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SpanFinder into spaCy from experimental (#12507)

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* span finder integrated into spacy from experimental

* black

* isort

* black

* default spankey constant

* black

* Update spacy/pipeline/spancat.py

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

* rename

* rename

* max_length and min_length as Optional[int] and strict checking

* black

* mypy fix for integer type infinity

* revert line order

* implement all comparison operators for inf int

* avoid two for loops over all docs by not precomputing

* interleave thresholding with span creation

* black

* revert to not interleaving (relized its faster)

* black

* Update spacy/errors.py

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

* update dosctring

* enforce that the gold and predicted documents have the same text

* new error for ensuring reference and predicted texts are the same

* remove todo

* adjust test

* black

* handle misaligned tokenization

* return correct variable

* failing overfit test

* only use a single spans_key like in spancat

* black

* remove debug lines

* typo

* remove comment

* remove near duplicate reduntant method

* use the 'spans_key' variable name everywhere

* Update spacy/pipeline/span_finder.py

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

* flaky test fix suggestion, hand set bias terms

* only test suggester and test result exhaustively

* make it clear that the span_finder_suggester is more general (not specific to span_finder)

* Update spacy/tests/pipeline/test_span_finder.py

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

* Apply suggestions from code review

* remove question comment

* move preset_spans_suggester test to spancat tests

* Add docs and unify default configs for spancat and span finder

* Add `allow_overlap=True` to span finder scorer

* Fix offset bug in set_annotations

* Ignore labels in span finder scorer

* Format

* Add span_finder to quickstart template

* Move settings to self.cfg, store min/max unset as None

* Remove debugging

* Update docstrings and docs

* Update spacy/pipeline/span_finder.py

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Fix imports

---------

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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12 changed files with 1135 additions and 26 deletions
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49
spacy/cli/templates/quickstart_training.jinja
View File

@ -3,7 +3,7 @@ the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = hardware != "cpu" and transformer_data -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "span_finder", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
[paths]
train = null
dev = null
@ -28,7 +28,7 @@ lang = "{{ lang }}"
tok2vec/transformer. #}
{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "span_finder" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
{%- else -%}
{%- set full_pipeline = components -%}
@ -127,6 +127,30 @@ grad_factor = 1.0
@layers = "reduce_mean.v1"
{% endif -%}
{% if "span_finder" in components -%}
[components.span_finder]
factory = "span_finder"
max_length = null
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.span_finder.model]
@architectures = "spacy.SpanFinder.v1"
[components.span_finder.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = 2
[components.span_finder.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.span_finder.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{% if "spancat" in components -%}
[components.spancat]
factory = "spancat"
@ -392,6 +416,27 @@ nO = null
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% if "span_finder" in components %}
[components.span_finder]
factory = "span_finder"
max_length = null
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.span_finder.model]
@architectures = "spacy.SpanFinder.v1"
[components.span_finder.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = 2
[components.span_finder.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% if "spancat" in components %}
[components.spancat]
factory = "spancat"

4
spacy/errors.py
View File

@ -973,6 +973,10 @@ class Errors(metaclass=ErrorsWithCodes):
E1052 = ("Unable to copy spans: the character offsets for the span at "
"index {i} in the span group do not align with the tokenization "
"in the target doc.")
E1053 = ("Both 'min_length' and 'max_length' should be larger than 0, but found"
" 'min_length': {min_length}, 'max_length': {max_length}")
E1054 = ("The text, including whitespace, must match between reference and "
"predicted docs when training {component}.")
# Deprecated model shortcuts, only used in errors and warnings

1
spacy/ml/models/__init__.py
View File

@ -1,6 +1,7 @@
from .entity_linker import * # noqa
from .multi_task import * # noqa
from .parser import * # noqa
from .span_finder import * # noqa
from .spancat import * # noqa
from .tagger import * # noqa
from .textcat import * # noqa

42
spacy/ml/models/span_finder.py Normal file
View File

@ -0,0 +1,42 @@
from typing import Callable, List, Tuple
from thinc.api import Model, chain, with_array
from thinc.types import Floats1d, Floats2d
from ...tokens import Doc
from ...util import registry
InT = List[Doc]
OutT = Floats2d
@registry.architectures("spacy.SpanFinder.v1")
def build_finder_model(
tok2vec: Model[InT, List[Floats2d]], scorer: Model[OutT, OutT]
) -> Model[InT, OutT]:
logistic_layer: Model[List[Floats2d], List[Floats2d]] = with_array(scorer)
model: Model[InT, OutT] = chain(tok2vec, logistic_layer, flattener())
model.set_ref("tok2vec", tok2vec)
model.set_ref("scorer", scorer)
model.set_ref("logistic_layer", logistic_layer)
return model
def flattener() -> Model[List[Floats2d], Floats2d]:
"""Flattens the input to a 1-dimensional list of scores"""
def forward(
model: Model[Floats1d, Floats1d], X: List[Floats2d], is_train: bool
) -> Tuple[Floats2d, Callable[[Floats2d], List[Floats2d]]]:
lens = model.ops.asarray1i([len(doc) for doc in X])
Y = model.ops.flatten(X)
def backprop(dY: Floats2d) -> List[Floats2d]:
return model.ops.unflatten(dY, lens)
return Y, backprop
return Model("Flattener", forward=forward)

