Pipeline

DependencyParser

class
String name:parserBase class:TrainablePipeTrainable:
Pipeline component for syntactic dependency parsing

A transition-based dependency parser component. The dependency parser jointly learns sentence segmentation and labelled dependency parsing, and can optionally learn to merge tokens that had been over-segmented by the tokenizer. The parser uses a variant of the non-monotonic arc-eager transition-system described by Honnibal and Johnson (2014), with the addition of a “break” transition to perform the sentence segmentation. Nivre (2005)’s pseudo-projective dependency transformation is used to allow the parser to predict non-projective parses.

The parser is trained using an imitation learning objective. It follows the actions predicted by the current weights, and at each state, determines which actions are compatible with the optimal parse that could be reached from the current state. The weights are updated such that the scores assigned to the set of optimal actions is increased, while scores assigned to other actions are decreased. Note that more than one action may be optimal for a given state.

Assigned Attributes

Dependency predictions are assigned to the Token.dep and Token.head fields. Beside the dependencies themselves, the parser decides sentence boundaries, which are saved in Token.is_sent_start and accessible via Doc.sents.

LocationValue
Token.depThe type of dependency relation (hash). int
Token.dep_The type of dependency relation. str
Token.headThe syntactic parent, or “governor”, of this token. Token
Token.is_sent_startA boolean value indicating whether the token starts a sentence. After the parser runs this will be True or False for all tokens. bool
Doc.sentsAn iterator over sentences in the Doc, determined by Token.is_sent_start values. Iterator[Span]

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
movesA list of transition names. Inferred from the data if not provided. Defaults to None. Optional[TransitionSystem]
update_with_oracle_cut_sizeDuring training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won’t need to change it. Defaults to 100. int
learn_tokensWhether to learn to merge subtokens that are split relative to the gold standard. Experimental. Defaults to False. bool
min_action_freqThe minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to “dep”. While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. Defaults to 30. int
modelThe Model powering the pipeline component. Defaults to TransitionBasedParser. Model[List[Doc], List[Floats2d]]
explosion/spaCy/master/spacy/pipeline/dep_parser.pyx
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DependencyParser.__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
modelThe Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
nameString name of the component instance. Used to add entries to the losses during training. str
movesA list of transition names. Inferred from the data if not provided. Optional[TransitionSystem]
keyword-only
update_with_oracle_cut_sizeDuring training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won’t need to change it. Defaults to 100. int
learn_tokensWhether to learn to merge subtokens that are split relative to the gold standard. Experimental. Defaults to False. bool
min_action_freqThe minimum frequency of labelled actions to retain. Rarer labelled actions have their label backed-off to “dep”. While this primarily affects the label accuracy, it can also affect the attachment structure, as the labels are used to represent the pseudo-projectivity transformation. int
scorerThe scoring method. Defaults to Scorer.score_deps for the attribute "dep" ignoring the labels p and punct and Scorer.score_spans for the attribute "sents". Optional[Callable]

DependencyParser.__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

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

DependencyParser.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.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Must contain at least one Example. 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[Dict[str, Dict[str, int]]]

DependencyParser.predict method

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

NameDescription
docsThe documents to predict. Iterable[Doc]

DependencyParser.set_annotations method

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

NameDescription
docsThe documents to modify. Iterable[Doc]
scoresThe scores to set, produced by DependencyParser.predict. Returns an internal helper class for the parse state. List[StateClass]

DependencyParser.update method

Learn from a batch of Example objects, updating the pipe’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]]

DependencyParser.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. StateClass

DependencyParser.create_optimizer method

Create an Optimizer for the pipeline component.

NameDescription

DependencyParser.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

DependencyParser.add_label method

Add a new label to the pipe. 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.

NameDescription
labelThe label to add. str

DependencyParser.set_output method

Change the output dimension of the component’s model by calling the model’s attribute resize_output. This is a function that takes the original model and the new output dimension nO, and changes the model in place. When resizing an already trained model, care should be taken to avoid the “catastrophic forgetting” problem.

NameDescription
nOThe new output dimension. int

DependencyParser.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]

DependencyParser.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]

DependencyParser.to_bytes method

Serialize the pipe to a bytestring.

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

DependencyParser.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]

DependencyParser.labels property

The labels currently added to the component.

NameDescription

DependencyParser.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 DependencyParser.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.