---
title: Language
teaser: A text-processing pipeline
tag: class
source: spacy/language.py
---
Usually you'll load this once per process as `nlp` and pass the instance around
your application. The `Language` class is created when you call
[`spacy.load()`](/api/top-level#spacy.load) and contains the shared vocabulary
and [language data](/usage/adding-languages), optional model data loaded from a
[model package](/models) or a path, and a
[processing pipeline](/usage/processing-pipelines) 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.
## Language.component {#component tag="classmethod" new="3"}
Register a custom pipeline component under a given name. This allows
initializing the component by name using
[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
[config files](/usage/training#config). 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`](/api/language#factory)
decorator. For more details and examples, see the
[usage documentation](/usage/processing-pipelines#custom-components).
> #### Example
>
> ```python
> from spacy.language import Language
>
> # Usage as a decorator
> @Language.component("my_component")
> def my_component(doc):
> # Do something to the doc
> return doc
>
> # Usage as a function
> Language.component("my_component2", func=my_component)
> ```
| Name | Type | Description |
| ----------------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. |
| `default_score_weights` | `Dict[str, float]` | 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. |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
## Language.factory {#factory tag="classmethod"}
Register a custom pipeline component factory under a given name. This allows
initializing the component by name using
[`Language.add_pipe`](/api/language#add_pipe) and referring to it in
[config files](/usage/training#config). 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 instances of the same component and allows
trainable components to add custom losses using the component instance name. The
`default_config` defines the default values of the remaining factory arguments.
It's merged into the [`nlp.config`](/api/language#config). For more details and
examples, see the
[usage documentation](/usage/processing-pipelines#custom-components).
> #### Example
>
> ```python
> from spacy.language import Language
>
> # Usage as a decorator
> @Language.factory(
> "my_component",
> default_config={"some_setting": True},
> )
> def create_my_component(nlp, name, some_setting):
> return MyComponent(some_setting)
>
> # Usage as function
> Language.factory(
> "my_component",
> default_config={"some_setting": True},
> func=create_my_component
> )
> ```
| Name | Type | Description |
| ----------------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | The name of the component factory. |
| _keyword-only_ | | |
| `default_config` | `Dict[str, any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. |
| `default_score_weights` | `Dict[str, float]` | 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. |
| `func` | `Optional[Callable]` | Optional function if not used a a decorator. |
## Language.\_\_init\_\_ {#init tag="method"}
Initialize a `Language` object.
> #### Example
>
> ```python
> from spacy.vocab import Vocab
> from spacy.language import Language
> nlp = Language(Vocab())
>
> from spacy.lang.en import English
> nlp = English()
> ```
| Name | Type | Description |
| ------------------ | ----------- | ------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. |
| _keyword-only_ | | |
| `max_length` | int | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. |
| `meta` | dict | Custom meta data for the `Language` class. Is written to by models to add model meta data. |
| `create_tokenizer` | `Callable` | Optional function that receives the `nlp` object and returns a tokenizer. |
| **RETURNS** | `Language` | The newly constructed object. |
## Language.from_config {#from_config tag="classmethod"}
Create a `Language` object from a loaded config. Will set up the tokenizer and
language data, add pipeline components based on the pipeline and components
define in the config and validate the results. 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.cfg`](/api/data-formats#config).
> #### Example
>
> ```python
> from thinc.api import Config
> from spacy.language import Language
>
> config = Config().from_disk("./config.cfg")
> nlp = Language.from_config(config)
> ```
| Name | Type | Description |
| -------------- | ---------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `config` | `Dict[str, Any]` / [`Config`](https://thinc.ai/docs/api-config#config) | The loaded config. |
| _keyword-only_ | |
| `disable` | `Iterable[str]` | List of pipeline component names to disable. |
| `auto_fill` | bool | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. |
| `validate` | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | `Language` | The initialized object. |
## Language.\_\_call\_\_ {#call tag="method"}
Apply the pipeline to some text. The text can span multiple sentences, and can
contain arbitrary whitespace. Alignment into the original string is preserved.
> #### Example
>
> ```python
> doc = nlp("An example sentence. Another sentence.")
> assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")
> ```
| Name | Type | Description |
| ----------- | ----------- | --------------------------------------------------------------------------------- |
| `text` | str | The text to be processed. |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| **RETURNS** | `Doc` | A container for accessing the annotations. |
## Language.pipe {#pipe tag="method"}
Process texts as a stream, and yield `Doc` objects in order. This is usually
more efficient than processing texts one-by-one.
> #### Example
>
> ```python
> texts = ["One document.", "...", "Lots of documents"]
> for doc in nlp.pipe(texts, batch_size=50):
> assert doc.is_parsed
> ```
| Name | Type | Description |
| -------------------------------------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `texts` | `Iterable[str]` | A sequence of strings. |
| `as_tuples` | bool | 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`. |
| `batch_size` | int | The number of texts to buffer. |
| `disable` | `List[str]` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `component_cfg` 2.1 | `Dict[str, Dict]` | Config parameters for specific pipeline components, keyed by component name. |
| `n_process` 2.2.2 | int | Number of processors to use, only supported in Python 3. Defaults to `1`. |
| **YIELDS** | `Doc` | Documents in the order of the original text. |
## Language.update {#update tag="method"}
Update the models in the pipeline.
> #### Example
>
> ```python
> for raw_text, entity_offsets in train_data:
> doc = nlp.make_doc(raw_text)
> example = Example.from_dict(doc, {"entities": entity_offsets})
> nlp.update([example], sgd=optimizer)
> ```
| Name | Type | Description |
| -------------------------------------------- | ------------------- | ---------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of `Example` objects to learn from. |
| _keyword-only_ | | |
| `drop` | float | The dropout rate. |
| `sgd` | `Optimizer` | An [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
| `losses` | `Dict[str, float]` | Dictionary to update with the loss, keyed by pipeline component. |
| `component_cfg` 2.1 | `Dict[str, Dict]` | Config parameters for specific pipeline components, keyed by component name. |
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
## Language.evaluate {#evaluate tag="method"}
Evaluate a model's pipeline components.
> #### Example
>
> ```python
> scores = nlp.evaluate(examples, verbose=True)
> print(scores)
> ```
| Name | Type | Description |
| -------------------------------------------- | ------------------------------- | ------------------------------------------------------------------------------------- |
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
| `verbose` | bool | Print debugging information. |
| `batch_size` | int | The batch size to use. |
| `scorer` | `Scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. |
| `component_cfg` 2.1 | `Dict[str, Dict]` | Config parameters for specific pipeline components, keyed by component name. |
| **RETURNS** | `Dict[str, Union[float, Dict]]` | A dictionary of evaluation scores. |
## Language.begin_training {#begin_training tag="method"}
Allocate models, pre-process training data and acquire an
[`Optimizer`](https://thinc.ai/docs/api-optimizers).
> #### Example
>
> ```python
> optimizer = nlp.begin_training(get_examples)
> ```
| Name | Type | Description |
| -------------------------------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------ |
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. If not set, a default one will be created. |
| `component_cfg` 2.1 | `Dict[str, Dict]` | Config parameters for specific pipeline components, keyed by component name. |
| `**cfg` | - | Config parameters (sent to all components). |
| **RETURNS** | `Optimizer` | An optimizer. |
## Language.use_params {#use_params tag="contextmanager, method"}
Replace weights of models in the pipeline with those 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.
> #### Example
>
> ```python
> with nlp.use_params(optimizer.averages):
> nlp.to_disk("/tmp/checkpoint")
> ```
| Name | Type | Description |
| -------- | ---- | --------------------------------------------- |
| `params` | dict | A dictionary of parameters keyed by model ID. |
## Language.create_pipe {#create_pipe tag="method" new="2"}
Create a pipeline component from a factory.
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`.
> #### Example
>
> ```python
> parser = nlp.create_pipe("parser")
> ```
| Name | Type | Description |
| ------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | 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. |
| `config` 3 | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `validate` 3 | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** | callable | The pipeline component. |
## Language.add_pipe {#add_pipe tag="method" new="2"}
Add a component to the processing pipeline. Expects a name that maps to a
component factory registered using
[`@Language.component`](/api/language#component) or
[`@Language.factory`](/api/language#factory). Components should be callables
that take a `Doc` object, modify it and return it. Only one of `before`,
`after`, `first` or `last` can be set. Default behavior is `last=True`.
