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Merge branch 'develop' into feature/component-scores

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This commit is contained in:
Ines Montani 2020-07-27 18:14:39 +02:00
commit 894e20c466
64 changed files with 3571 additions and 1471 deletions
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6
spacy/cli/package.py
View File

@ -19,6 +19,7 @@ def package_cli(
meta_path: Optional[Path] = Opt(None, "--meta-path", "--meta", "-m", help="Path to meta.json", exists=True, dir_okay=False),
create_meta: bool = Opt(False, "--create-meta", "-c", "-C", help="Create meta.json, even if one exists"),
version: Optional[str] = Opt(None, "--version", "-v", help="Package version to override meta"),
no_sdist: bool = Opt(False, "--no-sdist", "-NS", help="Don't build .tar.gz sdist, can be set if you want to run this step manually"),
force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing model in output directory"),
# fmt: on
):
@ -37,6 +38,7 @@ def package_cli(
meta_path=meta_path,
version=version,
create_meta=create_meta,
create_sdist=not no_sdist,
force=force,
silent=False,
)
@ -48,6 +50,7 @@ def package(
meta_path: Optional[Path] = None,
version: Optional[str] = None,
create_meta: bool = False,
create_sdist: bool = True,
force: bool = False,
silent: bool = True,
) -> None:
@ -61,7 +64,6 @@ def package(
msg.fail("Output directory not found", output_path, exits=1)
if meta_path and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta_path = meta_path or input_dir / "meta.json"
if not meta_path.exists() or not meta_path.is_file():
msg.fail("Can't load model meta.json", meta_path, exits=1)
@ -80,7 +82,6 @@ def package(
model_name_v = model_name + "-" + meta["version"]
main_path = output_dir / model_name_v
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(str(package_path))
@ -98,6 +99,7 @@ def package(
create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
create_file(package_path / "__init__.py", TEMPLATE_INIT)
msg.good(f"Successfully created package '{model_name_v}'", main_path)
if create_sdist:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "sdist"])
zip_file = main_path / "dist" / f"{model_name_v}.tar.gz"

9
spacy/default_config.cfg
View File

@ -39,18 +39,13 @@ score_weights = {}
# These settings are invalid for the transformer models.
init_tok2vec = null
discard_oversize = false
batch_by = "sequences"
raw_text = null
tag_map = null
morph_rules = null
base_model = null
vectors = null
[training.batch_size]
@schedules = "compounding.v1"
start = 1000
stop = 1000
compound = 1.001
batch_by = "words"
batch_size = 1000
[training.optimizer]
@optimizers = "Adam.v1"

3
spacy/language.py
View File

@ -110,6 +110,7 @@ class Language:
def __init__(
self,
vocab: Union[Vocab, bool] = True,
*,
max_length: int = 10 ** 6,
meta: Dict[str, Any] = {},
create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
@ -549,6 +550,7 @@ class Language:
resolved, filled = registry.resolve(cfg, validate=validate, overrides=overrides)
filled = filled[factory_name]
filled["factory"] = factory_name
filled.pop("@factories", None)
self._pipe_configs[name] = filled
return resolved[factory_name]
@ -1284,6 +1286,7 @@ class Language:
def from_config(
cls,
config: Union[Dict[str, Any], Config] = {},
*,
disable: Iterable[str] = tuple(),
overrides: Dict[str, Any] = {},
auto_fill: bool = True,

2
spacy/ml/models/textcat.py
View File

@ -53,7 +53,7 @@ def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO
return model
@registry.architectures.register("spacy.TextCat.v1")
@registry.architectures.register("spacy.TextCatEnsemble.v1")
def build_text_classifier(
width,
embed_size,

9
spacy/pipeline/dep_parser.pyx
View File

@ -73,7 +73,6 @@ cdef class DependencyParser(Parser):
DOCS: https://spacy.io/api/dependencyparser
"""
# cdef classes can't have decorators, so we're defining this here
TransitionSystem = ArcEager
@property
@ -107,6 +106,14 @@ cdef class DependencyParser(Parser):
return tuple(sorted(labels))
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
and Scorer.score_deps.
DOCS: https://spacy.io/api/dependencyparser#score
"""
def dep_getter(token, attr):
dep = getattr(token, attr)
dep = token.vocab.strings.as_string(dep).lower()

98
spacy/pipeline/entity_linker.py
View File

@ -86,6 +86,19 @@ class EntityLinker(Pipe):
incl_prior: bool,
incl_context: bool,
) -> None:
"""Initialize an entity linker.
vocab (Vocab): The shared vocabulary.
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.
kb (KnowledgeBase): TODO:
labels_discard (Iterable[str]): TODO:
incl_prior (bool): TODO:
incl_context (bool): TODO:
DOCS: https://spacy.io/api/entitylinker#init
"""
self.vocab = vocab
self.model = model
self.name = name
@ -119,6 +132,19 @@ class EntityLinker(Pipe):
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/entitylinker#begin_training
"""
self.require_kb()
nO = self.kb.entity_vector_length
self.set_output(nO)
@ -136,6 +162,20 @@ class EntityLinker(Pipe):
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.
set_annotations (bool): Whether or not to update the Example objects
with the predictions.
sgd (thinc.api.Optimizer): The optimizer.
losses (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/entitylinker#update
"""
self.require_kb()
if losses is None:
losses = {}
@ -215,18 +255,43 @@ class EntityLinker(Pipe):
return loss, gradients
def __call__(self, doc: Doc) -> Doc:
"""Apply the pipe to a Doc.
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/entitylinker#call
"""
kb_ids = self.predict([doc])
self.set_annotations([doc], kb_ids)
return doc
def pipe(self, stream: Iterable[Doc], batch_size: int = 128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""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 components are
applied to the Doc.
stream (Iterable[Doc]): A stream of documents.
batch_size (int): The number of documents to buffer.
YIELDS (Doc): PRocessed documents in order.
DOCS: https://spacy.io/api/entitylinker#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
kb_ids = self.predict(docs)
self.set_annotations(docs, kb_ids)
yield from docs
def predict(self, docs):
""" Return the KB IDs for each entity in each doc, including NIL if there is no prediction """
def predict(self, docs: Iterable[Doc]) -> List[str]:
"""Apply the pipeline's model to a batch of docs, without modifying them.
Returns the KB IDs for each entity in each doc, including NIL if there is
no prediction.
docs (Iterable[Doc]): The documents to predict.
RETURNS (List[int]): The models prediction for each document.
DOCS: https://spacy.io/api/entitylinker#predict
"""
self.require_kb()
entity_count = 0
final_kb_ids = []
@ -315,7 +380,14 @@ class EntityLinker(Pipe):
raise RuntimeError(err)
return final_kb_ids
def set_annotations(self, docs: Iterable[Doc], kb_ids: List[int]) -> None:
def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
DOCS: https://spacy.io/api/entitylinker#predict
"""
count_ents = len([ent for doc in docs for ent in doc.ents])
if count_ents != len(kb_ids):
raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
@ -328,6 +400,13 @@ class EntityLinker(Pipe):
token.ent_kb_id_ = kb_id
def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = tuple()) -> None:
"""Serialize the pipe to disk.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/entitylinker#to_disk
"""
serialize = {}
self.cfg["entity_width"] = self.kb.entity_vector_length
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
@ -339,6 +418,15 @@ class EntityLinker(Pipe):
def from_disk(
self, path: Union[str, Path], exclude: Iterable[str] = tuple()
) -> "EntityLinker":
"""Load the pipe from disk. Modifies the object in place and returns it.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (EntityLinker): The modified EntityLinker object.
DOCS: https://spacy.io/api/entitylinker#from_disk
"""
def load_model(p):
try:
self.model.from_bytes(p.open("rb").read())
@ -359,7 +447,7 @@ class EntityLinker(Pipe):
util.from_disk(path, deserialize, exclude)
return self
def rehearse(self, examples, sgd=None, losses=None, **config):
def rehearse(self, examples, *, sgd=None, losses=None, **config):
raise NotImplementedError
def add_label(self, label):

4
spacy/pipeline/entityruler.py
View File

@ -74,6 +74,10 @@ class EntityRuler:
nlp (Language): The shared nlp object to pass the vocab to the matchers
and process phrase patterns.
name (str): Instance name of the current pipeline component. Typically
passed in automatically from the factory when the component is
added. Used to disable the current entity ruler while creating
phrase patterns with the nlp object.
phrase_matcher_attr (int / str): Token attribute to match on, passed
to the internal PhraseMatcher as `attr`
validate (bool): Whether patterns should be validated, passed to

89
spacy/pipeline/morphologizer.pyx
View File

@ -63,6 +63,17 @@ class Morphologizer(Tagger):
labels_morph: Optional[dict] = None,
labels_pos: Optional[dict] = None,
):
"""Initialize a morphologizer.
vocab (Vocab): The shared vocabulary.
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.
labels_morph (dict): TODO:
labels_pos (dict): TODO:
DOCS: https://spacy.io/api/morphologizer#init
"""
self.vocab = vocab
self.model = model
self.name = name
@ -79,9 +90,17 @@ class Morphologizer(Tagger):
@property
def labels(self):
"""RETURNS (Tuple[str]): The labels currently added to the component."""
return tuple(self.cfg["labels_morph"].keys())
def add_label(self, label):
"""Add a new label to the pipe.
label (str): The label to add.
RETURNS (int): 1
DOCS: https://spacy.io/api/morphologizer#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
@ -101,7 +120,20 @@ class Morphologizer(Tagger):
self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
return 1
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None):
def begin_training(self, get_examples=lambda: [], *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/morphologizer#begin_training
"""
for example in get_examples():
for i, token in enumerate(example.reference):
pos = token.pos_
@ -123,6 +155,13 @@ class Morphologizer(Tagger):
return sgd
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
DOCS: https://spacy.io/api/morphologizer#predict
"""
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
@ -139,6 +178,15 @@ class Morphologizer(Tagger):
doc.is_morphed = True
def get_loss(self, examples, scores):
"""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.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/morphologizer#get_loss
"""
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
truths = []
for eg in examples:
@ -166,6 +214,15 @@ class Morphologizer(Tagger):
return float(loss), d_scores
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by
Scorer.score_token_attr for the attributes "pos" and "morph" and
Scorer.score_token_attr_per_feat for the attribute "morph".
DOCS: https://spacy.io/api/morphologizer#score
"""
results = {}
results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
results.update(Scorer.score_token_attr(examples, "morph", **kwargs))
@ -174,6 +231,13 @@ class Morphologizer(Tagger):
return results
def to_bytes(self, exclude=tuple()):
"""Serialize the pipe to a bytestring.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/morphologizer#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
serialize["vocab"] = self.vocab.to_bytes
@ -181,6 +245,14 @@ class Morphologizer(Tagger):
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple()):
"""Load the pipe from a bytestring.
bytes_data (bytes): The serialized pipe.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Morphologizer): The loaded Morphologizer.
DOCS: https://spacy.io/api/morphologizer#from_bytes
"""
def load_model(b):
try:
self.model.from_bytes(b)
@ -196,6 +268,13 @@ class Morphologizer(Tagger):
return self
def to_disk(self, path, exclude=tuple()):
"""Serialize the pipe to disk.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/morphologizer#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
"model": lambda p: p.open("wb").write(self.model.to_bytes()),
@ -204,6 +283,14 @@ class Morphologizer(Tagger):
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple()):
"""Load the pipe from disk. Modifies the object in place and returns it.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Morphologizer): The modified Morphologizer object.
DOCS: https://spacy.io/api/morphologizer#from_disk
"""
def load_model(p):
with p.open("rb") as file_:
try:

7
spacy/pipeline/ner.pyx
View File

@ -94,4 +94,11 @@ cdef class EntityRecognizer(Parser):
return tuple(sorted(labels))
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/entityrecognizer#score
"""
return Scorer.score_spans(examples, "ents", **kwargs)

4
spacy/pipeline/pipe.pyx
View File

@ -23,7 +23,7 @@ class Pipe:
name = None
def __init__(self, vocab, model, **cfg):
def __init__(self, vocab, model, name, **cfg):
"""Create a new pipe instance."""
raise NotImplementedError
@ -79,7 +79,7 @@ class Pipe:
def create_optimizer(self):
return create_default_optimizer()
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None):
def begin_training(self, get_examples=lambda: [], *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data exampes if available.
If no model has been initialized yet, the model is added."""
self.model.initialize()

34
spacy/pipeline/sentencizer.pyx
View File

@ -43,7 +43,7 @@ class Sentencizer(Pipe):
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
'｡', '。']
def __init__(self, name="sentencizer", *, punct_chars):
def __init__(self, name="sentencizer", *, punct_chars=None):
"""Initialize the sentencizer.
punct_chars (list): Punctuation characters to split on. Will be
@ -64,8 +64,8 @@ class Sentencizer(Pipe):
def __call__(self, doc):
"""Apply the sentencizer to a Doc and set Token.is_sent_start.
example (Doc or Example): The document to process.
RETURNS (Doc or Example): The processed Doc or Example.
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/sentencizer#call
"""
@ -85,14 +85,26 @@ class Sentencizer(Pipe):
return doc
def pipe(self, stream, batch_size=128):
"""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 components are
applied to the Doc.
stream (Iterable[Doc]): A stream of documents.
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/sentencizer#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
predictions = self.predict(docs)
self.set_annotations(docs, predictions)
yield from docs
def predict(self, docs):
"""Apply the pipeline's model to a batch of docs, without
modifying them.
"""Apply the pipe to a batch of docs, without modifying them.
docs (Iterable[Doc]): The documents to predict.
RETURNS: The predictions for each document.
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
@ -119,6 +131,11 @@ class Sentencizer(Pipe):
return guesses
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The tag IDs produced by Sentencizer.predict.
"""
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
@ -134,6 +151,13 @@ class Sentencizer(Pipe):
doc.c[j].sent_start = -1
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/sentencizer#score
"""
results = Scorer.score_spans(examples, "sents", **kwargs)
del results["sents_per_type"]
return results

77
spacy/pipeline/senter.pyx
View File

@ -47,6 +47,15 @@ class SentenceRecognizer(Tagger):
DOCS: https://spacy.io/api/sentencerecognizer
"""
def __init__(self, vocab, model, name="senter"):
"""Initialize a sentence recognizer.
vocab (Vocab): The shared vocabulary.
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.
DOCS: https://spacy.io/api/sentencerecognizer#init
"""
self.vocab = vocab
self.model = model
self.name = name
@ -55,12 +64,20 @@ class SentenceRecognizer(Tagger):
@property
def labels(self):
"""RETURNS (Tuple[str]): The labels."""
# labels are numbered by index internally, so this matches GoldParse
# and Example where the sentence-initial tag is 1 and other positions
# are 0
return tuple(["I", "S"])
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by SentenceRecognizer.predict.
DOCS: https://spacy.io/api/sentencerecognizer#predict
"""
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
@ -77,6 +94,15 @@ class SentenceRecognizer(Tagger):
doc.c[j].sent_start = -1
def get_loss(self, examples, scores):
"""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.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/sentencerecognizer#get_loss
"""
labels = self.labels
loss_func = SequenceCategoricalCrossentropy(names=labels, normalize=False)
truths = []
@ -96,7 +122,20 @@ class SentenceRecognizer(Tagger):
raise ValueError("nan value when computing loss")
return float(loss), d_scores
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None):
def begin_training(self, get_examples=lambda: [], *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/sentencerecognizer#begin_training
"""
self.set_output(len(self.labels))
self.model.initialize()
util.link_vectors_to_models(self.vocab)
@ -108,11 +147,24 @@ class SentenceRecognizer(Tagger):
raise NotImplementedError
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
DOCS: https://spacy.io/api/sentencerecognizer#score
"""
results = Scorer.score_spans(examples, "sents", **kwargs)
del results["sents_per_type"]
return results
def to_bytes(self, exclude=tuple()):
"""Serialize the pipe to a bytestring.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/sentencerecognizer#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
serialize["vocab"] = self.vocab.to_bytes
@ -120,6 +172,14 @@ class SentenceRecognizer(Tagger):
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple()):
"""Load the pipe from a bytestring.
bytes_data (bytes): The serialized pipe.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The loaded SentenceRecognizer.
DOCS: https://spacy.io/api/sentencerecognizer#from_bytes
"""
def load_model(b):
try:
self.model.from_bytes(b)
@ -135,6 +195,13 @@ class SentenceRecognizer(Tagger):
return self
def to_disk(self, path, exclude=tuple()):
"""Serialize the pipe to disk.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/sentencerecognizer#to_disk
"""
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
"model": lambda p: p.open("wb").write(self.model.to_bytes()),
@ -143,6 +210,14 @@ class SentenceRecognizer(Tagger):
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple()):
"""Load the pipe from disk. Modifies the object in place and returns it.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The modified SentenceRecognizer object.
DOCS: https://spacy.io/api/sentencerecognizer#from_disk
"""
def load_model(p):
with p.open("rb") as file_:
try:

154
spacy/pipeline/tagger.pyx
View File

@ -53,6 +53,16 @@ class Tagger(Pipe):
DOCS: https://spacy.io/api/tagger
"""
def __init__(self, vocab, model, name="tagger", *, set_morphology=False):
"""Initialize a part-of-speech tagger.
vocab (Vocab): The shared vocabulary.
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.
set_morphology (bool): Whether to set morphological features.
DOCS: https://spacy.io/api/tagger#init
"""
self.vocab = vocab
self.model = model
self.name = name
@ -62,20 +72,52 @@ class Tagger(Pipe):
@property
def labels(self):
"""The labels currently added to the component. Note that even for a
blank component, this will always include the built-in coarse-grained
part-of-speech tags by default.
RETURNS (Tuple[str]): The labels.
DOCS: https://spacy.io/api/tagger#labels
"""
return tuple(self.vocab.morphology.tag_names)
def __call__(self, doc):
"""Apply the pipe to a Doc.
doc (Doc): The document to process.
RETURNS (Doc): The processed Doc.
DOCS: https://spacy.io/api/tagger#call
"""
tags = self.predict([doc])
self.set_annotations([doc], tags)
return doc
def pipe(self, stream, batch_size=128):
def pipe(self, stream, *, batch_size=128):
"""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 components are
applied to the Doc.
stream (Iterable[Doc]): A stream of documents.
batch_size (int): The number of documents to buffer.
YIELDS (Doc): Processed documents in order.
DOCS: https://spacy.io/api/tagger#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
tag_ids = self.predict(docs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, docs):
"""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/tagger#predict
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
n_labels = len(self.labels)
@ -98,6 +140,13 @@ class Tagger(Pipe):
return guesses
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
batch_tag_ids: The IDs to set, produced by Tagger.predict.
DOCS: https://spacy.io/api/tagger#predict
"""
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
@ -123,10 +172,23 @@ class Tagger(Pipe):
doc.is_tagged = True
def update(self, examples, *, drop=0., sgd=None, losses=None, set_annotations=False):
"""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.
set_annotations (bool): Whether or not to update the Example objects
with the predictions.
sgd (thinc.api.Optimizer): The optimizer.
losses (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/tagger#update
"""
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
try:
if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
# Handle cases where there are no tokens in any docs.
@ -151,9 +213,20 @@ class Tagger(Pipe):
self.set_annotations(docs, self._scores2guesses(tag_scores))
return losses
def rehearse(self, examples, drop=0., sgd=None, losses=None):
"""Perform a 'rehearsal' update, where we try to match the output of
an initial model.
def rehearse(self, examples, *, drop=0., sgd=None, losses=None):
"""Perform a "rehearsal" update from a batch of data. Rehearsal updates
teach the current model to make predictions similar to an initial model,
to try to address the "catastrophic forgetting" problem. This feature is
experimental.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (thinc.api.Optimizer): The optimizer.
losses (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/tagger#rehearse
"""
try:
docs = [eg.predicted for eg in examples]
@ -176,6 +249,15 @@ class Tagger(Pipe):
losses[self.name] += (gradient**2).sum()
def get_loss(self, examples, scores):
"""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.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/tagger#get_loss
"""
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
truths = [eg.get_aligned("tag", as_string=True) for eg in examples]
d_scores, loss = loss_func(scores, truths)
@ -183,7 +265,20 @@ class Tagger(Pipe):
raise ValueError("nan value when computing loss")
return float(loss), d_scores
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None):
def begin_training(self, get_examples=lambda: [], *, pipeline=None, sgd=None):
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/tagger#begin_training
"""
lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"]
if not any(table in self.vocab.lookups for table in lemma_tables):
warnings.warn(Warnings.W022)
@ -229,6 +324,15 @@ class Tagger(Pipe):
return sgd
def add_label(self, label, values=None):
"""Add a new label to the pipe.
label (str): The label to add.
values (Dict[int, str]): Optional values to map to the label, e.g. a
tag map dictionary.
RETURNS (int): 1
DOCS: https://spacy.io/api/tagger#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
@ -256,6 +360,14 @@ class Tagger(Pipe):
yield
def score(self, examples, **kwargs):
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
RETURNS (Dict[str, Any]): The scores, produced by
Scorer.score_token_attr for the attributes "tag", "pos" and "lemma".
DOCS: https://spacy.io/api/tagger#score
"""
scores = {}
scores.update(Scorer.score_token_attr(examples, "tag", **kwargs))
scores.update(Scorer.score_token_attr(examples, "pos", **kwargs))
@ -263,6 +375,13 @@ class Tagger(Pipe):
return scores
def to_bytes(self, exclude=tuple()):
"""Serialize the pipe to a bytestring.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/tagger#to_bytes
"""
serialize = {}
serialize["model"] = self.model.to_bytes
serialize["vocab"] = self.vocab.to_bytes
@ -274,6 +393,14 @@ class Tagger(Pipe):
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple()):
"""Load the pipe from a bytestring.
bytes_data (bytes): The serialized pipe.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The loaded Tagger.
DOCS: https://spacy.io/api/tagger#from_bytes
"""
def load_model(b):
try:
self.model.from_bytes(b)
@ -302,6 +429,13 @@ class Tagger(Pipe):
return self
def to_disk(self, path, exclude=tuple()):
"""Serialize the pipe to disk.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/tagger#to_disk
"""
tag_map = dict(sorted(self.vocab.morphology.tag_map.items()))
morph_rules = dict(self.vocab.morphology.exc)
serialize = {
@ -314,6 +448,14 @@ class Tagger(Pipe):
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple()):
"""Load the pipe from disk. Modifies the object in place and returns it.
path (str / Path): Path to a directory.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (Tagger): The modified Tagger object.
DOCS: https://spacy.io/api/tagger#from_disk
"""
def load_model(p):
with p.open("rb") as file_:
try:

115
spacy/pipeline/textcat.py
View File

@ -14,7 +14,7 @@ from ..vocab import Vocab
default_model_config = """
[model]
@architectures = "spacy.TextCat.v1"
@architectures = "spacy.TextCatEnsemble.v1"
exclusive_classes = false
pretrained_vectors = null
width = 64
@ -79,6 +79,16 @@ class TextCategorizer(Pipe):
*,
labels: Iterable[str],
) -> None:
"""Initialize a text categorizer.
vocab (Vocab): The shared vocabulary.
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.
labels (Iterable[str]): The labels to use.
DOCS: https://spacy.io/api/textcategorizer#init
"""
self.vocab = vocab
self.model = model
self.name = name
@ -88,6 +98,10 @@ class TextCategorizer(Pipe):
@property
def labels(self) -> Tuple[str]:
"""RETURNS (Tuple[str]): The labels currently added to the component.
DOCS: https://spacy.io/api/textcategorizer#labels
"""
return tuple(self.cfg.setdefault("labels", []))
def require_labels(self) -> None:
@ -99,13 +113,30 @@ class TextCategorizer(Pipe):
def labels(self, value: Iterable[str]) -> None:
self.cfg["labels"] = tuple(value)
def pipe(self, stream: Iterator[str], batch_size: int = 128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""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 components are
applied to the Doc.
stream (Iterable[Doc]): A stream of documents.
batch_size (int): The number of documents to buffer.
YIELDS (Doc): PRocessed documents in order.
DOCS: https://spacy.io/api/textcategorizer#pipe
"""
for docs in util.minibatch(stream, size=batch_size):
scores = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
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/textcategorizer#predict
"""
tensors = [doc.tensor for doc in docs]
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
@ -117,6 +148,13 @@ class TextCategorizer(Pipe):
return scores
def set_annotations(self, docs: Iterable[Doc], scores) -> None:
"""Modify a batch of documents, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by TextCategorizer.predict.
DOCS: https://spacy.io/api/textcategorizer#predict
"""
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
@ -130,6 +168,20 @@ class TextCategorizer(Pipe):
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.
set_annotations (bool): Whether or not to update the Example objects
with the predictions.
sgd (thinc.api.Optimizer): The optimizer.
losses (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/textcategorizer#update
"""
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
@ -157,10 +209,25 @@ class TextCategorizer(Pipe):
def rehearse(
self,
examples: Iterable[Example],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> None:
) -> Dict[str, float]:
"""Perform a "rehearsal" update from a batch of data. Rehearsal updates
teach the current model to make predictions similar to an initial model,
to try to address the "catastrophic forgetting" problem. This feature is
experimental.
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (thinc.api.Optimizer): The optimizer.
losses (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/textcategorizer#rehearse
"""
if self._rehearsal_model is None:
return
try:
@ -184,6 +251,7 @@ class TextCategorizer(Pipe):
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += (gradient ** 2).sum()
return losses
def _examples_to_truth(
self, examples: List[Example]
@ -200,6 +268,15 @@ class TextCategorizer(Pipe):
return truths, not_missing
def get_loss(self, examples: Iterable[Example], scores) -> Tuple[float, float]:
"""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.
RETUTNRS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/textcategorizer#get_loss
"""
truths, not_missing = self._examples_to_truth(examples)
not_missing = self.model.ops.asarray(not_missing)
d_scores = (scores - truths) / scores.shape[0]
@ -208,6 +285,13 @@ class TextCategorizer(Pipe):
return float(mean_square_error), d_scores
def add_label(self, label: str) -> int:
"""Add a new label to the pipe.
label (str): The label to add.
RETURNS (int): 1.
DOCS: https://spacy.io/api/textcategorizer#add_label
"""
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
@ -228,10 +312,24 @@ class TextCategorizer(Pipe):
def begin_training(
self,
get_examples: Callable = lambda: [],
get_examples: Callable[[], Iterable[Example]] = lambda: [],
*,
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://spacy.io/api/textcategorizer#begin_training
"""
# TODO: begin_training is not guaranteed to see all data / labels ?
examples = list(get_examples())
for example in examples:
@ -257,9 +355,18 @@ class TextCategorizer(Pipe):
def score(
self,
examples: Iterable[Example],
*,
positive_label: Optional[str] = None,
**kwargs,
) -> Dict[str, Any]:
"""Score a batch of examples.
examples (Iterable[Example]): The examples to score.
positive_label (str): Optional positive label.
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_cats.
DOCS: https://spacy.io/api/textcategorizer#score
"""
return Scorer.score_cats(
examples,
"cats",

2
spacy/syntax/nn_parser.pyx
View File

@ -160,7 +160,7 @@ cdef class Parser:
self.set_annotations([doc], states)
return doc
def pipe(self, docs, int batch_size=256):
def pipe(self, docs, *, int batch_size=256):
"""Process a stream of documents.
stream: The sequence of documents to process.

