in Morphologizer

This also needed a docstring added.

For this component it's less clear what the right overwrite settings
are.

* Remove backward compat from sentencizer

This was simple

* Remove backward compat from senter

Another simple one

* Remove backward compat setting from tagger

* Add docstrings

* Update spacy/pipeline/morphologizer.pyx

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

* Update docs

---------

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

spacy/pipeline/entity_linker.py

@@ -27,9 +27,6 @@ ActivationsT = Dict[str, Union[List[Ragged], List[str]]]
 
 KNOWLEDGE_BASE_IDS = "kb_ids"
 
-# See #9050
-BACKWARD_OVERWRITE = True
-
 default_model_config = """
 [model]
 @architectures = "spacy.EntityLinker.v2"
@@ -60,7 +57,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
         "entity_vector_length": 64,
         "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
         "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
-        "overwrite": True,
+        "overwrite": False,
         "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
         "use_gold_ents": True,
         "candidates_batch_size": 1,
@@ -191,7 +188,7 @@ class EntityLinker(TrainablePipe):
         get_candidates_batch: Callable[
             [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
         ],
-        overwrite: bool = BACKWARD_OVERWRITE,
+        overwrite: bool = False,
         scorer: Optional[Callable] = entity_linker_score,
         use_gold_ents: bool,
         candidates_batch_size: int,
@@ -215,6 +212,7 @@ class EntityLinker(TrainablePipe):
             Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
             Iterable[Candidate]]
             ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
+        overwrite (bool): Whether to overwrite existing non-empty annotations.
         scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
         use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
             component must provide entity annotations.

spacy/pipeline/morphologizer.pyx

@@ -21,10 +21,6 @@ from ..scorer import Scorer
 from ..training import validate_examples, validate_get_examples
 from ..util import registry
 
-# See #9050
-BACKWARD_OVERWRITE = True
-BACKWARD_EXTEND = False
-
 default_model_config = """
 [model]
 @architectures = "spacy.Tagger.v2"
@@ -102,8 +98,8 @@ class Morphologizer(Tagger):
         model: Model,
         name: str = "morphologizer",
         *,
-        overwrite: bool = BACKWARD_OVERWRITE,
+        overwrite: bool = False,
-        extend: bool = BACKWARD_EXTEND,
+        extend: bool = False,
         scorer: Optional[Callable] = morphologizer_score,
         save_activations: bool = False,
     ):
@@ -113,6 +109,8 @@ class Morphologizer(Tagger):
         model (thinc.api.Model): The Thinc Model powering the pipeline component.
         name (str): The component instance name, used to add entries to the
             losses during training.
+        overwrite (bool): Whether to overwrite existing annotations.
+        extend (bool): Whether to extend existing annotations.
         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".

spacy/pipeline/sentencizer.pyx

@@ -10,9 +10,6 @@ from ..language import Language
 from ..scorer import Scorer
 from .. import util
 
-# see #9050
-BACKWARD_OVERWRITE = False
-
 @Language.factory(
     "sentencizer",
     assigns=["token.is_sent_start", "doc.sents"],
@@ -52,13 +49,14 @@ class Sentencizer(Pipe):
         name="sentencizer",
         *,
         punct_chars=None,
-        overwrite=BACKWARD_OVERWRITE,
+        overwrite=False,
         scorer=senter_score,
     ):
         """Initialize the sentencizer.
 
         punct_chars (list): Punctuation characters to split on. Will be
             serialized with the nlp object.
+        overwrite (bool): Whether to overwrite existing annotations.
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_spans for the attribute "sents".
 

spacy/pipeline/senter.pyx

@@ -18,8 +18,6 @@ from ..training import validate_examples, validate_get_examples
 from ..util import registry
 from .. import util
 
-# See #9050
-BACKWARD_OVERWRITE = False
 
 default_model_config = """
 [model]
@@ -83,7 +81,7 @@ class SentenceRecognizer(Tagger):
         model,
         name="senter",
         *,
-        overwrite=BACKWARD_OVERWRITE,
+        overwrite=False,
         scorer=senter_score,
         save_activations: bool = False,
     ):
@@ -93,6 +91,7 @@ class SentenceRecognizer(Tagger):
         model (thinc.api.Model): The Thinc Model powering the pipeline component.
         name (str): The component instance name, used to add entries to the
             losses during training.
+        overwrite (bool): Whether to overwrite existing annotations.
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_spans for the attribute "sents".
         save_activations (bool): save model activations in Doc when annotating.

spacy/pipeline/tagger.pyx

@@ -27,9 +27,6 @@ from .. import util
 
 ActivationsT = Dict[str, Union[List[Floats2d], List[Ints1d]]]
 
-# See #9050
-BACKWARD_OVERWRITE = False
-
 default_model_config = """
 [model]
 @architectures = "spacy.Tagger.v2"
@@ -99,7 +96,7 @@ class Tagger(TrainablePipe):
         model,
         name="tagger",
         *,
-        overwrite=BACKWARD_OVERWRITE,
+        overwrite=False,
         scorer=tagger_score,
         neg_prefix="!",
         save_activations: bool = False,
@@ -110,6 +107,7 @@ class Tagger(TrainablePipe):
         model (thinc.api.Model): The Thinc Model powering the pipeline component.
         name (str): The component instance name, used to add entries to the
             losses during training.
+        overwrite (bool): Whether to overwrite existing annotations.
         scorer (Optional[Callable]): The scoring method. Defaults to
             Scorer.score_token_attr for the attribute "tag".
         save_activations (bool): save model activations in Doc when annotating.

website/docs/api/entitylinker.mdx

@@ -63,7 +63,7 @@ architectures and their arguments and hyperparameters.
 | `entity_vector_length`                          | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~                                                                                                                                                                                                                               |
 | `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~~                                                                                                        |
 | `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]]~~                    |
-| `overwrite` <Tag variant="new">3.2</Tag>        | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                                                    |
+| `overwrite` <Tag variant="new">3.2</Tag>        | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~                                                                                                                                                                                                                   |
 | `scorer` <Tag variant="new">3.2</Tag>           | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~                                                                                                                                                                                     |
 | `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~                                                                                                                                                                        |
 | `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]~~ |

website/docs/api/morphologizer.mdx

@@ -45,7 +45,7 @@ architectures and their arguments and hyperparameters.
 | Setting                                         | Description                                                                                                                                                                                                                                                            |
 | ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `model`                                         | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                |
-| `overwrite` <Tag variant="new">3.2</Tag>        | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                  |
+| `overwrite` <Tag variant="new">3.2</Tag>        | Whether the values of existing features are overwritten. Defaults to `False`. ~~bool~~                                                                                                                                                                                 |
 | `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~~                                                                                                                      |
 | `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]~~ |
 | `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"probabilities"` and `"label_ids"`. ~~Union[bool, list[str]]~~                                                                                                                                       |