14
spacy/pipeline/__init__.py
View File

@ -2,21 +2,22 @@ from .attributeruler import AttributeRuler
from .dep_parser import DependencyParser
from .edit_tree_lemmatizer import EditTreeLemmatizer
from .entity_linker import EntityLinker
from .ner import EntityRecognizer
from .entityruler import EntityRuler
from .functions import merge_entities, merge_noun_chunks, merge_subtokens
from .lemmatizer import Lemmatizer
from .morphologizer import Morphologizer
from .ner import EntityRecognizer
from .pipe import Pipe
from .trainable_pipe import TrainablePipe
from .senter import SentenceRecognizer
from .sentencizer import Sentencizer
from .senter import SentenceRecognizer
from .span_finder import SpanFinder
from .span_ruler import SpanRuler
from .spancat import SpanCategorizer
from .tagger import Tagger
from .textcat import TextCategorizer
from .spancat import SpanCategorizer
from .span_ruler import SpanRuler
from .textcat_multilabel import MultiLabel_TextCategorizer
from .tok2vec import Tok2Vec
from .functions import merge_entities, merge_noun_chunks, merge_subtokens
from .trainable_pipe import TrainablePipe
__all__ = [
"AttributeRuler",
@ -31,6 +32,7 @@ __all__ = [
"SentenceRecognizer",
"Sentencizer",
"SpanCategorizer",
"SpanFinder",
"SpanRuler",
"Tagger",
"TextCategorizer",

