diff --git a/spacy/language.py b/spacy/language.py
index d2b89029d..fb86689bc 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -1248,17 +1248,12 @@ class Language:
component_cfg[name].setdefault("drop", drop)
pipe_kwargs[name].setdefault("batch_size", self.batch_size)
for name, proc in self.pipeline:
- # ignore statements are used here because mypy ignores hasattr
- if name not in exclude and hasattr(proc, "update"):
- proc.update(examples, sgd=None, losses=losses, **component_cfg[name]) # type: ignore
- if sgd not in (None, False):
- if (
- name not in exclude
- and isinstance(proc, ty.TrainableComponent)
- and proc.is_trainable
- and proc.model not in (True, False, None)
- ):
- proc.finish_update(sgd)
+ if (
+ name not in exclude
+ and isinstance(proc, ty.TrainableComponent)
+ and proc.is_trainable
+ ):
+ proc.update(examples, sgd=None, losses=losses, **component_cfg[name])
if name in annotates:
for doc, eg in zip(
_pipe(
@@ -1271,6 +1266,17 @@ class Language:
examples,
):
eg.predicted = doc
+ # Only finish the update after all component updates are done. Some
+ # components may share weights (such as tok2vec) and we only want
+ # to apply weight updates after all gradients are accumulated.
+ for name, proc in self.pipeline:
+ if (
+ name not in exclude
+ and isinstance(proc, ty.TrainableComponent)
+ and proc.is_trainable
+ ):
+ proc.finish_update(sgd)
+
return losses
def rehearse(
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index 6fe322b62..63d5cccc2 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/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.
diff --git a/spacy/pipeline/morphologizer.pyx b/spacy/pipeline/morphologizer.pyx
index 293add9e1..fabc51fee 100644
--- a/spacy/pipeline/morphologizer.pyx
+++ b/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,
- extend: bool = BACKWARD_EXTEND,
+ overwrite: bool = False,
+ 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".
diff --git a/spacy/pipeline/sentencizer.pyx b/spacy/pipeline/sentencizer.pyx
index 77f4e8adb..6c2565170 100644
--- a/spacy/pipeline/sentencizer.pyx
+++ b/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".
diff --git a/spacy/pipeline/senter.pyx b/spacy/pipeline/senter.pyx
index 42feeb277..a7d263e94 100644
--- a/spacy/pipeline/senter.pyx
+++ b/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.
diff --git a/spacy/pipeline/tagger.pyx b/spacy/pipeline/tagger.pyx
index a6be51c3c..101d8bcea 100644
--- a/spacy/pipeline/tagger.pyx
+++ b/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.
diff --git a/spacy/tests/doc/test_span.py b/spacy/tests/doc/test_span.py
index 21d247b74..a99f8b561 100644
--- a/spacy/tests/doc/test_span.py
+++ b/spacy/tests/doc/test_span.py
@@ -175,6 +175,18 @@ def test_modify_span_group(doc):
assert group[0].label == doc.vocab.strings["TEST"]
+def test_char_span_attributes(doc):
+ label = "LABEL"
+ kb_id = "KB_ID"
+ span_id = "SPAN_ID"
+ span1 = doc.char_span(20, 45, label=label, kb_id=kb_id, span_id=span_id)
+ span2 = doc[1:].char_span(15, 40, label=label, kb_id=kb_id, span_id=span_id)
+ assert span1.text == span2.text
+ assert span1.label_ == span2.label_ == label
+ assert span1.kb_id_ == span2.kb_id_ == kb_id
+ assert span1.id_ == span2.id_ == span_id
+
+
def test_spans_sent_spans(doc):
sents = list(doc.sents)
assert sents[0].start == 0
@@ -354,6 +366,14 @@ def test_spans_by_character(doc):
span1.start_char + 1, span1.end_char, label="GPE", alignment_mode="unk"
)
+ # Span.char_span + alignment mode "contract"
+ span2 = doc[0:2].char_span(
+ span1.start_char - 3, span1.end_char, label="GPE", alignment_mode="contract"
+ )
+ assert span1.start_char == span2.start_char
+ assert span1.end_char == span2.end_char
+ assert span2.label_ == "GPE"
+
def test_span_to_array(doc):
span = doc[1:-2]
diff --git a/spacy/tests/pipeline/test_annotates_on_update.py b/spacy/tests/pipeline/test_annotates_on_update.py
index 869b8b874..10fb22c97 100644
--- a/spacy/tests/pipeline/test_annotates_on_update.py
+++ b/spacy/tests/pipeline/test_annotates_on_update.py
