mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
554df9ef20
* Rename all MDX file to `.mdx`
* Lock current node version (#11885)
* Apply Prettier (#11996)
* Minor website fixes (#11974) [ci skip]
* fix table
* Migrate to Next WEB-17 (#12005)
* Initial commit
* Run `npx create-next-app@13 next-blog`
* Install MDX packages
Following: 77b5f79a4d/packages/next-mdx/readme.md
* Add MDX to Next
* Allow Next to handle `.md` and `.mdx` files.
* Add VSCode extension recommendation
* Disabled TypeScript strict mode for now
* Add prettier
* Apply Prettier to all files
* Make sure to use correct Node version
* Add basic implementation for `MDXRemote`
* Add experimental Rust MDX parser
* Add `/public`
* Add SASS support
* Remove default pages and styling
* Convert to module
This allows to use `import/export` syntax
* Add import for custom components
* Add ability to load plugins
* Extract function
This will make the next commit easier to read
* Allow to handle directories for page creation
* Refactoring
* Allow to parse subfolders for pages
* Extract logic
* Redirect `index.mdx` to parent directory
* Disabled ESLint during builds
* Disabled typescript during build
* Remove Gatsby from `README.md`
* Rephrase Docker part of `README.md`
* Update project structure in `README.md`
* Move and rename plugins
* Update plugin for wrapping sections
* Add dependencies for plugin
* Use plugin
* Rename wrapper type
* Simplify unnessary adding of id to sections
The slugified section ids are useless, because they can not be referenced anywhere anyway. The navigation only works if the section has the same id as the heading.
* Add plugin for custom attributes on Markdown elements
* Add plugin to readd support for tables
* Add plugin to fix problem with wrapped images
For more details see this issue: https://github.com/mdx-js/mdx/issues/1798
* Add necessary meta data to pages
* Install necessary dependencies
* Remove outdated MDX handling
* Remove reliance on `InlineList`
* Use existing Remark components
* Remove unallowed heading
Before `h1` components where not overwritten and would never have worked and they aren't used anywhere either.
* Add missing components to MDX
* Add correct styling
* Fix broken list
* Fix broken CSS classes
* Implement layout
* Fix links
* Fix broken images
* Fix pattern image
* Fix heading attributes
* Rename heading attribute
`new` was causing some weird issue, so renaming it to `version`
* Update comment syntax in MDX
* Merge imports
* Fix markdown rendering inside components
* Add model pages
* Simplify anchors
* Fix default value for theme
* Add Universe index page
* Add Universe categories
* Add Universe projects
* Fix Next problem with copy
Next complains when the server renders something different then the client, therfor we move the differing logic to `useEffect`
* Fix improper component nesting
Next doesn't allow block elements inside a `<p>`
* Replace landing page MDX with page component
* Remove inlined iframe content
* Remove ability to inline HTML content in iFrames
* Remove MDX imports
* Fix problem with image inside link in MDX
* Escape character for MDX
* Fix unescaped characters in MDX
* Fix headings with logo
* Allow to export static HTML pages
* Add prebuild script
This command is automatically run by Next
* Replace `svg-loader` with `react-inlinesvg`
`svg-loader` is no longer maintained
* Fix ESLint `react-hooks/exhaustive-deps`
* Fix dropdowns
* Change code language from `cli` to `bash`
* Remove unnessary language `none`
* Fix invalid code language
`markdown_` with an underscore was used to basically turn of syntax highlighting, but using unknown languages know throws an error.
* Enable code blocks plugin
* Readd `InlineCode` component
MDX2 removed the `inlineCode` component
> The special component name `inlineCode` was removed, we recommend to use `pre` for the block version of code, and code for both the block and inline versions
Source: https://mdxjs.com/migrating/v2/#update-mdx-content
* Remove unused code
* Extract function to own file
* Fix code syntax highlighting
* Update syntax for code block meta data
* Remove unused prop
* Fix internal link recognition
There is a problem with regex between Node and browser, and since Next runs the component on both, this create an error.
`Prop `rel` did not match. Server: "null" Client: "noopener nofollow noreferrer"`
This simplifies the implementation and fixes the above error.
* Replace `react-helmet` with `next/head`
* Fix `className` problem for JSX component
* Fix broken bold markdown
* Convert file to `.mjs` to be used by Node process
* Add plugin to replace strings
* Fix custom table row styling
* Fix problem with `span` inside inline `code`
React doesn't allow a `span` inside an inline `code` element and throws an error in dev mode.