2
spacy/tests/pipeline/test_pipe_factories.py
View File

@ -155,7 +155,7 @@ def test_pipe_class_component_model():
name = "test_class_component_model"
default_config = {
"model": {
"@architectures": "spacy.TextCat.v1",
"@architectures": "spacy.TextCatEnsemble.v1",
"exclusive_classes": False,
"pretrained_vectors": None,
"width": 64,

6
spacy/tests/pipeline/test_textcat.py
View File

@ -133,9 +133,9 @@ def test_overfitting_IO():
{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False},
{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True},
{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True},
{"@architectures": "spacy.TextCat.v1", "exclusive_classes": False, "ngram_size": 1, "pretrained_vectors": False, "width": 64, "conv_depth": 2, "embed_size": 2000, "window_size": 2, "dropout": None},
{"@architectures": "spacy.TextCat.v1", "exclusive_classes": True, "ngram_size": 5, "pretrained_vectors": False, "width": 128, "conv_depth": 2, "embed_size": 2000, "window_size": 1, "dropout": None},
{"@architectures": "spacy.TextCat.v1", "exclusive_classes": True, "ngram_size": 2, "pretrained_vectors": False, "width": 32, "conv_depth": 3, "embed_size": 500, "window_size": 3, "dropout": None},
{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "ngram_size": 1, "pretrained_vectors": False, "width": 64, "conv_depth": 2, "embed_size": 2000, "window_size": 2, "dropout": None},
{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": True, "ngram_size": 5, "pretrained_vectors": False, "width": 128, "conv_depth": 2, "embed_size": 2000, "window_size": 1, "dropout": None},
{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": True, "ngram_size": 2, "pretrained_vectors": False, "width": 32, "conv_depth": 3, "embed_size": 500, "window_size": 3, "dropout": None},
{"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True},
{"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False},
],

2
website/README.md
View File

@ -384,7 +384,7 @@ original file is shown at the top of the widget.
> ```
```python
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
https://github.com/explosion/spaCy/tree/master/spacy/language.py
```
### Infobox

8
website/docs/api/cli.md
View File

@ -535,13 +535,14 @@ then create a `.tar.gz` archive file that you can distribute and install with
<Infobox title="New in v3.0" variant="warning">
The `spacy package` command now also builds the `.tar.gz` archive automatically,
so you don't have to run `python setup.py sdist` separately anymore.
so you don't have to run `python setup.py sdist` separately anymore. To disable
this, you can set the `--no-sdist` flag.
</Infobox>
```bash
$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta]
[--version] [--force]
[--no-sdist] [--version] [--force]
```
> #### Example
@ -557,7 +558,8 @@ $ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta]
| `input_dir` | positional | Path to directory containing model data. |
| `output_dir` | positional | Directory to create package folder in. |
| `--meta-path`, `-m` <Tag variant="new">2</Tag> | option | Path to `meta.json` file (optional). |
| `--create-meta`, `-c` <Tag variant="new">2</Tag> | flag | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. |
| `--create-meta`, `-C` <Tag variant="new">2</Tag> | flag | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. |
| `--no-sdist`, `-NS`, | flag | Don't build the `.tar.gz` sdist automatically. Can be set if you want to run this step manually. |
| `--version`, `-v` <Tag variant="new">3</Tag> | option | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. |
| `--force`, `-f` | flag | Force overwriting of existing folder in output directory. |
| `--help`, `-h` | flag | Show help message and available arguments. |

8
website/docs/api/dependencymatcher.md Normal file
View File

@ -0,0 +1,8 @@
---
title: DependencyMatcher
teaser: Match sequences of tokens, based on the dependency parse
tag: class
source: spacy/matcher/dependencymatcher.pyx
---
TODO: write

152
website/docs/api/dependencyparser.md
View File

@ -1,23 +1,41 @@
---
title: DependencyParser
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/dep_parser.pyx
teaser: 'Pipeline component for syntactic dependency parsing'
api_base_class: /api/pipe
api_string_name: parser
api_trainable: true
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"parser"`.
## Config and implementation {#config}
## Default config {#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.
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
> #### Example
>
> ```python
> from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
> config = {
> "moves": None,
> # TODO: rest
> "model": DEFAULT_PARSER_MODEL,
> }
> nlp.add_pipe("parser", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------------------------------------- |
| `moves` | list | <!-- TODO: --> | `None` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/parser_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/dep_parser.pyx
```
## DependencyParser.\_\_init\_\_ {#init tag="method"}
@ -25,29 +43,33 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/parser_d
> #### Example
>
> ```python
> # Construction via create_pipe with default model
> parser = nlp.create_pipe("parser")
> # Construction via add_pipe with default model
> parser = nlp.add_pipe("parser")
>
> # Construction via create_pipe with custom model
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_parser"}}
> parser = nlp.create_pipe("parser", config)
> parser = nlp.add_pipe("parser", config=config)
>
> # Construction from class with custom model from file
> # Construction from class
> from spacy.pipeline import DependencyParser
> model = util.load_config("model.cfg", create_objects=True)["model"]
> parser = DependencyParser(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------- | ------------------ | ------------------------------------------------------------------------------- |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `DependencyParser` | The newly constructed object. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `moves` | list | <!-- TODO: --> |
| _keyword-only_ | | |
| `update_with_oracle_cut_size` | int | <!-- TODO: --> |
| `multitasks` | `Iterable` | <!-- TODO: --> |
| `learn_tokens` | bool | <!-- TODO: --> |
| `min_action_freq` | int | <!-- TODO: --> |
## DependencyParser.\_\_call\_\_ {#call tag="method"}
@ -62,8 +84,8 @@ and all pipeline components are applied to the `Doc` in order. Both
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> doc = nlp("This is a sentence.")
> parser = nlp.add_pipe("parser")
> # This usually happens under the hood
> processed = parser(doc)
> ```
@ -85,17 +107,38 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> for doc in parser.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## DependencyParser.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> parser = nlp.add_pipe("parser")
> optimizer = parser.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## DependencyParser.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
@ -103,7 +146,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> scores = parser.predict([doc1, doc2])
> ```
@ -119,7 +162,7 @@ Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> scores = parser.predict([doc1, doc2])
> parser.set_annotations([doc1, doc2], scores)
> ```
@ -138,7 +181,7 @@ model. Delegates to [`predict`](/api/dependencyparser#predict) and
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab, parser_model)
> parser = nlp.add_pipe("parser")
> optimizer = nlp.begin_training()
> losses = parser.update(examples, sgd=optimizer)
> ```
@ -150,7 +193,7 @@ model. Delegates to [`predict`](/api/dependencyparser#predict) and
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/dependencyparser#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| `losses` | `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. |
## DependencyParser.get_loss {#get_loss tag="method"}
@ -161,36 +204,31 @@ predicted scores.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> scores = parser.predict([eg.predicted for eg in examples])
> loss, d_loss = parser.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------- |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | `syntax.StateClass` | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## DependencyParser.begin_training {#begin_training tag="method"}
## DependencyParser.score {#score tag="method" new="3"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
Score a batch of examples.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> nlp.pipeline.append(parser)
> optimizer = parser.begin_training(pipeline=nlp.pipeline)
> scores = parser.score(examples)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. |
| ----------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans) and [`Scorer.score_deps`](/api/scorer#score_deps). |
## DependencyParser.create_optimizer {#create_optimizer tag="method"}
@ -200,13 +238,13 @@ component.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> optimizer = parser.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## DependencyParser.use_params {#use_params tag="method, contextmanager"}
@ -231,7 +269,7 @@ Add a new label to the pipe.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> parser.add_label("MY_LABEL")
> ```
@ -246,14 +284,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> parser.to_disk("/path/to/parser")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## DependencyParser.from_disk {#from_disk tag="method"}
@ -262,14 +300,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> parser.from_disk("/path/to/parser")
> ```
| Name | Type | Description |
| ----------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `DependencyParser` | The modified `DependencyParser` object. |
## DependencyParser.to_bytes {#to_bytes tag="method"}
@ -277,15 +315,15 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> parser_bytes = parser.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------- |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `DependencyParser` object. |
## DependencyParser.from_bytes {#from_bytes tag="method"}
@ -296,14 +334,14 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> parser_bytes = parser.to_bytes()
> parser = DependencyParser(nlp.vocab)
> parser = nlp.add_pipe("parser")
> parser.from_bytes(parser_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------------ | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `DependencyParser` | The `DependencyParser` object. |
## DependencyParser.labels {#labels tag="property"}

264
website/docs/api/entitylinker.md
View File

@ -1,27 +1,47 @@
---
title: EntityLinker
teaser:
Functionality to disambiguate a named entity in text to a unique knowledge
base identifier.
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/entity_linker.py
new: 2.2
teaser: 'Pipeline component for named entity linking and disambiguation'
api_base_class: /api/pipe
api_string_name: entity_linker
api_trainable: true
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"entity_linker"`.
## Config and implementation {#config}
## Default config {#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.
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
> #### Example
>
> ```python
> from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
> config = {
> "kb": None,
> "labels_discard": [],
> "incl_prior": True,
> "incl_context": True,
> "model": DEFAULT_NEL_MODEL,
> }
> nlp.add_pipe("entity_linker", config=config)
> ```
| Setting | Type | Description | Default |
| ---------------- | ------------------------------------------ | ----------------- | ----------------------------------------------- |
| `kb` | `KnowledgeBase` | <!-- TODO: --> | `None` |
| `labels_discard` | `Iterable[str]` | <!-- TODO: --> | `[]` |
| `incl_prior` | bool | <!-- TODO: --> |  `True` |
| `incl_context` | bool | <!-- TODO: --> | `True` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [EntityLinker](/api/architectures#EntityLinker) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/entity_linker_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
```
## EntityLinker.\_\_init\_\_ {#init tag="method"}
@ -29,30 +49,32 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/entity_l
> #### Example
>
> ```python
> # Construction via create_pipe with default model
> entity_linker = nlp.create_pipe("entity_linker")
> # Construction via add_pipe with default model
> entity_linker = nlp.add_pipe("entity_linker")
>
> # Construction via create_pipe with custom model
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_el"}}
> entity_linker = nlp.create_pipe("entity_linker", config)
> entity_linker = nlp.add_pipe("entity_linker", config=config)
>
> # Construction from class with custom model from file
> # Construction from class
> from spacy.pipeline import EntityLinker
> model = util.load_config("model.cfg", create_objects=True)["model"]
> entity_linker = EntityLinker(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ------- | ------- | ------------------------------------------------------------------------------- |
| ---------------- | --------------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `EntityLinker` | The newly constructed object. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `kb` | `KnowlegeBase` | <!-- TODO: --> |
| `labels_discard` | `Iterable[str]` | <!-- TODO: --> |
| `incl_prior` | bool | <!-- TODO: --> |
| `incl_context` | bool | <!-- TODO: --> |
## EntityLinker.\_\_call\_\_ {#call tag="method"}
@ -66,8 +88,8 @@ delegate to the [`predict`](/api/entitylinker#predict) and
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> doc = nlp("This is a sentence.")
> entity_linker = nlp.add_pipe("entity_linker")
> # This usually happens under the hood
> processed = entity_linker(doc)
> ```
@ -89,92 +111,18 @@ applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker")
> for doc in entity_linker.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## EntityLinker.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> kb_ids = entity_linker.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ------------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | `Iterable[str]` | The predicted KB identifiers for the entities in the `docs`. |
## EntityLinker.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed entity IDs for a list of named
entities.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> kb_ids = entity_linker.predict([doc1, doc2])
> entity_linker.set_annotations([doc1, doc2], kb_ids)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `kb_ids` | `Iterable[str]` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. |
## EntityLinker.update {#update tag="method"}
Learn from a batch of [`Example`](/api/example) objects, updating both the
pipe's entity linking model and context encoder. Delegates to
[`predict`](/api/entitylinker#predict) and
[`get_loss`](/api/entitylinker#get_loss).
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab, nel_model)
> optimizer = nlp.begin_training()
> losses = entity_linker.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/entitylinker#set_annotations). |
| `sgd` | `Optimizer` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
## EntityLinker.set_kb {#set_kb tag="method"}
Define the knowledge base (KB) used for disambiguating named entities to KB
identifiers.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.set_kb(kb)
> ```
| Name | Type | Description |
| ---- | --------------- | ------------------------------- |
| `kb` | `KnowledgeBase` | The [`KnowledgeBase`](/api/kb). |
## EntityLinker.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
@ -185,18 +133,94 @@ method, a knowledge base should have been defined with
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker", last=True)
> entity_linker.set_kb(kb)
> nlp.add_pipe(entity_linker, last=True)
> optimizer = entity_linker.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/entitylinker#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. | |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## EntityLinker.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them. Returns
the KB IDs for each entity in each doc, including `NIL` if there is no
prediction.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> kb_ids = entity_linker.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ------------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | `List[str]` | The predicted KB identifiers for the entities in the `docs`. |
## EntityLinker.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed entity IDs for a list of named
entities.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> kb_ids = entity_linker.predict([doc1, doc2])
> entity_linker.set_annotations([doc1, doc2], kb_ids)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `kb_ids` | `List[str]` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. |
## EntityLinker.update {#update tag="method"}
Learn from a batch of [`Example`](/api/example) objects, updating both the
pipe's entity linking model and context encoder. Delegates to
[`predict`](/api/entitylinker#predict).
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> optimizer = nlp.begin_training()
> losses = entity_linker.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/textcategorizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## EntityLinker.set_kb {#set_kb tag="method"}
Define the knowledge base (KB) used for disambiguating named entities to KB
identifiers.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.set_kb(kb)
> ```
| Name | Type | Description |
| ---- | --------------- | ------------------------------- |
| `kb` | `KnowledgeBase` | The [`KnowledgeBase`](/api/kb). |
## EntityLinker.create_optimizer {#create_optimizer tag="method"}
@ -205,13 +229,13 @@ Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker")
> optimizer = entity_linker.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## EntityLinker.use_params {#use_params tag="method, contextmanager"}
@ -220,7 +244,7 @@ Modify the pipe's EL model, to use the given parameter values.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker")
> with entity_linker.use_params(optimizer.averages):
> entity_linker.to_disk("/best_model")
> ```
@ -236,14 +260,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.to_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## EntityLinker.from_disk {#from_disk tag="method"}
@ -252,14 +276,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.from_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| ----------- | -------------- | -------------------------------------------------------------------------- |
| ----------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityLinker` | The modified `EntityLinker` object. |
## Serialization fields {#serialization-fields}

158
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View File

@ -1,23 +1,41 @@
---
title: EntityRecognizer
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/ner.pyx
teaser: 'Pipeline component for named entity recognition'
api_base_class: /api/pipe
api_string_name: ner
api_trainable: true
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"ner"`.
## Config and implementation {#config}
## Default config {#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.
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
> #### Example
>
> ```python
> from spacy.pipeline.ner import DEFAULT_NER_MODEL
> config = {
> "moves": None,
> # TODO: rest
> "model": DEFAULT_NER_MODEL,
> }
> nlp.add_pipe("ner", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------------------------------------- |
| `moves` | list | <!-- TODO: --> | `None` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/ner_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/ner.pyx
```
## EntityRecognizer.\_\_init\_\_ {#init tag="method"}
@ -25,29 +43,33 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/ner_defa
> #### Example
>
> ```python
> # Construction via create_pipe
> ner = nlp.create_pipe("ner")
> # Construction via add_pipe with default model
> ner = nlp.add_pipe("ner")
>
> # Construction via create_pipe with custom model
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_ner"}}
> parser = nlp.create_pipe("ner", config)
> parser = nlp.add_pipe("ner", config=config)
>
> # Construction from class with custom model from file
> # Construction from class
> from spacy.pipeline import EntityRecognizer
> model = util.load_config("model.cfg", create_objects=True)["model"]
> ner = EntityRecognizer(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------- | ------------------ | ------------------------------------------------------------------------------- |
| ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `EntityRecognizer` | The newly constructed object. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| `moves` | list | <!-- TODO: --> |
| _keyword-only_ | | |
| `update_with_oracle_cut_size` | int | <!-- TODO: --> |
| `multitasks` | `Iterable` | <!-- TODO: --> |
| `learn_tokens` | bool | <!-- TODO: --> |
| `min_action_freq` | int | <!-- TODO: --> |
## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
@ -62,8 +84,8 @@ and all pipeline components are applied to the `Doc` in order. Both
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> doc = nlp("This is a sentence.")
> ner = nlp.add_pipe("ner")
> # This usually happens under the hood
> processed = ner(doc)
> ```
@ -85,17 +107,38 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> for doc in ner.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| -------------- | --------------- | ------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## EntityRecognizer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> ner = nlp.add_pipe("ner")
> optimizer = ner.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/entityrecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## EntityRecognizer.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
@ -103,7 +146,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> scores = ner.predict([doc1, doc2])
> ```
@ -119,7 +162,7 @@ Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> scores = ner.predict([doc1, doc2])
> ner.set_annotations([doc1, doc2], scores)
> ```
@ -138,19 +181,19 @@ model. Delegates to [`predict`](/api/entityrecognizer#predict) and
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab, ner_model)
> ner = nlp.add_pipe("ner")
> optimizer = nlp.begin_training()
> losses = ner.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| ----------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/entityrecognizer#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## EntityRecognizer.get_loss {#get_loss tag="method"}
@ -161,36 +204,31 @@ predicted scores.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> scores = ner.predict([eg.predicted for eg in examples])
> loss, d_loss = ner.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------- |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | `List[StateClass]` | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## EntityRecognizer.begin_training {#begin_training tag="method"}
## EntityRecognizer.score {#score tag="method" new="3"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
Score a batch of examples.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> nlp.pipeline.append(ner)
> optimizer = ner.begin_training(pipeline=nlp.pipeline)
> scores = ner.score(examples)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/entityrecognizer#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
## EntityRecognizer.create_optimizer {#create_optimizer tag="method"}
@ -199,13 +237,13 @@ Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> optimizer = ner.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## EntityRecognizer.use_params {#use_params tag="method, contextmanager"}
@ -230,7 +268,7 @@ Add a new label to the pipe.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> ner.add_label("MY_LABEL")
> ```
@ -245,14 +283,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> ner.to_disk("/path/to/ner")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## EntityRecognizer.from_disk {#from_disk tag="method"}
@ -261,14 +299,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> ner.from_disk("/path/to/ner")
> ```
| Name | Type | Description |
| ----------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityRecognizer` | The modified `EntityRecognizer` object. |
## EntityRecognizer.to_bytes {#to_bytes tag="method"}
@ -276,15 +314,15 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> ner_bytes = ner.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------- |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `EntityRecognizer` object. |
## EntityRecognizer.from_bytes {#from_bytes tag="method"}
@ -295,14 +333,14 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> ner_bytes = ner.to_bytes()
> ner = EntityRecognizer(nlp.vocab)
> ner = nlp.add_pipe("ner")
> ner.from_bytes(ner_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------------ | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityRecognizer` | The `EntityRecognizer` object. |
## EntityRecognizer.labels {#labels tag="property"}

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@ -3,29 +3,60 @@ title: EntityRuler
tag: class
source: spacy/pipeline/entityruler.py
new: 2.1
teaser: 'Pipeline component for rule-based named entity recognition'
api_string_name: entity_ruler
api_trainable: false
---
The EntityRuler lets you add spans to the [`Doc.ents`](/api/doc#ents) using
The entity ruler lets you add spans to the [`Doc.ents`](/api/doc#ents) using
token-based rules or exact phrase matches. It can be combined with the
statistical [`EntityRecognizer`](/api/entityrecognizer) to boost accuracy, or
used on its own to implement a purely rule-based entity recognition system.
After initialization, the component is typically added to the processing
pipeline using [`nlp.add_pipe`](/api/language#add_pipe). For usage examples, see
the docs on
used on its own to implement a purely rule-based entity recognition system. For
usage examples, see the docs on
[rule-based entity recognition](/usage/rule-based-matching#entityruler).
## Config and implementation {#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).
> #### Example
>
> ```python
> config = {
> "phrase_matcher_attr": None,
> "validation": True,
> "overwrite_ents": False,
> "ent_id_sep": "||",
> }
> nlp.add_pipe("entity_ruler", config=config)
> ```
| Setting | Type | Description | Default |
| --------------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
| `phrase_matcher_attr` | str | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. | `None` |
| `validation` | bool | Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. | `False` |
| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. | `False` |
| `ent_id_sep` | str | Separator used internally for entity IDs. | `"||"` |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entityruler.py
```
## EntityRuler.\_\_init\_\_ {#init tag="method"}
Initialize the entity ruler. If patterns are supplied here, they need to be a
list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either
be a token pattern (list) or a phrase pattern (string). For example:
`{'label': 'ORG', 'pattern': 'Apple'}`.
`{"label": "ORG", "pattern": "Apple"}`.
> #### Example
>
> ```python
> # Construction via create_pipe
> ruler = nlp.create_pipe("entity_ruler")
> # Construction via add_pipe
> ruler = nlp.add_pipe("entity_ruler")
>
> # Construction from class
> from spacy.pipeline import EntityRuler
@ -33,14 +64,15 @@ be a token pattern (list) or a phrase pattern (string). For example:
> ```
| Name | Type | Description |
| --------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| --------------------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | `Language` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
| `patterns` | iterable | Optional patterns to load in. |
| `phrase_matcher_attr` | int / str | Optional attr to pass to the internal [`PhraseMatcher`](/api/phrasematcher). defaults to `None` |
| `name` <Tag variant="new">3</Tag> | str | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. |
| _keyword-only_ | | |
| `phrase_matcher_attr` | int / str | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. |
| `validate` | bool | Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. Defaults to `False`. |
| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. |
| `**cfg` | - | Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. |
| **RETURNS** | `EntityRuler` | The newly constructed object. |
| `ent_id_sep` | str | Separator used internally for entity IDs. Defaults to `"||"`. |
| `patterns` | iterable | Optional patterns to load in on initialization. |
## EntityRuler.\_\len\_\_ {#len tag="method"}
@ -49,7 +81,7 @@ The number of all patterns added to the entity ruler.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> assert len(ruler) == 0
> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
> assert len(ruler) == 1
@ -66,7 +98,7 @@ Whether a label is present in the patterns.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
> assert "ORG" in ruler
> assert not "PERSON" in ruler
@ -90,9 +122,8 @@ is chosen.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
> nlp.add_pipe(ruler)
>
> doc = nlp("A text about Apple.")
> ents = [(ent.text, ent.label_) for ent in doc.ents]
@ -117,7 +148,7 @@ of dicts) or a phrase pattern (string). For more details, see the usage guide on
> {"label": "ORG", "pattern": "Apple"},
> {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
> ]
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler.add_patterns(patterns)
> ```
@ -135,7 +166,7 @@ only the patterns are saved as JSONL. If a directory name is provided, a
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler.to_disk("/path/to/patterns.jsonl") # saves patterns only
> ruler.to_disk("/path/to/entity_ruler") # saves patterns and config
> ```
@ -154,7 +185,7 @@ configuration.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler.from_disk("/path/to/patterns.jsonl") # loads patterns only
> ruler.from_disk("/path/to/entity_ruler") # loads patterns and config
> ```
@ -171,7 +202,7 @@ Serialize the entity ruler patterns to a bytestring.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("entity_ruler")
> ruler_bytes = ruler.to_bytes()
> ```
@ -187,13 +218,13 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> ruler_bytes = ruler.to_bytes()
> ruler = EntityRuler(nlp)
> ruler = nlp.add_pipe("enity_ruler")
> ruler.from_bytes(ruler_bytes)
> ```
| Name | Type | Description |
| ---------------- | ------------- | ---------------------------------- |
| `patterns_bytes` | bytes | The bytestring to load. |
| ------------ | ------------- | ---------------------------------- |
| `bytes_data` | bytes | The bytestring to load. |
| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
## EntityRuler.labels {#labels tag="property"}