336
spacy/pipeline/span_finder.py Normal file
View File

@ -0,0 +1,336 @@
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
from thinc.api import Config, Model, Optimizer, set_dropout_rate
from thinc.types import Floats2d
from ..language import Language
from .trainable_pipe import TrainablePipe
from ..scorer import Scorer
from ..tokens import Doc, Span
from ..training import Example
from ..errors import Errors
from ..util import registry
from .spancat import DEFAULT_SPANS_KEY
span_finder_default_config = """
[model]
@architectures = "spacy.SpanFinder.v1"
[model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = 2
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 96
rows = [5000, 1000, 2500, 1000]
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
depth = 4
"""
DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model"]
@Language.factory(
"span_finder",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"model": DEFAULT_SPAN_FINDER_MODEL,
"spans_key": DEFAULT_SPANS_KEY,
"max_length": None,
"min_length": None,
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
},
default_score_weights={
f"span_finder_{DEFAULT_SPANS_KEY}_f": 1.0,
f"span_finder_{DEFAULT_SPANS_KEY}_p": 0.0,
f"span_finder_{DEFAULT_SPANS_KEY}_r": 0.0,
},
)
def make_span_finder(
nlp: Language,
name: str,
model: Model[Iterable[Doc], Floats2d],
spans_key: str,
threshold: float,
max_length: Optional[int],
min_length: Optional[int],
scorer: Optional[Callable],
) -> "SpanFinder":
"""Create a SpanFinder component. The component predicts whether a token is
the start or the end of a potential span.
model (Model[List[Doc], Floats2d]): A model instance that
is given a list of documents and predicts a probability for each token.
spans_key (str): Key of the 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.
threshold (float): Minimum probability to consider a prediction positive.
max_length (Optional[int]): Maximum length of the produced spans, defaults
to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans, defaults
to None meaning shortest span length is 1.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
"""
return SpanFinder(
nlp,
model=model,
threshold=threshold,
name=name,
scorer=scorer,
max_length=max_length,
min_length=min_length,
spans_key=spans_key,
)
@registry.scorers("spacy.span_finder_scorer.v1")
def make_span_finder_scorer():
return span_finder_score
def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
kwargs = dict(kwargs)
attr_prefix = "span_finder_"
key = kwargs["spans_key"]
kwargs.setdefault("attr", f"{attr_prefix}{key}")
kwargs.setdefault(
"getter", lambda doc, key: doc.spans.get(key[len(attr_prefix) :], [])
)
kwargs.setdefault("has_annotation", lambda doc: key in doc.spans)
kwargs.setdefault("allow_overlap", True)
kwargs.setdefault("labeled", False)
scores = Scorer.score_spans(examples, **kwargs)
scores.pop(f"{kwargs['attr']}_per_type", None)
return scores
def _char_indices(span: Span) -> Tuple[int, int]:
start = span[0].idx
end = span[-1].idx + len(span[-1])
return start, end
class SpanFinder(TrainablePipe):
"""Pipeline that learns span boundaries.
DOCS: https://spacy.io/api/spanfinder
"""
def __init__(
self,
nlp: Language,
model: Model[Iterable[Doc], Floats2d],
name: str = "span_finder",
*,
spans_key: str = DEFAULT_SPANS_KEY,
threshold: float = 0.5,
max_length: Optional[int] = None,
min_length: Optional[int] = None,
scorer: Optional[Callable] = span_finder_score,
) -> None:
"""Initialize the span finder.
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.
threshold (float): Minimum probability to consider a prediction
positive.
scorer (Optional[Callable]): The scoring method.
spans_key (str): Key of the 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.
max_length (Optional[int]): Maximum length of the produced spans,
defaults to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans,
defaults to None meaning shortest span length is 1.
DOCS: https://spacy.io/api/spanfinder#init
"""
self.vocab = nlp.vocab
if (max_length is not None and max_length < 1) or (
min_length is not None and min_length < 1
):
raise ValueError(
Errors.E1053.format(min_length=min_length, max_length=max_length)
)
self.model = model
self.name = name
self.scorer = scorer
self.cfg: Dict[str, Any] = {
"min_length": min_length,
"max_length": max_length,
"threshold": threshold,
"spans_key": spans_key,
}
def predict(self, docs: Iterable[Doc]):
"""Apply the pipeline's model to a batch of docs, without modifying
them.
docs (Iterable[Doc]): The documents to predict.
RETURNS: The models prediction for each document.
DOCS: https://spacy.io/api/spanfinder#predict
"""
scores = self.model.predict(docs)
return scores
def set_annotations(self, docs: Iterable[Doc], scores: Floats2d) -> None:
"""Modify a batch of Doc objects, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by SpanFinder predict method.
DOCS: https://spacy.io/api/spanfinder#set_annotations
"""
offset = 0
for i, doc in enumerate(docs):
doc.spans[self.cfg["spans_key"]] = []
starts = []
ends = []
doc_scores = scores[offset : offset + len(doc)]
for token, token_score in zip(doc, doc_scores):
if token_score[0] >= self.cfg["threshold"]:
starts.append(token.i)
if token_score[1] >= self.cfg["threshold"]:
ends.append(token.i)
for start in starts:
for end in ends:
span_length = end + 1 - start
if span_length < 1:
continue
if (
self.cfg["min_length"] is None
or self.cfg["min_length"] <= span_length
) and (
self.cfg["max_length"] is None
or span_length <= self.cfg["max_length"]
):
doc.spans[self.cfg["spans_key"]].append(doc[start : end + 1])
offset += len(doc)
def update(
self,
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model. Delegates to predict and get_loss.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (Optional[thinc.api.Optimizer]): The optimizer.
losses (Optional[Dict[str, float]]): Optional record of the loss during
training. Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/spanfinder#update
"""
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
predicted = [eg.predicted for eg in examples]
set_dropout_rate(self.model, drop)
scores, backprop_scores = self.model.begin_update(predicted)
loss, d_scores = self.get_loss(examples, scores)
backprop_scores(d_scores)
if sgd is not None:
self.finish_update(sgd)
losses[self.name] += loss
return losses
def get_loss(self, examples, scores) -> Tuple[float, Floats2d]:
"""Find the loss and gradient of loss for the batch of documents and
their predicted scores.
examples (Iterable[Examples]): The batch of examples.
scores: Scores representing the model's predictions.
RETURNS (Tuple[float, Floats2d]): The loss and the gradient.
DOCS: https://spacy.io/api/spanfinder#get_loss
"""
truths, masks = self._get_aligned_truth_scores(examples, self.model.ops)
d_scores = scores - self.model.ops.asarray2f(truths)
d_scores *= masks
loss = float((d_scores**2).sum())
return loss, d_scores
def _get_aligned_truth_scores(self, examples, ops) -> Tuple[Floats2d, Floats2d]:
"""Align scores of the predictions to the references for calculating
the loss.
"""
truths = []
masks = []
for eg in examples:
if eg.x.text != eg.y.text:
raise ValueError(Errors.E1054.format(component="span_finder"))
n_tokens = len(eg.predicted)
truth = ops.xp.zeros((n_tokens, 2), dtype="float32")
mask = ops.xp.ones((n_tokens, 2), dtype="float32")
if self.cfg["spans_key"] in eg.reference.spans:
for span in eg.reference.spans[self.cfg["spans_key"]]:
ref_start_char, ref_end_char = _char_indices(span)
pred_span = eg.predicted.char_span(
ref_start_char, ref_end_char, alignment_mode="expand"
)
pred_start_char, pred_end_char = _char_indices(pred_span)
start_match = pred_start_char == ref_start_char
end_match = pred_end_char == ref_end_char
if start_match:
truth[pred_span[0].i, 0] = 1
else:
mask[pred_span[0].i, 0] = 0
if end_match:
truth[pred_span[-1].i, 1] = 1
else:
mask[pred_span[-1].i, 1] = 0
truths.append(truth)
masks.append(mask)
truths = ops.xp.concatenate(truths, axis=0)
masks = ops.xp.concatenate(masks, axis=0)
return truths, masks
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
nlp: Optional[Language] = None,
) -> None:
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Optional[Language]): The current nlp object the component is part
of.
DOCS: https://spacy.io/api/spanfinder#initialize
"""
subbatch: List[Example] = []
for eg in get_examples():
if len(subbatch) < 10:
subbatch.append(eg)
if subbatch:
docs = [eg.reference for eg in subbatch]
Y, _ = self._get_aligned_truth_scores(subbatch, self.model.ops)
self.model.initialize(X=docs, Y=Y)
else:
self.model.initialize()

