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
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.
| 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] |
explosion/spaCy/master/spacy/pipeline/edit_tree_lemmatizer.py
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.
| 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__ 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.
| Name | Description |
|---|---|
doc | The document to process. Doc |
| RETURNS | The processed document. 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.
| 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 methodv3.0
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.
| Name | Description |
|---|---|
get_examples | Function that returns gold-standard annotations in the form of Example objects. Must contain at least one Example. 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 method
Apply the component’s model to a batch of Doc objects, without
modifying them.
| Name | Description |
|---|---|
docs | The documents to predict. Iterable[Doc] |
| RETURNS | The model’s prediction for each document. |
EditTreeLemmatizer.set_annotations method
Modify a batch of Doc objects, using pre-computed tree
identifiers.
| 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 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.
| 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 method
Find the loss and gradient of loss for the batch of documents and their predicted 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 method
Create an optimizer for the pipeline component.
| Name | Description |
|---|---|
| RETURNS | The optimizer. Optimizer |
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.
| Name | Description |
|---|---|
params | The parameter values to use in the model. dict |
EditTreeLemmatizer.to_disk method
Serialize the pipe to disk.
| 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 method
Load the pipe from disk. Modifies the object in place and returns it.
| 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 method
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 method
Load the pipe from a bytestring. Modifies the object in place and returns it.
| 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 property
The labels currently added to the component.
| Name | Description |
|---|---|
| RETURNS | The labels added to the component. Tuple[str, …] |
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.
| 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.
| 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. |