EditTreeLemmatizer

A trainable component for assigning base forms to tokens. This lemmatizer uses edit trees to transform tokens into base forms. The lemmatization model predicts which edit tree is applicable to a token. The edit tree data structure and construction method used by this lemmatizer were proposed in Joint Lemmatization and Morphological Tagging with Lemming (Thomas Müller et al., 2015).

For a lookup and rule-based lemmatizer, see Lemmatizer.

Assigned Attributes {id="assigned-attributes"}

Predictions are assigned to Token.lemma.

Config and implementation {id="config"}

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

Example

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from spacy.pipeline.edit_tree_lemmatizer import DEFAULT_EDIT_TREE_LEMMATIZER_MODEL
config = {"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL}
nlp.add_pipe("trainable_lemmatizer", config=config, name="lemmatizer")

%%GITHUB_SPACY/spacy/pipeline/edit_tree_lemmatizer.py

EditTreeLemmatizer.__init__ {id="init",tag="method"}

Example

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# Construction via add_pipe with default model
lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")

# Construction via create_pipe with custom model
config = {"model": {"@architectures": "my_tagger"}}
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config=config, name="lemmatizer")

# Construction from class
from spacy.pipeline import EditTreeLemmatizer
lemmatizer = EditTreeLemmatizer(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.

EditTreeLemmatizer.__call__ {id="call",tag="method"}

Apply the pipe to one document. The document is modified in place, and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

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doc = nlp("This is a sentence.")
lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
# This usually happens under the hood
processed = lemmatizer(doc)

EditTreeLemmatizer.pipe {id="pipe",tag="method"}

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

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

EditTreeLemmatizer.initialize {id="initialize",tag="method",version="3"}

Initialize the component for training. get_examples should be a function that returns an iterable of Example objects. At least one example should be supplied. The data examples are used to initialize the model of the component and can either be the full training data or a representative sample. Initialization includes validating the network, 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

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer.initialize(lambda: examples, nlp=nlp)

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

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

EditTreeLemmatizer.predict {id="predict",tag="method"}

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

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
tree_ids = lemmatizer.predict([doc1, doc2])

EditTreeLemmatizer.set_annotations {id="set_annotations",tag="method"}

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

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
tree_ids = lemmatizer.predict([doc1, doc2])
lemmatizer.set_annotations([doc1, doc2], tree_ids)

EditTreeLemmatizer.update {id="update",tag="method"}

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

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
optimizer = nlp.initialize()
losses = lemmatizer.update(examples, sgd=optimizer)

EditTreeLemmatizer.get_loss {id="get_loss",tag="method"}

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

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
scores = lemmatizer.model.begin_update([eg.predicted for eg in examples])
loss, d_loss = lemmatizer.get_loss(examples, scores)

EditTreeLemmatizer.create_optimizer {id="create_optimizer",tag="method"}

Create an optimizer for the pipeline component.

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
optimizer = lemmatizer.create_optimizer()

EditTreeLemmatizer.use_params {id="use_params",tag="method, contextmanager"}

Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
with lemmatizer.use_params(optimizer.averages):
    lemmatizer.to_disk("/best_model")

EditTreeLemmatizer.to_disk {id="to_disk",tag="method"}

Serialize the pipe to disk.

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer.to_disk("/path/to/lemmatizer")

EditTreeLemmatizer.from_disk {id="from_disk",tag="method"}

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

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer.from_disk("/path/to/lemmatizer")

EditTreeLemmatizer.to_bytes {id="to_bytes",tag="method"}

Example

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lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer_bytes = lemmatizer.to_bytes()

Serialize the pipe to a bytestring.

EditTreeLemmatizer.from_bytes {id="from_bytes",tag="method"}

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

Example

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lemmatizer_bytes = lemmatizer.to_bytes()
lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer.from_bytes(lemmatizer_bytes)

EditTreeLemmatizer.labels {id="labels",tag="property"}

The labels currently added to the component.

The EditTreeLemmatizer labels are not useful by themselves, since they are identifiers of edit trees.

EditTreeLemmatizer.label_data {id="label_data",tag="property",version="3"}

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

Example

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

Serialization fields {id="serialization-fields"}

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

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data = lemmatizer.to_disk("/path", exclude=["vocab"])