60
spacy/pipeline/spancat.py
View File

@ -1,22 +1,20 @@
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast, Union
from dataclasses import dataclass
from functools import partial
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
from thinc.api import Optimizer
from thinc.types import Ragged, Ints2d, Floats2d
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
import numpy
from thinc.api import Config, Model, Ops, Optimizer, get_current_ops, set_dropout_rate
from thinc.types import Floats2d, Ints1d, Ints2d, Ragged
from ..compat import Protocol, runtime_checkable
from ..scorer import Scorer
from ..language import Language
from .trainable_pipe import TrainablePipe
from ..tokens import Doc, SpanGroup, Span
from ..vocab import Vocab
from ..training import Example, validate_examples
from ..errors import Errors
from ..language import Language
from ..scorer import Scorer
from ..tokens import Doc, Span, SpanGroup
from ..training import Example, validate_examples
from ..util import registry
from ..vocab import Vocab
from .trainable_pipe import TrainablePipe
spancat_default_config = """
[model]
@ -33,8 +31,8 @@ hidden_size = 128
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 96
rows = [5000, 2000, 1000, 1000]
attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 1000, 2500, 1000]
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@ -71,6 +69,7 @@ maxout_pieces = 3
depth = 4
"""
DEFAULT_SPANS_KEY = "sc"
DEFAULT_SPANCAT_MODEL = Config().from_str(spancat_default_config)["model"]
DEFAULT_SPANCAT_SINGLELABEL_MODEL = Config().from_str(
spancat_singlelabel_default_config
@ -112,6 +111,29 @@ def ngram_suggester(
return output
def preset_spans_suggester(
docs: Iterable[Doc], spans_key: str, *, ops: Optional[Ops] = None
) -> Ragged:
if ops is None:
ops = get_current_ops()
spans = []
lengths = []
for doc in docs:
length = 0
if doc.spans[spans_key]:
for span in doc.spans[spans_key]:
spans.append([span.start, span.end])
length += 1
lengths.append(length)
lengths_array = cast(Ints1d, ops.asarray(lengths, dtype="i"))
if len(spans) > 0:
output = Ragged(ops.asarray(spans, dtype="i"), lengths_array)
else:
output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array)
return output
@registry.misc("spacy.ngram_suggester.v1")
def build_ngram_suggester(sizes: List[int]) -> Suggester:
"""Suggest all spans of the given lengths. Spans are returned as a ragged
@ -130,12 +152,20 @@ def build_ngram_range_suggester(min_size: int, max_size: int) -> Suggester:
return build_ngram_suggester(sizes)
@registry.misc("spacy.preset_spans_suggester.v1")
def build_preset_spans_suggester(spans_key: str) -> Suggester:
"""Suggest all spans that are already stored in doc.spans[spans_key].
This is useful when an upstream component is used to set the spans
on the Doc such as a SpanRuler or SpanFinder."""
return partial(preset_spans_suggester, spans_key=spans_key)
@Language.factory(
"spancat",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"spans_key": "sc",
"spans_key": DEFAULT_SPANS_KEY,
"max_positive": None,
"model": DEFAULT_SPANCAT_MODEL,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
@ -199,7 +229,7 @@ def make_spancat(
"spancat_singlelabel",
assigns=["doc.spans"],
default_config={
"spans_key": "sc",
"spans_key": DEFAULT_SPANS_KEY,
"model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
"negative_weight": 1.0,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},

