Pipeline

EditTreeLemmatizer

classv3.3
String name:trainable_lemmatizerBase class:TrainablePipeTrainable:
Pipeline component for lemmatization

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.

LocationValue
Token.lemmaThe 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.

SettingDescription
modelA 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]]
backoffToken attribute to use when no applicable edit tree is found. Defaults to orth. str
min_tree_freqMinimum frequency of an edit tree in the training set to be used. Defaults to 3. int
overwriteWhether existing annotation is overwritten. Defaults to False. bool
top_kThe number of most probable edit trees to try before resorting to backoff. Defaults to 1. int
scorerThe scoring method. Defaults to Scorer.score_token_attr for the attribute "lemma". Optional[Callable]
explosion/spaCy/master/spacy/pipeline/edit_tree_lemmatizer.py
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EditTreeLemmatizer.__init__ method

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.

NameDescription
vocabThe shared vocabulary. Vocab
modelA 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]]
nameString name of the component instance. Used to add entries to the losses during training. str
keyword-only
backoffToken attribute to use when no applicable edit tree is found. Defaults to orth. str
min_tree_freqMinimum frequency of an edit tree in the training set to be used. Defaults to 3. int
overwriteWhether existing annotation is overwritten. Defaults to False. bool
top_kThe number of most probable edit trees to try before resorting to backoff. Defaults to 1. int
scorerThe scoring method. Defaults to Scorer.score_token_attr for the attribute "lemma". Optional[Callable]

EditTreeLemmatizer.__call__ 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.

NameDescription
docThe document to process. Doc

EditTreeLemmatizer.pipe 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.

NameDescription
streamA stream of documents. Iterable[Doc]
keyword-only
batch_sizeThe number of documents to buffer. Defaults to 128. int

EditTreeLemmatizer.initialize methodv3.0

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.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]
labelsThe 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 method

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

NameDescription
docsThe documents to predict. Iterable[Doc]

EditTreeLemmatizer.set_annotations method

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

NameDescription
docsThe documents to modify. Iterable[Doc]
tree_idsThe identifiers of the edit trees to apply, produced by EditTreeLemmatizer.predict.

EditTreeLemmatizer.update 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.

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

EditTreeLemmatizer.get_loss method

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

NameDescription
examplesThe batch of examples. Iterable[Example]
scoresScores representing the model’s predictions.

EditTreeLemmatizer.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

EditTreeLemmatizer.use_params methodcontextmanager

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

NameDescription
paramsThe parameter values to use in the model. dict

EditTreeLemmatizer.to_disk method

Serialize the pipe to disk.

NameDescription
pathA 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
excludeString names of serialization fields to exclude. Iterable[str]

EditTreeLemmatizer.from_disk method

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

NameDescription
pathA path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EditTreeLemmatizer.to_bytes method

Serialize the pipe to a bytestring.

NameDescription
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EditTreeLemmatizer.from_bytes method

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

NameDescription
bytes_dataThe data to load from. bytes
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

EditTreeLemmatizer.labels property

The labels currently added to the component.

NameDescription

EditTreeLemmatizer.label_data propertyv3.0

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.

NameDescription

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.

NameDescription
vocabThe shared Vocab.
cfgThe config file. You usually don’t want to exclude this.
modelThe binary model data. You usually don’t want to exclude this.
treesThe edit trees. You usually don’t want to exclude this.