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1043 lines
65 KiB
Markdown
1043 lines
65 KiB
Markdown
---
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title: Language
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teaser: A text-processing pipeline
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tag: class
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source: spacy/language.py
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---
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Usually you'll load this once per process as `nlp` and pass the instance around
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your application. The `Language` class is created when you call
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[`spacy.load`](/api/top-level#spacy.load) and contains the shared vocabulary and
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[language data](/usage/adding-languages), optional binary weights, e.g. provided
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by a [trained pipeline](/models), and the
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[processing pipeline](/usage/processing-pipelines) containing components like
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the tagger or parser that are called on a document in order. You can also add
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your own processing pipeline components that take a `Doc` object, modify it and
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return it.
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## Language.\_\_init\_\_ {#init tag="method"}
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Initialize a `Language` object.
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> #### Example
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>
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> ```python
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> # Construction from subclass
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> from spacy.lang.en import English
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> nlp = English()
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>
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> # Construction from scratch
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> from spacy.vocab import Vocab
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> from spacy.language import Language
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> nlp = Language(Vocab())
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> ```
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| Name | Description |
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| ------------------ | ------------------------------------------------------------------------------------------------------------------------ |
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| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
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| _keyword-only_ | |
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| `max_length` | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. ~~int~~ |
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| `meta` | Custom meta data for the `Language` class. Is written to by pipelines to add meta data. ~~dict~~ |
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| `create_tokenizer` | Optional function that receives the `nlp` object and returns a tokenizer. ~~Callable[[Language], Callable[[str], Doc]]~~ |
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## Language.from_config {#from_config tag="classmethod" new="3"}
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Create a `Language` object from a loaded config. Will set up the tokenizer and
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language data, add pipeline components based on the pipeline and components
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define in the config and validate the results. If no config is provided, the
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default config of the given language is used. This is also how spaCy loads a
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model under the hood based on its [`config.cfg`](/api/data-formats#config).
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> #### Example
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>
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> ```python
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> from thinc.api import Config
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> from spacy.language import Language
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>
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> config = Config().from_disk("./config.cfg")
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> nlp = Language.from_config(config)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ |
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| _keyword-only_ | |
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| `disable` | List of pipeline component names to disable. ~~Iterable[str]~~ |
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| `auto_fill` | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~ |
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| `validate` | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
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| **RETURNS** | The initialized object. ~~Language~~ |
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## Language.component {#component tag="classmethod" new="3"}
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Register a custom pipeline component under a given name. This allows
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initializing the component by name using
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[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
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[config files](/usage/training#config). This classmethod and decorator is
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intended for **simple stateless functions** that take a `Doc` and return it. For
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more complex stateful components that allow settings and need access to the
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shared `nlp` object, use the [`Language.factory`](/api/language#factory)
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decorator. For more details and examples, see the
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[usage documentation](/usage/processing-pipelines#custom-components).
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> #### Example
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>
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> ```python
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> from spacy.language import Language
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>
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> # Usage as a decorator
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> @Language.component("my_component")
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> def my_component(doc):
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> # Do something to the doc
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> return doc
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>
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> # Usage as a function
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> Language.component("my_component2", func=my_component)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `name` | The name of the component factory. ~~str~~ |
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| _keyword-only_ | |
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| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
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| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
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| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~ |
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| `func` | Optional function if not used a a decorator. ~~Optional[Callable[[Doc], Doc]]~~ |
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## Language.factory {#factory tag="classmethod"}
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Register a custom pipeline component factory under a given name. This allows
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initializing the component by name using
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[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
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[config files](/usage/training#config). The registered factory function needs to
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take at least two **named arguments** which spaCy fills in automatically: `nlp`
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for the current `nlp` object and `name` for the component instance name. This
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can be useful to distinguish multiple instances of the same component and allows
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trainable components to add custom losses using the component instance name. The
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`default_config` defines the default values of the remaining factory arguments.
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It's merged into the [`nlp.config`](/api/language#config). For more details and
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examples, see the
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[usage documentation](/usage/processing-pipelines#custom-components).
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> #### Example
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>
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> ```python
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> from spacy.language import Language
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>
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> # Usage as a decorator
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> @Language.factory(
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> "my_component",
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> default_config={"some_setting": True},
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> )
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> def create_my_component(nlp, name, some_setting):
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> return MyComponent(some_setting)
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>
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> # Usage as function
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> Language.factory(
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> "my_component",
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> default_config={"some_setting": True},
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> func=create_my_component
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> )
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> ```
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| Name | Description |
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| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `name` | The name of the component factory. ~~str~~ |
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| _keyword-only_ | |
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| `default_config` | The default config, describing the default values of the factory arguments. ~~Dict[str, Any]~~ |
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| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
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| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
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| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~ |
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| `scores` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
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| `default_score_weights` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. ~~Dict[str, float]~~ |
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| `func` | Optional function if not used a a decorator. ~~Optional[Callable[[...], Callable[[Doc], Doc]]]~~ |
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## Language.\_\_call\_\_ {#call tag="method"}
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Apply the pipeline to some text. The text can span multiple sentences, and can
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contain arbitrary whitespace. Alignment into the original string is preserved.