242
spacy/tests/pipeline/test_span_finder.py Normal file
View File

@ -0,0 +1,242 @@
import pytest
from thinc.api import Config
from spacy.language import Language
from spacy.lang.en import English
from spacy.pipeline.span_finder import span_finder_default_config
from spacy.tokens import Doc
from spacy.training import Example
from spacy import util
from spacy.util import registry
from spacy.util import fix_random_seed, make_tempdir
SPANS_KEY = "pytest"
TRAIN_DATA = [
("Who is Shaka Khan?", {"spans": {SPANS_KEY: [(7, 17)]}}),
(
"I like London and Berlin.",
{"spans": {SPANS_KEY: [(7, 13), (18, 24)]}},
),
]
TRAIN_DATA_OVERLAPPING = [
("Who is Shaka Khan?", {"spans": {SPANS_KEY: [(7, 17)]}}),
(
"I like London and Berlin",
{"spans": {SPANS_KEY: [(7, 13), (18, 24), (7, 24)]}},
),
("", {"spans": {SPANS_KEY: []}}),
]
def make_examples(nlp, data=TRAIN_DATA):
train_examples = []
for t in data:
eg = Example.from_dict(nlp.make_doc(t[0]), t[1])
train_examples.append(eg)
return train_examples
@pytest.mark.parametrize(
"tokens_predicted, tokens_reference, reference_truths",
[
(
["Mon", ".", "-", "June", "16"],
["Mon.", "-", "June", "16"],
[(0, 0), (0, 0), (0, 0), (1, 1), (0, 0)],
),
(
["Mon.", "-", "J", "une", "16"],
["Mon.", "-", "June", "16"],
[(0, 0), (0, 0), (1, 0), (0, 1), (0, 0)],
),
(
["Mon", ".", "-", "June", "16"],
["Mon.", "-", "June", "1", "6"],
[(0, 0), (0, 0), (0, 0), (1, 1), (0, 0)],
),
(
["Mon.", "-J", "un", "e 16"],
["Mon.", "-", "June", "16"],
[(0, 0), (0, 0), (0, 0), (0, 0)],
),
pytest.param(
["Mon.-June", "16"],
["Mon.", "-", "June", "16"],
[(0, 1), (0, 0)],
),
pytest.param(
["Mon.-", "June", "16"],
["Mon.", "-", "J", "une", "16"],
[(0, 0), (1, 1), (0, 0)],
),
pytest.param(
["Mon.-", "June 16"],
["Mon.", "-", "June", "16"],
[(0, 0), (1, 0)],
),
],
)
def test_loss_alignment_example(tokens_predicted, tokens_reference, reference_truths):
nlp = Language()
predicted = Doc(
nlp.vocab, words=tokens_predicted, spaces=[False] * len(tokens_predicted)
)
reference = Doc(
nlp.vocab, words=tokens_reference, spaces=[False] * len(tokens_reference)
)
example = Example(predicted, reference)
example.reference.spans[SPANS_KEY] = [example.reference.char_span(5, 9)]
span_finder = nlp.add_pipe("span_finder", config={"spans_key": SPANS_KEY})
nlp.initialize()
ops = span_finder.model.ops
if predicted.text != reference.text:
with pytest.raises(
ValueError, match="must match between reference and predicted"
):
span_finder._get_aligned_truth_scores([example], ops)
return
truth_scores, masks = span_finder._get_aligned_truth_scores([example], ops)
assert len(truth_scores) == len(tokens_predicted)
ops.xp.testing.assert_array_equal(truth_scores, ops.xp.asarray(reference_truths))
def test_span_finder_model():
nlp = Language()
docs = [nlp("This is an example."), nlp("This is the second example.")]
docs[0].spans[SPANS_KEY] = [docs[0][3:4]]
docs[1].spans[SPANS_KEY] = [docs[1][3:5]]
total_tokens = 0
for doc in docs:
total_tokens += len(doc)
config = Config().from_str(span_finder_default_config).interpolate()
model = registry.resolve(config)["model"]
model.initialize(X=docs)
predictions = model.predict(docs)
assert len(predictions) == total_tokens
assert len(predictions[0]) == 2
def test_span_finder_component():
nlp = Language()
docs = [nlp("This is an example."), nlp("This is the second example.")]
docs[0].spans[SPANS_KEY] = [docs[0][3:4]]
docs[1].spans[SPANS_KEY] = [docs[1][3:5]]
span_finder = nlp.add_pipe("span_finder", config={"spans_key": SPANS_KEY})
nlp.initialize()
docs = list(span_finder.pipe(docs))
assert SPANS_KEY in docs[0].spans
@pytest.mark.parametrize(
"min_length, max_length, span_count",
[(0, 0, 0), (None, None, 8), (2, None, 6), (None, 1, 2), (2, 3, 2)],
)
def test_set_annotations_span_lengths(min_length, max_length, span_count):
nlp = Language()
doc = nlp("Me and Jenny goes together like peas and carrots.")
if min_length == 0 and max_length == 0:
with pytest.raises(ValueError, match="Both 'min_length' and 'max_length'"):
span_finder = nlp.add_pipe(
"span_finder",
config={
"max_length": max_length,
"min_length": min_length,
"spans_key": SPANS_KEY,
},
)
return
span_finder = nlp.add_pipe(
"span_finder",
config={
"max_length": max_length,
"min_length": min_length,
"spans_key": SPANS_KEY,
},
)
nlp.initialize()
# Starts [Me, Jenny, peas]
# Ends [Jenny, peas, carrots]
scores = [
(1, 0),
(0, 0),
(1, 1),
(0, 0),
(0, 0),
(0, 0),
(1, 1),
(0, 0),
(0, 1),
(0, 0),
]
span_finder.set_annotations([doc], scores)
assert doc.spans[SPANS_KEY]
assert len(doc.spans[SPANS_KEY]) == span_count
# Assert below will fail when max_length is set to 0
if max_length is None:
max_length = float("inf")
if min_length is None:
min_length = 1
assert all(min_length <= len(span) <= max_length for span in doc.spans[SPANS_KEY])
def test_overfitting_IO():
# Simple test to try and quickly overfit the span_finder component - ensuring the ML models work correctly
fix_random_seed(0)
nlp = English()
span_finder = nlp.add_pipe("span_finder", config={"spans_key": SPANS_KEY})
train_examples = make_examples(nlp)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
assert span_finder.model.get_dim("nO") == 2
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["span_finder"] < 0.001
# test the trained model
test_text = "I like London and Berlin"
doc = nlp(test_text)
spans = doc.spans[SPANS_KEY]
assert len(spans) == 3
assert set([span.text for span in spans]) == {
"London",
"Berlin",
"London and Berlin",
}
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
spans2 = doc2.spans[SPANS_KEY]
assert len(spans2) == 3
assert set([span.text for span in spans2]) == {
"London",
"Berlin",
"London and Berlin",
}
# Test scoring
scores = nlp.evaluate(train_examples)
assert f"span_finder_{SPANS_KEY}_f" in scores
# It's not perfect 1.0 F1 because it's designed to overgenerate for now.
assert scores[f"span_finder_{SPANS_KEY}_p"] == 0.75
assert scores[f"span_finder_{SPANS_KEY}_r"] == 1.0
# also test that the spancat works for just a single entity in a sentence
doc = nlp("London")
assert len(doc.spans[SPANS_KEY]) == 1