As of v3.0, the [`Language.add_pipe`](/api/language#add_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). It now takes care of creating the
component, adds it to the pipeline and returns it.
> #### Example
>
> ```python
> @Language.component("component")
> def component_func(doc):
> # modify Doc and return it return doc
>
> nlp.add_pipe("component", before="ner")
> component = nlp.add_pipe("component", name="custom_name", last=True)
> ```
| Name | Type | Description |
| -------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory_name` | str | Name of the registered component factory. |
| `name` | str | 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. |
| _keyword-only_ | | |
| `before` | str / int | Component name or index to insert component directly before. |
| `after` | str / int | Component name or index to insert component directly after: |
| `first` | bool | Insert component first / not first in the pipeline. |
| `last` | bool | Insert component last / not last in the pipeline. |
| `config` 3 | `Dict[str, Any]` | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. |
| `validate` 3 | bool | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. |
| **RETURNS** 3 | callable | The pipeline component. |
## 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 | Type | Description |
| ----------- | ---- | ---------------------------------------------------------- |
| `name` | str | Name of the pipeline factory to check. |
| **RETURNS** | bool | Whether a factory of that name is registered on the class. |
## 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 | Type | Description |
| ----------- | ---- | -------------------------------------------------------- |
| `name` | str | Name of the pipeline component to check. |
| **RETURNS** | bool | Whether a component of that name exists in the pipeline. |
## 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 | Type | Description |
| ----------- | -------- | -------------------------------------- |
| `name` | str | Name of the pipeline component to get. |
| **RETURNS** | callable | The pipeline component. |
## Language.replace_pipe {#replace_pipe tag="method" new="2"}
Replace a component in the pipeline.
> #### Example
>
> ```python
> nlp.replace_pipe("parser", my_custom_parser)
> ```
| Name | Type | Description |
| ----------- | -------- | --------------------------------- |
| `name` | str | Name of the component to replace. |
| `component` | callable | The pipeline component to insert. |
## 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 | Type | Description |
| ---------- | ---- | -------------------------------- |
| `old_name` | str | Name of the component to rename. |
| `new_name` | str | New name of the component. |
## 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 | Type | Description |
| ----------- | ----- | ----------------------------------------------------- |
| `name` | str | Name of the component to remove. |
| **RETURNS** | tuple | A `(name, component)` tuple of the removed component. |
## 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.
> #### 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()
> ```
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"])
```
| Name | Type | Description |
| ----------- | --------------- | ------------------------------------------------------------------------------------ |
| `disable` | str / list | Name(s) of pipeline components to disable. |
| `enable` | str / list | Names(s) of pipeline components that will not be disabled. |
| **RETURNS** | `DisabledPipes` | The disabled pipes that can be restored by calling the object's `.restore()` method. |
## 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 | Type | Description |
| ----------- | ----------------------------- | ------------------ |
| `name` | str | The factory name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) | The factory meta. |
## 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 | Type | Description |
| ----------- | ----------------------------- | ---------------------------- |
| `name` | str | The pipeline component name. |
| **RETURNS** | [`FactoryMeta`](#factorymeta) | The factory meta. |
## Language.meta {#meta tag="property"}
Custom meta data for the Language class. If a model is loaded, contains meta
data of the model. The `Language.meta` is also what's serialized as the
`meta.json` when you save an `nlp` object to disk.
> #### Example
>
> ```python
> print(nlp.meta)
> ```
| Name | Type | Description |
| ----------- | ---- | -------------- |
| **RETURNS** | dict | The meta data. |
## 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 | Type | Description |
| ----------- | --------------------------------------------------- | ----------- |
| **RETURNS** | [`Config`](https://thinc.ai/docs/api-config#config) | The config. |
## Language.to_disk {#to_disk tag="method" new="2"}
Save the current state to a directory. If a model is loaded, this will **include
the model**.
> #### Example
>
> ```python
> nlp.to_disk("/path/to/models")
> ```
| Name | Type | Description |
| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
## Language.from_disk {#from_disk tag="method" new="2"}
Loads state from a directory. Modifies the object in place and returns it. If
the saved `Language` object contains a model, the model will be loaded. 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.language import Language
> nlp = Language().from_disk("/path/to/model")
>
> # using language-specific subclass
> from spacy.lang.en import English
> nlp = English().from_disk("/path/to/en_model")
> ```
| Name | Type | Description |
| ----------- | ------------ | ----------------------------------------------------------------------------------------- |
| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Language` | The modified `Language` object. |
## Language.to_bytes {#to_bytes tag="method"}
Serialize the current state to a binary string.