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> #### Example
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>
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> ```python
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> doc = nlp("An example sentence. Another sentence.")
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> assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")
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> ```
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| Name | Description |
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| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| `text` | The text to be processed. ~~str~~ |
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| _keyword-only_ | |
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| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
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| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
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| **RETURNS** | A container for accessing the annotations. ~~Doc~~ |
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## Language.pipe {#pipe tag="method"}
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Process texts as a stream, and yield `Doc` objects in order. This is usually
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more efficient than processing texts one-by-one.
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> #### Example
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>
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> ```python
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> texts = ["One document.", "...", "Lots of documents"]
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> for doc in nlp.pipe(texts, batch_size=50):
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> assert doc.is_parsed
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> ```
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| Name | Description |
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| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `texts` | A sequence of strings. ~~Iterable[str]~~ |
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| _keyword-only_ | |
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| `as_tuples` | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ |
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| `batch_size` | The number of texts to buffer. ~~int~~ |
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| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
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| `cleanup` | If `True`, unneeded strings are freed to control memory use. Experimental. ~~bool~~ |
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| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
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| `n_process` <Tag variant="new">2.2.2</Tag> | Number of processors to use. Defaults to `1`. ~~int~~ |
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| **YIELDS** | Documents in the order of the original text. ~~Doc~~ |
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## Language.begin_training {#begin_training tag="method"}
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Initialize the pipeline for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples can either be the full training data or a representative sample. They
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are used to **initialize the models** of trainable pipeline components and are
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passed each component's [`begin_training`](/api/pipe#begin_training) method, if
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available. Initialization includes validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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<Infobox variant="warning" title="Changed in v3.0">
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The `Language.update` method now takes a **function** that is called with no
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arguments and returns a sequence of [`Example`](/api/example) objects instead of
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tuples of `Doc` and `GoldParse` objects.
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</Infobox>
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> #### Example
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>
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> ```python
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> get_examples = lambda: examples
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> optimizer = nlp.begin_training(get_examples)
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> ```
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| Name | Description |
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| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Optional[Callable[[], Iterable[Example]]]~~ |
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| _keyword-only_ | |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## Language.resume_training {#resume_training tag="method,experimental" new="3"}
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Continue training a trained pipeline. Create and return an optimizer, and
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initialize "rehearsal" for any pipeline component that has a `rehearse` method.
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Rehearsal is used to prevent models from "forgetting" their initialized
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"knowledge". To perform rehearsal, collect samples of text you want the models
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to retain performance on, and call [`nlp.rehearse`](/api/language#rehearse) with
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a batch of [Example](/api/example) objects.
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> #### Example
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>
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> ```python
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> optimizer = nlp.resume_training()
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> nlp.rehearse(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## Language.update {#update tag="method"}
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Update the models in the pipeline.
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<Infobox variant="warning" title="Changed in v3.0">
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The `Language.update` method now takes a batch of [`Example`](/api/example)
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objects instead of the raw texts and annotations or `Doc` and `GoldParse`
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objects. An [`Example`](/api/example) streamlines how data is passed around. It
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stores two `Doc` objects: one for holding the gold-standard reference data, and
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one for holding the predictions of the pipeline.
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For most use cases, you shouldn't have to write your own training scripts
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anymore. Instead, you can use [`spacy train`](/api/cli#train) with a config file
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and custom registered functions if needed. See the
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[training documentation](/usage/training) for details.
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</Infobox>
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> #### Example
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>
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> ```python
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> for raw_text, entity_offsets in train_data:
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> doc = nlp.make_doc(raw_text)
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> example = Example.from_dict(doc, {"entities": entity_offsets})
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> nlp.update([example], sgd=optimizer)
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> ```
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| Name | Description |
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| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
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| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## Language.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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the "catastrophic forgetting" problem. This feature is experimental.
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> #### Example
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>
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> ```python
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> optimizer = nlp.resume_training()
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> losses = nlp.rehearse(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------- |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## Language.evaluate {#evaluate tag="method"}
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Evaluate a pipeline's components.
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<Infobox variant="warning" title="Changed in v3.0">
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The `Language.update` method now takes a batch of [`Example`](/api/example)
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objects instead of tuples of `Doc` and `GoldParse` objects.