* Add `_document` to be able to customize `<html>` and `<body>`
* Add `lang="en"`
* Store Netlify settings in file
This way we don't need to update via Netlify UI, which can be tricky if changing build settings.
* Add sitemap
* Add Smartypants
* Add PWA support
* Add `manifest.webmanifest`
* Fix bug with anchor links after reloading
There was no need for the previous implementation, since the browser handles this nativly. Additional the manual scrolling into view was actually broken, because the heading would disappear behind the menu bar.
* Rename custom event
I was googeling for ages to find out what kind of event `inview` is, only to figure out it was a custom event with a name that sounds pretty much like a native one. 🫠
* Fix missing comment syntax highlighting
* Refactor Quickstart component
The previous implementation was hidding the irrelevant lines via data-props and dynamically generated CSS. This created problems with Next and was also hard to follow. CSS was used to do what React is supposed to handle.
The new implementation simplfy filters the list of children (React elements) via their props.
* Fix syntax highlighting for Training Quickstart
* Unify code rendering
* Improve error logging in Juniper
* Fix Juniper component
* Automatically generate "Read Next" link
* Add Plausible
* Use recent DocSearch component and adjust styling
* Fix images
* Turn of image optimization
> Image Optimization using Next.js' default loader is not compatible with `next export`.
We currently deploy to Netlify via `next export`
* Dont build pages starting with `_`
* Remove unused files
* Add Next plugin to Netlify
* Fix button layout
MDX automatically adds `p` tags around text on a new line and Prettier wants to put the text on a new line. Hacking with JSX string.
* Add 404 page
* Apply Prettier
* Update Prettier for `package.json`
Next sometimes wants to patch `package-lock.json`. The old Prettier setting indended with 4 spaces, but Next always indends with 2 spaces. Since `npm install` automatically uses the indendation from `package.json` for `package-lock.json` and to avoid the format switching back and forth, both files are now set to 2 spaces.
* Apply Next patch to `package-lock.json`
When starting the dev server Next would warn `warn - Found lockfile missing swc dependencies, patching...` and update the `package-lock.json`. These are the patched changes.
* fix link
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* small backslash fixes
* adjust to new style
Co-authored-by: Marcus Blättermann <marcus@essenmitsosse.de>
1141 lines
76 KiB
Plaintext
1141 lines
76 KiB
Plaintext
---
|
|
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/linguistic-features#language-data), optional binary
|
|
weights, e.g. provided by a [trained pipeline](/models), and the
|
|
[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.\_\_init\_\_ {id="init",tag="method"}
|
|
|
|
Initialize a `Language` object. Note that the `meta` is only used for meta
|
|
information in [`Language.meta`](/api/language#meta) and not to configure the
|
|
`nlp` object or to override the config. To initialize from a config, use
|
|
[`Language.from_config`](/api/language#from_config) instead.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction from subclass
|
|
> from spacy.lang.en import English
|
|
> nlp = English()
|
|
>
|
|
> # Construction from scratch
|
|
> from spacy.vocab import Vocab
|
|
> from spacy.language import Language
|
|
> nlp = Language(Vocab())
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ------------------ | ------------------------------------------------------------------------------------------------------------------------ |
|
|
| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
|
|
| _keyword-only_ | |
|
|
| `max_length` | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. ~~int~~ |
|
|
| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ |
|
|
| `create_tokenizer` | Optional function that receives the `nlp` object and returns a tokenizer. ~~Callable[[Language], Callable[[str], Doc]]~~ |
|
|
| `batch_size` | Default batch size for [`pipe`](#pipe) and [`evaluate`](#evaluate). Defaults to `1000`. ~~int~~ |
|
|
|
|
## Language.from_config {id="from_config",tag="classmethod",version="3"}
|
|
|
|
Create a `Language` object from a loaded config. Will set up the tokenizer and
|
|
language data, add pipeline components based on the pipeline and add pipeline
|
|
components based on the definitions specified in the config. If no config is
|
|
provided, the default config of the given language is used. This is also how
|
|
spaCy loads a model under the hood based on its
|
|
[`config.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 | Description |
|
|
| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `config` | The loaded config. ~~Union[Dict[str, Any], Config]~~ |
|
|
| _keyword-only_ | |
|
|
| `vocab` | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~ |
|
|
| `disable` | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). Is merged with the config entry `nlp.disabled`. ~~Union[str, Iterable[str]]~~ |
|
|
| `enable` <Tag variant="new">3.4</Tag> | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [nlp.enable_pipe](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ |
|
|
| `exclude` | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~ |
|
|
| `meta` | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~ |
|
|
| `auto_fill` | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~ |
|
|
| `validate` | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~ |
|
|
| **RETURNS** | The initialized object. ~~Language~~ |
|
|
|
|
## Language.component {id="component",tag="classmethod",version="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 | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `name` | The name of the component factory. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| `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~~ |
|
|
| `func` | Optional function if not used as a decorator. ~~Optional[Callable[[Doc], Doc]]~~ |
|
|
|
|
## Language.factory {id="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 | Description |
|
|
| ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `name` | The name of the component factory. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| `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~~ |
|
|
| `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. If a weight is set to `None`, the score will not be logged or weighted. ~~Dict[str, Optional[float]]~~ |
|
|
| `func` | Optional function if not used as a decorator. ~~Optional[Callable[[...], Callable[[Doc], Doc]]]~~ |
