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The Language class is created when you call spacy. load and contains the shared vocabulary and language dataoptional binary weights, e. provided by a trained pipelineand the processing pipeline containing components like the tagger or parser that are called on a document in order.
You can also add your own processing pipeline components that take a Doc object, modify it and return it. Initialize a Language object. Note that the meta is only used for meta information in Language. meta and not to configure the nlp object or to override the config. To initialize from a config, docave resume language en doc, use Language.
Create a Language object from a loaded config. Will set up the tokenizer and language data, add pipeline components based on docave resume language en doc pipeline and add pipeline components based on the definitions specified in the config. If no config is provided, the default config of the given language is used.
This is also how spaCy loads a model under the hood based on its config. Register a custom pipeline component under a given name. This allows initializing the component by name using Language. This classmethod and decorator is intended for simple stateless functions that take a Doc and return it. For more complex stateful components that allow settings and need access to the shared nlp object, use the Language.
factory decorator. For more details and examples, see the usage documentation. Register a custom pipeline component factory under a given name, docave resume language en doc. The registered factory function needs to take at least two named arguments which spaCy fills in automatically: nlp for the current nlp object and name for the component instance name. This can be useful to distinguish multiple docave resume language en doc of the same component and allows trainable components to add custom losses using the component instance name.
Apply the pipeline to some text. The text can span multiple sentences, and can contain arbitrary whitespace, docave resume language en doc.
Alignment into the original string is preserved. Process texts as a stream, and yield Doc objects in order. This is usually more efficient than processing texts one-by-one.
Define a callback that will be invoked when an error is thrown during processing of one or more documents. Initialize the pipeline for training and return an Optimizer.
Under the hood, it uses the settings defined in the [initialize] config block to set up the vocabulary, load in vectors and tok2vec weights and pass optional arguments to the initialize methods implemented by pipeline components or the tokenizer.
This method is typically called automatically when you run spacy train. See the usage guide on the config lifecycle and initialization for details. The data examples 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. initializethe pipeline components will be initialized with generic data. In this case, it is crucial that the output dimension of each component has already been defined either in the configor by calling pipe.
for the tagger or textcat. Continue training a trained pipeline. To perform rehearsal, collect samples of text you want the models to retain performance on, and call nlp. rehearse with a batch of Example objects. This feature is experimental. Replace weights of models in the pipeline with docave resume language en doc provided in the params dictionary. Can be used as a context manager, in which case, models go back to their original weights after the block.
Add a component to the processing pipeline. Expects a name that maps to a component factory registered using Language, docave resume language en doc. component or Language. Components should be callables that take a Doc object, modify it and return it.
Only one of beforeafterdocave resume language en doc, first or last can be set. Check whether a factory name is registered on the Language class or subclass. Will check for language-specific factories registered on the subclass, as well as general-purpose factories registered on the Language base class, available to all subclasses.
Check whether a component is present in the pipeline. Equivalent to name in nlp. Rename a component in the pipeline.
Useful to create custom docave resume language en doc for pre-defined and pre-loaded components. Disabled components are listed in nlp. disabled and included in nlp. componentsbut not in nlp. If the component is already disabled, this method does nothing. Enable a previously disabled component e. via Language. If the component is already enabled, this method does nothing.
Disable one or more pipeline components. If used as a context manager, the pipeline will be restored to the initial state at the end of the block. Otherwise, a DisabledPipes object is returned, that has a.
restore method you can use to undo your changes. You can specify either disable as a list or stringor enable. In the latter case, all components not in the enable list docave resume language en doc be disabled. Get the factory meta docave resume language en doc for a given pipeline component name. Expects the name of the component factory. The factory meta is an instance of the Docave resume language en doc dataclass and contains the information about the component and its default provided by the Language.
Expects the name of the component instance in the pipeline. Analyze the current pipeline components and show a summary of the attributes they assign and require, and the scores they set.
The data is based on the information provided in the Language. component and Language. if a component specifies a required property that is not set by a previous component, a warning is shown. Find listener layers connecting to a shared token-to-vector embedding component of a given pipeline component model and replace them with a standalone copy of the token-to-vector layer. The listener layer allows other components to connect to a shared token-to-vector embedding component like Tok2Vec or Transformer.
Replacing listeners can be useful when training a pipeline with components sourced from an existing pipeline: if multiple components e. tagger, parser, NER listen to the same token-to-vector component, but some of them are frozen and not updated, their performance may degrade significally as the token-to-vector component is updated with new data. Meta data for the Language class, including name, version, data sources, license, author information and more, docave resume language en doc.
If docave resume language en doc trained pipeline is loaded, docave resume language en doc, this contains meta data of the pipeline. The Language. json when you save an nlp object to disk. See the meta data format for more details. Export a trainable config.
cfg for the current nlp object. Includes the current pipeline, all configs used to create the currently active pipeline components, as well as the default training config that can be used with spacy train. config returns a Thinc Config objectwhich is a subclass of the built-in dict. Save the current state to a directory. This means that if a trained pipeline is loaded, all components and their weights will be saved to disk.
Loads state from a directory, including all data that was saved with the Language object. Modifies the object in place and returns it. Load state from a binary string. Note that this method is commonly used via the subclasses like English or German to make language-specific functionality like the lexical attribute getters available to the loaded object.
The following attributes can be set on the Language. Defaults class to customize the default language data:. 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.
The FactoryMeta contains the information about the component and its default provided by the Language. spaCy �� Out now: spaCy v3.
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