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</Infobox>
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> #### Example
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>
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> ```python
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> scores = nlp.evaluate(examples)
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> print(scores)
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> ```
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| Name | Description |
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| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `batch_size` | The batch size to use. ~~int~~ |
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| `scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~ |
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| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
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| `scorer_cfg` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~ |
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| **RETURNS** | A dictionary of evaluation scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## Language.use_params {#use_params tag="contextmanager, method"}
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Replace weights of models in the pipeline with those provided in the params
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dictionary. Can be used as a context manager, in which case, models go back to
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their original weights after the block.
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> #### Example
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>
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> ```python
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> with nlp.use_params(optimizer.averages):
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> nlp.to_disk("/tmp/checkpoint")
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> ```
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| Name | Description |
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| -------- | ------------------------------------------------------ |
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| `params` | A dictionary of parameters keyed by model ID. ~~dict~~ |
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## Language.add_pipe {#add_pipe tag="method" new="2"}
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Add a component to the processing pipeline. Expects a name that maps to a
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component factory registered using
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[`@Language.component`](/api/language#component) or
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[`@Language.factory`](/api/language#factory). Components should be callables
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that take a `Doc` object, modify it and return it. Only one of `before`,
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`after`, `first` or `last` can be set. Default behavior is `last=True`.
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<Infobox title="Changed in v3.0" variant="warning">
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As of v3.0, the [`Language.add_pipe`](/api/language#add_pipe) method doesn't
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take callables anymore and instead expects the **name of a component factory**
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registered using [`@Language.component`](/api/language#component) or
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[`@Language.factory`](/api/language#factory). It now takes care of creating the
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component, adds it to the pipeline and returns it.
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</Infobox>
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> #### Example
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||
>
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> ```python
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> @Language.component("component")
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> def component_func(doc):
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> # modify Doc and return it return doc
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>
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> nlp.add_pipe("component", before="ner")
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> component = nlp.add_pipe("component", name="custom_name", last=True)
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>
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> # Add component from source pipeline
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> source_nlp = spacy.load("en_core_web_sm")
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> nlp.add_pipe("ner", source=source_nlp)
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> ```
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| Name | Description |
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| ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `factory_name` | Name of the registered component factory. ~~str~~ |
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| `name` | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~ |
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| _keyword-only_ | |
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| `before` | Component name or index to insert component directly before. ~~Optional[Union[str, int]]~~ |
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| `after` | Component name or index to insert component directly after. ~~Optional[Union[str, int]]~~ |
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| `first` | Insert component first / not first in the pipeline. ~~Optional[bool]~~ |
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| `last` | Insert component last / not last in the pipeline. ~~Optional[bool]~~ |
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| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
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| `source` <Tag variant="new">3</Tag> | Optional source pipeline to copy component from. If a source is provided, the `factory_name` is interpreted as the name of the component in the source pipeline. Make sure that the vocab, vectors and settings of the source pipeline match the target pipeline. ~~Optional[Language]~~ |
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| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
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||
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
|
||
|
||
## Language.create_pipe {#create_pipe tag="method" new="2"}
|
||
|
||
Create a pipeline component from a factory.
|
||
|
||
<Infobox title="Changed in v3.0" variant="warning">
|
||
|
||
As of v3.0, the [`Language.add_pipe`](/api/language#add_pipe) method also takes
|
||
the string name of the factory, creates the component, adds it to the pipeline
|
||
and returns it. The `Language.create_pipe` method is now mostly used internally.
|
||
To create a component and add it to the pipeline, you should always use
|
||
`Language.add_pipe`.
|
||
|
||
</Infobox>
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> parser = nlp.create_pipe("parser")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `factory_name` | Name of the registered component factory. ~~str~~ |
|
||
| `name` | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~ |
|
||
| _keyword-only_ | |
|
||
| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
|
||
| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
|
||
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
|
||
|
||
## Language.has_factory {#has_factory tag="classmethod" new="3"}
|
||
|
||
Check whether a factory name is registered on the `Language` class or subclass.
