spaCy/website/docs/api/morphologizer.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
Morphologizer	class	spacy/pipeline/morphologizer.pyx	3	Pipeline component for predicting morphological features	/api/tagger	morphologizer	true

A trainable pipeline component to predict morphological features and coarse-grained POS tags following the Universal Dependencies UPOS and FEATS annotation guidelines.

Assigned Attributes

Predictions are saved to Token.morph and Token.pos.

Location	Value
Token.pos	The UPOS part of speech (hash). int
Token.pos_	The UPOS part of speech. str
Token.morph	Morphological features. MorphAnalysis

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.morphologizer import DEFAULT_MORPH_MODEL
config = {"model": DEFAULT_MORPH_MODEL}
nlp.add_pipe("morphologizer", config=config)

Setting	Description
model	The model to use. Defaults to Tagger. Model[List[Doc], List[Floats2d]]
overwrite 3.2	Whether the values of existing features are overwritten. Defaults to True. bool
extend 3.2	Whether existing feature types (whose values may or may not be overwritten depending on overwrite) are preserved. Defaults to False. bool
scorer 3.2	The scoring method. Defaults to Scorer.score_token_attr for the attributes "pos" and "morph" and Scorer.score_token_attr_per_feat for the attribute "morph". Optional[Callable]

%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx

Morphologizer.__init__

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.

The overwrite and extend settings determine how existing annotation is handled (with the example for existing annotation A=B|C=D + predicted annotation C=E|X=Y):

• overwrite=True, extend=True: overwrite values of existing features, add any new features (A=B|C=D + C=E|X=Y → A=B|C=E|X=Y)
• overwrite=True, extend=False: overwrite completely, removing any existing features (A=B|C=D + C=E|X=Y → C=E|X=Y)
• overwrite=False, extend=True: keep values of existing features, add any new features (A=B|C=D + C=E|X=Y → A=B|C=D|X=Y)
• overwrite=False, extend=False: do not modify the existing annotation if set (A=B|C=D + C=E|X=Y → A=B|C=D)

Example

# 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(nlp.vocab, model)

Name	Description
vocab	The shared vocabulary. Vocab
model	The Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
overwrite 3.2	Whether the values of existing features are overwritten. Defaults to True. bool
extend 3.2	Whether existing feature types (whose values may or may not be overwritten depending on overwrite) are preserved. Defaults to False. bool
scorer 3.2	The scoring method. Defaults to Scorer.score_token_attr for the attributes "pos" and "morph" and Scorer.score_token_attr_per_feat for the attribute "morph". Optional[Callable]

Morphologizer.__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.")
morphologizer = nlp.add_pipe("morphologizer")
# This usually happens under the hood
processed = morphologizer(doc)

Name	Description
doc	The document to process. Doc
RETURNS	The processed document. Doc

Morphologizer.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

morphologizer = nlp.add_pipe("morphologizer")
for doc in morphologizer.pipe(docs, batch_size=50):
    pass

Name	Description
stream	A stream of documents. Iterable[Doc]
keyword-only
batch_size	The number of documents to buffer. Defaults to 128. int
YIELDS	The processed documents in order. Doc

Morphologizer.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 and lets you customize arguments it receives via the [initialize.components] block in the config.

Example

morphologizer = nlp.add_pipe("morphologizer")
morphologizer.initialize(lambda: [], nlp=nlp)

### config.cfg
[initialize.components.morphologizer]

[initialize.components.morphologizer.labels]
@readers = "spacy.read_labels.v1"
path = "corpus/labels/morphologizer.json

Name	Description
get_examples	Function that returns gold-standard annotations in the form of Example objects. Callable], Iterable[Example
keyword-only
nlp	The current nlp object. Defaults to None. Optional[Language]
labels	The label information to add to the component, as provided by the label_data property after initialization. To generate a reusable JSON file from your data, you should run the init labels command. If no labels are provided, the get_examples callback is used to extract the labels from the data, which may be a lot slower. Optional[dict]

Morphologizer.predict

Apply the component's model to a batch of Doc objects, without modifying them.