|
|
|
|
## Language.\_\_call\_\_ {id="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.
|
|
|
|
Instead of text, a `Doc` can be passed as input, in which case tokenization is
|
|
skipped, but the rest of the pipeline is run.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> doc = nlp("An example sentence. Another sentence.")
|
|
> assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `text` | The text to be processed, or a Doc. ~~Union[str, Doc]~~ |
|
|
| _keyword-only_ | |
|
|
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
|
|
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
|
|
| **RETURNS** | A container for accessing the annotations. ~~Doc~~ |
|
|
|
|
## Language.pipe {id="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.
|
|
|
|
Instead of text, a `Doc` object can be passed as input. In this case
|
|
tokenization is skipped but the rest of the pipeline is run.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> texts = ["One document.", "...", "Lots of documents"]
|
|
> for doc in nlp.pipe(texts, batch_size=50):
|
|
> assert doc.has_annotation("DEP")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `texts` | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~ |
|
|
| _keyword-only_ | |
|
|
| `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~~ |
|
|
| `batch_size` | The number of texts to buffer. ~~Optional[int]~~ |
|
|
| `disable` | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~ |
|
|
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
|
|
| `n_process` | Number of processors to use. Defaults to `1`. ~~int~~ |
|
|
| **YIELDS** | Documents in the order of the original text. ~~Doc~~ |
|
|
|
|
## Language.set_error_handler {id="set_error_handler",tag="method",version="3"}
|
|
|
|
Define a callback that will be invoked when an error is thrown during processing
|
|
of one or more documents. Specifically, this function will call
|
|
[`set_error_handler`](/api/pipe#set_error_handler) on all the pipeline
|
|
components that define that function. The error handler will be invoked with the
|
|
original component's name, the component itself, the list of documents that was
|
|
being processed, and the original error.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> def warn_error(proc_name, proc, docs, e):
|
|
> print(f"An error occurred when applying component {proc_name}.")
|
|
>
|
|
> nlp.set_error_handler(warn_error)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | -------------------------------------------------------------------------------------------------------------- |
|
|
| `error_handler` | A function that performs custom error handling. ~~Callable[[str, Callable[[Doc], Doc], List[Doc], Exception]~~ |
|
|
|
|
## Language.initialize {id="initialize",tag="method",version="3"}
|
|
|
|
Initialize the pipeline for training and return an
|
|
[`Optimizer`](https://thinc.ai/docs/api-optimizers). Under the hood, it uses the
|
|
settings defined in the [`[initialize]`](/api/data-formats#config-initialize)
|
|
config block to set up the vocabulary, load in vectors and tok2vec weights and
|
|
pass optional arguments to the `initialize` methods implemented by pipeline
|
|
components or the tokenizer. This method is typically called automatically when
|
|
you run [`spacy train`](/api/cli#train). See the usage guide on the
|
|
[config lifecycle](/usage/training#config-lifecycle) and
|
|
[initialization](/usage/training#initialization) for details.
