spaCy/website/docs/api/edittreelemmatizer.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
EditTreeLemmatizer	class	spacy/pipeline/edit_tree_lemmatizer.py	3.3	Pipeline component for lemmatization	/api/pipe	trainable_lemmatizer	true

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

Predictions are assigned to Token.lemma.

Location	Value
Token.lemma	The lemma (hash). int
Token.lemma_	The lemma. str

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.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")

Setting	Description
model	A model instance that predicts the edit tree probabilities. The output vectors should match the number of edit trees in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to 1). Defaults to Tagger. Model[List[Doc], List[Floats2d]]
backoff	Token attribute to use when no applicable edit tree is found. Defaults to orth. str
min_tree_freq	Minimum frequency of an edit tree in the training set to be used. Defaults to 3. int
overwrite	Whether existing annotation is overwritten. Defaults to False. bool
top_k	The number of most probable edit trees to try before resorting to backoff. Defaults to 1. int
scorer	The scoring method. Defaults to Scorer.score_token_attr for the attribute "lemma". Optional[Callable]

%%GITHUB_SPACY/spacy/pipeline/edit_tree_lemmatizer.py

EditTreeLemmatizer.__init__

Example

# 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.

Name	Description
vocab	The shared vocabulary. Vocab
model	A model instance that predicts the edit tree probabilities. The output vectors should match the number of edit trees in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to 1). Model[List[Doc], List[Floats2d]]
name	String name of the component instance. Used to add entries to the losses during training. str
keyword-only
backoff	Token attribute to use when no applicable edit tree is found. Defaults to orth. str
min_tree_freq	Minimum frequency of an edit tree in the training set to be used. Defaults to 3. int
overwrite	Whether existing annotation is overwritten. Defaults to False. bool
top_k	The number of most probable edit trees to try before resorting to backoff. Defaults to 1. int
scorer	The scoring method. Defaults to Scorer.score_token_attr for the attribute "lemma". Optional[Callable]

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

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

EditTreeLemmatizer.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

lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
for doc in lemmatizer.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

EditTreeLemmatizer.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

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

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

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

EditTreeLemmatizer.predict

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

Example

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

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

EditTreeLemmatizer.set_annotations

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

Example

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

Name	Description
docs	The documents to modify. Iterable[Doc]
tree_ids	The identifiers of the edit trees to apply, produced by EditTreeLemmatizer.predict.

EditTreeLemmatizer.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

lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
optimizer = nlp.initialize()
losses = lemmatizer.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]

EditTreeLemmatizer.get_loss

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

Example

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)

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]

EditTreeLemmatizer.create_optimizer

Create an optimizer for the pipeline component.

Example

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

Name	Description
RETURNS	The optimizer. Optimizer

EditTreeLemmatizer.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

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

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

EditTreeLemmatizer.to_disk

Serialize the pipe to disk.

Example

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

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]

EditTreeLemmatizer.from_disk

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

Example

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

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 EditTreeLemmatizer object. EditTreeLemmatizer

EditTreeLemmatizer.to_bytes

Example

lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
lemmatizer_bytes = lemmatizer.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 EditTreeLemmatizer object. bytes

EditTreeLemmatizer.from_bytes

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

Example

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

EditTreeLemmatizer.labels

The labels currently added to the component.

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

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

EditTreeLemmatizer.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 EditTreeLemmatizer.initialize to initialize the model with a pre-defined label set.

Example

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

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

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 = lemmatizer.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.
trees	The edit trees. You usually don't want to exclude this.