@@ -54,9 +54,11 @@ def test_annotates_on_update():
return AssertSents(name)
class AssertSents:
+ model = None
+ is_trainable = True
+
def __init__(self, name, **cfg):
self.name = name
- pass
def __call__(self, doc):
if not doc.has_annotation("SENT_START"):
@@ -64,10 +66,16 @@ def test_annotates_on_update():
return doc
def update(self, examples, *, drop=0.0, sgd=None, losses=None):
+ losses.setdefault(self.name, 0.0)
+
for example in examples:
if not example.predicted.has_annotation("SENT_START"):
raise ValueError("No sents")
- return {}
+
+ return losses
+
+ def finish_update(self, sgd=None):
+ pass
nlp = English()
nlp.add_pipe("sentencizer")
diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py
index 42ffae22d..dc7ce46fe 100644
--- a/spacy/tests/test_cli.py
+++ b/spacy/tests/test_cli.py
@@ -1017,8 +1017,6 @@ def test_local_remote_storage_pull_missing():
def test_cli_find_threshold(capsys):
- thresholds = numpy.linspace(0, 1, 10)
-
def make_examples(nlp: Language) -> List[Example]:
docs: List[Example] = []
@@ -1082,8 +1080,6 @@ def test_cli_find_threshold(capsys):
scores_key="cats_macro_f",
silent=True,
)
- assert best_threshold != thresholds[0]
- assert thresholds[0] < best_threshold < thresholds[9]
assert best_score == max(res.values())
assert res[1.0] == 0.0
@@ -1091,7 +1087,7 @@ def test_cli_find_threshold(capsys):
nlp, _ = init_nlp((("spancat", {}),))
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
- res = find_threshold(
+ best_threshold, best_score, res = find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="spancat",
@@ -1099,10 +1095,8 @@ def test_cli_find_threshold(capsys):
scores_key="spans_sc_f",
silent=True,
)
- assert res[0] != thresholds[0]
- assert thresholds[0] < res[0] < thresholds[8]
- assert res[1] >= 0.6
- assert res[2][1.0] == 0.0
+ assert best_score == max(res.values())
+ assert res[1.0] == 0.0
# Having multiple textcat_multilabel components should work, since the name has to be specified.
nlp, _ = init_nlp((("textcat_multilabel", {}),))
diff --git a/spacy/tests/test_cli_app.py b/spacy/tests/test_cli_app.py
index 9b099ccb5..648a52374 100644
--- a/spacy/tests/test_cli_app.py
+++ b/spacy/tests/test_cli_app.py
@@ -9,7 +9,7 @@ import spacy
from spacy.cli._util import app
from spacy.language import Language
from spacy.tokens import DocBin
-from .util import make_tempdir
+from .util import make_tempdir, normalize_whitespace
def test_convert_auto():
@@ -247,8 +247,8 @@ def test_benchmark_accuracy_alias():
# Verify that the `evaluate` alias works correctly.
result_benchmark = CliRunner().invoke(app, ["benchmark", "accuracy", "--help"])
result_evaluate = CliRunner().invoke(app, ["evaluate", "--help"])
- assert result_benchmark.stdout == result_evaluate.stdout.replace(
- "spacy evaluate", "spacy benchmark accuracy"
+ assert normalize_whitespace(result_benchmark.stdout) == normalize_whitespace(
+ result_evaluate.stdout.replace("spacy evaluate", "spacy benchmark accuracy")
)
diff --git a/spacy/tests/test_language.py b/spacy/tests/test_language.py
index f2d6d5fc0..3d0905dd3 100644
--- a/spacy/tests/test_language.py
+++ b/spacy/tests/test_language.py
@@ -10,8 +10,9 @@ from spacy.training import Example
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.util import registry, ignore_error, raise_error, find_matching_language
+from spacy.util import load_model_from_config
import spacy
-from thinc.api import CupyOps, NumpyOps, get_current_ops
+from thinc.api import Config, CupyOps, NumpyOps, get_array_module, get_current_ops
from .util import add_vecs_to_vocab, assert_docs_equal
@@ -25,6 +26,51 @@ try:
except ImportError:
pass
+TAGGER_CFG_STRING = """
+ [nlp]
+ lang = "en"
+ pipeline = ["tok2vec","tagger"]
+
+ [components]
+
+ [components.tagger]
+ factory = "tagger"
+
+ [components.tagger.model]
+ @architectures = "spacy.Tagger.v2"
+ nO = null
+
+ [components.tagger.model.tok2vec]
+ @architectures = "spacy.Tok2VecListener.v1"
+ width = ${components.tok2vec.model.encode.width}
+
+ [components.tok2vec]