|
|
|
|
`get_examples` should be a function that returns an iterable of
|
|
[`Example`](/api/example) objects. The data examples can either be the full
|
|
training data or a representative sample. They are used to **initialize the
|
|
models** of trainable pipeline components and are passed each component's
|
|
[`initialize`](/api/pipe#initialize) method, if available. Initialization
|
|
includes validating the network,
|
|
[inferring missing shapes](/usage/layers-architectures#thinc-shape-inference)
|
|
and setting up the label scheme based on the data.
|
|
|
|
If no `get_examples` function is provided when calling `nlp.initialize`, the
|
|
pipeline components will be initialized with generic data. In this case, it is
|
|
crucial that the output dimension of each component has already been defined
|
|
either in the [config](/usage/training#config), or by calling
|
|
[`pipe.add_label`](/api/pipe#add_label) for each possible output label (e.g. for
|
|
the tagger or textcat).
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
|
|
|
|
This method was previously called `begin_training`. It now also takes a
|
|
**function** that is called with no arguments and returns a sequence of
|
|
[`Example`](/api/example) objects instead of tuples of `Doc` and `GoldParse`
|
|
objects.
|
|
|
|
</Infobox>
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> get_examples = lambda: examples
|
|
> optimizer = nlp.initialize(get_examples)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Optional[Callable[[], Iterable[Example]]]~~ |
|
|
| _keyword-only_ | |
|
|
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## Language.resume_training {id="resume_training",tag="method,experimental",version="3"}
|
|
|
|
Continue training a trained pipeline. Create and return an optimizer, and
|
|
initialize "rehearsal" for any pipeline component that has a `rehearse` method.
|
|
Rehearsal is used to prevent models from "forgetting" their initialized
|
|
"knowledge". To perform rehearsal, collect samples of text you want the models
|
|
to retain performance on, and call [`nlp.rehearse`](/api/language#rehearse) with
|
|
a batch of [Example](/api/example) objects.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> optimizer = nlp.resume_training()
|
|
> nlp.rehearse(examples, sgd=optimizer)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------- |
|
|
| _keyword-only_ | |
|
|
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## Language.update {id="update",tag="method"}
|
|
|
|
Update the models in the pipeline.
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0">
|
|
|
|
The `Language.update` method now takes a batch of [`Example`](/api/example)
|
|
objects instead of the raw texts and annotations or `Doc` and `GoldParse`
|
|
objects. An [`Example`](/api/example) streamlines how data is passed around. It
|
|
stores two `Doc` objects: one for holding the gold-standard reference data, and
|
|
one for holding the predictions of the pipeline.
|
|
|
|
For most use cases, you shouldn't have to write your own training scripts
|
|
anymore. Instead, you can use [`spacy train`](/api/cli#train) with a config file
|
|
and custom registered functions if needed. See the
|
|
[training documentation](/usage/training) for details.
|
|
|
|
</Infobox>
|
|
|
|
> #### 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 | Description |
|
|
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
|
|
| _keyword-only_ | |
|
|
| `drop` | The dropout rate. ~~float~~ |
|
|
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
|
|
| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
|
|
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
|
|
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
|
|
|
|
## Language.rehearse {id="rehearse",tag="method,experimental",version="3"}
|
|
|
|
Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
|
|
current model to make predictions similar to an initial model, to try to address
|
|
the "catastrophic forgetting" problem. This feature is experimental.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> optimizer = nlp.resume_training()
|
|
> losses = nlp.rehearse(examples, sgd=optimizer)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------- |
|
|
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
|
|
| _keyword-only_ | |
|
|
| `drop` | The dropout rate. ~~float~~ |
|
|
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
|
|
| `losses` | Dictionary to update with the loss, keyed by pipeline component. ~~Optional[Dict[str, float]]~~ |
|
|
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
|
|
|
|
## Language.evaluate {id="evaluate",tag="method"}
|
|
|
|
Evaluate a pipeline's components.