|
||
Will check for
|
||
[language-specific factories](/usage/processing-pipelines#factories-language)
|
||
registered on the subclass, as well as general-purpose factories registered on
|
||
the `Language` base class, available to all subclasses.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> from spacy.language import Language
|
||
> from spacy.lang.en import English
|
||
>
|
||
> @English.component("component")
|
||
> def component(doc):
|
||
> return doc
|
||
>
|
||
> assert English.has_factory("component")
|
||
> assert not Language.has_factory("component")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------------------------------------- |
|
||
| `name` | Name of the pipeline factory to check. ~~str~~ |
|
||
| **RETURNS** | Whether a factory of that name is registered on the class. ~~bool~~ |
|
||
|
||
## Language.has_pipe {#has_pipe tag="method" new="2"}
|
||
|
||
Check whether a component is present in the pipeline. Equivalent to
|
||
`name in nlp.pipe_names`.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> @Language.component("component")
|
||
> def component(doc):
|
||
> return doc
|
||
>
|
||
> nlp.add_pipe("component", name="my_component")
|
||
> assert "my_component" in nlp.pipe_names
|
||
> assert nlp.has_pipe("my_component")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ----------------------------------------------------------------- |
|
||
| `name` | Name of the pipeline component to check. ~~str~~ |
|
||
| **RETURNS** | Whether a component of that name exists in the pipeline. ~~bool~~ |
|
||
|
||
## Language.get_pipe {#get_pipe tag="method" new="2"}
|
||
|
||
Get a pipeline component for a given component name.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> parser = nlp.get_pipe("parser")
|
||
> custom_component = nlp.get_pipe("custom_component")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------------------ |
|
||
| `name` | Name of the pipeline component to get. ~~str~~ |
|
||
| **RETURNS** | The pipeline component. ~~Callable[[Doc], Doc]~~ |
|
||
|
||
## Language.replace_pipe {#replace_pipe tag="method" new="2"}
|
||
|
||
Replace a component in the pipeline.
|
||
|
||
<Infobox title="Changed in v3.0" variant="warning">
|
||
|
||
As of v3.0, the `Language.replace_pipe` method doesn't take callables anymore
|
||
and instead expects the **name of a component factory** registered using
|
||
[`@Language.component`](/api/language#component) or
|
||
[`@Language.factory`](/api/language#factory).
|
||
|
||
</Infobox>
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.replace_pipe("parser", my_custom_parser)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||
| `name` | Name of the component to replace. ~~str~~ |
|
||
| `component` | The factory name of the component to insert. ~~str~~ |
|
||
| _keyword-only_ | |
|
||
| `config` <Tag variant="new">3</Tag> | Optional config parameters to use for the new component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~ |
|
||
| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
|
||
|
||
## Language.rename_pipe {#rename_pipe tag="method" new="2"}
|
||
|
||
Rename a component in the pipeline. Useful to create custom names for
|
||
pre-defined and pre-loaded components. To change the default name of a component
|
||
added to the pipeline, you can also use the `name` argument on
|
||
[`add_pipe`](/api/language#add_pipe).
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.rename_pipe("parser", "spacy_parser")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ---------- | ---------------------------------------- |
|
||
| `old_name` | Name of the component to rename. ~~str~~ |
|
||
| `new_name` | New name of the component. ~~str~~ |
|
||
|
||
## Language.remove_pipe {#remove_pipe tag="method" new="2"}
|
||
|
||
Remove a component from the pipeline. Returns the removed component name and
|
||
component function.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> name, component = nlp.remove_pipe("parser")
|
||
> assert name == "parser"
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------------------------------------------------------------ |
|
||
| `name` | Name of the component to remove. ~~str~~ |
|
||
| **RETURNS** | A `(name, component)` tuple of the removed component. ~~Tuple[str, Callable[[Doc], Doc]]~~ |
|
||
|
||
## Language.disable_pipe {#disable_pipe tag="method" new="3"}
|
||
|
||
Temporarily disable a pipeline component so it's not run as part of the
|
||
pipeline. Disabled components are listed in
|
||
[`nlp.disabled`](/api/language#attributes) and included in
|
||
[`nlp.components`](/api/language#attributes), but not in
|
||
[`nlp.pipeline`](/api/language#pipeline), so they're not run when you process a
|
||
`Doc` with the `nlp` object. If the component is already disabled, this method
|
||
does nothing.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.add_pipe("ner")
|
||
> nlp.add_pipe("textcat")
|
||
> assert nlp.pipe_names == ["ner", "textcat"]
|
||
> nlp.disable_pipe("ner")
|
||
> assert nlp.pipe_names == ["textcat"]
|
||
> assert nlp.component_names == ["ner", "textcat"]
|
||
> assert nlp.disabled == ["ner"]
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------ | ----------------------------------------- |
|
||
| `name` | Name of the component to disable. ~~str~~ |
|
||
|
||
## Language.enable_pipe {#enable_pipe tag="method" new="3"}
|
||
|
||
Enable a previously disable component (e.g. via
|
||
[`Language.disable_pipes`](/api/language#disable_pipes)) so it's run as part of
|
||
the pipeline, [`nlp.pipeline`](/api/language#pipeline). If the component is
|
||
already enabled, this method does nothing.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.disable_pipe("ner")
|
||
> assert "ner" in nlp.disabled
|
||
> assert not "ner" in nlp.pipe_names
|
||
> nlp.enable_pipe("ner")
|
||
> assert not "ner" in nlp.disabled
|
||
> assert "ner" in nlp.pipe_names
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------ | ---------------------------------------- |
|
||
| `name` | Name of the component to enable. ~~str~~ |
|
||
|
||
## Language.select_pipes {#select_pipes tag="contextmanager, method" new="3"}
|
||
|
||
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
|
||
string), or `enable`. In the latter case, all components not in the `enable`
|
||
list, will be disabled. Under the hood, this method calls into
|
||
[`disable_pipe`](/api/language#disable_pipe) and
|
||
[`enable_pipe`](/api/language#enable_pipe).