> #### Example
>
> ```python
> nlp_bytes = nlp.to_bytes()
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------------------------------------------------------- |
| `exclude` | list | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | bytes | The serialized form of the `Language` object. |
## 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 | Type | Description |
| ------------ | ---------- | ----------------------------------------------------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `exclude` | list | Names of pipeline components or [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `Language` | The `Language` object. |
## Attributes {#attributes}
| Name | Type | Description |
| --------------------------------------------- | ---------------------- | ---------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | A container for the lexical types. |
| `tokenizer` | `Tokenizer` | The tokenizer. |
| `make_doc` | `Callable` | Callable that takes a string and returns a `Doc`. |
| `pipeline` | `List[str, Callable]` | List of `(name, component)` tuples describing the current processing pipeline, in order. |
| `pipe_names` 2 | `List[str]` | List of pipeline component names, in order. |
| `pipe_labels` 2.2 | `Dict[str, List[str]]` | List of labels set by the pipeline components, if available, keyed by component name. |
| `pipe_factories` 2.2 | `Dict[str, str]` | Dictionary of pipeline component names, mapped to their factory names. |
| `factories` | `Dict[str, Callable]` | All available factory functions, keyed by name. |
| `factory_names` 3 | `List[str]` | List of all available factory names. |
| `path` 2 | `Path` | Path to the model data directory, if a model is loaded. Otherwise `None`. |
## Class attributes {#class-attributes}
| Name | Type | Description |
| ---------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Defaults` | class | Settings, data and factory methods for creating the `nlp` object and processing pipeline. |
| `lang` | str | Two-letter language ID, i.e. [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). |
| `default_config` | dict | 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). |
## 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`.
**Example:** [`stop_words.py`][stop_words.py] |
| `tokenizer_exceptions` | Tokenizer exception rules, string mapped to list of token attributes.
**Example:** [`de/tokenizer_exceptions.py`][de/tokenizer_exceptions.py] |
| `prefixes`, `suffixes`, `infixes` | Prefix, suffix and infix rules for the default tokenizer.
**Example:** [`puncutation.py`][punctuation.py] |
| `token_match` | Optional regex for matching strings that should never be split, overriding the infix rules.
**Example:** [`fr/tokenizer_exceptions.py`][fr/tokenizer_exceptions.py] |
| `url_match` | Regular expression for matching URLs. Prefixes and suffixes are removed before applying the match.
**Example:** [`tokenizer_exceptions.py`][tokenizer_exceptions.py] |
| `lex_attr_getters` | Custom functions for setting lexical attributes on tokens, e.g. `like_num`.
**Example:** [`lex_attrs.py`][lex_attrs.py] |
| `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).
**Example:** [`syntax_iterators.py`][syntax_iterators.py]. |
| `writing_system` | Information about the language's writing system, available via `Vocab.writing_system`. Defaults to: `{"direction": "ltr", "has_case": True, "has_letters": True}.`.
**Example:** [`zh/__init__.py`][zh/__init__.py] |
| `config` | Default [config](/usage/training#config) added to `nlp.config`. This can include references to custom tokenizers or lemmatizers.
**Example:** [`zh/__init__.py`][zh/__init__.py] |
[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("./model-data", exclude=["ner"])
> ```
| Name | Description |
| ----------- | -------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `tokenizer` | Tokenization rules and exceptions. |
| `meta` | The meta data, available as `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 added to the pipeline and stored on the `Language` class for each
component instance and factory instance.
| Name | Type | Description |
| ----------------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `factory` | str | The name of the registered component factory. |
| `default_config` | `Dict[str, Any]` | The default config, describing the default values of the factory arguments. |
| `assigns` | `Iterable[str]` | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `requires` | `Iterable[str]` | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. |
| `retokenizes` | bool | Whether the component changes tokenization. Used for pipeline analysis. |
| `scores` | `Iterable[str]` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. |
| `default_score_weights` | `Dict[str, float]` | 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. |