18 KiB
title | tag | source | new | teaser | api_base_class | api_string_name | api_trainable |
---|---|---|---|---|---|---|---|
SentenceRecognizer | class | spacy/pipeline/senter.pyx | 3 | Pipeline component for sentence segmentation | /api/tagger | senter | true |
A trainable pipeline component for sentence segmentation. For a simpler,
rule-based strategy, see the Sentencizer
.
Assigned Attributes
Predicted values will be assigned to Token.is_sent_start
. The resulting
sentences can be accessed using Doc.sents
.
Location | Value |
---|---|
Token.is_sent_start |
A boolean value indicating whether the token starts a sentence. This will be either True or False for all tokens. |
Doc.sents |
An iterator over sentences in the Doc , determined by Token.is_sent_start values. |
Config and implementation
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
or in your
config.cfg
for training. See the
model architectures documentation for details on the
architectures and their arguments and hyperparameters.
Example
from spacy.pipeline.senter import DEFAULT_SENTER_MODEL config = {"model": DEFAULT_SENTER_MODEL,} nlp.add_pipe("senter", config=config)
Setting | Description |
---|---|
model |
The Model powering the pipeline component. Defaults to Tagger. |
%%GITHUB_SPACY/spacy/pipeline/senter.pyx
SentenceRecognizer.__init__
Initialize the sentence recognizer.
Example
# 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(nlp.vocab, model)
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
nlp.add_pipe
.
Name | Description |
---|---|
vocab |
The shared vocabulary. |
model |
The Model powering the pipeline component. |
name |
String name of the component instance. Used to add entries to the losses during training. |
SentenceRecognizer.__call__
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__
and
pipe
delegate to the
predict
and
set_annotations
methods.
Example
doc = nlp("This is a sentence.") senter = nlp.add_pipe("senter") # This usually happens under the hood processed = senter(doc)
Name | Description |
---|---|
doc |
The document to process. |
RETURNS | The processed document. |
SentenceRecognizer.pipe
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__
and pipe
delegate to the
predict
and
set_annotations
methods.
Example
senter = nlp.add_pipe("senter") for doc in senter.pipe(docs, batch_size=50): pass
Name | Description |
---|---|
stream |
A stream of documents. |
keyword-only | |
batch_size |
The number of documents to buffer. Defaults to 128 . |
YIELDS | The processed documents in order. |
SentenceRecognizer.initialize
Initialize the component for training. get_examples
should be a function that
returns an iterable of Example
objects. The data examples are
used to initialize the model of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
inferring missing shapes and
setting up the label scheme based on the data. This method is typically called
by Language.initialize
.
Example
senter = nlp.add_pipe("senter") senter.initialize(lambda: [], nlp=nlp)
Name | Description |
---|---|
get_examples |
Function that returns gold-standard annotations in the form of Example objects. |
keyword-only | |
nlp |
The current nlp object. Defaults to None . |
SentenceRecognizer.predict
Apply the component's model to a batch of Doc
objects, without
modifying them.
Example
senter = nlp.add_pipe("senter") scores = senter.predict([doc1, doc2])
Name | Description |
---|---|
docs |
The documents to predict. |
RETURNS | The model's prediction for each document. |
SentenceRecognizer.set_annotations
Modify a batch of Doc
objects, using pre-computed scores.
Example
senter = nlp.add_pipe("senter") scores = senter.predict([doc1, doc2]) senter.set_annotations([doc1, doc2], scores)
Name | Description |
---|---|
docs |
The documents to modify. |
scores |
The scores to set, produced by SentenceRecognizer.predict . |
SentenceRecognizer.update
Learn from a batch of Example
objects containing the
predictions and gold-standard annotations, and update the component's model.
Delegates to predict
and
get_loss
.
Example
senter = nlp.add_pipe("senter") optimizer = nlp.initialize() losses = senter.update(examples, sgd=optimizer)
Name | Description |
---|---|
examples |
A batch of Example objects to learn from. |
keyword-only | |
drop |
The dropout rate. |
sgd |
An optimizer. Will be created via create_optimizer if not set. |
losses |
Optional record of the loss during training. Updated using the component name as the key. |
RETURNS | The updated losses dictionary. |
SentenceRecognizer.rehearse
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
senter = nlp.add_pipe("senter") optimizer = nlp.resume_training() losses = senter.rehearse(examples, sgd=optimizer)
Name | Description |
---|---|
examples |
A batch of Example objects to learn from. |
keyword-only | |
drop |
The dropout rate. |
sgd |
An optimizer. Will be created via create_optimizer if not set. |
losses |
Optional record of the loss during training. Updated using the component name as the key. |
RETURNS | The updated losses dictionary. |
SentenceRecognizer.get_loss
Find the loss and gradient of loss for the batch of documents and their predicted scores.
Example
senter = nlp.add_pipe("senter") scores = senter.predict([eg.predicted for eg in examples]) loss, d_loss = senter.get_loss(examples, scores)
Name | Description |
---|---|
examples |
The batch of examples. |
scores |
Scores representing the model's predictions. |
RETURNS | The loss and the gradient, i.e. (loss, gradient) . |
SentenceRecognizer.score
Score a batch of examples.
Example
scores = senter.score(examples)
Name | Description |
---|---|
examples |
The examples to score. |
RETURNS | The scores, produced by Scorer.score_token_attr for the attributes "pos" , "tag" and "lemma" . |
SentenceRecognizer.create_optimizer
Create an optimizer for the pipeline component.
Example
senter = nlp.add_pipe("senter") optimizer = senter.create_optimizer()
Name | Description |
---|---|
RETURNS | The optimizer. |
SentenceRecognizer.use_params
Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.
Example
senter = nlp.add_pipe("senter") with senter.use_params(optimizer.averages): senter.to_disk("/best_model")
Name | Description |
---|---|
params |
The parameter values to use in the model. |
SentenceRecognizer.to_disk
Serialize the pipe to disk.
Example
senter = nlp.add_pipe("senter") senter.to_disk("/path/to/senter")
Name | Description |
---|---|
path |
A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path -like objects. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
SentenceRecognizer.from_disk
Load the pipe from disk. Modifies the object in place and returns it.
Example
senter = nlp.add_pipe("senter") senter.from_disk("/path/to/senter")
Name | Description |
---|---|
path |
A path to a directory. Paths may be either strings or Path -like objects. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
RETURNS | The modified SentenceRecognizer object. |
SentenceRecognizer.to_bytes
Example
senter = nlp.add_pipe("senter") senter_bytes = senter.to_bytes()
Serialize the pipe to a bytestring.
Name | Description |
---|---|
keyword-only | |
exclude |
String names of serialization fields to exclude. |
RETURNS | The serialized form of the SentenceRecognizer object. |
SentenceRecognizer.from_bytes
Load the pipe from a bytestring. Modifies the object in place and returns it.
Example
senter_bytes = senter.to_bytes() senter = nlp.add_pipe("senter") senter.from_bytes(senter_bytes)
Name | Description |
---|---|
bytes_data |
The data to load from. |
keyword-only | |
exclude |
String names of serialization fields to exclude. |
RETURNS | The SentenceRecognizer object. |
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
data = senter.to_disk("/path", exclude=["vocab"])
Name | Description |
---|---|
vocab |
The shared 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. |