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> with nlp.select_pipes(disable=["tagger", "parser"]):
|
||
> nlp.begin_training()
|
||
>
|
||
> with nlp.select_pipes(enable="ner"):
|
||
> nlp.begin_training()
|
||
>
|
||
> disabled = nlp.select_pipes(disable=["tagger", "parser"])
|
||
> nlp.begin_training()
|
||
> disabled.restore()
|
||
> ```
|
||
|
||
<Infobox title="Changed in v3.0" variant="warning" id="disable_pipes">
|
||
|
||
As of spaCy v3.0, the `disable_pipes` method has been renamed to `select_pipes`:
|
||
|
||
```diff
|
||
- nlp.disable_pipes(["tagger", "parser"])
|
||
+ nlp.select_pipes(disable=["tagger", "parser"])
|
||
```
|
||
|
||
</Infobox>
|
||
|
||
| Name | Description |
|
||
| -------------- | ------------------------------------------------------------------------------------------------------ |
|
||
| _keyword-only_ | |
|
||
| `disable` | Name(s) of pipeline components to disable. ~~Optional[Union[str, Iterable[str]]]~~ |
|
||
| `enable` | Names(s) of pipeline components that will not be disabled. ~~Optional[Union[str, Iterable[str]]]~~ |
|
||
| **RETURNS** | The disabled pipes that can be restored by calling the object's `.restore()` method. ~~DisabledPipes~~ |
|
||
|
||
## Language.get_factory_meta {#get_factory_meta tag="classmethod" new="3"}
|
||
|
||
Get the factory meta information for a given pipeline component name. Expects
|
||
the name of the component **factory**. The factory meta is an instance of the
|
||
[`FactoryMeta`](/api/language#factorymeta) dataclass and contains the
|
||
information about the component and its default provided by the
|
||
[`@Language.component`](/api/language#component) or
|
||
[`@Language.factory`](/api/language#factory) decorator.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> factory_meta = Language.get_factory_meta("ner")
|
||
> assert factory_meta.factory == "ner"
|
||
> print(factory_meta.default_config)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | --------------------------------- |
|
||
| `name` | The factory name. ~~str~~ |
|
||
| **RETURNS** | The factory meta. ~~FactoryMeta~~ |
|
||
|
||
## Language.get_pipe_meta {#get_pipe_meta tag="method" new="3"}
|
||
|
||
Get the factory meta information for a given pipeline component name. Expects
|
||
the name of the component **instance** in the pipeline. The factory meta is an
|
||
instance of the [`FactoryMeta`](/api/language#factorymeta) dataclass and
|
||
contains the information about the component and its default provided by the
|
||
[`@Language.component`](/api/language#component) or
|
||
[`@Language.factory`](/api/language#factory) decorator.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.add_pipe("ner", name="entity_recognizer")
|
||
> factory_meta = nlp.get_pipe_meta("entity_recognizer")
|
||
> assert factory_meta.factory == "ner"
|
||
> print(factory_meta.default_config)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------ |
|
||
| `name` | The pipeline component name. ~~str~~ |
|
||
| **RETURNS** | The factory meta. ~~FactoryMeta~~ |
|
||
|
||
## Language.analyze_pipes {#analyze_pipes tag="method" new="3"}
|
||
|
||
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`](/api/language#component) and
|
||
[`@Language.factory`](/api/language#factory) decorator. If requirements aren't
|
||
met, e.g. if a component specifies a required property that is not set by a
|
||
previous component, a warning is shown.
|
||
|
||
<Infobox variant="warning" title="Important note">
|
||
|
||
The pipeline analysis is static and does **not actually run the components**.
|
||
This means that it relies on the information provided by the components
|
||
themselves. If a custom component declares that it assigns an attribute but it
|
||
doesn't, the pipeline analysis won't catch that.