+ factory = "tok2vec"
+
+ [components.tok2vec.model]
+ @architectures = "spacy.Tok2Vec.v2"
+
+ [components.tok2vec.model.embed]
+ @architectures = "spacy.MultiHashEmbed.v1"
+ width = ${components.tok2vec.model.encode.width}
+ rows = [2000, 1000, 1000, 1000]
+ attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+ include_static_vectors = false
+
+ [components.tok2vec.model.encode]
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
+ width = 96
+ depth = 4
+ window_size = 1
+ maxout_pieces = 3
+ """
+
+
+TAGGER_TRAIN_DATA = [
+ ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
+ ("Eat blue ham", {"tags": ["V", "J", "N"]}),
+]
+
TAGGER_TRAIN_DATA = [
("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
@@ -91,6 +137,26 @@ def test_language_update(nlp):
example = Example.from_dict(doc, wrongkeyannots)
+def test_language_update_updates():
+ config = Config().from_str(TAGGER_CFG_STRING)
+ nlp = load_model_from_config(config, auto_fill=True, validate=True)
+
+ train_examples = []
+ for t in TAGGER_TRAIN_DATA:
+ train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+ optimizer = nlp.initialize(get_examples=lambda: train_examples)
+
+ docs_before_update = list(nlp.pipe([eg.predicted.copy() for eg in train_examples]))
+ nlp.update(train_examples, sgd=optimizer)
+ docs_after_update = list(nlp.pipe([eg.predicted.copy() for eg in train_examples]))
+
+ xp = get_array_module(docs_after_update[0].tensor)
+ assert xp.any(
+ xp.not_equal(docs_before_update[0].tensor, docs_after_update[0].tensor)
+ )
+
+
def test_language_evaluate(nlp):
text = "hello world"
annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
diff --git a/spacy/tests/util.py b/spacy/tests/util.py
index d5f3c39ff..c2647558d 100644
--- a/spacy/tests/util.py
+++ b/spacy/tests/util.py
@@ -1,6 +1,7 @@
import numpy
import tempfile
import contextlib
+import re
import srsly
from spacy.tokens import Doc
from spacy.vocab import Vocab
@@ -95,3 +96,7 @@ def assert_packed_msg_equal(b1, b2):
for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
assert k1 == k2
assert v1 == v2
+
+
+def normalize_whitespace(s):
+ return re.sub(r"\s+", " ", s)
diff --git a/spacy/tokens/doc.pyi b/spacy/tokens/doc.pyi
index 1c7c18bf3..93cd8de05 100644
--- a/spacy/tokens/doc.pyi
+++ b/spacy/tokens/doc.pyi
@@ -108,6 +108,7 @@ class Doc:
kb_id: Union[int, str] = ...,
vector: Optional[Floats1d] = ...,
alignment_mode: str = ...,
+ span_id: Union[int, str] = ...,
) -> Span: ...
def similarity(self, other: Union[Doc, Span, Token, Lexeme]) -> float: ...
@property
diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx
index 2b3b83e6a..2eca1aafd 100644
--- a/spacy/tokens/doc.pyx
+++ b/spacy/tokens/doc.pyx
@@ -528,9 +528,9 @@ cdef class Doc:
doc (Doc): The parent document.
start_idx (int): The index of the first character of the span.
end_idx (int): The index of the first character after the span.
- label (uint64 or string): A label to attach to the Span, e.g. for
+ label (Union[int, str]): A label to attach to the Span, e.g. for
named entities.
- kb_id (uint64 or string): An ID from a KB to capture the meaning of a
+ kb_id (Union[int, str]): An ID from a KB to capture the meaning of a
named entity.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
the span.
@@ -539,6 +539,7 @@ cdef class Doc:
with token boundaries), "contract" (span of all tokens completely
within the character span), "expand" (span of all tokens at least
partially covered by the character span). Defaults to "strict".
+ span_id (Union[int, str]): An identifier to associate with the span.
RETURNS (Span): The newly constructed object.
DOCS: https://spacy.io/api/doc#char_span
diff --git a/spacy/tokens/span.pyi b/spacy/tokens/span.pyi
index 5168f3b03..979e74e7e 100644
--- a/spacy/tokens/span.pyi
+++ b/spacy/tokens/span.pyi
@@ -96,6 +96,9 @@ class Span:
label: Union[int, str] = ...,
kb_id: Union[int, str] = ...,
vector: Optional[Floats1d] = ...,
+ id: Union[int, str] = ...,
+ alignment_mode: str = ...,
+ span_id: Union[int, str] = ...,
) -> Span: ...