2
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@ -223,7 +223,7 @@ in `example.predicted`.
> #### Example
>
> ```python
> nlp.add_pipe(my_ner)
> nlp.add_pipe("my_ner")
> doc = nlp("Mr and Mrs Smith flew to New York")
> tokens_ref = ["Mr and Mrs", "Smith", "flew", "to", "New York"]
> example = Example.from_dict(doc, {"words": tokens_ref})

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@ -15,6 +15,88 @@ the tagger or parser that are called on a document in order. You can also add
your own processing pipeline components that take a `Doc` object, modify it and
return it.
## Language.component {#component tag="classmethod" new="3"}
Register a custom pipeline component under a given name. This allows
initializing the component by name using
[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
[config files](/usage/training#config). This classmethod and decorator is
intended for **simple stateless functions** that take a `Doc` and return it. For
more complex stateful components that allow settings and need access to the
shared `nlp` object, use the [`Language.factory`](/api/language#factory)
decorator. For more details and examples, see the
[usage documentation](/usage/processing-pipelines#custom-components).
> #### Example
>
> ```python
> from spacy.language import Language
>
> # Usage as a decorator
> @Language.component("my_component")
> def my_component(doc):
> # Do something to the doc
> return doc
>
> # Usage as a function
> Language.component("my_component2", func=my_component)
> ```
| Name | Type | Description |
| -------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something --> |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something --> |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something --> |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
## Language.factory {#factory tag="classmethod"}
Register a custom pipeline component factory under a given name. This allows
initializing the component by name using
[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
[config files](/usage/training#config). The registered factory function needs to
take at least two **named arguments** which spaCy fills in automatically: `nlp`
for the current `nlp` object and `name` for the component instance name. This
can be useful to distinguish multiple instances of the same component and allows
trainable components to add custom losses using the component instance name. The
`default_config` defines the default values of the remaining factory arguments.
It's merged into the [`nlp.config`](/api/language#config). For more details and
examples, see the
[usage documentation](/usage/processing-pipelines#custom-components).
> #### Example
>
> ```python
> from spacy.language import Language
>
> # Usage as a decorator
> @Language.factory(
> "my_component",
> default_config={"some_setting": True},
> )
> def create_my_component(nlp, name, some_setting):
> return MyComponent(some_setting)
>
> # Usage as function
> Language.factory(
> "my_component",
> default_config={"some_setting": True},
> func=create_my_component
> )
> ```
| Name | Type | Description |
| ---------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `default_config` | `Dict[str, any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something --> |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something --> |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something --> |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
## Language.\_\_init\_\_ {#init tag="method"}
Initialize a `Language` object.
@ -31,12 +113,41 @@ Initialize a `Language` object.
> ```
| Name | Type | Description |
| ----------- | ---------- | ------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A `Vocab` object. If `True`, a vocab is created via `Language.Defaults.create_vocab`. |
| `make_doc` | callable | A function that takes text and returns a `Doc` object. Usually a `Tokenizer`. |
| ------------------ | ----------- | ------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. |
| _keyword-only_ | | |
| `max_length` | int | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. |
| `meta` | dict | Custom meta data for the `Language` class. Is written to by models to add model meta data. |
| `create_tokenizer` |  `Callable` | Optional function that receives the `nlp` object and returns a tokenizer. |
| **RETURNS** | `Language` | The newly constructed object. |
## Language.from_config {#from_config tag="classmethod"}
Create a `Language` object from a loaded config. Will set up the tokenizer and
language data, add pipeline components based on the pipeline and components
define in the config and validate the results. If no config is provided, the
default config of the given language is used. This is also how spaCy loads a
model under the hood based on its [`config.cfg`](/api/data-formats#config).
> #### Example
>
> ```python
> from thinc.api import Config
> from spacy.language import Language
>
> config = Config().from_disk("./config.cfg")
> nlp = Language.from_config(config)
> ```
| Name | Type | Description |
| -------------- | ---------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `config` | `Dict[str, Any]` / [`Config`](https://thinc.ai/docs/api-config#config) | The loaded config. |
| _keyword-only_ | |
| `disable` | `Iterable[str]` | List of pipeline component names to disable. |
| `auto_fill` | bool | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. |
| `validate` | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | `Language` | The initialized object. |
## Language.\_\_call\_\_ {#call tag="method"}
Apply the pipeline to some text. The text can span multiple sentences, and can
@ -162,43 +273,99 @@ their original weights after the block.
Create a pipeline component from a factory.
<Infobox title="Changed in v3.0" variant="warning">
As of v3.0, the [`Language.add_pipe`](/api/language#add_pipe) method also takes
the string name of the factory, creates the component, adds it to the pipeline
and returns it. The `Language.create_pipe` method is now mostly used internally.
To create a component and add it to the pipeline, you should always use
`Language.add_pipe`.
</Infobox>
> #### Example
>
> ```python
> parser = nlp.create_pipe("parser")
> nlp.add_pipe(parser)
> ```
| Name | Type | Description |
| ----------- | -------- | ---------------------------------------------------------------------------------- |
| `name` | str | Factory name to look up in [`Language.factories`](/api/language#class-attributes). |
| `config` | dict | Configuration parameters to initialize component. |
| ------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `validate` <Tag variant="new">3</Tag> | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | callable | The pipeline component. |
## Language.add_pipe {#add_pipe tag="method" new="2"}
Add a component to the processing pipeline. Valid components are callables that
take a `Doc` object, modify it and return it. Only one of `before`, `after`,
`first` or `last` can be set. Default behavior is `last=True`.
Add a component to the processing pipeline. Expects a name that maps to a
component factory registered using
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory). Components should be callables
that take a `Doc` object, modify it and return it. Only one of `before`,
`after`, `first` or `last` can be set. Default behavior is `last=True`.
<Infobox title="Changed in v3.0" variant="warning">
As of v3.0, the [`Language.add_pipe`](/api/language#add_pipe) method doesn't
take callables anymore and instead expects the name of a component factory
registered using [`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory). It now takes care of creating the
component, adds it to the pipeline and returns it.
</Infobox>
> #### Example
>
> ```python
> def component(doc):
> @Language.component("component")
> def component_func(doc):
> # modify Doc and return it return doc
>
> nlp.add_pipe(component, before="ner")
> nlp.add_pipe(component, name="custom_name", last=True)
> nlp.add_pipe("component", before="ner")
> component = nlp.add_pipe("component", name="custom_name", last=True)
> ```
| Name | Type | Description |
| ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `component` | callable | The pipeline component. |
| `name` | str | Name of pipeline component. Overwrites existing `component.name` attribute if available. If no `name` is set and the component exposes no name attribute, `component.__name__` is used. An error is raised if the name already exists in the pipeline. |
| `before` | str | Component name to insert component directly before. |
| `after` | str | Component name to insert component directly after: |
| -------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. |
| _keyword-only_ | | |
| `before` | str / int | Component name or index to insert component directly before. |
| `after` | str / int | Component name or index to insert component directly after: |
| `first` | bool | Insert component first / not first in the pipeline. |
| `last` | bool | Insert component last / not last in the pipeline. |
| `config` <Tag variant="new">3</Tag> | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `validate` <Tag variant="new">3</Tag> | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** <Tag variant="new">3</Tag> | callable | The pipeline component. |
## Language.has_factory {#has_factory tag="classmethod" new="3"}
Check whether a factory name is registered on the `Language` class or subclass.
Will check for
[language-specific factories](/usage/processing-pipelines#factories-language)
registered on the subclass, as well as general-purpose factories registered on
the `Language` base class, available to all subclasses.
> #### Example
>
> ```python
> from spacy.language import Language
> from spacy.lang.en import English
>
> @English.component("component")
> def component(doc):
> return doc
>
> assert English.has_factory("component")
> assert not Language.has_factory("component")
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------------------------- |
| `name` | str | Name of the pipeline factory to check. |
| **RETURNS** | bool | Whether a factory of that name is registered on the class. |
## Language.has_pipe {#has_pipe tag="method" new="2"}
@ -208,9 +375,13 @@ Check whether a component is present in the pipeline. Equivalent to
> #### Example
>
> ```python
> nlp.add_pipe(lambda doc: doc, name="component")
> assert "component" in nlp.pipe_names
> assert nlp.has_pipe("component")
> @Language.component("component")
> def component(doc):
> return doc
>
> nlp.add_pipe("component", name="my_component")
> assert "my_component" in nlp.pipe_names
> assert nlp.has_pipe("my_component")
> ```
| Name | Type | Description |
@ -324,6 +495,88 @@ As of spaCy v3.0, the `disable_pipes` method has been renamed to `select_pipes`:
| `enable` | str / list | Names(s) of pipeline components that will not be disabled. |
| **RETURNS** | `DisabledPipes` | The disabled pipes that can be restored by calling the object's `.restore()` method. |
## Language.get_factory_meta {#get_factory_meta tag="classmethod" new="3"}
Get the factory meta information for a given pipeline component name. Expects
the name of the component **factory**. The factory meta is an instance of the
[`FactoryMeta`](/api/language#factorymeta) dataclass and contains the
information about the component and its default provided by the
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory) decorator.
> #### Example
>
> ```python
> factory_meta = Language.get_factory_meta("ner")
> assert factory_meta.factory == "ner"
> print(factory_meta.default_config)
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ------------------ |
| `name` | str | The factory name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) |  The factory meta. |
## Language.get_pipe_meta {#get_pipe_meta tag="method" new="3"}
Get the factory meta information for a given pipeline component name. Expects
the name of the component **instance** in the pipeline. The factory meta is an
instance of the [`FactoryMeta`](/api/language#factorymeta) dataclass and
contains the information about the component and its default provided by the
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory) decorator.
> #### Example
>
> ```python
> nlp.add_pipe("ner", name="entity_recognizer")
> factory_meta = nlp.get_pipe_meta("entity_recognizer")
> assert factory_meta.factory == "ner"
> print(factory_meta.default_config)
> ```
| Name | Type | Description |
| ----------- | ----------------------------- | ---------------------------- |
| `name` | str | The pipeline component name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) |  The factory meta. |
## Language.meta {#meta tag="property"}
Custom meta data for the Language class. If a model is loaded, contains meta
data of the model. The `Language.meta` is also what's serialized as the
`meta.json` when you save an `nlp` object to disk.
> #### Example
>
> ```python
> print(nlp.meta)
> ```
| Name | Type | Description |
| ----------- | ---- | -------------- |
| **RETURNS** | dict | The meta data. |
## Language.config {#config tag="property" new="3"}
Export a trainable [`config.cfg`](/api/data-formats#config) for the current
`nlp` object. Includes the current pipeline, all configs used to create the
currently active pipeline components, as well as the default training config
that can be used with [`spacy train`](/api/cli#train). `Language.config` returns
a [Thinc `Config` object](https://thinc.ai/docs/api-config#config), which is a
subclass of the built-in `dict`. It supports the additional methods `to_disk`
(serialize the config to a file) and `to_str` (output the config as a string).
> #### Example
>
> ```python
> nlp.config.to_disk("./config.cfg")
> print(nlp.config.to_str())
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | ----------- |
| **RETURNS** | [`Config`](https://thinc.ai/docs/api-config#config) | The config. |
## Language.to_disk {#to_disk tag="method" new="2"}
Save the current state to a directory. If a model is loaded, this will **include
@ -406,22 +659,25 @@ available to the loaded object.
## Attributes {#attributes}
| Name | Type | Description |
| ------------------------------------------ | ----------- | ----------------------------------------------------------------------------------------------- |
| --------------------------------------------- | ---------------------- | ---------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | A container for the lexical types. |
| `tokenizer` | `Tokenizer` | The tokenizer. |
| `make_doc` | `callable` | Callable that takes a string and returns a `Doc`. |
| `pipeline` | list | List of `(name, component)` tuples describing the current processing pipeline, in order. |
| `pipe_names` <Tag variant="new">2</Tag> | list | List of pipeline component names, in order. |
| `pipe_labels` <Tag variant="new">2.2</Tag> | dict | List of labels set by the pipeline components, if available, keyed by component name. |
| `meta` | dict | Custom meta data for the Language class. If a model is loaded, contains meta data of the model. |
| `make_doc` | `Callable` | Callable that takes a string and returns a `Doc`. |
| `pipeline` | `List[str, Callable]` | List of `(name, component)` tuples describing the current processing pipeline, in order. |
| `pipe_names` <Tag variant="new">2</Tag> | `List[str]` | List of pipeline component names, in order. |
| `pipe_labels` <Tag variant="new">2.2</Tag> | `Dict[str, List[str]]` | List of labels set by the pipeline components, if available, keyed by component name. |
| `pipe_factories` <Tag variant="new">2.2</Tag> | `Dict[str, str]` | Dictionary of pipeline component names, mapped to their factory names. |
| `factories` | `Dict[str, Callable]` | All available factory functions, keyed by name. |
| `factory_names` <Tag variant="new">3</Tag> | `List[str]` | List of all available factory names. |
| `path` <Tag variant="new">2</Tag> | `Path` | Path to the model data directory, if a model is loaded. Otherwise `None`. |
## Class attributes {#class-attributes}
| Name | Type | Description |
| ---------- | ----- | ----------------------------------------------------------------------------------------------- |
| ---------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Defaults` | class | Settings, data and factory methods for creating the `nlp` object and processing pipeline. |
| `lang` | str | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). |
| `default_config` | dict | Base [config](/usage/training#config) to use for [Language.config](/api/language#config). Defaults to [`default_config.cfg`](https://github.com/explosion/spaCy/tree/develop/spacy/default_config.cfg). |
## Defaults {#defaults}
@ -502,3 +758,19 @@ serialization by passing in the string names via the `exclude` argument.
| `tokenizer` | Tokenization rules and exceptions. |
| `meta` | The meta data, available as `Language.meta`. |
| ... | String names of pipeline components, e.g. `"ner"`. |
## FactoryMeta {#factorymeta new="3" tag="dataclass"}
The `FactoryMeta` contains the information about the component and its default
provided by the [`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory) decorator. It's created whenever a
component is added to the pipeline and stored on the `Language` class for each
component instance and factory instance.
| Name | Type | Description |
| ---------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory` | str | The name of the registered component factory. |
| `default_config` | `Dict[str, Any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something --> |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->  |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something -->  |

2
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@ -5,6 +5,8 @@ tag: class
source: spacy/lemmatizer.py
---
<!-- TODO: rewrite once it's converted to pipe -->
The `Lemmatizer` supports simple part-of-speech-sensitive suffix rules and
lookup tables.

3
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View File

@ -143,9 +143,10 @@ patterns = [[{"TEXT": "Google"}, {"TEXT": "Now"}], [{"TEXT": "GoogleNow"}]]
</Infobox>
| Name | Type | Description |
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `patterns` | list | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. |
| _keyword-only_ | | |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
## Matcher.remove {#remove tag="method" new="2"}