|
||
|
||
</Infobox>
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp = spacy.blank("en")
|
||
> nlp.add_pipe("tagger")
|
||
> nlp.add_pipe("entity_linker")
|
||
> analysis = nlp.analyze_pipes()
|
||
> ```
|
||
|
||
<Accordion title="Example output" spaced>
|
||
|
||
```json
|
||
### Structured
|
||
{
|
||
"summary": {
|
||
"tagger": {
|
||
"assigns": ["token.tag"],
|
||
"requires": [],
|
||
"scores": ["tag_acc", "pos_acc", "lemma_acc"],
|
||
"retokenizes": false
|
||
},
|
||
"entity_linker": {
|
||
"assigns": ["token.ent_kb_id"],
|
||
"requires": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
|
||
"scores": [],
|
||
"retokenizes": false
|
||
}
|
||
},
|
||
"problems": {
|
||
"tagger": [],
|
||
"entity_linker": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"]
|
||
},
|
||
"attrs": {
|
||
"token.ent_iob": { "assigns": [], "requires": ["entity_linker"] },
|
||
"doc.ents": { "assigns": [], "requires": ["entity_linker"] },
|
||
"token.ent_kb_id": { "assigns": ["entity_linker"], "requires": [] },
|
||
"doc.sents": { "assigns": [], "requires": ["entity_linker"] },
|
||
"token.tag": { "assigns": ["tagger"], "requires": [] },
|
||
"token.ent_type": { "assigns": [], "requires": ["entity_linker"] }
|
||
}
|
||
}
|
||
```
|
||
|
||
```
|
||
### Pretty
|
||
============================= Pipeline Overview =============================
|
||
|
||
# Component Assigns Requires Scores Retokenizes
|
||
- ------------- --------------- -------------- --------- -----------
|
||
0 tagger token.tag tag_acc False
|
||
pos_acc
|
||
lemma_acc
|
||
|
||
1 entity_linker token.ent_kb_id doc.ents False
|
||
doc.sents
|
||
token.ent_iob
|
||
token.ent_type
|
||
|
||
|
||
================================ Problems (4) ================================
|
||
⚠ 'entity_linker' requirements not met: doc.ents, doc.sents,
|
||
token.ent_iob, token.ent_type
|
||
```
|
||
|
||
</Accordion>
|
||
|
||
| Name | Description |
|
||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| _keyword-only_ | |
|
||
| `keys` | The values to display in the table. Corresponds to attributes of the [`FactoryMeta`](/api/language#factorymeta). Defaults to `["assigns", "requires", "scores", "retokenizes"]`. ~~List[str]~~ |
|
||
| `pretty` | Pretty-print the results as a table. Defaults to `False`. ~~bool~~ |
|
||
| **RETURNS** | Dictionary containing the pipe analysis, keyed by `"summary"` (component meta by pipe), `"problems"` (attribute names by pipe) and `"attrs"` (pipes that assign and require an attribute, keyed by attribute). ~~Optional[Dict[str, Any]]~~ |
|
||
|
||
## Language.meta {#meta tag="property"}
|
||
|
||
Custom meta data for the Language class. If a trained pipeline is loaded, this
|
||
contains meta data of the pipeline. The `Language.meta` is also what's
|
||
serialized as the [`meta.json`](/api/data-formats#meta) when you save an `nlp`
|
||
object to disk.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> print(nlp.meta)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | --------------------------------- |
|
||
| **RETURNS** | The meta data. ~~Dict[str, Any]~~ |
|
||
|
||
## Language.config {#config tag="property" new="3"}
|
||
|
||
Export a trainable [`config.cfg`](/api/data-formats#config) 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`](/api/cli#train). `Language.config` returns
|
||
a [Thinc `Config` object](https://thinc.ai/docs/api-config#config), which is a
|
||
subclass of the built-in `dict`. It supports the additional methods `to_disk`
|
||
(serialize the config to a file) and `to_str` (output the config as a string).
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.config.to_disk("./config.cfg")
|
||
> print(nlp.config.to_str())
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ---------------------- |
|
||
| **RETURNS** | The config. ~~Config~~ |
|
||
|
||
## Language.to_disk {#to_disk tag="method" new="2"}
|
||
|
||
Save the current state to a directory. Under the hood, this method delegates to
|
||
the `to_disk` methods of the individual pipeline components, if available. This
|
||
means that if a trained pipeline is loaded, all components and their weights
|
||
will be saved to disk.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp.to_disk("/path/to/pipeline")
|
||
> ```
|
||
|
||
| 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` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
|
||
## Language.from_disk {#from_disk tag="method" new="2"}
|
||
|
||
Loads state from a directory, including all data that was saved with the
|
||
`Language` object. Modifies the object in place and returns it.