@property
def conjuncts(self) -> Tuple[Token]: ...
diff --git a/spacy/tokens/span.pyx b/spacy/tokens/span.pyx
index b605434fd..aefea4f71 100644
--- a/spacy/tokens/span.pyx
+++ b/spacy/tokens/span.pyx
@@ -382,7 +382,7 @@ cdef class Span:
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
-
+
cpdef np.ndarray to_array(self, object py_attr_ids):
"""Given a list of M attribute IDs, export the tokens to a numpy
`ndarray` of shape `(N, M)`, where `N` is the length of the document.
@@ -656,22 +656,29 @@ cdef class Span:
else:
return self.doc[root]
- def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None, id=0):
+ def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None, id=0, alignment_mode="strict", span_id=0):
"""Create a `Span` object from the slice `span.text[start : end]`.
start (int): The index of the first character of the span.
end (int): The index of the first character after the span.
- label (uint64 or string): A label to attach to the Span, e.g. for
+ label (Union[int, str]): A label to attach to the Span, e.g. for
named entities.
- kb_id (uint64 or string): An ID from a KB to capture the meaning of a named entity.
+ kb_id (Union[int, str]): An ID from a KB to capture the meaning of a named entity.
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
the span.
+ id (Union[int, str]): Unused.
+ alignment_mode (str): How character indices are aligned to token
+ boundaries. Options: "strict" (character indices must be aligned
+ with token boundaries), "contract" (span of all tokens completely
+ within the character span), "expand" (span of all tokens at least
+ partially covered by the character span). Defaults to "strict".
+ span_id (Union[int, str]): An identifier to associate with the span.
RETURNS (Span): The newly constructed object.
"""
cdef SpanC* span_c = self.span_c()
start_idx += span_c.start_char
end_idx += span_c.start_char
- return self.doc.char_span(start_idx, end_idx, label=label, kb_id=kb_id, vector=vector)
+ return self.doc.char_span(start_idx, end_idx, label=label, kb_id=kb_id, vector=vector, alignment_mode=alignment_mode, span_id=span_id)
@property
def conjuncts(self):
diff --git a/spacy/training/loop.py b/spacy/training/loop.py
index fc929816d..fcc023a0d 100644
--- a/spacy/training/loop.py
+++ b/spacy/training/loop.py
@@ -210,7 +210,7 @@ def train_while_improving(
subbatch,
drop=dropout,
losses=losses,
- sgd=False, # type: ignore[arg-type]
+ sgd=None,
exclude=exclude,
annotates=annotating_components,
)
diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx
index 0bf708183..9777650a9 100644
--- a/website/docs/api/cli.mdx
+++ b/website/docs/api/cli.mdx
@@ -1410,12 +1410,13 @@ $ python -m spacy project assets [project_dir]
> $ python -m spacy project assets [--sparse]
> ```
-| Name | Description |
-| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
-| `--sparse`, `-S` | Enable [sparse checkout](https://git-scm.com/docs/git-sparse-checkout) to only check out and download what's needed. Requires Git v22.2+. ~~bool (flag)~~ |
-| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
-| **CREATES** | Downloaded or copied assets defined in the `project.yml`. |
+| Name | Description |
+| ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_dir` | Path to project directory. Defaults to current working directory. ~~Path (positional)~~ |
+| `--extra`, `-e` 3.3.1 | Download assets marked as "extra". Default false. ~~bool (flag)~~ |
+| `--sparse`, `-S` | Enable [sparse checkout](https://git-scm.com/docs/git-sparse-checkout) to only check out and download what's needed. Requires Git v22.2+. ~~bool (flag)~~ |
+| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
+| **CREATES** | Downloaded or copied assets defined in the `project.yml`. |
### project run {id="project-run",tag="command"}
diff --git a/website/docs/api/doc.mdx b/website/docs/api/doc.mdx
index a303d628e..1a3f6179f 100644
--- a/website/docs/api/doc.mdx
+++ b/website/docs/api/doc.mdx
@@ -37,7 +37,7 @@ Construct a `Doc` object. The most common way to get a `Doc` object is via the
| `words` | A list of strings or integer hash values to add to the document as words. ~~Optional[List[Union[str,int]]]~~ |
| `spaces` | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. ~~Optional[List[bool]]~~ |
| _keyword-only_ | |
-| `user\_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
+| `user_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
| `tags` 3 | A list of strings, of the same length as `words`, to assign as `token.tag` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `pos` 3 | A list of strings, of the same length as `words`, to assign as `token.pos` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `morphs` 3 | A list of strings, of the same length as `words`, to assign as `token.morph` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
@@ -209,15 +209,16 @@ alignment mode `"strict".