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View File

@ -3,27 +3,41 @@ title: Morphologizer
tag: class
source: spacy/pipeline/morphologizer.pyx
new: 3
teaser: 'Pipeline component for predicting morphological features'
api_base_class: /api/tagger
api_string_name: morphologizer
api_trainable: true
---
A trainable pipeline component to predict morphological features and
coarse-grained POS tags following the Universal Dependencies
[UPOS](https://universaldependencies.org/u/pos/index.html) and
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
annotation guidelines. This class is a subclass of `Pipe` and follows the same
API. The component is also available via the string name `"morphologizer"`.
After initialization, it is typically added to the processing pipeline using
[`nlp.add_pipe`](/api/language#add_pipe).
annotation guidelines.
## Default config {#config}
## Config and implementation {#config}
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
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.morphologizer import DEFAULT_MORPH_MODEL
> config = {"model": DEFAULT_MORPH_MODEL}
> nlp.add_pipe("morphologizer", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------- |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [Tagger](/api/architectures#Tagger) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/morphologizer_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/morphologizer.pyx
```
## Morphologizer.\_\_init\_\_ {#init tag="method"}
@ -33,40 +47,45 @@ Initialize the morphologizer.
> #### Example
>
> ```python
> # Construction via create_pipe
> morphologizer = nlp.create_pipe("morphologizer")
> # Construction via add_pipe with default model
> morphologizer = nlp.add_pipe("morphologizer")
>
> # Construction via create_pipe with custom model
> config = {"model": {"@architectures": "my_morphologizer"}}
> morphologizer = nlp.add_pipe("morphologizer", config=config)
>
> # Construction from class
> from spacy.pipeline import Morphologizer
> morphologizer = Morphologizer()
> morphologizer = Morphologizer(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------- | -------- | ------------------------------------------------------------------------------- |
| -------------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `Morphologizer` | The newly constructed object. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `labels_morph` | dict | <!-- TODO: --> |
| `labels_pos` | dict | <!-- TODO: --> |
## Morphologizer.\_\_call\_\_ {#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/morphologizer#call) and [`pipe`](/api/morphologizer#pipe) delegate to the
[`predict`](/api/morphologizer#predict) and
[`__call__`](/api/morphologizer#call) and [`pipe`](/api/morphologizer#pipe)
delegate to the [`predict`](/api/morphologizer#predict) and
[`set_annotations`](/api/morphologizer#set_annotations) methods.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> doc = nlp("This is a sentence.")
> morphologizer = nlp.add_pipe("morphologizer")
> # This usually happens under the hood
> processed = morphologizer(doc)
> ```
@ -81,23 +100,46 @@ and all pipeline components are applied to the `Doc` in order. Both
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/morphologizer#call) and
[`pipe`](/api/morphologizer#pipe) delegate to the [`predict`](/api/morphologizer#predict) and
[`pipe`](/api/morphologizer#pipe) delegate to the
[`predict`](/api/morphologizer#predict) and
[`set_annotations`](/api/morphologizer#set_annotations) methods.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> for doc in morphologizer.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## Morphologizer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> morphologizer = nlp.add_pipe("morphologizer")
> nlp.pipeline.append(morphologizer)
> optimizer = morphologizer.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/sentencerecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Morphologizer.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
@ -105,7 +147,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> scores = morphologizer.predict([doc1, doc2])
> ```
@ -121,13 +163,13 @@ Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> scores = morphologizer.predict([doc1, doc2])
> morphologizer.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------ |
| -------- | --------------- | ------------------------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `Morphologizer.predict`. |
@ -140,18 +182,18 @@ pipe's model. Delegates to [`predict`](/api/morphologizer#predict) and
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab, morphologizer_model)
> morphologizer = nlp.add_pipe("morphologizer")
> optimizer = nlp.begin_training()
> losses = morphologizer.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/morphologizer#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/sentencerecognizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
@ -163,36 +205,16 @@ predicted scores.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> scores = morphologizer.predict([eg.predicted for eg in examples])
> loss, d_loss = morphologizer.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------- |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
## Morphologizer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> nlp.pipeline.append(morphologizer)
> optimizer = morphologizer.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/morphologizer#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## Morphologizer.create_optimizer {#create_optimizer tag="method"}
@ -201,13 +223,13 @@ Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> optimizer = morphologizer.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Morphologizer.use_params {#use_params tag="method, contextmanager"}
@ -216,7 +238,7 @@ Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> with morphologizer.use_params():
> morphologizer.to_disk("/best_model")
> ```
@ -233,12 +255,12 @@ both `pos` and `morph`, the label should include the UPOS as the feature `POS`.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin")
> ```
| Name | Type | Description |
| -------- | ---- | --------------------------------------------------------------- |
| ------- | ---- | ----------------- |
| `label` | str | The label to add. |
## Morphologizer.to_disk {#to_disk tag="method"}
@ -248,14 +270,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> morphologizer.to_disk("/path/to/morphologizer")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## Morphologizer.from_disk {#from_disk tag="method"}
@ -264,14 +286,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> morphologizer.from_disk("/path/to/morphologizer")
> ```
| Name | Type | Description |
| ----------- | ------------ | -------------------------------------------------------------------------- |
| ----------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Morphologizer` | The modified `Morphologizer` object. |
## Morphologizer.to_bytes {#to_bytes tag="method"}
@ -279,15 +301,15 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> morphologizer_bytes = morphologizer.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------- |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Morphologizer` object. |
## Morphologizer.from_bytes {#from_bytes tag="method"}
@ -298,20 +320,20 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> morphologizer_bytes = morphologizer.to_bytes()
> morphologizer = Morphologizer(nlp.vocab)
> morphologizer = nlp.add_pipe("morphologizer")
> morphologizer.from_bytes(morphologizer_bytes)
> ```
| Name | Type | Description |
| ------------ | -------- | ------------------------------------------------------------------------- |
| ------------ | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Morphologizer` | The `Morphologizer` object. |
## Morphologizer.labels {#labels tag="property"}
The labels currently added to the component in Universal Dependencies [FEATS
format](https://universaldependencies.org/format.html#morphological-annotation).
The labels currently added to the component in Universal Dependencies
[FEATS format](https://universaldependencies.org/format.html#morphological-annotation).
Note that even for a blank component, this will always include the internal
empty label `_`. If POS features are used, the labels will include the
coarse-grained POS as the feature `POS`.
@ -340,7 +362,7 @@ serialization by passing in the string names via the `exclude` argument.
> ```
| 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. |

3
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@ -166,9 +166,10 @@ patterns = [nlp("health care reform"), nlp("healthcare reform")]
</Infobox>
| Name | Type | Description |
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| -------------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `docs` | list | `Doc` objects of the phrases to match. |
| _keyword-only_ | | |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
## PhraseMatcher.remove {#remove tag="method" new="2.2"}

6
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@ -0,0 +1,6 @@
---
title: Pipe
tag: class
---
TODO: write

26
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View File

@ -11,8 +11,7 @@ menu:
## merge_noun_chunks {#merge_noun_chunks tag="function"}
Merge noun chunks into a single token. Also available via the string name
`"merge_noun_chunks"`. After initialization, the component is typically added to
the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
`"merge_noun_chunks"`.
> #### Example
>
@ -20,9 +19,7 @@ the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
> texts = [t.text for t in nlp("I have a blue car")]
> assert texts == ["I", "have", "a", "blue", "car"]
>
> merge_nps = nlp.create_pipe("merge_noun_chunks")
> nlp.add_pipe(merge_nps)
>
> nlp.add_pipe("merge_noun_chunks")
> texts = [t.text for t in nlp("I have a blue car")]
> assert texts == ["I", "have", "a blue car"]
> ```
@ -44,8 +41,7 @@ all other components.
## merge_entities {#merge_entities tag="function"}
Merge named entities into a single token. Also available via the string name
`"merge_entities"`. After initialization, the component is typically added to
the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
`"merge_entities"`.
> #### Example
>
@ -53,8 +49,7 @@ the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
> texts = [t.text for t in nlp("I like David Bowie")]
> assert texts == ["I", "like", "David", "Bowie"]
>
> merge_ents = nlp.create_pipe("merge_entities")
> nlp.add_pipe(merge_ents)
> nlp.add_pipe("merge_entities")
>
> texts = [t.text for t in nlp("I like David Bowie")]
> assert texts == ["I", "like", "David Bowie"]
@ -76,12 +71,9 @@ components to the end of the pipeline and after all other components.
## merge_subtokens {#merge_subtokens tag="function" new="2.1"}
Merge subtokens into a single token. Also available via the string name
`"merge_subtokens"`. After initialization, the component is typically added to
the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
As of v2.1, the parser is able to predict "subtokens" that should be merged into
one single token later on. This is especially relevant for languages like
Chinese, Japanese or Korean, where a "word" isn't defined as a
`"merge_subtokens"`. As of v2.1, the parser is able to predict "subtokens" that
should be merged into one single token later on. This is especially relevant for
languages like Chinese, Japanese or Korean, where a "word" isn't defined as a
whitespace-delimited sequence of characters. Under the hood, this component uses
the [`Matcher`](/api/matcher) to find sequences of tokens with the dependency
label `"subtok"` and then merges them into a single token.
@ -96,9 +88,7 @@ label `"subtok"` and then merges them into a single token.
> print([(token.text, token.dep_) for token in doc])
> # [('拜', 'subtok'), ('托', 'subtok')]
>
> merge_subtok = nlp.create_pipe("merge_subtokens")
> nlp.add_pipe(merge_subtok)
>
> nlp.add_pipe("merge_subtokens")
> doc = nlp("拜托")
> print([token.text for token in doc])
> # ['拜托']

356
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View File

@ -1,26 +1,40 @@
---
title: SentenceRecognizer
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/senter.pyx
new: 3
teaser: 'Pipeline component for sentence segmentation'
api_base_class: /api/tagger
api_string_name: senter
api_trainable: true
---
A trainable pipeline component for sentence segmentation. For a simpler,
ruse-based strategy, see the [`Sentencizer`](/api/sentencizer). This class is a
subclass of `Pipe` and follows the same API. The component is also available via
the string name `"senter"`. After initialization, it is typically added to the
processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
ruse-based strategy, see the [`Sentencizer`](/api/sentencizer).
## Default config {#config}
## Config and implementation {#config}
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
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.senter import DEFAULT_SENTER_MODEL
> config = {"model": DEFAULT_SENTER_MODEL,}
> nlp.add_pipe("senter", config=config)
> ```
| Setting | Type | Description | Default |
| ------- | ------------------------------------------ | ----------------- | ----------------------------------- |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [Tagger](/api/architectures#Tagger) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/senter_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/senter.pyx
```
## SentenceRecognizer.\_\_init\_\_ {#init tag="method"}
@ -30,12 +44,322 @@ Initialize the sentence recognizer.
> #### Example
>
> ```python
> # Construction via create_pipe
> senter = nlp.create_pipe("senter")
> # Construction via add_pipe with default model
> senter = nlp.add_pipe("senter")
>
> # Construction via create_pipe with custom model
> config = {"model": {"@architectures": "my_senter"}}
> senter = nlp.add_pipe("senter", config=config)
>
> # Construction from class
> from spacy.pipeline import SentenceRecognizer
> senter = SentenceRecognizer()
> senter = SentenceRecognizer(nlp.vocab, model)
> ```
<!-- TODO: document, similar to other trainable pipeline components -->
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#add_pipe).
| Name | Type | Description |
| ------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
## SentenceRecognizer.\_\_call\_\_ {#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/sentencerecognizer#call) and
[`pipe`](/api/sentencerecognizer#pipe) delegate to the
[`predict`](/api/sentencerecognizer#predict) and
[`set_annotations`](/api/sentencerecognizer#set_annotations) methods.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> senter = nlp.add_pipe("senter")
> # This usually happens under the hood
> processed = senter(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
## SentenceRecognizer.pipe {#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/sentencerecognizer#call)
and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
[`predict`](/api/sentencerecognizer#predict) and
[`set_annotations`](/api/sentencerecognizer#set_annotations) methods.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> for doc in senter.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## SentenceRecognizer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> optimizer = senter.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/sentencerecognizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## SentenceRecognizer.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> scores = senter.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | --------------- | ----------------------------------------- |
| `docs` | `Iterable[Doc]` | The documents to predict. |
| **RETURNS** | - | The model's prediction for each document. |
## SentenceRecognizer.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> scores = senter.predict([doc1, doc2])
> senter.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | --------------- | ------------------------------------------------------------ |
| `docs` | `Iterable[Doc]` | The documents to modify. |
| `scores` | - | The scores to set, produced by `SentenceRecognizer.predict`. |
## SentenceRecognizer.update {#update tag="method"}
Learn from a batch of documents and gold-standard information, updating the
pipe's model. Delegates to [`predict`](/api/sentencerecognizer#predict) and
[`get_loss`](/api/sentencerecognizer#get_loss).
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> optimizer = nlp.begin_training()
> losses = senter.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/sentencerecognizer#set_annotations). |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
## SentenceRecognizer.rehearse {#rehearse tag="method,experimental"}
Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
current model to make predictions similar to an initial model, to try to address
the "catastrophic forgetting" problem. This feature is experimental.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> optimizer = nlp.begin_training()
> losses = senter.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## SentenceRecognizer.get_loss {#get_loss tag="method"}
Find the loss and gradient of loss for the batch of documents and their
predicted scores.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> scores = senter.predict([eg.predicted for eg in examples])
> loss, d_loss = senter.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## SentenceRecognizer.score {#score tag="method" new="3"}
Score a batch of examples.
> #### Example
>
> ```python
> scores = senter.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
## SentenceRecognizer.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> optimizer = senter.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## SentenceRecognizer.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> with senter.use_params():
> senter.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| `params` | - | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
## SentenceRecognizer.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> senter.to_disk("/path/to/senter")
> ```
| Name | Type | Description |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## SentenceRecognizer.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> senter.from_disk("/path/to/senter")
> ```
| Name | Type | Description |
| ----------- | -------------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `SentenceRecognizer` | The modified `SentenceRecognizer` object. |
## SentenceRecognizer.to_bytes {#to_bytes tag="method"}
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> senter_bytes = senter.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `SentenceRecognizer` object. |
## SentenceRecognizer.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> senter_bytes = senter.to_bytes()
> senter = nlp.add_pipe("senter")
> senter.from_bytes(senter_bytes)
> ```
| Name | Type | Description |
| ------------ | -------------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `SentenceRecognizer` | The `SentenceRecognizer` object. |
## Serialization fields {#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 = senter.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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@ -1,16 +1,40 @@
---
title: Sentencizer
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/sentencizer.pyx
teaser: 'Pipeline component for rule-based sentence boundary detection'
api_base_class: /api/pipe
api_string_name: sentencizer
api_trainable: false
---
A simple pipeline component, to allow custom sentence boundary detection logic
that doesn't require the dependency parse. By default, sentence segmentation is
performed by the [`DependencyParser`](/api/dependencyparser), so the
`Sentencizer` lets you implement a simpler, rule-based strategy that doesn't
require a statistical model to be loaded. The component is also available via
the string name `"sentencizer"`. After initialization, it is typically added to
the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
require a statistical model to be loaded.
## Config and implementation {#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).
> #### Example
>
> ```python
> config = {"punct_chars": None}
> nlp.add_pipe("entity_ruler", config=config)
> ```
| Setting | Type | Description | Default |
| ------------- | ----------- | ---------------------------------------------------------------------------------------------------------- | ------- |
| `punct_chars` | `List[str]` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults if not set. | `None` |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/sentencizer.pyx
```
## Sentencizer.\_\_init\_\_ {#init tag="method"}
@ -19,8 +43,8 @@ Initialize the sentencizer.
> #### Example
>
> ```python
> # Construction via create_pipe
> sentencizer = nlp.create_pipe("sentencizer")
> # Construction via add_pipe
> sentencizer = nlp.add_pipe("sentencizer")
>
> # Construction from class
> from spacy.pipeline import Sentencizer
@ -28,9 +52,9 @@ Initialize the sentencizer.
> ```
| Name | Type | Description |
| ------------- | ------------- | ----------------------------------------------------------------------------------------------- |
| `punct_chars` | list | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. |
| **RETURNS** | `Sentencizer` | The newly constructed object. |
| -------------- | ----------- | ----------------------------------------------------------------------------------------------- |
| _keyword-only_ | | |
| `punct_chars` | `List[str]` | Optional custom list of punctuation characters that mark sentence ends. See below for defaults. |
```python
### punct_chars defaults
@ -58,8 +82,7 @@ the component has been added to the pipeline using
> from spacy.lang.en import English
>
> nlp = English()
> sentencizer = nlp.create_pipe("sentencizer")
> nlp.add_pipe(sentencizer)
> nlp.add_pipe("sentencizer")
> doc = nlp("This is a sentence. This is another sentence.")
> assert len(list(doc.sents)) == 2
> ```
@ -69,6 +92,42 @@ the component has been added to the pipeline using
| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries. |
## Sentencizer.pipe {#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.
> #### Example
>
> ```python
> sentencizer = nlp.add_pipe("sentencizer")
> for doc in sentencizer.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
## Sentencizer.score {#score tag="method" new="3"}
Score a batch of examples.
> #### Example
>
> ```python
> scores = sentencizer.score(examples)
> ```
| Name | Type | Description |
| ----------- | ------------------- | ------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_spans`](/api/scorer#score_spans). |
## Sentencizer.to_disk {#to_disk tag="method"}
Save the sentencizer settings (punctuation characters) a directory. Will create
@ -78,13 +137,14 @@ a file `sentencizer.json`. This also happens automatically when you save an
> #### Example
>
> ```python
> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
> sentencizer.to_disk("/path/to/sentencizer.jsonl")
> config = {"punct_chars": [".", "?", "!", "。"]}
> sentencizer = nlp.add_pipe("sentencizer", config=config)
> sentencizer.to_disk("/path/to/sentencizer.json")
> ```
| Name | Type | Description |
| ------ | ------------ | ---------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| ------ | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a JSON file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
## Sentencizer.from_disk {#from_disk tag="method"}
@ -95,7 +155,7 @@ added to its pipeline.
> #### Example
>
> ```python
> sentencizer = Sentencizer()
> sentencizer = nlp.add_pipe("sentencizer")
> sentencizer.from_disk("/path/to/sentencizer.json")
> ```
@ -111,7 +171,8 @@ Serialize the sentencizer settings to a bytestring.
> #### Example
>
> ```python
> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
> config = {"punct_chars": [".", "?", "!", "。"]}
> sentencizer = nlp.add_pipe("sentencizer", config=config)
> sentencizer_bytes = sentencizer.to_bytes()
> ```
@ -127,7 +188,7 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> sentencizer_bytes = sentencizer.to_bytes()
> sentencizer = Sentencizer()
> sentencizer = nlp.add_pipe("sentencizer")
> sentencizer.from_bytes(sentencizer_bytes)
> ```

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@ -1,41 +1,70 @@
---
title: Tagger
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/tagger.pyx
teaser: 'Pipeline component for part-of-speech tagging'
api_base_class: /api/pipe
api_string_name: tagger
api_trainable: true
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"tagger"`.
## Config and implementation {#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.tagger import DEFAULT_TAGGER_MODEL
> config = {
> "set_morphology": False,
> "model": DEFAULT_TAGGER_MODEL,
> }
> nlp.add_pipe("tagger", config=config)
> ```
| Setting | Type | Description | Default |
| ---------------- | ------------------------------------------ | -------------------------------------- | ----------------------------------- |
| `set_morphology` | bool | Whether to set morphological features. | `False` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [Tagger](/api/architectures#Tagger) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/tagger.pyx
```
## Tagger.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via create_pipe
> tagger = nlp.create_pipe("tagger")
> # Construction via add_pipe with default model
> tagger = nlp.add_pipe("tagger")
>
> # Construction via create_pipe with custom model
> config = {"model": {"@architectures": "my_tagger"}}
> parser = nlp.create_pipe("tagger", config)
> parser = nlp.add_pipe("tagger", config=config)
>
> # Construction from class with custom model from file
> # Construction from class
> from spacy.pipeline import Tagger
> model = util.load_config("model.cfg", create_objects=True)["model"]
> tagger = Tagger(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Type | Description |
| ----------- | -------- | ------------------------------------------------------------------------------- |
| ---------------- | ------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `Tagger` | The newly constructed object. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `set_morphology` | bool | Whether to set morphological features. |
## Tagger.\_\_call\_\_ {#call tag="method"}
@ -49,8 +78,8 @@ and all pipeline components are applied to the `Doc` in order. Both
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> doc = nlp("This is a sentence.")
> tagger = nlp.add_pipe("tagger")
> # This usually happens under the hood
> processed = tagger(doc)
> ```
@ -71,17 +100,38 @@ applied to the `Doc` in order. Both [`__call__`](/api/tagger#call) and
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> for doc in tagger.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| -------------- | --------------- | ------------------------------------------------------ |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| _keyword-only_ | | |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## Tagger.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> tagger = nlp.add_pipe("tagger")
> optimizer = tagger.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/tagger#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Tagger.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
@ -89,7 +139,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> scores = tagger.predict([doc1, doc2])
> ```
@ -105,7 +155,7 @@ Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> scores = tagger.predict([doc1, doc2])
> tagger.set_annotations([doc1, doc2], scores)
> ```
@ -124,21 +174,44 @@ pipe's model. Delegates to [`predict`](/api/tagger#predict) and
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab, tagger_model)
> tagger = nlp.add_pipe("tagger")
> optimizer = nlp.begin_training()
> losses = tagger.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| ----------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/tagger#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
## Tagger.rehearse {#rehearse tag="method,experimental"}
Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
current model to make predictions similar to an initial model, to try to address
the "catastrophic forgetting" problem. This feature is experimental.
> #### Example
>
> ```python
> tagger = nlp.add_pipe("tagger")
> optimizer = nlp.begin_training()
> losses = tagger.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## Tagger.get_loss {#get_loss tag="method"}
Find the loss and gradient of loss for the batch of documents and their
@ -147,36 +220,31 @@ predicted scores.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> scores = tagger.predict([eg.predicted for eg in examples])
> loss, d_loss = tagger.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------- |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## Tagger.begin_training {#begin_training tag="method"}
## Tagger.score {#score tag="method" new="3"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
Score a batch of examples.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> nlp.pipeline.append(tagger)
> optimizer = tagger.begin_training(pipeline=nlp.pipeline)
> scores = tagger.score(examples)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/tagger#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. |
| ----------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `examples` | `Iterable[Example]` | The examples to score. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"`, `"tag"` and `"lemma"`. |
## Tagger.create_optimizer {#create_optimizer tag="method"}
@ -185,13 +253,13 @@ Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> optimizer = tagger.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## Tagger.use_params {#use_params tag="method, contextmanager"}
@ -200,7 +268,7 @@ Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> with tagger.use_params():
> tagger.to_disk("/best_model")
> ```
@ -217,14 +285,14 @@ Add a new label to the pipe.
>
> ```python
> from spacy.symbols import POS
> tagger = Tagger(nlp.vocab)
> tagger.add_label("MY_LABEL", {POS: 'NOUN'})
> tagger = nlp.add_pipe("tagger")
> tagger.add_label("MY_LABEL", {POS: "NOUN"})
> ```
| Name | Type | Description |
| -------- | ---- | --------------------------------------------------------------- |
| -------- | ---------------- | --------------------------------------------------------------- |
| `label` | str | The label to add. |
| `values` | dict | Optional values to map to the label, e.g. a tag map dictionary. |
| `values` | `Dict[int, str]` | Optional values to map to the label, e.g. a tag map dictionary. |
## Tagger.to_disk {#to_disk tag="method"}
@ -233,14 +301,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> tagger.to_disk("/path/to/tagger")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## Tagger.from_disk {#from_disk tag="method"}
@ -249,14 +317,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> tagger.from_disk("/path/to/tagger")
> ```
| Name | Type | Description |
| ----------- | ------------ | -------------------------------------------------------------------------- |
| ----------- | --------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tagger` | The modified `Tagger` object. |
## Tagger.to_bytes {#to_bytes tag="method"}
@ -264,15 +332,15 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> tagger_bytes = tagger.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------- |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Tagger` object. |
## Tagger.from_bytes {#from_bytes tag="method"}
@ -283,14 +351,14 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> tagger_bytes = tagger.to_bytes()
> tagger = Tagger(nlp.vocab)
> tagger = nlp.add_pipe("tagger")
> tagger.from_bytes(tagger_bytes)
> ```
| Name | Type | Description |
| ------------ | -------- | ------------------------------------------------------------------------- |
| ------------ | --------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Tagger` | The `Tagger` object. |
## Tagger.labels {#labels tag="property"}
@ -307,8 +375,8 @@ tags by default, e.g. `VERB`, `NOUN` and so on.
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |
| ----------- | ------------ | ---------------------------------- |
| **RETURNS** | `Tuple[str]` | The labels added to the component. |
## Serialization fields {#serialization-fields}

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@ -1,56 +1,71 @@
---
title: TextCategorizer
tag: class
source: spacy/pipeline/pipes.pyx
source: spacy/pipeline/textcat.py
new: 2
teaser: 'Pipeline component for text classification'
api_base_class: /api/pipe
api_string_name: textcat
api_trainable: true
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"textcat"`.
## Config and implementation {#config}
## Default config {#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.
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
> #### Example
>
> ```python
> from spacy.pipeline.textcat import DEFAULT_TEXTCAT_MODEL
> config = {
> "labels": [],
> "model": DEFAULT_TEXTCAT_MODEL,
> }
> nlp.add_pipe("textcat", config=config)
> ```
| Setting | Type | Description | Default |
| -------- | ------------------------------------------ | ------------------ | ----------------------------------------------------- |
| `labels` | `Iterable[str]` | The labels to use. | `[]` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TextCatEnsemble](/api/architectures#TextCatEnsemble) |
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/textcat_defaults.cfg
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/textcat.py
```
<!-- TODO: do we also need to document the other defaults here? -->
## TextCategorizer.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via create_pipe
> textcat = nlp.create_pipe("textcat")
> # Construction via add_pipe with default model
> textcat = nlp.add_pipe("textcat")
>
> # Construction via create_pipe with custom model
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_textcat"}}
> parser = nlp.create_pipe("textcat", config)
> parser = nlp.add_pipe("textcat", config=config)
>
> # Construction from class with custom model from file
> # Construction from class
> from spacy.pipeline import TextCategorizer
> model = util.load_config("model.cfg", create_objects=True)["model"]
> textcat = TextCategorizer(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.create_pipe`](/api/language#create_pipe).
[`nlp.add_pipe`](/api/language#create_pipe).
| Name | Type | Description |
| ----------- | ----------------- | ------------------------------------------------------------------------------- |
| -------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `TextCategorizer` | The newly constructed object. |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
| `labels` | `Iterable[str]` | The labels to use. |
<!-- TODO move to config page
### Architectures {#architectures new="2.1"}
@ -81,8 +96,8 @@ delegate to the [`predict`](/api/textcategorizer#predict) and
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> doc = nlp("This is a sentence.")
> textcat = nlp.add_pipe("textcat")
> # This usually happens under the hood
> processed = textcat(doc)
> ```
@ -104,16 +119,37 @@ applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> for doc in textcat.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | --------------- | ------------------------------------------------------ |
| -------------- | --------------- | ----------------------------------------------------- |
| `stream` | `Iterable[Doc]` | A stream of documents. |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
| _keyword-only_ | | |
| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | The processed documents in order. |
## TextCategorizer.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
> #### Example
>
> ```python
> textcat = nlp.add_pipe("textcat")
> optimizer = textcat.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
| _keyword-only_ | | |
| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/textcategorizer#create_optimizer) if not set. |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## TextCategorizer.predict {#predict tag="method"}
@ -122,7 +158,7 @@ Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> scores = textcat.predict([doc1, doc2])
> ```
@ -138,7 +174,7 @@ Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> scores = textcat.predict(docs)
> textcat.set_annotations(docs, scores)
> ```
@ -157,19 +193,42 @@ pipe's model. Delegates to [`predict`](/api/textcategorizer#predict) and
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab, textcat_model)
> textcat = nlp.add_pipe("textcat")
> optimizer = nlp.begin_training()
> losses = textcat.update(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| ----------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| ----------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/textcategorizer#set_annotations). |
| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## TextCategorizer.rehearse {#rehearse tag="method,experimental"}
Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
current model to make predictions similar to an initial model, to try to address
the "catastrophic forgetting" problem. This feature is experimental.
> #### Example
>
> ```python
> textcat = nlp.add_pipe("textcat")
> optimizer = nlp.begin_training()
> losses = textcat.rehearse(examples, sgd=optimizer)
> ```
| Name | Type | Description |
| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
| `losses` | `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. |
## TextCategorizer.get_loss {#get_loss tag="method"}
@ -180,36 +239,32 @@ predicted scores.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> scores = textcat.predict([eg.predicted for eg in examples])
> loss, d_loss = textcat.get_loss(examples, scores)
> ```
| Name | Type | Description |
| ----------- | ------------------- | --------------------------------------------------- |
| ----------- | --------------------- | --------------------------------------------------- |
| `examples` | `Iterable[Example]` | The batch of examples. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
## TextCategorizer.begin_training {#begin_training tag="method"}
## TextCategorizer.score {#score tag="method" new="3"}
Initialize the pipe for training, using data examples if available. Return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
Score a batch of examples.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> nlp.pipeline.append(textcat)
> optimizer = textcat.begin_training(pipeline=nlp.pipeline)
> scores = textcat.score(examples)
> ```
| Name | Type | Description |
| -------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/textcategorizer#create_optimizer) if not set. |
| **RETURNS** | `Optimizer` | An optimizer. |
| ---------------- | ------------------- | ---------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | The examples to score. | _keyword-only_ | | |
| `positive_label` | str | Optional positive label. |
| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_cats`](/api/scorer#score_cats). |
## TextCategorizer.create_optimizer {#create_optimizer tag="method"}
@ -218,29 +273,13 @@ Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> optimizer = textcat.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------- |
| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> with textcat.use_params(optimizer.averages):
> textcat.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| `params` | dict | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
| ----------- | --------------------------------------------------- | -------------- |
| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
## TextCategorizer.add_label {#add_label tag="method"}
@ -249,7 +288,7 @@ Add a new label to the pipe.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> textcat.add_label("MY_LABEL")
> ```
@ -257,6 +296,22 @@ Add a new label to the pipe.
| ------- | ---- | ----------------- |
| `label` | str | The label to add. |
## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> textcat = nlp.add_pipe("textcat")
> with textcat.use_params():
> textcat.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| `params` | - | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
## TextCategorizer.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
@ -264,14 +319,14 @@ Serialize the pipe to disk.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> textcat.to_disk("/path/to/textcat")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| --------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
## TextCategorizer.from_disk {#from_disk tag="method"}
@ -280,14 +335,14 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> textcat.from_disk("/path/to/textcat")
> ```
| Name | Type | Description |
| ----------- | ----------------- | -------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `TextCategorizer` | The modified `TextCategorizer` object. |
## TextCategorizer.to_bytes {#to_bytes tag="method"}
@ -295,15 +350,15 @@ Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> textcat_bytes = textcat.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------- |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| ----------- | --------------- | ------------------------------------------------------------------------- |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `TextCategorizer` object. |
## TextCategorizer.from_bytes {#from_bytes tag="method"}
@ -314,14 +369,14 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
>
> ```python
> textcat_bytes = textcat.to_bytes()
> textcat = TextCategorizer(nlp.vocab)
> textcat = nlp.add_pipe("textcat")
> textcat.from_bytes(textcat_bytes)
> ```
| Name | Type | Description |
| ------------ | ----------------- | ------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `TextCategorizer` | The `TextCategorizer` object. |
## TextCategorizer.labels {#labels tag="property"}

18
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@ -2,18 +2,10 @@
title: Tok2Vec
source: spacy/pipeline/tok2vec.py
new: 3
teaser: null
api_base_class: /api/pipe
api_string_name: tok2vec
api_trainable: true
---
TODO: document
## Default config {#config}
This is the default configuration used to initialize the model powering the
pipeline component. See the [model architectures](/api/architectures)
documentation for details on the architectures and their arguments and
hyperparameters. To learn more about how to customize the config and train
custom models, check out the [training config](/usage/training#config) docs.
```python
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/tok2vec_defaults.cfg
```
TODO:

2
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@ -31,7 +31,7 @@ the
> nlp = English()
> # Create a Tokenizer with the default settings for English
> # including punctuation rules and exceptions
> tokenizer = nlp.Defaults.create_tokenizer(nlp)
> tokenizer = nlp.tokenizer
> ```
| Name | Type | Description |

21
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@ -32,9 +32,10 @@ loaded in via [`Language.from_disk`](/api/language#from_disk).
> ```
| Name | Type | Description |
| ----------- | ------------ | --------------------------------------------------------------------------------- |
| ------------------------------------------ | ----------------- | --------------------------------------------------------------------------------- |
| `name` | str / `Path` | Model to load, i.e. package name or path. |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `component_cfg` <Tag variant="new">3</Tag> | `Dict[str, dict]` | Optional config overrides for pipeline components, keyed by component names. |
| **RETURNS** | `Language` | A `Language` object with the loaded model. |
Essentially, `spacy.load()` is a convenience wrapper that reads the language ID
@ -43,9 +44,10 @@ class, loads in the model data and returns it.
```python
### Abstract example
cls = util.get_lang_class(lang) # get language for ID, e.g. 'en'
nlp = cls() # initialise the language
for name in pipeline: component = nlp.create_pipe(name) # create each pipeline component nlp.add_pipe(component) # add component to pipeline
cls = util.get_lang_class(lang) # get language for ID, e.g. "en"
nlp = cls() # initialize the language
for name in pipeline:
nlp.add_pipe(name) # add component to pipeline
nlp.from_disk(model_data_path) # load in model data
```
@ -57,14 +59,13 @@ Create a blank model of a given language class. This function is the twin of
> #### Example
>
> ```python
> nlp_en = spacy.blank("en")
> nlp_de = spacy.blank("de")
> nlp_en = spacy.blank("en") # equivalent to English()
> nlp_de = spacy.blank("de") # equivalent to German()
> ```
| Name | Type | Description |
| ----------- | ----------- | ------------------------------------------------------------------------------------------------ |
| ----------- | ---------- | ------------------------------------------------------------------------------------------------ |
| `name` | str | [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) of the language class to load. |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| **RETURNS** | `Language` | An empty `Language` object of the appropriate subclass. |
#### spacy.info {#spacy.info tag="function"}
@ -79,13 +80,14 @@ meta data as a dictionary instead, you can use the `meta` attribute on your
> ```python
> spacy.info()
> spacy.info("en_core_web_sm")
> spacy.info(markdown=True)
> markdown = spacy.info(markdown=True, silent=True)
> ```
| Name | Type | Description |
| ---------- | ---- | ------------------------------------------------ |
| `model` | str | A model, i.e. a package name or path (optional). |
| `markdown` | bool | Print information as Markdown. |
| `silent` | bool | Don't print anything, just return. |
### spacy.explain {#spacy.explain tag="function"}
@ -479,7 +481,6 @@ you can use the [`set_lang_class`](/api/top-level#util.set_lang_class) helper.
> for lang_id in ["en", "de"]:
> lang_class = util.get_lang_class(lang_id)
> lang = lang_class()
> tokenizer = lang.Defaults.create_tokenizer()
> ```
| Name | Type | Description |

14
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@ -31,10 +31,11 @@ you can add vectors to later.
> ```
| Name | Type | Description |
| ----------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| -------------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| _keyword-only_ | | |
| `shape` | tuple | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. |
| `data` | `ndarray[ndim=1, dtype='float32']` | The vector data. |
| `keys` | iterable | A sequence of keys aligned with the data. |
| `shape` | tuple | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. |
| `name` | str | A name to identify the vectors table. |
| **RETURNS** | `Vectors` | The newly created object. |
@ -139,8 +140,9 @@ mapping separately. If you need to manage the strings, you should use the
> ```
| Name | Type | Description |
| ----------- | ---------------------------------- | ----------------------------------------------------- |
| -------------- | ---------------------------------- | ----------------------------------------------------- |
| `key` | str / int | The key to add. |
| _keyword-only_ | | |
| `vector` | `ndarray[ndim=1, dtype='float32']` | An optional vector to add for the key. |
| `row` | int | An optional row number of a vector to map the key to. |
| **RETURNS** | int | The row the vector was added to. |
@ -226,7 +228,8 @@ Look up one or more keys by row, or vice versa.
> ```
| Name | Type | Description |
| ----------- | ------------------------------------- | ------------------------------------------------------------------------ |
| -------------- | ------------------------------------- | ------------------------------------------------------------------------ |
| _keyword-only_ | | |
| `key` | str / int | Find the row that the given key points to. Returns int, `-1` if missing. |
| `keys` | iterable | Find rows that the keys point to. Returns `ndarray`. |
| `row` | int | Find the first key that points to the row. Returns int. |
@ -319,8 +322,9 @@ performed in chunks, to avoid consuming too much memory. You can set the
> ```
| Name | Type | Description |
| ------------ | --------- | ------------------------------------------------------------------ |
| -------------- | --------- | ------------------------------------------------------------------ |
| `queries` | `ndarray` | An array with one or more vectors. |
| _keyword-only_ | | |
| `batch_size` | int | The batch size to use. Default to `1024`. |
| `n` | int | The number of entries to return for each query. Defaults to `1`. |
| `sort` | bool | Whether to sort the entries returned by score. Defaults to `True`. |

3
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@ -137,7 +137,8 @@ have to call this to change the size of the vectors. Only one of the `width` and
> ```
| Name | Type | Description |
| ------- | ---- | -------------------------------------- |
| -------------- | ---- | -------------------------------------- |
| _keyword-only_ | | |
| `width` | int | The new width (keyword argument only). |
| `shape` | int | The new shape (keyword argument only). |

61
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@ -1,30 +1,33 @@
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 923 200" width="923" height="200">
<style>
.svg__pipeline__text { fill: #1a1e23; font: 20px Arial, sans-serif }
.svg__pipeline__text-small { fill: #1a1e23; font: bold 18px Arial, sans-serif }
.svg__pipeline__text-code { fill: #1a1e23; font: 600 16px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
</style>
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<text class="svg__pipeline__text" dy="0.75em" width="28" height="19" transform="translate(846.5 75.5)">Doc</text>
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4
website/docs/usage/101/_architecture.md
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@ -19,7 +19,7 @@ an **annotated document**. It also orchestrates training and serialization.
### Container objects {#architecture-containers}
| Name | Description |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`Doc`](/api/doc) | A container for accessing linguistic annotations. |
| [`Span`](/api/span) | A slice from a `Doc` object. |
| [`Token`](/api/token) | An individual token — i.e. a word, punctuation symbol, whitespace, etc. |
@ -52,5 +52,3 @@ an **annotated document**. It also orchestrates training and serialization.
| [`StringStore`](/api/stringstore) | Map strings to and from hash values. |
| [`Vectors`](/api/vectors) | Container class for vector data keyed by string. |
| [`Example`](/api/example) | Collection for training annotations. |
|