19
spacy/tests/pipeline/test_spancat.py
View File

@ -406,6 +406,21 @@ def test_ngram_sizes(en_tokenizer):
assert_array_equal(OPS.to_numpy(ngrams_3.lengths), [0, 1, 3, 6, 9])
def test_preset_spans_suggester():
nlp = Language()
docs = [nlp("This is an example."), nlp("This is the second example.")]
docs[0].spans[SPAN_KEY] = [docs[0][3:4]]
docs[1].spans[SPAN_KEY] = [docs[1][0:4], docs[1][3:5]]
suggester = registry.misc.get("spacy.preset_spans_suggester.v1")(spans_key=SPAN_KEY)
candidates = suggester(docs)
assert type(candidates) == Ragged
assert len(candidates) == 2
assert list(candidates.dataXd[0]) == [3, 4]
assert list(candidates.dataXd[1]) == [0, 4]
assert list(candidates.dataXd[2]) == [3, 5]
assert list(candidates.lengths) == [1, 2]
def test_overfitting_IO():
# Simple test to try and quickly overfit the spancat component - ensuring the ML models work correctly
fix_random_seed(0)
@ -428,7 +443,7 @@ def test_overfitting_IO():
spans = doc.spans[SPAN_KEY]
assert len(spans) == 2
assert len(spans.attrs["scores"]) == 2
assert min(spans.attrs["scores"]) > 0.9
assert min(spans.attrs["scores"]) > 0.8
assert set([span.text for span in spans]) == {"London", "Berlin"}
assert set([span.label_ for span in spans]) == {"LOC"}
@ -440,7 +455,7 @@ def test_overfitting_IO():
spans2 = doc2.spans[SPAN_KEY]
assert len(spans2) == 2
assert len(spans2.attrs["scores"]) == 2
assert min(spans2.attrs["scores"]) > 0.9
assert min(spans2.attrs["scores"]) > 0.8
assert set([span.text for span in spans2]) == {"London", "Berlin"}
assert set([span.label_ for span in spans2]) == {"LOC"}