> assert span.text == "New York"
> ```
-| Name | Description |
-| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `start` | The index of the first character of the span. ~~int~~ |
-| `end` | The index of the last character after the span. ~~int~~ |
-| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
-| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
-| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
-| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
-| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
+| Name | Description |
+| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `start` | The index of the first character of the span. ~~int~~ |
+| `end` | The index of the last character after the span. ~~int~~ |
+| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
+| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
+| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
+| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
+| `span_id` 3.3.1 | An identifier to associate with the span. ~~Union[int, str]~~ |
+| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Doc.set_ents {id="set_ents",tag="method",version="3"}
diff --git a/website/docs/api/entitylinker.mdx b/website/docs/api/entitylinker.mdx
index 238b62a2e..12b2f6bef 100644
--- a/website/docs/api/entitylinker.mdx
+++ b/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` 3.2 | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
+| `overwrite` 3.2 | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
| `scorer` 3.2 | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
| `save_activations` 4.0 | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
| `threshold` 3.4 | 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]~~ |
diff --git a/website/docs/api/morphologizer.mdx b/website/docs/api/morphologizer.mdx
index 4660ec312..9514bc773 100644
--- a/website/docs/api/morphologizer.mdx
+++ b/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` 3.2 | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
+| `overwrite` 3.2 | Whether the values of existing features are overwritten. Defaults to `False`. ~~bool~~ |
| `extend` 3.2 | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
| `scorer` 3.2 | 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` 4.0 | Save activations in `Doc` when annotating. Saved activations are `"probabilities"` and `"label_ids"`. ~~Union[bool, list[str]]~~ |
diff --git a/website/docs/api/span.mdx b/website/docs/api/span.mdx
index 878bb30c3..e62d9c724 100644
--- a/website/docs/api/span.mdx
+++ b/website/docs/api/span.mdx
@@ -186,14 +186,17 @@ the character indices don't map to a valid span.
> assert span.text == "New York"
> ```
-| Name | Description |
-| ----------- | ----------------------------------------------------------------------------------------- |
-| `start` | The index of the first character of the span. ~~int~~ |
-| `end` | The index of the last character after the span. ~~int~~ |
-| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
-| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
-| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
-| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
+| Name | Description |
+| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `start` | The index of the first character of the span. ~~int~~ |
+| `end` | The index of the last character after the span. ~~int~~ |
+| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
+| `kb_id` | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
+| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
+| `id` | Unused. ~~Union[int, str]~~ |
+| `alignment_mode` 3.5.1 | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
+| `span_id` 3.5.1 | An identifier to associate with the span. ~~Union[int, str]~~ |
+| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Span.similarity {id="similarity",tag="method",model="vectors"}
diff --git a/website/docs/models/index.mdx b/website/docs/models/index.mdx
index 371e4460f..366d44f0e 100644
--- a/website/docs/models/index.mdx
+++ b/website/docs/models/index.mdx
@@ -21,8 +21,8 @@ menu:
## Package naming conventions {id="conventions"}
In general, spaCy expects all pipeline packages to follow the naming convention
-of `[lang]\_[name]`. For spaCy's pipelines, we also chose to divide the name
-into three components:
+of `[lang]_[name]`. For spaCy's pipelines, we also chose to divide the name into
+three components:
1. **Type:** Capabilities (e.g. `core` for general-purpose pipeline with
tagging, parsing, lemmatization and named entity recognition, or `dep` for
diff --git a/website/docs/usage/v3-5.mdx b/website/docs/usage/v3-5.mdx
index ac61338e3..3ca64f8a2 100644
--- a/website/docs/usage/v3-5.mdx
+++ b/website/docs/usage/v3-5.mdx
@@ -155,6 +155,21 @@ An error is now raised when unsupported values are given as input to train a
`textcat` or `textcat_multilabel` model - ensure that values are `0.0` or `1.0`
as explained in the [docs](/api/textcategorizer#assigned-attributes).
+### Using the default knowledge base
+
+As `KnowledgeBase` is now an abstract class, you should call the constructor of
+the new `InMemoryLookupKB` instead when you want to use spaCy's default KB
+implementation:
+
+```diff
+- kb = KnowledgeBase()
++ kb = InMemoryLookupKB()
+```
+
+If you've written a custom KB that inherits from `KnowledgeBase`, you'll need to
+implement its abstract methods, or alternatively inherit from `InMemoryLookupKB`
+instead.
+
### Updated scorers for tokenization and textcat {id="scores"}
We fixed a bug that inflated the `token_acc` scores in v3.0-v3.4. The reported