22
website/docs/usage/101/_pipelines.md
View File

@ -13,7 +13,7 @@ passed on to the next component.
> component.
| Name | Component | Creates | Description |
| ----------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
| ------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
| **tokenizer** | [`Tokenizer`](/api/tokenizer) | `Doc` | Segment text into tokens. |
| **tagger** | [`Tagger`](/api/tagger) | `Doc[i].tag` | Assign part-of-speech tags. |
| **parser** | [`DependencyParser`](/api/dependencyparser) | `Doc[i].head`, `Doc[i].dep`, `Doc.sents`, `Doc.noun_chunks` | Assign dependency labels. |
@ -24,17 +24,20 @@ passed on to the next component.
The processing pipeline always **depends on the statistical model** and its
capabilities. For example, a pipeline can only include an entity recognizer
component if the model includes data to make predictions of entity labels. This
is why each model will specify the pipeline to use in its meta data, as a simple
list containing the component names:
is why each model will specify the pipeline to use in its meta data and
[config](/usage/training#config), as a simple list containing the component
names:
```json
"pipeline": ["tagger", "parser", "ner"]
```ini
pipeline = ["tagger", "parser", "ner"]
```
import Accordion from 'components/accordion.js'
<Accordion title="Does the order of pipeline components matter?" id="pipeline-components-order">
<!-- TODO: note on v3 tok2vec own model vs. upstream listeners -->
In spaCy v2.x, the statistical components like the tagger or parser are
independent and don't share any data between themselves. For example, the named
entity recognizer doesn't use any features set by the tagger and parser, and so
@ -48,11 +51,10 @@ pre-defined sentence boundaries, so if a previous component in the pipeline sets
them, its dependency predictions may be different. Similarly, it matters if you
add the [`EntityRuler`](/api/entityruler) before or after the statistical entity
recognizer: if it's added before, the entity recognizer will take the existing
entities into account when making predictions.
The [`EntityLinker`](/api/entitylinker), which resolves named entities to
knowledge base IDs, should be preceded by
a pipeline component that recognizes entities such as the
[`EntityRecognizer`](/api/entityrecognizer).
entities into account when making predictions. The
[`EntityLinker`](/api/entitylinker), which resolves named entities to knowledge
base IDs, should be preceded by a pipeline component that recognizes entities
such as the [`EntityRecognizer`](/api/entityrecognizer).
</Accordion>

27
website/docs/usage/101/_training.md
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@ -1,26 +1,30 @@
spaCy's models are **statistical** and every "decision" they make – for example,
spaCy's tagger, parser, text categorizer and many other components are powered
by **statistical models**. Every "decision" these components make – for example,
which part-of-speech tag to assign, or whether a word is a named entity – is a
**prediction**. This prediction is based on the examples the model has seen
**prediction** based on the model's current **weight values**. The weight
values are estimated based on examples the model has seen
during **training**. To train a model, you first need training data – examples
of text, and the labels you want the model to predict. This could be a
part-of-speech tag, a named entity or any other information.
The model is then shown the unlabelled text and will make a prediction. Because
we know the correct answer, we can give the model feedback on its prediction in
the form of an **error gradient** of the **loss function** that calculates the
difference between the training example and the expected output. The greater the
difference, the more significant the gradient and the updates to our model.
Training is an iterative process in which the model's predictions are compared
against the reference annotations in order to estimate the **gradient of the
loss**. The gradient of the loss is then used to calculate the gradient of the
weights through [backpropagation](https://thinc.ai/backprop101). The gradients
indicate how the weight values should be changed so that the model's
predictions become more similar to the reference labels over time.
> - **Training data:** Examples and their annotations.
> - **Text:** The input text the model should predict a label for.
> - **Label:** The label the model should predict.
> - **Gradient:** Gradient of the loss function calculating the difference
> between input and expected output.
> - **Gradient:** The direction and rate of change for a numeric value.
> Minimising the gradient of the weights should result in predictions that
> are closer to the reference labels on the training data.
![The training process](../../images/training.svg)
When training a model, we don't just want it to memorize our examples – we want
it to come up with a theory that can be **generalized across other examples**.
it to come up with a theory that can be **generalized across unseen data**.
After all, we don't just want the model to learn that this one instance of
"Amazon" right here is a company – we want it to learn that "Amazon", in
contexts _like this_, is most likely a company. That's why the training data
@ -34,5 +38,4 @@ it's learning the right things, you don't only need **training data** – you'll
also need **evaluation data**. If you only test the model with the data it was
trained on, you'll have no idea how well it's generalizing. If you want to train
a model from scratch, you usually need at least a few hundred examples for both
training and evaluation. To update an existing model, you can already achieve
decent results with very few examples – as long as they're representative.
training and evaluation.

14
website/docs/usage/linguistic-features.md
View File

@ -909,9 +909,8 @@ If you're using a statistical model, writing to the `nlp.Defaults` or
`English.Defaults` directly won't work, since the regular expressions are read
from the model and will be compiled when you load it. If you modify
`nlp.Defaults`, you'll only see the effect if you call
[`spacy.blank`](/api/top-level#spacy.blank) or `Defaults.create_tokenizer()`. If
you want to modify the tokenizer loaded from a statistical model, you should
modify `nlp.tokenizer` directly.
[`spacy.blank`](/api/top-level#spacy.blank). If you want to modify the tokenizer
loaded from a statistical model, you should modify `nlp.tokenizer` directly.
</Infobox>
@ -1386,8 +1385,7 @@ import spacy
from spacy.lang.en import English
nlp = English() # just the language with no model
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer)
nlp.add_pipe("sentencizer")
doc = nlp("This is a sentence. This is another sentence.")
for sent in doc.sents:
print(sent.text)
@ -1422,6 +1420,7 @@ take advantage of dependency-based sentence segmentation.
```python
### {executable="true"}
from spacy.language import Language
import spacy
text = "this is a sentence...hello...and another sentence."
@ -1430,13 +1429,14 @@ nlp = spacy.load("en_core_web_sm")
doc = nlp(text)
print("Before:", [sent.text for sent in doc.sents])
@Language.component("set_custom_coundaries")
def set_custom_boundaries(doc):
for token in doc[:-1]:
if token.text == "...":
doc[token.i+1].is_sent_start = True
doc[token.i + 1].is_sent_start = True
return doc
nlp.add_pipe(set_custom_boundaries, before="parser")
nlp.add_pipe("set_custom_boundaries", before="parser")
doc = nlp(text)
print("After:", [sent.text for sent in doc.sents])
```

655
website/docs/usage/processing-pipelines.md
View File

@ -97,32 +97,40 @@ but also your own custom processing functions. A pipeline component can be added
to an already existing `nlp` object, specified when initializing a `Language`
class, or defined within a [model package](/usage/saving-loading#models).
When you load a model, spaCy first consults the model's
[`meta.json`](/usage/saving-loading#models). The meta typically includes the
model details, the ID of a language class, and an optional list of pipeline
components. spaCy then does the following:
> #### meta.json (excerpt)
> #### config.cfg (excerpt)
>
> ```json
> {
> "lang": "en",
> "name": "core_web_sm",
> "description": "Example model for spaCy",
> "pipeline": ["tagger", "parser", "ner"]
> }
> ```ini
> [nlp]
> lang = "en"
> pipeline = ["tagger", "parser"]
>
> [components]
>
> [components.tagger]
> factory = "tagger"
> # settings for the tagger component
>
> [components.parser]
> factory = "parser"
> # settings for the parser component
> ```
When you load a model, spaCy first consults the model's
[`meta.json`](/usage/saving-loading#models) and
[`config.cfg`](/usage/training#config). The config tells spaCy what language
class to use, which components are in the pipeline, and how those components
should be created. spaCy will then do the following:
1. Load the **language class and data** for the given ID via
[`get_lang_class`](/api/top-level#util.get_lang_class) and initialize it. The
`Language` class contains the shared vocabulary, tokenization rules and the
language-specific annotation scheme.
2. Iterate over the **pipeline names** and create each component using
[`create_pipe`](/api/language#create_pipe), which looks them up in
`Language.factories`.
3. Add each pipeline component to the pipeline in order, using
[`add_pipe`](/api/language#add_pipe).
4. Make the **model data** available to the `Language` class by calling
language-specific settings.
2. Iterate over the **pipeline names** and look up each component name in the
`[components]` block. The `factory` tells spaCy which
[component factory](#custom-components-factories) to use for adding the
component with with [`add_pipe`](/api/language#add_pipe). The settings are
passed into the factory.
3. Make the **model data** available to the `Language` class by calling
[`from_disk`](/api/language#from_disk) with the path to the model data
directory.
@ -132,17 +140,25 @@ So when you call this...
nlp = spacy.load("en_core_web_sm")
```
... the model's `meta.json` tells spaCy to use the language `"en"` and the
... the model's `config.cfg` tells spaCy to use the language `"en"` and the
pipeline `["tagger", "parser", "ner"]`. spaCy will then initialize
`spacy.lang.en.English`, and create each pipeline component and add it to the
processing pipeline. It'll then load in the model's data from its data directory
and return the modified `Language` class for you to use as the `nlp` object.
<Infobox title="Changed in v3.0" variant="warning">
spaCy v3.0 introduces a `config.cfg`, which includes more detailed settings for
the model pipeline, its components and the
[training process](/usage/training#config). You can export the config of your
current `nlp` object by calling [`nlp.config.to_disk`](/api/language#config).
</Infobox>
Fundamentally, a [spaCy model](/models) consists of three components: **the
weights**, i.e. binary data loaded in from a directory, a **pipeline** of
functions called in order, and **language data** like the tokenization rules and
annotation scheme. All of this is specific to each model, and defined in the
model's `meta.json` – for example, a Spanish NER model requires different
language-specific settings. For example, a Spanish NER model requires different
weights, language data and pipeline components than an English parsing and
tagging model. This is also why the pipeline state is always held by the
`Language` class. [`spacy.load`](/api/top-level#spacy.load) puts this all
@ -158,9 +174,8 @@ data_path = "path/to/en_core_web_sm/en_core_web_sm-2.0.0"
cls = spacy.util.get_lang_class(lang) # 1. Get Language instance, e.g. English()
nlp = cls() # 2. Initialize it
for name in pipeline:
component = nlp.create_pipe(name) # 3. Create the pipeline components
nlp.add_pipe(component) # 4. Add the component to the pipeline
nlp.from_disk(model_data_path) # 5. Load in the binary data
nlp.add_pipe(name) # 3. Add the component to the pipeline
nlp.from_disk(model_data_path) # 4. Load in the binary data
```
When you call `nlp` on a text, spaCy will **tokenize** it and then **call each
@ -190,26 +205,23 @@ print(nlp.pipe_names)
### Built-in pipeline components {#built-in}
spaCy ships with several built-in pipeline components that are also available in
the `Language.factories`. This means that you can initialize them by calling
[`nlp.create_pipe`](/api/language#create_pipe) with their string names and
require them in the pipeline settings in your model's `meta.json`.
spaCy ships with several built-in pipeline components that are registered with
string names. This means that you can initialize them by calling
[`nlp.add_pipe`](/api/language#add_pipe) with their names and spaCy will know
how to create them. See the [API documentation](/api) for a full list of
available pipeline components and component functions.
> #### Usage
>
> ```python
> # Option 1: Import and initialize
> from spacy.pipeline import EntityRuler
> ruler = EntityRuler(nlp)
> nlp.add_pipe(ruler)
>
> # Option 2: Using nlp.create_pipe
> sentencizer = nlp.create_pipe("sentencizer")
> nlp.add_pipe(sentencizer)
> nlp = spacy.blank("en")
> nlp.add_pipe("sentencizer")
> # add_pipe returns the added component
> ruler = nlp.add_pipe("entity_ruler")
> ```
| String name | Component | Description |
| ------------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
| --------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `tagger` | [`Tagger`](/api/tagger) | Assign part-of-speech-tags. |
| `parser` | [`DependencyParser`](/api/dependencyparser) | Assign dependency labels. |
| `ner` | [`EntityRecognizer`](/api/entityrecognizer) | Assign named entities. |
@ -217,9 +229,10 @@ require them in the pipeline settings in your model's `meta.json`.
| `textcat` | [`TextCategorizer`](/api/textcategorizer) | Assign text categories. |
| `entity_ruler` | [`EntityRuler`](/api/entityruler) | Assign named entities based on pattern rules. |
| `sentencizer` | [`Sentencizer`](/api/sentencizer) | Add rule-based sentence segmentation without the dependency parse. |
| `merge_noun_chunks` | [`merge_noun_chunks`](/api/pipeline-functions#merge_noun_chunks) | Merge all noun chunks into a single token. Should be added after the tagger and parser. |
| `merge_entities` | [`merge_entities`](/api/pipeline-functions#merge_entities) | Merge all entities into a single token. Should be added after the entity recognizer. |
| `merge_subtokens` | [`merge_subtokens`](/api/pipeline-functions#merge_subtokens) | Merge subtokens predicted by the parser into single tokens. Should be added after the parser. |
<!-- TODO: update with more components -->
<!-- TODO: explain default config and factories -->
### Disabling and modifying pipeline components {#disabling}
@ -233,7 +246,6 @@ list:
```python
### Disable loading
nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser"])
nlp = English().from_disk("/model", disable=["ner"])
```
In some cases, you do want to load all pipeline components and their weights,
@ -297,15 +309,18 @@ nlp.replace_pipe("tagger", my_custom_tagger)
## Creating custom pipeline components {#custom-components}
A component receives a `Doc` object and can modify it – for example, by using
the current weights to make a prediction and set some annotation on the
document. By adding a component to the pipeline, you'll get access to the `Doc`
at any point **during processing** – instead of only being able to modify it
afterwards.
A pipeline component is a function that receives a `Doc` object, modifies it and
returns it – – for example, by using the current weights to make a prediction
and set some annotation on the document. By adding a component to the pipeline,
you'll get access to the `Doc` at any point **during processing** – instead of
only being able to modify it afterwards.
> #### Example
>
> ```python
> from spacy.language import Language
>
> @Language.component("my_component")
> def my_component(doc):
> # do something to the doc here
> return doc
@ -316,6 +331,12 @@ afterwards.
| `doc` | `Doc` | The `Doc` object processed by the previous component. |
| **RETURNS** | `Doc` | The `Doc` object processed by this pipeline component. |
The [`@Language.component`](/api/language#component) decorator lets you turn a
simple function into a pipeline component. It takes at least one argument, the
**name** of the component factory. You can use this name to add an instance of
your component to the pipeline. It can also be listed in your model config, so
you can save, load and train models using your component.
Custom components can be added to the pipeline using the
[`add_pipe`](/api/language#add_pipe) method. Optionally, you can either specify
a component to add it **before or after**, tell spaCy to add it **first or
@ -325,23 +346,43 @@ last** in the pipeline, or define a **custom name**. If no name is set and no
> #### Example
>
> ```python
> nlp.add_pipe(my_component)
> nlp.add_pipe(my_component, first=True)
> nlp.add_pipe(my_component, before="parser")
> nlp.add_pipe("my_component")
> nlp.add_pipe("my_component", first=True)
> nlp.add_pipe("my_component", before="parser")
> ```
| Argument | Type | Description |
| -------- | ---- | ------------------------------------------------------------------------ |
| -------- | --------- | ------------------------------------------------------------------------ |
| `last` | bool | If set to `True`, component is added **last** in the pipeline (default). |
| `first` | bool | If set to `True`, component is added **first** in the pipeline. |
| `before` | str | String name of component to add the new component **before**. |
| `after` | str | String name of component to add the new component **after**. |
| `before` | str / int | String name or index to add the new component **before**. |
| `after` | str / int | String name or index to add the new component **after**. |
### Example: A simple pipeline component {#custom-components-simple}
<Infobox title="Changed in v3.0" variant="warning">
As of v3.0, components need to be registered using the
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory) decorator so spaCy knows that a
function is a component. [`nlp.add_pipe`](/api/language#add_pipe) now takes the
**string name** of the component factory instead of the component function. This
doesn't only save you lines of code, it also allows spaCy to validate and track
your custom components, and make sure they can be saved and loaded.
```diff
- ruler = nlp.create_pipe("entity_ruler")
- nlp.add_pipe(ruler)
+ ruler = nlp.add_pipe("entity_ruler")
```
</Infobox>
### Examples: Simple stateless pipeline components {#custom-components-simple}
The following component receives the `Doc` in the pipeline and prints some
information about it: the number of tokens, the part-of-speech tags of the
tokens and a conditional message based on the document length.
tokens and a conditional message based on the document length. The
[`@Language.component`](/api/language#component) decorator lets you register the
component under the name `"info_component"`.
> #### ✏️ Things to try
>
@ -352,11 +393,16 @@ tokens and a conditional message based on the document length.
> this change reflected in `nlp.pipe_names`.
> 3. Print `nlp.pipeline`. You'll see a list of tuples describing the component
> name and the function that's called on the `Doc` object in the pipeline.
> 4. Change the first argument to `@Language.component`, the name, to something
> else. spaCy should now complain that it doesn't know a component of the
> name `"info_component"`.
```python
### {executable="true"}
import spacy
from spacy.language import Language
@Language.component("info_component")
def my_component(doc):
print(f"After tokenization, this doc has {len(doc)} tokens.")
print("The part-of-speech tags are:", [token.pos_ for token in doc])
@ -365,76 +411,16 @@ def my_component(doc):
return doc
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(my_component, name="print_info", last=True)
nlp.add_pipe("info_component", name="print_info", last=True)
print(nlp.pipe_names) # ['tagger', 'parser', 'ner', 'print_info']
doc = nlp("This is a sentence.")
```
Of course, you can also wrap your component as a class to allow initializing it
with custom settings and hold state within the component. This is useful for
**stateful components**, especially ones which **depend on shared data**. In the
following example, the custom component `EntityMatcher` can be initialized with
`nlp` object, a terminology list and an entity label. Using the
[`PhraseMatcher`](/api/phrasematcher), it then matches the terms in the `Doc`
and adds them to the existing entities.
<Infobox title="Important note" variant="warning">
As of v2.1.0, spaCy ships with the [`EntityRuler`](/api/entityruler), a pipeline
component for easy, rule-based named entity recognition. Its implementation is
similar to the `EntityMatcher` code shown below, but it includes some additional
features like support for phrase patterns and token patterns, handling overlaps
with existing entities and pattern export as JSONL.
We'll still keep the pipeline component example below, as it works well to
illustrate complex components. But if you're planning on using this type of
component in your application, you might find the `EntityRuler` more convenient.
[See here](/usage/rule-based-matching#entityruler) for more details and
examples.
</Infobox>
```python
### {executable="true"}
import spacy
from spacy.matcher import PhraseMatcher
from spacy.tokens import Span
class EntityMatcher:
name = "entity_matcher"
def __init__(self, nlp, terms, label):
patterns = [nlp.make_doc(text) for text in terms]
self.matcher = PhraseMatcher(nlp.vocab)
self.matcher.add(label, patterns)
def __call__(self, doc):
matches = self.matcher(doc)
for match_id, start, end in matches:
span = Span(doc, start, end, label=match_id)
doc.ents = list(doc.ents) + [span]
return doc
nlp = spacy.load("en_core_web_sm")
terms = ("cat", "dog", "tree kangaroo", "giant sea spider")
entity_matcher = EntityMatcher(nlp, terms, "ANIMAL")
nlp.add_pipe(entity_matcher, after="ner")
print(nlp.pipe_names) # The components in the pipeline
doc = nlp("This is a text about Barack Obama and a tree kangaroo")
print([(ent.text, ent.label_) for ent in doc.ents])
```
### Example: Custom sentence segmentation logic {#component-example1}
Let's say you want to implement custom logic to improve spaCy's sentence
boundary detection. Currently, sentence segmentation is based on the dependency
parse, which doesn't always produce ideal results. The custom logic should
therefore be applied **after** tokenization, but _before_ the dependency parsing
– this way, the parser can also take advantage of the sentence boundaries.
Here's another example of a pipeline component that implements custom logic to
improve the sentence boundaries set by the dependency parser. The custom logic
should therefore be applied **after** tokenization, but _before_ the dependency
parsing – this way, the parser can also take advantage of the sentence
boundaries.
> #### ✏️ Things to try
>
@ -448,90 +434,318 @@ therefore be applied **after** tokenization, but _before_ the dependency parsing
```python
### {executable="true"}
import spacy
from spacy.language import Language
@Language.component("custom_sentencizer")
def custom_sentencizer(doc):
for i, token in enumerate(doc[:-2]):
# Define sentence start if pipe + titlecase token
if token.text == "|" and doc[i+1].is_title:
doc[i+1].is_sent_start = True
if token.text == "|" and doc[i + 1].is_title:
doc[i + 1].is_sent_start = True
else:
# Explicitly set sentence start to False otherwise, to tell
# the parser to leave those tokens alone
doc[i+1].is_sent_start = False
doc[i + 1].is_sent_start = False
return doc
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(custom_sentencizer, before="parser") # Insert before the parser
nlp.add_pipe("custom_sentencizer", before="parser") # Insert before the parser
doc = nlp("This is. A sentence. | This is. Another sentence.")
for sent in doc.sents:
print(sent.text)
```
### Example: Pipeline component for entity matching and tagging with custom attributes {#component-example2}
### Component factories and stateful components {#custom-components-factories}
This example shows how to create a spaCy extension that takes a terminology list
(in this case, single- and multi-word company names), matches the occurrences in
a document, labels them as `ORG` entities, merges the tokens and sets custom
`is_tech_org` and `has_tech_org` attributes. For efficient matching, the example
uses the [`PhraseMatcher`](/api/phrasematcher) which accepts `Doc` objects as
match patterns and works well for large terminology lists. It also ensures your
patterns will always match, even when you customize spaCy's tokenization rules.
When you call `nlp` on a text, the custom pipeline component is applied to the
`Doc`.
```python
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_entities.py
```
Wrapping this functionality in a pipeline component allows you to reuse the
module with different settings, and have all pre-processing taken care of when
you call `nlp` on your text and receive a `Doc` object.
### Adding factories {#custom-components-factories}
When spaCy loads a model via its `meta.json`, it will iterate over the
`"pipeline"` setting, look up every component name in the internal factories and
call [`nlp.create_pipe`](/api/language#create_pipe) to initialize the individual
components, like the tagger, parser or entity recognizer. If your model uses
custom components, this won't work – so you'll have to tell spaCy **where to
find your component**. You can do this by writing to the `Language.factories`:
Component factories are callables that take settings and return a **pipeline
component function**. This is useful if your component is stateful and if you
need to customize their creation, or if you need access to the current `nlp`
object or the shared vocab. Component factories can be registered using the
[`@Language.factory`](/api/language#factory) decorator and they need at least
**two named arguments** that are filled in automatically when the component is
added to the pipeline:
> #### Example
>
> ```python
> from spacy.language import Language
>
> @Language.factory("my_component")
> def my_component(nlp, name):
> return MyComponent()
> ```
| Argument | Type | Description |
| -------- | --------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
| `nlp` | [`Language`](/api/language) | The current `nlp` object. Can be used to access the |
| `name` | str | The **instance name** of the component in the pipeline. This lets you identify different instances of the same component. |
All other settings can be passed in by the user via the `config` argument on
[`nlp.add_pipe`](/api/language). The
[`@Language.factory`](/api/language#factory) decorator also lets you define a
`default_config` that's used as a fallback.
```python
### With config {highlight="4,9"}
import spacy
from spacy.language import Language
Language.factories["entity_matcher"] = lambda nlp, **cfg: EntityMatcher(nlp, **cfg)
@Language.factory("my_component", default_config={"some_setting": True})
def my_component(nlp, name, some_setting: bool):
return MyComponent(some_setting=some_setting)
nlp = spacy.blank("en")
nlp.add_pipe("my_component", config={"some_setting": False})
```
You can also ship the above code and your custom component in your packaged
model's `__init__.py`, so it's executed when you load your model. The `**cfg`
config parameters are passed all the way down from
[`spacy.load`](/api/top-level#spacy.load), so you can load the model and its
components with custom settings:
<Accordion title="How is @Language.factory different from @Language.component?" id="factories-decorator-component">
The [`@Language.component`](/api/language#component) decorator is essentially a
**shortcut** for stateless pipeline component that don't need any settings. This
means you don't have to always write a function that returns your function if
there's no state to be passed through – spaCy can just take care of this for
you. The following two code examples are equivalent:
```python
nlp = spacy.load("your_custom_model", terms=["tree kangaroo"], label="ANIMAL")
# Statless component with @Language.factory
@Language.factory("my_component")
def create_my_component():
def my_component(doc):
# Do something to the doc
return doc
return my_component
# Stateless component with @Language.component
@Language.component("my_component")
def my_component(doc):
# Do something to the doc
return doc
```
<Infobox title="Important note" variant="warning">
</Accordion>
When you load a model via its package name, like `en_core_web_sm`, spaCy will
import the package and then call its `load()` method. This means that custom
code in the model's `__init__.py` will be executed, too. This is **not the
case** if you're loading a model from a path containing the model data. Here,
spaCy will only read in the `meta.json`. If you want to use custom factories
with a model loaded from a path, you need to add them to `Language.factories`
_before_ you load the model.
<Accordion title="Can I add the @Language.factory decorator to a class?" id="factories-class-decorator" spaced>
Yes, the [`@Language.factory`](/api/language#factory) decorator can be added to
a function or a class. If it's added to a class, it expects the `__init__`
method to take the arguments `nlp` and `name`, and will populate all other
arguments from the config. That said, it's often cleaner and more intuitive to
make your factory a separate function. That's also how spaCy does it internally.
</Accordion>
### Example: Stateful component with settings
This example shows a **stateful** pipeline component for handling acronyms:
based on a dictionary, it will detect acronyms and their expanded forms in both
directions and add them to a list as the custom `doc._.acronyms`
[extension attribute](#custom-components-attributes). Under the hood, it uses
the [`PhraseMatcher`](/api/phrasematcher) to find instances of the phrases.
The factory function takes three arguments: the shared `nlp` object and
component instance `name`, which are passed in automatically by spaCy, and a
`case_sensitive` config setting that makes the matching and acronym detection
case-sensitive.
> #### ✏️ Things to try
>
> 1. Change the `config` passed to `nlp.add_pipe` and set `"case_sensitive"` to
> `True`. You should see that the expanded acronym for "LOL" isn't detected
> anymore.
> 2. Add some more terms to the `DICTIONARY` and update the processed text so
> they're detected.
> 3. Add a `name` argument to `nlp.add_pipe` to change the component name. Print
> `nlp.pipe_names` to see the change reflected in the pipeline.
> 4. Print the config of the current `nlp` object with
> `print(nlp.config.to_str())` and inspect the `[components]` block. You
> should see an entry for the acronyms component, referencing the factory
> `acronyms` and the config settings.
```python
### {executable="true"}
from spacy.language import Language
from spacy.tokens import Doc
from spacy.matcher import PhraseMatcher
import spacy
DICTIONARY = {"lol": "laughing out loud", "brb": "be right back"}
DICTIONARY.update({value: key for key, value in DICTIONARY.items()})
@Language.factory("acronyms", default_config={"case_sensitive": False})
def create_acronym_component(nlp: Language, name: str, case_sensitive: bool):
return AcronymComponent(nlp, case_sensitive)
class AcronymComponent:
def __init__(self, nlp: Language, case_sensitive: bool):
# Create the matcher and match on Token.lower if case-insensitive
matcher_attr = "TEXT" if case_sensitive else "LOWER"
self.matcher = PhraseMatcher(nlp.vocab, attr=matcher_attr)
self.matcher.add("ACRONYMS", [nlp.make_doc(term) for term in DICTIONARY])
self.case_sensitive = case_sensitive
# Register custom extension on the Doc
if not Doc.has_extension("acronyms"):
Doc.set_extension("acronyms", default=[])
def __call__(self, doc: Doc) -> Doc:
# Add the matched spans when doc is processed
for _, start, end in self.matcher(doc):
span = doc[start:end]
acronym = DICTIONARY.get(span.text if self.case_sensitive else span.text.lower())
doc._.acronyms.append((span, acronym))
return doc
# Add the component to the pipeline and configure it
nlp = spacy.blank("en")
nlp.add_pipe("acronyms", config={"case_sensitive": False})
# Process a doc and see the results
doc = nlp("LOL, be right back")
print(doc._.acronyms)
```
### Python type hints and pydantic validation {#type-hints new="3"}
spaCy's configs are powered by our machine learning library Thinc's
[configuration system](https://thinc.ai/docs/usage-config), which supports
[type hints](https://docs.python.org/3/library/typing.html) and even
[advanced type annotations](https://thinc.ai/docs/usage-config#advanced-types)
using [`pydantic`](https://github.com/samuelcolvin/pydantic). If your component
factory provides type hints, the values that are passed in will be **checked
against the expected types**. If the value can't be cast to an integer, spaCy
will raise an error. `pydantic` also provides strict types like `StrictFloat`,
which will force the value to be an integer and raise an error if it's not – for
instance, if your config defines a float.
<Infobox variant="warning">
If you're not using
[strict types](https://pydantic-docs.helpmanual.io/usage/types/#strict-types),
values that can be **cast to** the given type will still be accepted. For
example, `1` can be cast to a `float` or a `bool` type, but not to a
`List[str]`. However, if the type is
[`StrictFloat`](https://pydantic-docs.helpmanual.io/usage/types/#strict-types),
only a float will be accepted.
</Infobox>
The following example shows a custom pipeline component for debugging. It can be
added anywhere in the pipeline and logs information about the `nlp` object and
the `Doc` that passes through. The `log_level` config setting lets the user
customize what log statements are shown – for instance, `"INFO"` will show info
logs and more critical logging statements, whereas `"DEBUG"` will show
everything. The value is annotated as a `StrictStr`, so it will only accept a
string value.
> #### ✏️ Things to try
>
> 1. Change the `config` passed to `nlp.add_pipe` to use the log level `"INFO"`.
> You should see that only the statement logged with `logger.info` is shown.
> 2. Change the `config` passed to `nlp.add_pipe` so that it contains unexpected
> values – for example, a boolean instead of a string: `"log_level": False`.
> You should see a validation error.
> 3. Check out the docs on `pydantic`'s
> [constrained types](https://pydantic-docs.helpmanual.io/usage/types/#constrained-types)
> and write a type hint for `log_level` that only accepts the exact string
> values `"DEBUG"`, `"INFO"` or `"CRITICAL"`.
```python
### {executable="true"}
import spacy
from spacy.language import Language
from spacy.tokens import Doc
from pydantic import StrictStr
import logging
@Language.factory("debug", default_config={"log_level": "DEBUG"})
class DebugComponent:
def __init__(self, nlp: Language, name: str, log_level: StrictStr):
self.logger = logging.getLogger(f"spacy.{name}")
self.logger.setLevel(log_level)
self.logger.info(f"Pipeline: {nlp.pipe_names}")
def __call__(self, doc: Doc) -> Doc:
self.logger.debug(f"Doc: {len(doc)} tokens, is_tagged: {doc.is_tagged}")
return doc
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("debug", config={"log_level": "DEBUG"})
doc = nlp("This is a text...")
```
### Language-specific factories {#factories-language new="3"}
There are many use case where you might want your pipeline components to be
language-specific. Sometimes this requires entirely different implementation per
language, sometimes the only difference is in the settings or data. spaCy allows
you to register factories of the **same name** on both the `Language` base
class, as well as its **subclasses** like `English` or `German`. Factories are
resolved starting with the specific subclass. If the subclass doesn't define a
component of that name, spaCy will check the `Language` base class.
Here's an example of a pipeline component that overwrites the normalized form of
a token, the `Token.norm_` with an entry from a language-specific lookup table.
It's registered twice under the name `"token_normalizer"` – once using
`@English.factory` and once using `@German.factory`:
```python
### {executable="true"}
from spacy.lang.en import English
from spacy.lang.de import German
class TokenNormalizer:
def __init__(self, norm_table):
self.norm_table = norm_table
def __call__(self, doc):
for token in doc:
# Overwrite the token.norm_ if there's an entry in the data
token.norm_ = self.norm_table.get(token.text, token.norm_)
return doc
@English.factory("token_normalizer")
def create_en_normalizer(nlp, name):
return TokenNormalizer({"realise": "realize", "colour": "color"})
@German.factory("token_normalizer")
def create_de_normalizer(nlp, name):
return TokenNormalizer({"daß": "dass", "wußte": "wusste"})
nlp_en = English()
nlp_en.add_pipe("token_normalizer") # uses the English factory
print([token.norm_ for token in nlp_en("realise colour daß wußte")])
nlp_de = German()
nlp_de.add_pipe("token_normalizer") # uses the German factory
print([token.norm_ for token in nlp_de("realise colour daß wußte")])
```
<Infobox title="Implementation details">
Under the hood, language-specific factories are added to the
[`factories` registry](/api/top-level#registry) prefixed with the language code,
e.g. `"en.token_normalizer"`. When resolving the factory in
[`nlp.add_pipe`](/api/language#add_pipe), spaCy first checks for a
language-specific version of the factory using `nlp.lang` and if none is
available, falls back to looking up the regular factory name.
</Infobox>
<!-- TODO:
### Trainable components {#trainable new="3"}
-->
## Extension attributes {#custom-components-attributes new="2"}
As of v2.0, spaCy allows you to set any custom attributes and methods on the
`Doc`, `Span` and `Token`, which become available as `Doc._`, `Span._` and
`Token._` – for example, `Token._.my_attr`. This lets you store additional
information relevant to your application, add new features and functionality to
spaCy, and implement your own models trained with other machine learning
libraries. It also lets you take advantage of spaCy's data structures and the
`Doc` object as the "single source of truth".
spaCy allows you to set any custom attributes and methods on the `Doc`, `Span`
and `Token`, which become available as `Doc._`, `Span._` and `Token._` – for
example, `Token._.my_attr`. This lets you store additional information relevant
to your application, add new features and functionality to spaCy, and implement
your own models trained with other machine learning libraries. It also lets you
take advantage of spaCy's data structures and the `Doc` object as the "single
source of truth".
<Accordion title="Why ._ and not just a top-level attribute?" id="why-dot-underscore">
@ -641,7 +855,73 @@ attributes on the `Doc`, `Span` and `Token` – for example, the capital,
latitude/longitude coordinates and even the country flag.
```python
https://github.com/explosion/spaCy/tree/master/examples/pipeline/custom_component_countries_api.py
### {executable="true"}
import requests
from spacy.lang.en import English
from spacy.language import Language
from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc, Span, Token
@Language.factory("rest_countries")
class RESTCountriesComponent:
def __init__(self, nlp, name, label="GPE"):
r = requests.get("https://restcountries.eu/rest/v2/all")
r.raise_for_status() # make sure requests raises an error if it fails
countries = r.json()
# Convert API response to dict keyed by country name for easy lookup
self.countries = {c["name"]: c for c in countries}
self.label = label
# Set up the PhraseMatcher with Doc patterns for each country name
self.matcher = PhraseMatcher(nlp.vocab)
self.matcher.add("COUNTRIES", [nlp.make_doc(c) for c in self.countries.keys()])
# Register attribute on the Token. We'll be overwriting this based on
# the matches, so we're only setting a default value, not a getter.
Token.set_extension("is_country", default=False)
Token.set_extension("country_capital", default=False)
Token.set_extension("country_latlng", default=False)
Token.set_extension("country_flag", default=False)
# Register attributes on Doc and Span via a getter that checks if one of
# the contained tokens is set to is_country == True.
Doc.set_extension("has_country", getter=self.has_country)
Span.set_extension("has_country", getter=self.has_country)
def __call__(self, doc):
spans = [] # keep the spans for later so we can merge them afterwards
for _, start, end in self.matcher(doc):
# Generate Span representing the entity & set label
entity = Span(doc, start, end, label=self.label)
spans.append(entity)
# Set custom attribute on each token of the entity
# Can be extended with other data returned by the API, like
# currencies, country code, flag, calling code etc.
for token in entity:
token._.set("is_country", True)
token._.set("country_capital", self.countries[entity.text]["capital"])
token._.set("country_latlng", self.countries[entity.text]["latlng"])
token._.set("country_flag", self.countries[entity.text]["flag"])
# Iterate over all spans and merge them into one token
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
# Overwrite doc.ents and add entity – be careful not to replace!
doc.ents = list(doc.ents) + spans
return doc # don't forget to return the Doc!
def has_country(self, tokens):
"""Getter for Doc and Span attributes. Since the getter is only called
when we access the attribute, we can refer to the Token's 'is_country'
attribute here, which is already set in the processing step."""
return any([t._.get("is_country") for t in tokens])
nlp = English()
nlp.add_pipe("rest_countries", config={"label": "GPE"})
doc = nlp("Some text about Colombia and the Czech Republic")
print("Pipeline", nlp.pipe_names) # pipeline contains component name
print("Doc has countries", doc._.has_country) # Doc contains countries
for token in doc:
if token._.is_country:
print(token.text, token._.country_capital, token._.country_latlng, token._.country_flag)
print("Entities", [(e.text, e.label_) for e in doc.ents])
```
In this case, all data can be fetched on initialization in one request. However,
@ -800,11 +1080,6 @@ function that takes a `Doc`, modifies it and returns it.
[`load_model_from_path`](/api/top-level#util.load_model_from_path) utility
functions.
```diff
+ nlp.add_pipe(my_custom_component)
+ return nlp.from_disk(model_path)
```
- Once you're ready to share your extension with others, make sure to **add docs
and installation instructions** (you can always link to this page for more
info). Make it easy for others to install and use your extension, for example
@ -838,10 +1113,12 @@ wrapper has to do is compute the entity spans and overwrite the `doc.ents`.
> overlapping entity spans are not allowed.
```python
### {highlight="1,6-7"}
### {highlight="1,8-9"}
import your_custom_entity_recognizer
from spacy.gold import offsets_from_biluo_tags
from spacy.language import Language
@Language.component("custom_ner_wrapper")
def custom_ner_wrapper(doc):
words = [token.text for token in doc]
custom_entities = your_custom_entity_recognizer(words)
@ -865,22 +1142,24 @@ because it returns the integer ID of the string _and_ makes sure it's added to
the vocab. This is especially important if the custom model uses a different
label scheme than spaCy's default models.
> #### Example: spacy-stanfordnlp
> #### Example: spacy-stanza
>
> For an example of an end-to-end wrapper for statistical tokenization, tagging
> and parsing, check out
> [`spacy-stanfordnlp`](https://github.com/explosion/spacy-stanfordnlp). It uses
> a very similar approach to the example in this section – the only difference
> is that it fully replaces the `nlp` object instead of providing a pipeline
> component, since it also needs to handle tokenization.
> [`spacy-stanza`](https://github.com/explosion/spacy-stanza). It uses a very
> similar approach to the example in this section – the only difference is that
> it fully replaces the `nlp` object instead of providing a pipeline component,
> since it also needs to handle tokenization.
```python
### {highlight="1,9,15-17"}
### {highlight="1,11,17-19"}
import your_custom_model
from spacy.language import Language
from spacy.symbols import POS, TAG, DEP, HEAD
from spacy.tokens import Doc
import numpy
@Language.component("custom_model_wrapper")
def custom_model_wrapper(doc):
words = [token.text for token in doc]
spaces = [token.whitespace for token in doc]

8
website/docs/usage/projects.md
View File

@ -450,6 +450,14 @@ git init # Initialize a Git repo
dvc init # Initialize a DVC project
```
<Infobox title="Important note on privacy" variant="warning">
DVC enables usage analytics by default, so if you're working in a
privacy-sensitive environment, make sure to
[**opt-out manually**](https://dvc.org/doc/user-guide/analytics#opting-out).
</Infobox>
The [`spacy project dvc`](/api/cli#project-dvc) command creates a `dvc.yaml`
config file based on a workflow defined in your `project.yml`. Whenever you
update your project, you can re-run the command to update your DVC config. You