|
||
|
||
<Infobox variant="warning" title="Important note">
|
||
|
||
Keep in mind that this method **only loads serialized state** and doesn't set up
|
||
the `nlp` object. This means that it requires the correct language class to be
|
||
initialized and all pipeline components to be added to the pipeline. If you want
|
||
to load a serialized pipeline from a directory, you should use
|
||
[`spacy.load`](/api/top-level#spacy.load), which will set everything up for you.
|
||
|
||
</Infobox>
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> from spacy.language import Language
|
||
> nlp = Language().from_disk("/path/to/pipeline")
|
||
>
|
||
> # Using language-specific subclass
|
||
> from spacy.lang.en import English
|
||
> nlp = English().from_disk("/path/to/pipeline")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ----------------------------------------------------------------------------------------------------------- |
|
||
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
||
| _keyword-only_ | |
|
||
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
| **RETURNS** | The modified `Language` object. ~~Language~~ |
|
||
|
||
## Language.to_bytes {#to_bytes tag="method"}
|
||
|
||
Serialize the current state to a binary string.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> nlp_bytes = nlp.to_bytes()
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ------------------------------------------------------------------------------------------------------ |
|
||
| _keyword-only_ | |
|
||
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~iterable~~ |
|
||
| **RETURNS** | The serialized form of the `Language` object. ~~bytes~~ |
|
||
|
||
## Language.from_bytes {#from_bytes tag="method"}
|
||
|
||
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](/usage/adding-languages#lex-attrs)
|
||
available to the loaded object.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> from spacy.lang.en import English
|
||
> nlp_bytes = nlp.to_bytes()
|
||
> nlp2 = English()
|
||
> nlp2.from_bytes(nlp_bytes)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ----------------------------------------------------------------------------------------------------------- |
|
||
| `bytes_data` | The data to load from. ~~bytes~~ |
|
||
| _keyword-only_ | |
|
||
| `exclude` | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
| **RETURNS** | The `Language` object. ~~Language~~ |
|
||
|
||
## Attributes {#attributes}
|
||
|
||
| Name | Description |
|
||
| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `vocab` | A container for the lexical types. ~~Vocab~~ |
|
||
| `tokenizer` | The tokenizer. ~~Tokenizer~~ |
|
||
| `make_doc` | Callable that takes a string and returns a `Doc`. ~~Callable[[str], Doc]~~ |
|
||
| `pipeline` | List of `(name, component)` tuples describing the current processing pipeline, in order. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ |
|
||
| `pipe_names` <Tag variant="new">2</Tag> | List of pipeline component names, in order. ~~List[str]~~ |
|
||
| `pipe_labels` <Tag variant="new">2.2</Tag> | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~ |
|
||
| `pipe_factories` <Tag variant="new">2.2</Tag> | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~ |
|
||
| `factories` | All available factory functions, keyed by name. ~~Dict[str, Callable[[...], Callable[[Doc], Doc]]]~~ |
|
||
| `factory_names` <Tag variant="new">3</Tag> | List of all available factory names. ~~List[str]~~ |
|
||
| `components` <Tag variant="new">3</Tag> | List of all available `(name, component)` tuples, including components that are currently disabled. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ |
|
||
| `component_names` <Tag variant="new">3</Tag> | List of all available component names, including components that are currently disabled. ~~List[str]~~ |
|
||
| `disabled` <Tag variant="new">3</Tag> | Names of components that are currently disabled and don't run as part of the pipeline. ~~List[str]~~ |
|
||
| `path` <Tag variant="new">2</Tag> | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~ |
|
||
|
||
## Class attributes {#class-attributes}
|
||
|
||
| Name | Description |
|
||
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||
| `Defaults` | Settings, data and factory methods for creating the `nlp` object and processing pipeline. ~~Defaults~~ |
|
||
| `lang` | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). ~~str~~ |
|
||
| `default_config` | Base [config](/usage/training#config) to use for [Language.config](/api/language#config). Defaults to [`default_config.cfg`](https://github.com/explosion/spaCy/tree/develop/spacy/default_config.cfg). ~~Config~~ |
|
||
|
||
## Defaults {#defaults}
|
||
|
||
The following attributes can be set on the `Language.Defaults` class to
|
||
customize the default language data:
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> from spacy.language import language
|
||
> from spacy.lang.tokenizer_exceptions import URL_MATCH
|
||
> from thinc.api import Config