Example

morphologizer = nlp.add_pipe("morphologizer")
scores = morphologizer.predict([doc1, doc2])

Name	Description
docs	The documents to predict. Iterable[Doc]
RETURNS	The model's prediction for each document.

Morphologizer.set_annotations

Modify a batch of Doc objects, using pre-computed scores.

Example

morphologizer = nlp.add_pipe("morphologizer")
scores = morphologizer.predict([doc1, doc2])
morphologizer.set_annotations([doc1, doc2], scores)

Name	Description
docs	The documents to modify. Iterable[Doc]
scores	The scores to set, produced by Morphologizer.predict.

Morphologizer.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

morphologizer = nlp.add_pipe("morphologizer")
optimizer = nlp.initialize()
losses = morphologizer.update(examples, sgd=optimizer)

Name	Description
examples	A batch of Example objects to learn from. Iterable[Example]
keyword-only
drop	The dropout rate. float
sgd	An optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
losses	Optional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]
RETURNS	The updated losses dictionary. Dict[str, float]

Morphologizer.get_loss

Find the loss and gradient of loss for the batch of documents and their predicted scores.

Example

morphologizer = nlp.add_pipe("morphologizer")
scores = morphologizer.predict([eg.predicted for eg in examples])
loss, d_loss = morphologizer.get_loss(examples, scores)

Name	Description
examples	The batch of examples. Iterable[Example]
scores	Scores representing the model's predictions.
RETURNS	The loss and the gradient, i.e. (loss, gradient). Tuple[float, float]

Morphologizer.create_optimizer

Create an optimizer for the pipeline component.

Example

morphologizer = nlp.add_pipe("morphologizer")
optimizer = morphologizer.create_optimizer()

Name	Description
RETURNS	The optimizer. Optimizer

Morphologizer.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

morphologizer = nlp.add_pipe("morphologizer")
with morphologizer.use_params(optimizer.averages):
    morphologizer.to_disk("/best_model")

Name	Description
params	The parameter values to use in the model. dict

Morphologizer.add_label

Add a new label to the pipe. If the Morphologizer should set annotations for both pos and morph, the label should include the UPOS as the feature POS. Raises an error if the output dimension is already set, or if the model has already been fully initialized. Note that you don't have to call this method if you provide a representative data sample to the initialize method. In this case, all labels found in the sample will be automatically added to the model, and the output dimension will be inferred automatically.

Example

morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin")

Name	Description
label	The label to add. str
RETURNS	0 if the label is already present, otherwise 1. int

Morphologizer.to_disk

Serialize the pipe to disk.

Example

morphologizer = nlp.add_pipe("morphologizer")
morphologizer.to_disk("/path/to/morphologizer")

Name	Description
path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]

Morphologizer.from_disk

Load the pipe from disk. Modifies the object in place and returns it.

Example

morphologizer = nlp.add_pipe("morphologizer")
morphologizer.from_disk("/path/to/morphologizer")

Name	Description
path	A path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The modified Morphologizer object. Morphologizer

Morphologizer.to_bytes

Example

morphologizer = nlp.add_pipe("morphologizer")
morphologizer_bytes = morphologizer.to_bytes()

Serialize the pipe to a bytestring.

Name	Description
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The serialized form of the Morphologizer object. bytes

Morphologizer.from_bytes

Load the pipe from a bytestring. Modifies the object in place and returns it.

Example

morphologizer_bytes = morphologizer.to_bytes()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.from_bytes(morphologizer_bytes)

Name	Description
bytes_data	The data to load from. bytes
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The Morphologizer object. Morphologizer

Morphologizer.labels

The labels currently added to the component in the Universal Dependencies FEATS format. 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.

Example

morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin")
assert "Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin" in morphologizer.labels

Name	Description
RETURNS	The labels added to the component. Tuple[str, ...]

Morphologizer.label_data

The labels currently added to the component and their internal meta information. This is the data generated by init labels and used by Morphologizer.initialize to initialize the model with a pre-defined label set.

Example

labels = morphologizer.label_data
morphologizer.initialize(lambda: [], nlp=nlp, labels=labels)

Name	Description
RETURNS	The label data added to the component. dict

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 = morphologizer.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.