106
website/docs/usage/rule-based-matching.md
View File

@ -506,11 +506,16 @@ attribute `bad_html` on the token.
```python
### {executable="true"}
import spacy
from spacy.language import Language
from spacy.matcher import Matcher
from spacy.tokens import Token
# We're using a class because the component needs to be initialized with
# the shared vocab via the nlp object
# We're using a component factory because the component needs to be initialized
# with the shared vocab via the nlp object
@Language.factory("html_merger")
def create_bad_html_merger(nlp, name):
return BadHTMLMerger(nlp)
class BadHTMLMerger:
def __init__(self, nlp):
patterns = [
@ -536,8 +541,7 @@ class BadHTMLMerger:
return doc
nlp = spacy.load("en_core_web_sm")
html_merger = BadHTMLMerger(nlp)
nlp.add_pipe(html_merger, last=True) # Add component to the pipeline
nlp.add_pipe("html_merger", last=True) # Add component to the pipeline
doc = nlp("Hello<br>world! <br/> This is a test.")
for token in doc:
print(token.text, token._.bad_html)
@ -546,10 +550,16 @@ for token in doc:
Instead of hard-coding the patterns into the component, you could also make it
take a path to a JSON file containing the patterns. This lets you reuse the
component with different patterns, depending on your application:
component with different patterns, depending on your application. When adding
the component to the pipeline with [`nlp.add_pipe`](/api/language#add_pipe), you
can pass in the argument via the `config`:
```python
html_merger = BadHTMLMerger(nlp, path="/path/to/patterns.json")
@Language.factory("html_merger", default_config={"path": None})
def create_bad_html_merger(nlp, name, path):
return BadHTMLMerger(nlp, path=path)
nlp.add_pipe("html_merger", config={"path": "/path/to/patterns.json"})
```
<Infobox title="Processing pipelines" emoji="📖">
@ -835,7 +845,7 @@ patterns can contain single or multiple tokens.
import spacy
from spacy.matcher import PhraseMatcher
nlp = spacy.load('en_core_web_sm')
nlp = spacy.load("en_core_web_sm")
matcher = PhraseMatcher(nlp.vocab)
terms = ["Barack Obama", "Angela Merkel", "Washington, D.C."]
# Only run nlp.make_doc to speed things up
@ -975,14 +985,12 @@ chosen.
```python
### {executable="true"}
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
nlp = English()
ruler = EntityRuler(nlp)
ruler = nlp.add_pipe("entity_ruler")
patterns = [{"label": "ORG", "pattern": "Apple"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "francisco"}]}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc = nlp("Apple is opening its first big office in San Francisco.")
print([(ent.text, ent.label_) for ent in doc.ents])
@ -1000,13 +1008,11 @@ can set `overwrite_ents=True` on initialization.
```python
### {executable="true"}
import spacy
from spacy.pipeline import EntityRuler
nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp)
ruler = nlp.add_pipe("entity_ruler")
patterns = [{"label": "ORG", "pattern": "MyCorp Inc."}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc = nlp("MyCorp Inc. is a company in the U.S.")
print([(ent.text, ent.label_) for ent in doc.ents])
@ -1014,12 +1020,12 @@ print([(ent.text, ent.label_) for ent in doc.ents])
#### Validating and debugging EntityRuler patterns {#entityruler-pattern-validation new="2.1.8"}
The `EntityRuler` can validate patterns against a JSON schema with the option
`validate=True`. See details under
The entity ruler can validate patterns against a JSON schema with the config
setting `"validate"`. See details under
[Validating and debugging patterns](#pattern-validation).
```python
ruler = EntityRuler(nlp, validate=True)
ruler = nlp.add_pipe("entity_ruler", config={"validate": True})
```
### Adding IDs to patterns {#entityruler-ent-ids new="2.2.2"}
@ -1031,15 +1037,13 @@ the same entity.
```python
### {executable="true"}
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
nlp = English()
ruler = EntityRuler(nlp)
ruler = nlp.add_pipe("entity_ruler")
patterns = [{"label": "ORG", "pattern": "Apple", "id": "apple"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "francisco"}], "id": "san-francisco"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "fran"}], "id": "san-francisco"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc1 = nlp("Apple is opening its first big office in San Francisco.")
print([(ent.text, ent.label_, ent.ent_id_) for ent in doc1.ents])
@ -1068,7 +1072,7 @@ line.
```python
ruler.to_disk("./patterns.jsonl")
new_ruler = EntityRuler(nlp).from_disk("./patterns.jsonl")
new_ruler = nlp.add_pipe("entity_ruler").from_disk("./patterns.jsonl")
```
<Infobox title="Integration with Prodigy">
@ -1086,9 +1090,8 @@ pipeline, its patterns are automatically exported to the model directory:
```python
nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp)
ruler = nlp.add_pipe("entity_ruler")
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
nlp.add_pipe(ruler)
nlp.to_disk("/path/to/model")
```
@ -1100,35 +1103,30 @@ powerful model packages with binary weights _and_ rules included!
### Using a large number of phrase patterns {#entityruler-large-phrase-patterns new="2.2.4"}
<!-- TODO: double-check that this still works if the ruler is added to the pipeline on creation, and include suggestion if needed -->
When using a large amount of **phrase patterns** (roughly > 10000) it's useful
to understand how the `add_patterns` function of the EntityRuler works. For each
**phrase pattern**, the EntityRuler calls the nlp object to construct a doc
to understand how the `add_patterns` function of the entity ruler works. For
each **phrase pattern**, the EntityRuler calls the nlp object to construct a doc
object. This happens in case you try to add the EntityRuler at the end of an
existing pipeline with, for example, a POS tagger and want to extract matches
based on the pattern's POS signature.
In this case you would pass a config value of `phrase_matcher_attr="POS"` for
the EntityRuler.
based on the pattern's POS signature. In this case you would pass a config value
of `"phrase_matcher_attr": "POS"` for the entity ruler.
Running the full language pipeline across every pattern in a large list scales
linearly and can therefore take a long time on large amounts of phrase patterns.
As of spaCy 2.2.4 the `add_patterns` function has been refactored to use
nlp.pipe on all phrase patterns resulting in about a 10x-20x speed up with
5,000-100,000 phrase patterns respectively.
Even with this speedup (but especially if you're using an older version) the
`add_patterns` function can still take a long time.
An easy workaround to make this function run faster is disabling the other
language pipes while adding the phrase patterns.
5,000-100,000 phrase patterns respectively. Even with this speedup (but
especially if you're using an older version) the `add_patterns` function can
still take a long time. An easy workaround to make this function run faster is
disabling the other language pipes while adding the phrase patterns.
```python
entityruler = EntityRuler(nlp)
ruler = nlp.add_pipe("entity_ruler")
patterns = [{"label": "TEST", "pattern": str(i)} for i in range(100000)]
with nlp.select_pipes(enable="tagger"):
entityruler.add_patterns(patterns)
ruler.add_patterns(patterns)
```
## Combining models and rules {#models-rules}
@ -1189,9 +1187,11 @@ have in common is that _if_ they occur, they occur in the **previous token**
right before the person entity.
```python
### {highlight="7-11"}
### {highlight="9-13"}
from spacy.language import Language
from spacy.tokens import Span
@Language.component("expand_person_entities")
def expand_person_entities(doc):
new_ents = []
for ent in doc.ents:
@ -1210,18 +1210,20 @@ def expand_person_entities(doc):
```
The above function takes a `Doc` object, modifies its `doc.ents` and returns it.
This is exactly what a [pipeline component](/usage/processing-pipelines) does,
so in order to let it run automatically when processing a text with the `nlp`
object, we can use [`nlp.add_pipe`](/api/language#add_pipe) to add it to the
current pipeline.
Using the [`@Language.component`](/api/language#component) decorator, we can
register it as a [pipeline component](/usage/processing-pipelines) so it can run
automatically when processing a text. We can use
[`nlp.add_pipe`](/api/language#add_pipe) to add it to the current pipeline.
```python
### {executable="true"}
import spacy
from spacy.language import Language
from spacy.tokens import Span
nlp = spacy.load("en_core_web_sm")
@Language.component("expand_person_entities")
def expand_person_entities(doc):
new_ents = []
for ent in doc.ents:
@ -1236,7 +1238,7 @@ def expand_person_entities(doc):
return doc
# Add the component after the named entity recognizer
nlp.add_pipe(expand_person_entities, after='ner')
nlp.add_pipe("expand_person_entities", after="ner")
doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
print([(ent.text, ent.label_) for ent in doc.ents])
@ -1347,7 +1349,7 @@ for ent in person_entities:
# children, e.g. at -> Acme Corp Inc.
orgs = [token for token in prep.children if token.ent_type_ == "ORG"]
# If the verb is in past tense, the company was a previous company
print({'person': ent, 'orgs': orgs, 'past': head.tag_ == "VBD"})
print({"person": ent, "orgs": orgs, "past": head.tag_ == "VBD"})
```
To apply this logic automatically when we process a text, we can add it to the
@ -1374,11 +1376,12 @@ the entity `Span` – for example `._.orgs` or `._.prev_orgs` and
```python
### {executable="true"}
import spacy
from spacy.pipeline import merge_entities
from spacy.language import Language
from spacy import displacy
nlp = spacy.load("en_core_web_sm")
@Language.component("extract_person_orgs")
def extract_person_orgs(doc):
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
for ent in person_entities:
@ -1391,12 +1394,12 @@ def extract_person_orgs(doc):
return doc
# To make the entities easier to work with, we'll merge them into single tokens
nlp.add_pipe(merge_entities)
nlp.add_pipe(extract_person_orgs)
nlp.add_pipe("merge_entities")
nlp.add_pipe("extract_person_orgs")
doc = nlp("Alex Smith worked at Acme Corp Inc.")
# If you're not in a Jupyter / IPython environment, use displacy.serve
displacy.render(doc, options={'fine_grained': True})
displacy.render(doc, options={"fine_grained": True})
```
If you change the sentence structure above, for example to "was working", you'll
@ -1409,7 +1412,8 @@ information is in the attached auxiliary "was":
To solve this, we can adjust the rules to also check for the above construction:
```python
### {highlight="9-11"}
### {highlight="10-12"}
@Language.component("extract_person_orgs")
def extract_person_orgs(doc):
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
for ent in person_entities:

310
website/docs/usage/saving-loading.md
View File

@ -15,6 +15,8 @@ import Serialization101 from 'usage/101/\_serialization.md'
### Serializing the pipeline {#pipeline}
<!-- TODO: update this -->
When serializing the pipeline, keep in mind that this will only save out the
**binary data for the individual components** to allow spaCy to restore them –
not the entire objects. This is a good thing, because it makes serialization
@ -22,32 +24,35 @@ safe. But it also means that you have to take care of storing the language name
and pipeline component names as well, and restoring them separately before you
can load in the data.
> #### Saving the model meta
> #### Saving the meta and config
>
> The `nlp.meta` attribute is a JSON-serializable dictionary and contains all
> model meta information, like the language and pipeline, but also author and
> license information.
> The [`nlp.meta`](/api/language#meta) attribute is a JSON-serializable
> dictionary and contains all model meta information like the author and license
> information. The [`nlp.config`](/api/language#config) attribute is a
> dictionary containing the training configuration, pipeline component factories
> and other settings. It is saved out with a model as the `config.cfg`.
```python
### Serialize
bytes_data = nlp.to_bytes()
lang = nlp.meta["lang"] # "en"
pipeline = nlp.meta["pipeline"] # ["tagger", "parser", "ner"]
lang = nlp.config["nlp"]["lang"] # "en"
pipeline = nlp.config["nlp"]["pipeline"] # ["tagger", "parser", "ner"]
```
```python
### Deserialize
nlp = spacy.blank(lang)
for pipe_name in pipeline:
pipe = nlp.create_pipe(pipe_name)
nlp.add_pipe(pipe)
nlp.add_pipe(pipe_name)
nlp.from_bytes(bytes_data)
```
This is also how spaCy does it under the hood when loading a model: it loads the
model's `meta.json` containing the language and pipeline information,
initializes the language class, creates and adds the pipeline components and
_then_ loads in the binary data. You can read more about this process
model's `config.cfg` containing the language and pipeline information,
initializes the language class, creates and adds the pipeline components based
on the defined
[factories](/usage/processing-pipeline#custom-components-factories) and _then_
loads in the binary data. You can read more about this process
[here](/usage/processing-pipelines#pipelines).
### Serializing Doc objects efficiently {#docs new="2.2"}
@ -192,10 +197,9 @@ add to that data and saves and loads the data to and from a JSON file.
> recognizer and including all rules _with_ the model data.
```python
### {highlight="15-19,21-26"}
### {highlight="14-18,20-25"}
@Language.factory("my_component")
class CustomComponent:
name = "my_component"
def __init__(self):
self.data = []
@ -228,9 +232,8 @@ component's `to_disk` method.
```python
### {highlight="2-4"}
nlp = spacy.load("en_core_web_sm")
my_component = CustomComponent()
my_component = nlp.add_pipe("my_component")
my_component.add({"hello": "world"})
nlp.add_pipe(my_component)
nlp.to_disk("/path/to/model")
```
@ -247,7 +250,8 @@ file `data.json` in its subdirectory:
├── parser # data for "parser" component
├── tagger # data for "tagger" component
├── vocab # model vocabulary
├── meta.json # model meta.json with name, language and pipeline
├── meta.json # model meta.json
├── config.cfg # model config
└── tokenizer # tokenization rules
```
@ -260,19 +264,14 @@ instance, you could add a
trained with a different library like TensorFlow or PyTorch and make spaCy load
its weights automatically when you load the model package.
<Infobox title="Important note on loading components" variant="warning">
<Infobox title="Important note on loading custom components" variant="warning">
When you load a model from disk, spaCy will check the `"pipeline"` in the
model's `meta.json` and look up the component name in the internal factories. To
make sure spaCy knows how to initialize `"my_component"`, you'll need to add it
to the factories:
```python
from spacy.language import Language
Language.factories["my_component"] = lambda nlp, **cfg: CustomComponent()
```
For more details, see the documentation on
When you load back a model with custom components, make sure that the components
are **available** and that the [`@Language.component`](/api/language#component)
or [`@Language.factory`](/api/language#factory) decorators are executed _before_
your model is loaded back. Otherwise, spaCy won't know how to resolve the string
name of a component factory like `"my_component"` back to a function. For more
details, see the documentation on
[adding factories](/usage/processing-pipelines#custom-components-factories) or
use [entry points](#entry-points) to make your extension package expose your
custom components to spaCy automatically.
@ -293,40 +292,31 @@ installed in the same environment – that's it.
| Entry point | Description |
| ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`spacy_factories`](#entry-points-components) | Group of entry points for pipeline component factories to add to [`Language.factories`](/usage/processing-pipelines#custom-components-factories), keyed by component name. |
| [`spacy_factories`](#entry-points-components) | Group of entry points for pipeline component factories, keyed by component name. Can be used to expose custom components defined by another package. |
| [`spacy_languages`](#entry-points-languages) | Group of entry points for custom [`Language` subclasses](/usage/adding-languages), keyed by language shortcut. |
| `spacy_lookups` <Tag variant="new">2.2</Tag> | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package. |
| [`spacy_displacy_colors`](#entry-points-displacy) <Tag variant="new">2.2</Tag> | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
### Custom components via entry points {#entry-points-components}
When you load a model, spaCy will generally use the model's `meta.json` to set
When you load a model, spaCy will generally use the model's `config.cfg` to set
up the language class and construct the pipeline. The pipeline is specified as a
list of strings, e.g. `"pipeline": ["tagger", "paser", "ner"]`. For each of
those strings, spaCy will call `nlp.create_pipe` and look up the name in the
[built-in factories](/usage/processing-pipelines#custom-components-factories).
If your model wanted to specify its own custom components, you usually have to
write to `Language.factories` _before_ loading the model.
list of strings, e.g. `pipeline = ["tagger", "paser", "ner"]`. For each of those
strings, spaCy will call `nlp.add_pipe` and look up the name in all factories
defined by the decorators [`@Language.component`](/api/language#component) and
[`@Language.factory`](/api/language#factory). This means that you have to import
your custom components _before_ loading the model.
```python
pipe = nlp.create_pipe("custom_component") # fails 👎
Language.factories["custom_component"] = CustomComponentFactory
pipe = nlp.create_pipe("custom_component") # works 👍
```
This is inconvenient and usually required shipping a bunch of component
initialization code with the model. Using entry points, model packages and
extension packages can now define their own `"spacy_factories"`, which will be
added to the built-in factories when the `Language` class is initialized. If a
package in the same environment exposes spaCy entry points, all of this happens
automatically and no further user action is required.
Using entry points, model packages and extension packages can define their own
`"spacy_factories"`, which will be loaded automatically in the background when
the `Language` class is initialized. So if a user has your package installed,
they'll be able to use your components – even if they **don't import them**!
To stick with the theme of
[this entry points blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
consider the following custom spaCy extension which is initialized with the
shared `nlp` object and will print a snake when it's called as a pipeline
component.
consider the following custom spaCy
[pipeline component](/usage/processing-pipelines#custom-coponents) that prints a
snake when it's called:
> #### Package directory structure
>
@ -337,32 +327,38 @@ component.
```python
### snek.py
from spacy.language import Language
snek = """
--..,_ _,.--.
`'.'. .'`__ o `;__.
`'.'. .'`__ o `;__. {text}
'.'. .'.'` '---'` `
'.`'--....--'`.'
`'--....--'`
"""
class SnekFactory:
def __init__(self, nlp, **cfg):
self.nlp = nlp
def __call__(self, doc):
print(snek)
@Language.component("snek")
def snek_component(doc):
print(snek.format(text=doc.text))
return doc
```
Since it's a very complex and sophisticated module, you want to split it off
into its own package so you can version it and upload it to PyPi. You also want
your custom model to be able to define `"pipeline": ["snek"]` in its
`meta.json`. For that, you need to be able to tell spaCy where to find the
factory for `"snek"`. If you don't do this, spaCy will raise an error when you
try to load the model because there's no built-in `"snek"` factory. To add an
your custom model to be able to define `pipeline = ["snek"]` in its
`config.cfg`. For that, you need to be able to tell spaCy where to find the
component `"snek"`. If you don't do this, spaCy will raise an error when you try
to load the model because there's no built-in `"snek"` component. To add an
entry to the factories, you can now expose it in your `setup.py` via the
`entry_points` dictionary:
> #### Entry point syntax
>
> Python entry points for a group are formatted as a **list of strings**, with
> each string following the syntax of `name = module:object`. In this example,
> the created entry point is named `snek` and points to the function
> `snek_component` in the module `snek`, i.e. `snek.py`.
```python
### setup.py {highlight="5-7"}
from setuptools import setup
@ -370,73 +366,74 @@ from setuptools import setup
setup(
name="snek",
entry_points={
"spacy_factories": ["snek = snek:SnekFactory"]
"spacy_factories": ["snek = snek:snek_component"]
}
)
```
The entry point definition tells spaCy that the name `snek` can be found in the
module `snek` (i.e. `snek.py`) as `SnekFactory`. The same package can expose
multiple entry points. To make them available to spaCy, all you need to do is
install the package:
The same package can expose multiple entry points, by the way. To make them
available to spaCy, all you need to do is install the package in your
environment:
```bash
$ python setup.py develop
```
spaCy is now able to create the pipeline component `'snek'`:
spaCy is now able to create the pipeline component `"snek"` – even though you
never imported `snek_component`. When you save the
[`nlp.config`](/api/language#config) to disk, it includes an entry for your
`"snek"` component and any model you train with this config will include the
component and know how to load it – if your `snek` package is installed.
> #### config.cfg (excerpt)
>
> ```diff
> [nlp]
> lang = "en"
> + pipeline = ["snek"]
>
> [components]
>
> + [components.snek]
> + factory = "snek"
> ```
```
>>> from spacy.lang.en import English
>>> nlp = English()
>>> snek = nlp.create_pipe("snek") # this now works! 🐍🎉
>>> nlp.add_pipe(snek)
>>> nlp.add_pipe("snek") # this now works! 🐍🎉
>>> doc = nlp("I am snek")
--..,_ _,.--.
`'.'. .'`__ o `;__.
`'.'. .'`__ o `;__. I am snek
'.'. .'.'` '---'` `
'.`'--....--'`.'
`'--....--'`
```
Arguably, this gets even more exciting when you train your `en_core_snek_sm`
model. To make sure `snek` is installed with the model, you can add it to the
model's `setup.py`. You can then tell spaCy to construct the model pipeline with
the `snek` component by setting `"pipeline": ["snek"]` in the `meta.json`.
Instead of making your snek component a simple
[stateless component](/usage/processing-pipelines#custom-components-simple), you
could also make it a
[factory](/usage/processing-pipelines#custom-components-factories) that takes
settings. Your users can then pass in an optional `config` when they add your
component to the pipeline and customize its appearance – for example, the
`snek_style`.
> #### meta.json
> #### config.cfg (excerpt)
>
> ```diff
> {
> "lang": "en",
> "name": "core_snek_sm",
> "version": "1.0.0",
> + "pipeline": ["snek"]
> }
> [components.snek]
> factory = "snek"
> + snek_style = "basic"
> ```
In theory, the entry point mechanism also lets you overwrite built-in factories
– including the tokenizer. By default, spaCy will output a warning in these
cases, to prevent accidental overwrites and unintended results.
#### Advanced components with settings {#advanced-cfg}
The `**cfg` keyword arguments that the factory receives are passed down all the
way from `spacy.load`. This means that the factory can respond to custom
settings defined when loading the model – for example, the style of the snake to
load:
```python
nlp = spacy.load("en_core_snek_sm", snek_style="cute")
```
```python
SNEKS = {"basic": snek, "cute": cute_snek} # collection of sneks
@Language.factory("snek", default_config={"snek_style": "basic"})
class SnekFactory:
def __init__(self, nlp, **cfg):
def __init__(self, nlp: Language, name: str, snek_style: str):
self.nlp = nlp
self.snek_style = cfg.get("snek_style", "basic")
self.snek_style = snek_style
self.snek = SNEKS[self.snek_style]
def __call__(self, doc):
@ -444,6 +441,14 @@ class SnekFactory:
return doc
```
```diff
### setup.py
entry_points={
- "spacy_factories": ["snek = snek:snek_component"]
+ "spacy_factories": ["snek = snek:SnekFactory"]
}
```
The factory can also implement other pipeline component like `to_disk` and
`from_disk` for serialization, or even `update` to make the component trainable.
If a component exposes a `from_disk` method and is included in a model's
@ -452,12 +457,12 @@ model. When you save out a model using `nlp.to_disk` and the component exposes a
`to_disk` method, it will be called with the disk path.
```python
def to_disk(self, path, **kwargs):
def to_disk(self, path, exclude=tuple()):
snek_path = path / "snek.txt"
with snek_path.open("w", encoding="utf8") as snek_file:
snek_file.write(self.snek)
def from_disk(self, path, **cfg):
def from_disk(self, path, exclude=tuple()):
snek_path = path / "snek.txt"
with snek_path.open("r", encoding="utf8") as snek_file:
self.snek = snek_file.read()
@ -473,24 +478,20 @@ the `snek.txt` and make it available to the component.
To stay with the theme of the previous example and
[this blog post on entry points](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
let's imagine you wanted to implement your own `SnekLanguage` class for your
custom model – but you don't necessarily want to modify spaCy's code to
[add a language](/usage/adding-languages). In your package, you could then
implement the following:
custom model – but you don't necessarily want to modify spaCy's code to add a
language. In your package, you could then implement the following
[custom language subclass](/usage/linguistic-features#language-subclass):
```python
### snek.py
from spacy.language import Language
from spacy.attrs import LANG
class SnekDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: "snk"
stop_words = set(["sss", "hiss"])
class SnekLanguage(Language):
lang = "snk"
Defaults = SnekDefaults
# Some custom snek language stuff here
```
Alongside the `spacy_factories`, there's also an entry point option for
@ -510,31 +511,12 @@ setup(
)
```
In spaCy, you can then load the custom `sk` language and it will be resolved to
In spaCy, you can then load the custom `snk` language and it will be resolved to
`SnekLanguage` via the custom entry point. This is especially relevant for model
packages, which could then specify `"lang": "snk"` in their `meta.json` without
spaCy raising an error because the language is not available in the core
packages you train, which could then specify `lang = snk` in their `config.cfg`
without spaCy raising an error because the language is not available in the core
library.
> #### meta.json
>
> ```diff
> {
> - "lang": "en",
> + "lang": "snk",
> "name": "core_snek_sm",
> "version": "1.0.0",
> "pipeline": ["snek"]
> }
> ```
```python
from spacy.util import get_lang_class
SnekLanguage = get_lang_class("snk")
nlp = SnekLanguage()
```
### Custom displaCy colors via entry points {#entry-points-displacy new="2.2"}
If you're training a named entity recognition model for a custom domain, you may
@ -611,7 +593,7 @@ manually and place it in the model data directory, or supply a path to it using
the `--meta` flag. For more info on this, see the [`package`](/api/cli#package)
docs.
> #### meta.json
> #### meta.json (example)
>
> ```json
> {
@ -622,8 +604,7 @@ docs.
> "description": "Example model for spaCy",
> "author": "You",
> "email": "you@example.com",
> "license": "CC BY-SA 3.0",
> "pipeline": ["tagger", "parser", "ner"]
> "license": "CC BY-SA 3.0"
> }
> ```
@ -631,7 +612,9 @@ docs.
$ python -m spacy package /home/me/data/en_example_model /home/me/my_models
```
This command will create a model package directory that should look like this:
This command will create a model package directory and will run
`python setup.py sdist` in that directory to create `.tar.gz` archive of your
model package that can be installed using `pip install`.
```yaml
### Directory structure
@ -639,58 +622,29 @@ This command will create a model package directory that should look like this:
├── MANIFEST.in # to include meta.json
├── meta.json # model meta data
├── setup.py # setup file for pip installation
└── en_example_model # model directory
├── __init__.py # init for pip installation
└── en_example_model-1.0.0 # model data
├── en_example_model # model directory
│ ├── __init__.py # init for pip installation
│ └── en_example_model-1.0.0 # model data
└── dist
└── en_example_model-1.0.0.tar.gz # installable package
```
You can also find templates for all files on
[GitHub](https://github.com/explosion/spacy-models/tree/master/template). If
you're creating the package manually, keep in mind that the directories need to
be named according to the naming conventions of `lang_name` and
You can also find templates for all files in the
[`cli/package.py` source](https://github.com/explosion/spacy/tree/master/spacy/cli/package.py).
If you're creating the package manually, keep in mind that the directories need
to be named according to the naming conventions of `lang_name` and
`lang_name-version`.
### Customizing the model setup {#models-custom}
The meta.json includes the model details, like name, requirements and license,
and lets you customize how the model should be initialized and loaded. You can
define the language data to be loaded and the
[processing pipeline](/usage/processing-pipelines) to execute.
| Setting | Type | Description |
| ---------- | ---- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lang` | str | ID of the language class to initialize. |
| `pipeline` | list | A list of strings mapping to the IDs of pipeline factories to apply in that order. If not set, spaCy's [default pipeline](/usage/processing-pipelines) will be used. |
The `load()` method that comes with our model package templates will take care
of putting all this together and returning a `Language` object with the loaded
pipeline and data. If your model requires custom
[pipeline components](/usage/processing-pipelines) or a custom language class,
you can also **ship the code with your model**. For examples of this, check out
the implementations of spaCy's
[`load_model_from_init_py`](/api/top-level#util.load_model_from_init_py) and
[`load_model_from_path`](/api/top-level#util.load_model_from_path) utility
functions.
### Building the model package {#models-building}
To build the package, run the following command from within the directory. For
more information on building Python packages, see the docs on Python's
[setuptools](https://setuptools.readthedocs.io/en/latest/).
```bash
$ python setup.py sdist
```
This will create a `.tar.gz` archive in a directory `/dist`. The model can be
installed by pointing pip to the path of the archive:
```bash
$ pip install /path/to/en_example_model-1.0.0.tar.gz
```
You can then load the model via its name, `en_example_model`, or import it
directly as a module and then call its `load()` method.
you can also **ship the code with your model** and include it in the
`__init__.py` – for example, to register custom
[pipeline components](/usage/processing-pipelines#custom-components) before the
`nlp` object is created.
### Loading a custom model package {#loading}