|
||
>
|
||
> DEFAULT_CONFIFG = """
|
||
> [nlp.tokenizer]
|
||
> @tokenizers = "MyCustomTokenizer.v1"
|
||
> """
|
||
>
|
||
> class Defaults(Language.Defaults):
|
||
> stop_words = set()
|
||
> tokenizer_exceptions = {}
|
||
> prefixes = tuple()
|
||
> suffixes = tuple()
|
||
> infixes = tuple()
|
||
> token_match = None
|
||
> url_match = URL_MATCH
|
||
> lex_attr_getters = {}
|
||
> syntax_iterators = {}
|
||
> writing_system = {"direction": "ltr", "has_case": True, "has_letters": True}
|
||
> config = Config().from_str(DEFAULT_CONFIG)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `stop_words` | List of stop words, used for `Token.is_stop`.<br />**Example:** [`stop_words.py`][stop_words.py] ~~Set[str]~~ |
|
||
| `tokenizer_exceptions` | Tokenizer exception rules, string mapped to list of token attributes.<br />**Example:** [`de/tokenizer_exceptions.py`][de/tokenizer_exceptions.py] ~~Dict[str, List[dict]]~~ |
|
||
| `prefixes`, `suffixes`, `infixes` | Prefix, suffix and infix rules for the default tokenizer.<br />**Example:** [`puncutation.py`][punctuation.py] ~~Optional[List[Union[str, Pattern]]]~~ |
|
||
| `token_match` | Optional regex for matching strings that should never be split, overriding the infix rules.<br />**Example:** [`fr/tokenizer_exceptions.py`][fr/tokenizer_exceptions.py] ~~Optional[Pattern]~~ |
|
||
| `url_match` | Regular expression for matching URLs. Prefixes and suffixes are removed before applying the match.<br />**Example:** [`tokenizer_exceptions.py`][tokenizer_exceptions.py] ~~Optional[Pattern]~~ |
|
||
| `lex_attr_getters` | Custom functions for setting lexical attributes on tokens, e.g. `like_num`.<br />**Example:** [`lex_attrs.py`][lex_attrs.py] ~~Dict[int, Callable[[str], Any]]~~ |
|
||
| `syntax_iterators` | Functions that compute views of a `Doc` object based on its syntax. At the moment, only used for [noun chunks](/usage/linguistic-features#noun-chunks).<br />**Example:** [`syntax_iterators.py`][syntax_iterators.py]. ~~Dict[str, Callable[[Union[Doc, Span]], Iterator[Span]]]~~ |
|
||
| `writing_system` | Information about the language's writing system, available via `Vocab.writing_system`. Defaults to: `{"direction": "ltr", "has_case": True, "has_letters": True}.`.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] ~~Dict[str, Any]~~ |
|
||
| `config` | Default [config](/usage/training#config) added to `nlp.config`. This can include references to custom tokenizers or lemmatizers.<br />**Example:** [`zh/__init__.py`][zh/__init__.py] ~~Config~~ |
|
||
|
||
[stop_words.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/en/stop_words.py
|
||
[tokenizer_exceptions.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/tokenizer_exceptions.py
|
||
[de/tokenizer_exceptions.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/de/tokenizer_exceptions.py
|
||
[fr/tokenizer_exceptions.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/fr/tokenizer_exceptions.py
|
||
[punctuation.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/punctuation.py
|
||
[lex_attrs.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/en/lex_attrs.py
|
||
[syntax_iterators.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/en/syntax_iterators.py
|
||
[zh/__init__.py]:
|
||
https://github.com/explosion/spaCy/tree/master/spacy/lang/zh/__init__.py
|
||
|
||
## Serialization fields {#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
|
||
>
|
||
> ```python
|
||
> data = nlp.to_bytes(exclude=["tokenizer", "vocab"])
|
||
> nlp.from_disk("/pipeline", exclude=["ner"])
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------------------------------------ |
|
||
| `vocab` | The shared [`Vocab`](/api/vocab). |
|
||
| `tokenizer` | Tokenization rules and exceptions. |
|
||
| `meta` | The meta data, available as [`Language.meta`](/api/language#meta). |
|
||
| ... | String names of pipeline components, e.g. `"ner"`. |
|
||
|
||
## FactoryMeta {#factorymeta new="3" tag="dataclass"}
|
||
|
||
The `FactoryMeta` contains the information about the component and its default
|
||
provided by the [`@Language.component`](/api/language#component) or
|
||
[`@Language.factory`](/api/language#factory) decorator. It's created whenever a
|
||
component is defined and stored on the `Language` class for each component
|
||
instance and factory instance.
|
||
|
||
| Name | Description |
|
||
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||
| `factory` | The name of the registered component factory. ~~str~~ |
|
||
| `default_config` | The default config, describing the default values of the factory arguments. ~~Dict[str, Any]~~ |
|
||
| `assigns` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
|
||
| `requires` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
|
||
| `retokenizes` | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~bool~~ |
|
||
| `scores` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
|
||
| `default_score_weights` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. ~~Dict[str, float]~~ |
|