21
website/docs/api/spancategorizer.mdx
View File

@ -105,7 +105,7 @@ architectures and their arguments and hyperparameters.
>
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_spancat"}}
> parser = nlp.add_pipe("spancat", config=config)
> spancat = nlp.add_pipe("spancat", config=config)
>
> # Construction from class
> from spacy.pipeline import SpanCategorizer
@ -524,3 +524,22 @@ has two columns, indicating the start and end position.
| `min_size` | The minimal phrase lengths to suggest (inclusive). ~~[int]~~ |
| `max_size` | The maximal phrase lengths to suggest (exclusive). ~~[int]~~ |
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
### spacy.preset_spans_suggester.v1 {id="preset_spans_suggester"}
> #### Example Config
>
> ```ini
> [components.spancat.suggester]
> @misc = "spacy.preset_spans_suggester.v1"
> spans_key = "my_spans"
> ```
Suggest all spans that are already stored in doc.spans[spans_key]. This is
useful when an upstream component is used to set the spans on the Doc such as a
[`SpanRuler`](/api/spanruler) or [`SpanFinder`](/api/spanfinder).
| Name | Description |
| ----------- | ----------------------------------------------------------------------------- |
| `spans_key` | Key of [`Doc.spans`](/api/doc/#spans) that provides spans to suggest. ~~str~~ |
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |

372
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@ -0,0 +1,372 @@
---
title: SpanFinder
tag: class,experimental
source: spacy/pipeline/span_finder.py
version: 3.6
teaser:
'Pipeline component for identifying potentially overlapping spans of text'
api_base_class: /api/pipe
api_string_name: span_finder
api_trainable: true
---
The span finder identifies potentially overlapping, unlabeled spans. It
identifies tokens that start or end spans and annotates unlabeled spans between
starts and ends, with optional filters for min and max span length. It is
intended for use in combination with a component like
[`SpanCategorizer`](/api/spancategorizer) that may further filter or label the
spans. Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the
doc under `doc.spans[spans_key]`, where `spans_key` is a component config
setting.
## Assigned Attributes {id="assigned-attributes"}
Predictions will be saved to `Doc.spans[spans_key]` as a
[`SpanGroup`](/api/spangroup).
`spans_key` defaults to `"sc"`, but can be passed as a parameter. The
`span_finder` component will overwrite any existing spans under the spans key
`doc.spans[spans_key]`.
| Location | Value |
| ---------------------- | ---------------------------------- |
| `Doc.spans[spans_key]` | The unlabeled spans. ~~SpanGroup~~ |
## Config and implementation {id="config"}
The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config). See the
[model architectures](/api/architectures) documentation for details on the
architectures and their arguments and hyperparameters.
> #### Example
>
> ```python
> from spacy.pipeline.span_finder import DEFAULT_SPAN_FINDER_MODEL
> config = {
> "threshold": 0.5,
> "spans_key": "my_spans",
> "max_length": None,
> "min_length": None,
> "model": DEFAULT_SPAN_FINDER_MODEL,
> }
> nlp.add_pipe("span_finder", config=config)
> ```
| Setting | Description |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | A model instance that is given a list of documents and predicts a probability for each token. ~~Model[List[Doc], Floats2d]~~ |
| `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~~ |
| `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ |
| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/span_finder.py
```
## SpanFinder.\_\_init\_\_ {id="init",tag="method"}
> #### Example
>
> ```python
> # Construction via add_pipe with default model
> span_finder = nlp.add_pipe("span_finder")
>
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_span_finder"}}
> span_finder = nlp.add_pipe("span_finder", config=config)
>
> # Construction from class
> from spacy.pipeline import SpanFinder
> span_finder = SpanFinder(nlp.vocab, model)
> ```
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | A model instance that is given a list of documents and predicts a probability for each token. ~~Model[List[Doc], Floats2d]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `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~~ |
| `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ |
| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
## SpanFinder.\_\_call\_\_ {id="call",tag="method"}
Apply the pipe to one document. The document is modified in place, and returned.
This usually happens under the hood when the `nlp` object is called on a text
and all pipeline components are applied to the `Doc` in order. Both
[`__call__`](/api/spanfinder#call) and [`pipe`](/api/spanfinder#pipe) delegate
to the [`predict`](/api/spanfinder#predict) and
[`set_annotations`](/api/spanfinder#set_annotations) methods.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> span_finder = nlp.add_pipe("span_finder")
> # This usually happens under the hood
> processed = span_finder(doc)
> ```
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## SpanFinder.pipe {id="pipe",tag="method"}
Apply the pipe to a stream of documents. This usually happens under the hood
when the `nlp` object is called on a text and all pipeline components are
applied to the `Doc` in order. Both [`__call__`](/api/spanfinder#call) and
[`pipe`](/api/spanfinder#pipe) delegate to the
[`predict`](/api/spanfinder#predict) and
[`set_annotations`](/api/spanfinder#set_annotations) methods.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> for doc in span_finder.pipe(docs, batch_size=50):
> pass
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## SpanFinder.initialize {id="initialize",tag="method"}
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. **At least one example
should be supplied.** The data examples are used to **initialize the model** of
the component and can either be the full training data or a representative
sample. Initialization includes validating the network and
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) This
method is typically called by [`Language.initialize`](/api/language#initialize)
and lets you customize arguments it receives via the
[`[initialize.components]`](/api/data-formats#config-initialize) block in the
config.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> span_finder.initialize(lambda: examples, nlp=nlp)
> ```
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## SpanFinder.predict {id="predict",tag="method"}
Apply the component's model to a batch of [`Doc`](/api/doc) objects without
modifying them.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> scores = span_finder.predict([doc1, doc2])
> ```
| Name | Description |
| ----------- | ------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The model's prediction for each document. |
## SpanFinder.set_annotations {id="set_annotations",tag="method"}
Modify a batch of [`Doc`](/api/doc) objects using pre-computed scores.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> scores = span_finder.predict(docs)
> span_finder.set_annotations(docs, scores)
> ```
| Name | Description |
| -------- | ---------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `scores` | The scores to set, produced by `SpanFinder.predict`. |
## SpanFinder.update {id="update",tag="method"}
Learn from a batch of [`Example`](/api/example) objects containing the
predictions and gold-standard annotations, and update the component's model.
Delegates to [`predict`](/api/spanfinder#predict) and
[`get_loss`](/api/spanfinder#get_loss).
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> optimizer = nlp.initialize()
> losses = span_finder.update(examples, sgd=optimizer)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## SpanFinder.get_loss {id="get_loss",tag="method"}
Find the loss and gradient of loss for the batch of documents and their
predicted scores.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> scores = span_finder.predict([eg.predicted for eg in examples])
> loss, d_loss = span_finder.get_loss(examples, scores)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------ |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `spans_scores` | Scores representing the model's predictions. ~~Tuple[Ragged, Floats2d]~~ |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, Floats2d]~~ |
## SpanFinder.create_optimizer {id="create_optimizer",tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> optimizer = span_finder.create_optimizer()
> ```
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## SpanFinder.use_params {id="use_params",tag="method, contextmanager"}
Modify the pipe's model to use the given parameter values.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> with span_finder.use_params(optimizer.averages):
> span_finder.to_disk("/best_model")
> ```
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## SpanFinder.to_disk {id="to_disk",tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> span_finder.to_disk("/path/to/span_finder")
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## SpanFinder.from_disk {id="from_disk",tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> span_finder.from_disk("/path/to/span_finder")
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `SpanFinder` object. ~~SpanFinder~~ |
## SpanFinder.to_bytes {id="to_bytes",tag="method"}
> #### Example
>
> ```python
> span_finder = nlp.add_pipe("span_finder")
> span_finder_bytes = span_finder.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `SpanFinder` object. ~~bytes~~ |
## SpanFinder.from_bytes {id="from_bytes",tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> span_finder_bytes = span_finder.to_bytes()
> span_finder = nlp.add_pipe("span_finder")
> span_finder.from_bytes(span_finder_bytes)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `SpanFinder` object. ~~SpanFinder~~ |
## Serialization fields {id="serialization-fields"}
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the `exclude` argument.
> #### Example
>
> ```python
> data = span_finder.to_disk("/path", exclude=["vocab"])
> ```
| Name | Description |
| ------- | -------------------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `cfg` | The config file. You usually don't want to exclude this. |
| `model` | The binary model data. You usually don't want to exclude this. |

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website/meta/sidebars.json
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@ -106,6 +106,7 @@
{ "text": "SentenceRecognizer", "url": "/api/sentencerecognizer" },
{ "text": "Sentencizer", "url": "/api/sentencizer" },
{ "text": "SpanCategorizer", "url": "/api/spancategorizer" },
{ "text": "SpanFinder", "url": "/api/spanfinder" },
{ "text": "SpanResolver", "url": "/api/span-resolver" },
{ "text": "SpanRuler", "url": "/api/spanruler" },
{ "text": "Tagger", "url": "/api/tagger" },