50
website/docs/usage/training.md
View File

@ -150,11 +150,11 @@ schedules, optimizers or any other custom components. The main top-level
sections of a config file are:
| Section | Description |
| ------------- | -------------------------------------------------------------------------------------------------------------------- |
| ------------- | --------------------------------------------------------------------------------------------------------------------- |
| `training` | Settings and controls for the training and evaluation process. |
| `pretraining` | Optional settings and controls for the [language model pretraining](#pretraining). |
| `nlp` | Definition of the `nlp` object, its tokenizer and [processing pipeline](/docs/processing-pipelines) component names. |
| `components` | Definitions of the [pipeline components](/docs/processing-pipelines) and their models. |
| `nlp` | Definition of the `nlp` object, its tokenizer and [processing pipeline](/usage/processing-pipelines) component names. |
| `components` | Definitions of the [pipeline components](/usage/processing-pipelines) and their models. |
<Infobox title="Config format and settings" emoji="📖">
@ -328,18 +328,15 @@ spaCy's configs are powered by our machine learning library Thinc's
[type hints](https://docs.python.org/3/library/typing.html) and even
[advanced type annotations](https://thinc.ai/docs/usage-config#advanced-types)
using [`pydantic`](https://github.com/samuelcolvin/pydantic). If your registered
function provides For example, `start: int` in the example above will ensure
that the value received as the argument `start` is an integer. If the value
can't be cast to an integer, spaCy will raise an error.
function provides type hints, the values that are passed in will be checked
against the expected types. For example, `start: int` in the example above will
ensure that the value received as the argument `start` is an integer. If the
value can't be cast to an integer, spaCy will raise an error.
`start: pydantic.StrictInt` will force the value to be an integer and raise an
error if it's not – for instance, if your config defines a float.
</Infobox>
### Defining custom architectures {#custom-architectures}
<!-- TODO: this could maybe be a more general example of using Thinc to compose some layers? We don't want to go too deep here and probably want to focus on a simple architecture example to show how it works -->
### Wrapping PyTorch and TensorFlow {#custom-frameworks}
<!-- TODO: -->
@ -352,6 +349,10 @@ mattis pretium.
</Project>
### Defining custom architectures {#custom-architectures}
<!-- TODO: this could maybe be a more general example of using Thinc to compose some layers? We don't want to go too deep here and probably want to focus on a simple architecture example to show how it works -->
## Parallel Training with Ray {#parallel-training}
<!-- TODO: document Ray integration -->
@ -445,19 +446,6 @@ annotations:
</Infobox>
> - **Training data**: The training examples.
> - **Text and label**: The current example.
> - **Doc**: A `Doc` object created from the example text.
> - **Example**: An `Example` object holding both predictions and gold-standard
> annotations.
> - **nlp**: The `nlp` object with the model.
> - **Optimizer**: A function that holds state between updates.
> - **Update**: Update the model's weights.
<!-- TODO: update graphic & related text -->
![The training loop](../images/training-loop.svg)
Of course, it's not enough to only show a model a single example once.
Especially if you only have few examples, you'll want to train for a **number of
iterations**. At each iteration, the training data is **shuffled** to ensure the
@ -468,12 +456,16 @@ it harder for the model to memorize the training data. For example, a `0.25`
dropout means that each feature or internal representation has a 1/4 likelihood
of being dropped.
> - [`begin_training`](/api/language#begin_training): Start the training and
> return an [`Optimizer`](https://thinc.ai/docs/api-optimizers) object to
> update the model's weights.
> - [`update`](/api/language#update): Update the model with the training
> examplea.
> - [`to_disk`](/api/language#to_disk): Save the updated model to a directory.
> - [`nlp`](/api/language): The `nlp` object with the model.
> - [`nlp.begin_training`](/api/language#begin_training): Start the training and
> return an optimizer to update the model's weights.
> - [`Optimizer`](https://thinc.ai/docs/api-optimizers): Function that holds
> state between updates.
> - [`nlp.update`](/api/language#update): Update model with examples.
> - [`Example`](/api/example): object holding predictions and gold-standard
> annotations.
> - [`nlp.to_disk`](/api/language#to_disk): Save the updated model to a
> directory.
```python
### Example training loop

6
website/docs/usage/transformers.md Normal file
View File

@ -0,0 +1,6 @@
---
title: Transformers
teaser: Using transformer models like BERT in spaCy
---
TODO: ...

255
website/docs/usage/v3.md
View File

@ -6,6 +6,7 @@ menu:
- ['New Features', 'features']
- ['Backwards Incompatibilities', 'incompat']
- ['Migrating from v2.x', 'migrating']
- ['Migrating plugins', 'plugins']
---
## Summary {#summary}
@ -14,20 +15,250 @@ menu:
## Backwards Incompatibilities {#incompat}
### Removed deprecated methods, attributes and arguments {#incompat-removed}
### Removed or renamed objects, methods, attributes and arguments {#incompat-removed}
| Removed | Replacement |
| -------------------------------------------------------- | ----------------------------------------- |
| `GoldParse` | [`Example`](/api/example) |
| `spacy debug-data` | [`spacy debug data`](/api/cli#debug-data) |
| `spacy link`, `util.set_data_path`, `util.get_data_path` | not needed, model symlinks are deprecated |
### Removed deprecated methods, attributes and arguments {#incompat-removed-deprecated}
The following deprecated methods, attributes and arguments were removed in v3.0.
Most of them have been deprecated for quite a while now and many would
previously raise errors. Many of them were also mostly internals. If you've been
working with more recent versions of spaCy v2.x, it's unlikely that your code
relied on them.
Most of them have been **deprecated for a while** and many would previously
raise errors. Many of them were also mostly internals. If you've been working
with more recent versions of spaCy v2.x, it's **unlikely** that your code relied
on them.
| Class | Removed |
| --------------------- | ------------------------------------------------------- |
| [`Doc`](/api/doc) | `Doc.tokens_from_list`, `Doc.merge` |
| [`Span`](/api/span) | `Span.merge`, `Span.upper`, `Span.lower`, `Span.string` |
| [`Token`](/api/token) | `Token.string` |
<!-- TODO: complete (see release notes Dropbox Paper doc) -->
| Removed | Replacement |
| ----------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Doc.tokens_from_list` | [`Doc.__init__`](/api/doc#init) |
| `Doc.merge`, `Span.merge` | [`Doc.retokenize`](/api/doc#retokenize) |
| `Token.string`, `Span.string`, `Span.upper`, `Span.lower` | [`Span.text`](/api/span#attributes), [`Token.text`](/api/token#attributes) |
| `Language.tagger`, `Language.parser`, `Language.entity` | [`Language.get_pipe`](/api/language#get_pipe) |
| keyword-arguments like `vocab=False` on `to_disk`, `from_disk`, `to_bytes`, `from_bytes` | `exclude=["vocab"]` |
| `n_threads` argument on [`Tokenizer`](/api/tokenizer), [`Matcher`](/api/matcher), [`PhraseMatcher`](/api/phrasematcher) | `n_process` |
| `SentenceSegmenter` hook, `SimilarityHook` | [user hooks](/usage/processing-pipelines#custom-components-user-hooks), [`Sentencizer`](/api/sentencizer), [`SentenceRecognizer`](/api/sentenceregognizer), |
## Migrating from v2.x {#migrating}
### Custom pipeline components and factories {#migrating-pipeline-components}
Custom pipeline components now have to be registered explicitly using the
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory) decorator. For simple functions
that take a `Doc` and return it, all you have to do is add the
`@Language.component` decorator to it and assign it a name:
```diff
### Stateless function components
+ from spacy.language import Language
+ @Language.component("my_component")
def my_component(doc):
return doc
```
For class components that are initialized with settings and/or the shared `nlp`
object, you can use the `@Language.factory` decorator. Also make sure that that
the method used to initialize the factory has **two named arguments**: `nlp`
(the current `nlp` object) and `name` (the string name of the component
instance).
```diff
### Stateful class components
+ from spacy.language import Language
+ @Language.factory("my_component")
class MyComponent:
- def __init__(self, nlp):
+ def __init__(self, nlp, name):
self.nlp = nlp
def __call__(self, doc):
return doc
```
Instead of decorating your class, you could also add a factory function that
takes the arguments `nlp` and `name` and returns an instance of your component:
```diff
### Stateful class components with factory function
+ from spacy.language import Language
+ @Language.factory("my_component")
+ def create_my_component(nlp, name):
+ return MyComponent(nlp)
class MyComponent:
def __init__(self, nlp):
self.nlp = nlp
def __call__(self, doc):
return doc
```
The `@Language.component` and `@Language.factory` decorators now take care of
adding an entry to the component factories, so spaCy knows how to load a
component back in from its string name. You won't have to write to
`Language.factories` manually anymore.
```diff
- Language.factories["my_component"] = lambda nlp, **cfg: MyComponent(nlp)
```
#### Adding components to the pipeline {#migrating-add-pipe}
The [`nlp.add_pipe`](/api/language#add_pipe) method now takes the **string
name** of the component factory instead of a callable component. This allows
spaCy to track and serialize components that have been added and their settings.
```diff
+ @Language.component("my_component")
def my_component(doc):
return doc
- nlp.add_pipe(my_component)
+ nlp.add_pipe("my_component")
```
[`nlp.add_pipe`](/api/language#add_pipe) now also returns the pipeline component
itself, so you can access its attributes. The
[`nlp.create_pipe`](/api/language#create_pipe) method is now mostly internals
and you typically shouldn't have to use it in your code.
```diff
- parser = nlp.create_pipe("parser")
- nlp.add_pipe(parser)
+ parser = nlp.add_pipe("parser")
```
### Training models {#migrating-training}
To train your models, you should now pretty much always use the
[`spacy train`](/api/cli#train) CLI. You shouldn't have to put together your own
training scripts anymore, unless you _really_ want to. The training commands now
use a [flexible config file](/usage/training#config) that describes all training
settings and hyperparameters, as well as your pipeline, model components and
architectures to use. The `--code` argument lets you pass in code containing
[custom registered functions](/usage/training#custom-code) that you can
reference in your config.
#### Binary .spacy training data format {#migrating-training-format}
spaCy now uses a new
[binary training data format](/api/data-formats#binary-training), which is much
smaller and consists of `Doc` objects, serialized via the
[`DocBin`](/api/docbin). You can convert your existing JSON-formatted data using
the [`spacy convert`](/api/cli#convert) command, which outputs `.spacy` files:
```bash
$ python -m spacy convert ./training.json ./output
```
#### Training config {#migrating-training-config}
<!-- TODO: update once we have recommended "getting started with a new config" workflow -->
```diff
### {wrap="true"}
- python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0
+ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --output ./output
```
<Project id="some_example_project">
The easiest way to get started with an end-to-end training process is to clone a
[project](/usage/projects) template. Projects let you manage multi-step
workflows, from data preprocessing to training and packaging your model.
</Project>
#### Migrating training scripts to CLI command and config {#migrating-training-scripts}
<!-- TODO: write -->
#### Packaging models {#migrating-training-packaging}
The [`spacy package`](/api/cli#package) command now automatically builds the
installable `.tar.gz` sdist of the Python package, so you don't have to run this
step manually anymore. You can disable the behavior by setting the `--no-sdist`
flag.
```diff
python -m spacy package ./model ./packages
- cd /output/en_model-0.0.0
- python setup.py sdist
```
## Migration notes for plugin maintainers {#plugins}
Thanks to everyone who's been contributing to the spaCy ecosystem by developing
and maintaining one of the many awesome [plugins and extensions](/universe).
We've tried to keep breaking changes to a minimum and make it as easy as
possible for you to upgrade your packages for spaCy v3.
### Custom pipeline components
The most common use case for plugins is providing pipeline components and
extension attributes.
- Use the [`@Language.factory`](/api/language#factory) decorator to register
your component and assign it a name. This allows users to refer to your
components by name and serialize pipelines referencing them. Remove all manual
entries to the `Language.factories`.
- Make sure your component factories take at least two **named arguments**:
`nlp` (the current `nlp` object) and `name` (the instance name of the added
component so you can identify multiple instances of the same component).
- Update all references to [`nlp.add_pipe`](/api/language#add_pipe) in your docs
to use **string names** instead of the component functions.
```python
### {highlight="1-5"}
from spacy.language import Language
@Language.factory("my_component", default_config={"some_setting": False})
def create_component(nlp: Language, name: str, some_setting: bool):
return MyCoolComponent(some_setting=some_setting)
class MyCoolComponent:
def __init__(self, some_setting):
self.some_setting = some_setting
def __call__(self, doc):
# Do something to the doc
return doc
```
> #### Result in config.cfg
>
> ```ini
> [components.my_component]
> factory = "my_component"
> some_setting = true
> ```
```diff
import spacy
from your_plugin import MyCoolComponent
nlp = spacy.load("en_core_web_sm")
- component = MyCoolComponent(some_setting=True)
- nlp.add_pipe(component)
+ nlp.add_pipe("my_component", config={"some_setting": True})
```
<Infobox title="Important note on registering factories" variant="warning">
The [`@Language.factory`](/api/language#factory) decorator takes care of letting
spaCy know that a component of that name is available. This means that your
users can add it to the pipeline using its **string name**. However, this
requires the decorator to be executed – so users will still have to **import
your plugin**. Alternatively, your plugin could expose an
[entry point](/usage/saving-loading#entry-points), which spaCy can read from.
This means that spaCy knows how to initialize `my_component`, even if your
package isn't imported.
</Infobox>

2
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View File

@ -229,3 +229,5 @@ vectors.data = torch.Tensor(vectors.data).cuda(0)
## Other embeddings {#embeddings}
<!-- TODO: explain spacy-transformers, doc.tensor, tok2vec? -->
<!-- TODO: mention sense2vec somewhere? -->

24
website/gatsby-node.js
View File

@ -14,6 +14,10 @@ function getNodeTitle({ childMdx }) {
return (frontmatter.title || '').replace("'", '’')
}
function findNode(pages, slug) {
return slug ? pages.find(({ node }) => node.fields.slug === slug) : null
}
exports.createPages = ({ graphql, actions }) => {
const { createPage } = actions
@ -70,6 +74,9 @@ exports.createPages = ({ graphql, actions }) => {
title
teaser
source
api_base_class
api_string_name
api_trainable
tag
new
next
@ -115,10 +122,18 @@ exports.createPages = ({ graphql, actions }) => {
const section = frontmatter.section || page.node.relativeDirectory
const sectionMeta = sections[section] || {}
const title = getNodeTitle(page.node)
const next = frontmatter.next
? pages.find(({ node }) => node.fields.slug === frontmatter.next)
: null
const next = findNode(pages, frontmatter.next)
const baseClass = findNode(pages, frontmatter.api_base_class)
const apiDetails = {
stringName: frontmatter.api_string_name,
baseClass: baseClass
? {
title: getNodeTitle(baseClass.node),
slug: frontmatter.api_base_class,
}
: null,
trainable: frontmatter.api_trainable,
}
createPage({
path: replacePath(page.node.fields.slug),
component: DEFAULT_TEMPLATE,
@ -131,6 +146,7 @@ exports.createPages = ({ graphql, actions }) => {
sectionTitle: sectionMeta.title,
menu: frontmatter.menu || [],
teaser: frontmatter.teaser,
apiDetails,
source: frontmatter.source,
sidebar: frontmatter.sidebar,
tag: frontmatter.tag,

18
website/meta/sidebars.json
View File

@ -19,6 +19,7 @@
{ "text": "Rule-based Matching", "url": "/usage/rule-based-matching" },
{ "text": "Processing Pipelines", "url": "/usage/processing-pipelines" },
{ "text": "Vectors & Embeddings", "url": "/usage/vectors-embeddings" },
{ "text": "Transformers", "url": "/usage/transformers", "tag": "new" },
{ "text": "Training Models", "url": "/usage/training", "tag": "new" },
{ "text": "spaCy Projects", "url": "/usage/projects", "tag": "new" },
{ "text": "Saving & Loading", "url": "/usage/saving-loading" },
@ -66,6 +67,7 @@
{
"label": "Containers",
"items": [
{ "text": "Language", "url": "/api/language" },
{ "text": "Doc", "url": "/api/doc" },
{ "text": "Token", "url": "/api/token" },
{ "text": "Span", "url": "/api/span" },
@ -77,7 +79,6 @@
{
"label": "Pipeline",
"items": [
{ "text": "Language", "url": "/api/language" },
{ "text": "Tokenizer", "url": "/api/tokenizer" },
{ "text": "Tok2Vec", "url": "/api/tok2vec" },
{ "text": "Lemmatizer", "url": "/api/lemmatizer" },
@ -85,14 +86,21 @@
{ "text": "Tagger", "url": "/api/tagger" },
{ "text": "DependencyParser", "url": "/api/dependencyparser" },
{ "text": "EntityRecognizer", "url": "/api/entityrecognizer" },
{ "text": "EntityRuler", "url": "/api/entityruler" },
{ "text": "EntityLinker", "url": "/api/entitylinker" },
{ "text": "TextCategorizer", "url": "/api/textcategorizer" },
{ "text": "Matcher", "url": "/api/matcher" },
{ "text": "PhraseMatcher", "url": "/api/phrasematcher" },
{ "text": "EntityRuler", "url": "/api/entityruler" },
{ "text": "Sentencizer", "url": "/api/sentencizer" },
{ "text": "SentenceRecognizer", "url": "/api/sentencerecognizer" },
{ "text": "Other Functions", "url": "/api/pipeline-functions" }
{ "text": "Other Functions", "url": "/api/pipeline-functions" },
{ "text": "Pipe", "url": "/api/pipe" }
]
},
{
"label": "Matchers",
"items": [
{ "text": "Matcher", "url": "/api/matcher" },
{ "text": "PhraseMatcher", "url": "/api/phrasematcher" },
{ "text": "DependencyMatcher", "url": "/api/dependencymatcher" }
]
},
{

11
website/src/components/link.js
View File

@ -87,6 +87,17 @@ const Link = ({
)
}
export const OptionalLink = ({ to, href, children, ...props }) => {
const dest = to || href
return dest ? (
<Link to={dest} {...props}>
{children}
</Link>
) : (
children || null
)
}
Link.defaultProps = {
hidden: false,
hideIcon: false,

70
website/src/components/title.js
View File

@ -4,11 +4,41 @@ import classNames from 'classnames'
import Button from './button'
import Tag from './tag'
import { H1 } from './typography'
import { OptionalLink } from './link'
import { InlineCode } from './code'
import { H1, Label, InlineList, Help } from './typography'
import Icon from './icon'
import classes from '../styles/title.module.sass'
const Title = ({ id, title, tag, version, teaser, source, image, children, ...props }) => (
const MetaItem = ({ label, url, children, help }) => (
<span>
<Label className={classes.label}>{label}:</Label>
<OptionalLink to={url}>{children}</OptionalLink>
{help && (
<>
{' '}
<Help>{help}</Help>
</>
)}
</span>
)
const Title = ({
id,
title,
tag,
version,
teaser,
source,
image,
apiDetails,
children,
...props
}) => {
const hasApiDetails = Object.values(apiDetails).some(v => v)
const metaIconProps = { className: classes.metaIcon, width: 18 }
return (
<header className={classes.root}>
{(image || source) && (
<div className={classes.corner}>
@ -28,18 +58,50 @@ const Title = ({ id, title, tag, version, teaser, source, image, children, ...pr
<H1 className={classes.h1} id={id} {...props}>
{title}
</H1>
{(tag || version) && (
<div className={classes.tags}>
{tag && <Tag spaced>{tag}</Tag>}
{version && (
<Tag variant="new" spaced>
{version}
</Tag>
)}
</div>
)}
{hasApiDetails && (
<InlineList Component="div" className={classes.teaser}>
{apiDetails.stringName && (
<MetaItem
label="String name"
//help="String name of the component to use with nlp.add_pipe"
>
<InlineCode>{apiDetails.stringName}</InlineCode>
</MetaItem>
)}
{apiDetails.baseClass && (
<MetaItem label="Base class" url={apiDetails.baseClass.slug}>
<InlineCode>{apiDetails.baseClass.title}</InlineCode>
</MetaItem>
)}
{apiDetails.trainable != null && (
<MetaItem label="Trainable">
<span aria-label={apiDetails.trainable ? 'yes' : 'no'}>
{apiDetails.trainable ? (
<Icon name="yes" variant="success" {...metaIconProps} />
) : (
<Icon name="no" {...metaIconProps} />
)}
</span>
</MetaItem>
)}
</InlineList>
)}
{teaser && <div className={classNames('heading-teaser', classes.teaser)}>{teaser}</div>}
{children}
</header>
)
)
}
Title.propTypes = {
title: PropTypes.string,

4
website/src/components/typography.js
View File

@ -59,9 +59,9 @@ export const InlineList = ({ Component = 'p', gutterBottom = true, className, ch
return <Component className={listClassNames}>{children}</Component>
}
export const Help = ({ children }) => (
export const Help = ({ children, size = 16 }) => (
<span className={classes.help} data-tooltip={children}>
<Icon name="help2" width={16} />
<Icon name="help2" width={size} />
</span>
)

2
website/src/styles/layout.sass
View File

@ -414,7 +414,7 @@ body [id]:target
.cm-number
color: var(--syntax-number)
.cm-def
.cm-def, .cm-meta
color: var(--syntax-function)
// Jupyter

13
website/src/styles/table.module.sass
View File

@ -17,13 +17,17 @@
background: var(--color-subtle-opaque)
.footer
background: var(--color-theme-light)
--color-inline-code-bg: var(--color-theme-opaque)
background: var(--color-theme-light) !important
border-top: 2px solid var(--color-theme)
& > td:first-child
font-family: var(--font-secondary)
color: var(--color-theme)
& > td:nth-child(2) a
border: 0
.td
padding: 1rem
@ -79,6 +83,13 @@
white-space: nowrap
z-index: 5
&:first-child // directly after thead/tr
border-bottom: 0
td em
top: -7px
// Responsive table
// Shadows adapted from "CSS only Responsive Tables" by David Bushell
// http://codepen.io/dbushell/pen/wGaamR

13
website/src/styles/title.module.sass
View File

@ -34,3 +34,16 @@
.corner
float: right
.label
display: inline
margin-right: var(--spacing-xs)
.meta-icon
position: relative
top: -2px
.tags
display: inline-block
position: relative
top: 0.5rem

2
website/src/templates/docs.js
View File

@ -30,6 +30,7 @@ const Docs = ({ pageContext, children }) => (
menu,
theme,
version,
apiDetails,
} = pageContext
const { sidebars = [], modelsRepo, languages, nightly } = site.siteMetadata
const isModels = section === 'models'
@ -102,6 +103,7 @@ const Docs = ({ pageContext, children }) => (
tag={tag}
version={version}
id="_title"
apiDetails={apiDetails}
/>
{children}
{subFooter}