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0">
|
|
|
|
The `Language.evaluate` method now takes a batch of [`Example`](/api/example)
|
|
objects instead of tuples of `Doc` and `GoldParse` objects.
|
|
|
|
</Infobox>
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> scores = nlp.evaluate(examples)
|
|
> print(scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
|
|
| _keyword-only_ | |
|
|
| `batch_size` | The batch size to use. ~~Optional[int]~~ |
|
|
| `scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. ~~Optional[Scorer]~~ |
|
|
| `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~ |
|
|
| `scorer_cfg` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. ~~Optional[Dict[str, Any]]~~ |
|
|
| **RETURNS** | A dictionary of evaluation scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
|
|
|
|
## Language.use_params {id="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 | Description |
|
|
| -------- | ------------------------------------------------------ |
|
|
| `params` | A dictionary of parameters keyed by model ID. ~~dict~~ |
|
|
|
|
## Language.add_pipe {id="add_pipe",tag="method",version="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`.
|
|
|
|
<Infobox title="Changed in v3.0" variant="warning">
|
|
|
|
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.
|
|
|
|
</Infobox>
|
|
|
|
> #### 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)
|
|
>
|
|
> # Add component from source pipeline
|
|
> source_nlp = spacy.load("en_core_web_sm")
|
|
> nlp.add_pipe("ner", source=source_nlp)
|
|
> ```
|
|
|
|
| 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_ | |
|
|
| `before` | Component name or index to insert component directly before. ~~Optional[Union[str, int]]~~ |
|
|
| `after` | Component name or index to insert component directly after. ~~Optional[Union[str, int]]~~ |
|
|
| `first` | Insert component first / not first in the pipeline. ~~Optional[bool]~~ |
|
|
| `last` | Insert component last / not last in the pipeline. ~~Optional[bool]~~ |
|
|
| `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. ~~Dict[str, Any]~~ |
|
|
| `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]~~ |
|
|
| `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.create_pipe {id="create_pipe",tag="method",version="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. ~~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 {id="has_factory",tag="classmethod",version="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 {id="has_pipe",tag="method",version="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 {id="get_pipe",tag="method",version="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 {id="replace_pipe",tag="method",version="2"}
|
|
|
|
Replace a component in the pipeline and return the new component.
|
|
|
|
<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
|
|
> new_parser = 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~~ |
|
|
| **RETURNS** | The new pipeline component. ~~Callable[[Doc], Doc]~~ |
|
|
|
|
## Language.rename_pipe {id="rename_pipe",tag="method",version="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 {id="remove_pipe",tag="method",version="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 {id="disable_pipe",tag="method",version="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 {id="enable_pipe",tag="method",version="3"}
|
|
|
|
Enable a previously disabled 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 {id="select_pipes",tag="contextmanager, method",version="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.initialize()
|
|
>
|
|
> with nlp.select_pipes(enable="ner"):
|
|
> nlp.initialize()
|
|
>
|
|
> disabled = nlp.select_pipes(disable=["tagger", "parser"])
|
|
> nlp.initialize()
|
|
> 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 component(s) to disable. ~~Optional[Union[str, Iterable[str]]]~~ |
|
|
| `enable` | Name(s) of pipeline component(s) 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 {id="get_factory_meta",tag="classmethod",version="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 {id="get_pipe_meta",tag="method",version="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 {id="analyze_pipes",tag="method",version="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 {title="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
|
|
|
|
1 entity_linker token.ent_kb_id doc.ents nel_micro_f False
|
|
doc.sents nel_micro_r
|
|
token.ent_iob nel_micro_p
|
|
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.replace_listeners {id="replace_listeners",tag="method",version="3"}
|
|
|
|
Find [listener layers](/usage/embeddings-transformers#embedding-layers)
|
|
(connecting to a shared token-to-vector embedding component) of a given pipeline
|
|
component model and replace them with a standalone copy of the token-to-vector
|
|
layer. The listener layer allows other components to connect to a shared
|
|
token-to-vector embedding component like [`Tok2Vec`](/api/tok2vec) or
|
|
[`Transformer`](/api/transformer). Replacing listeners can be useful when
|
|
training a pipeline with components sourced from an existing pipeline: if
|
|
multiple components (e.g. tagger, parser, NER) listen to the same
|
|
token-to-vector component, but some of them are frozen and not updated, their
|
|
performance may degrade significally as the token-to-vector component is updated
|
|
with new data. To prevent this, listeners can be replaced with a standalone
|
|
token-to-vector layer that is owned by the component and doesn't change if the
|
|
component isn't updated.
|
|
|
|
This method is typically not called directly and only executed under the hood
|
|
when loading a config with
|
|
[sourced components](/usage/training#config-components) that define
|
|
`replace_listeners`.
|
|
|
|
> ```python
|
|
> ### Example
|
|
> nlp = spacy.load("en_core_web_sm")
|
|
> nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg (excerpt)
|
|
> [training]
|
|
> frozen_components = ["tagger"]
|
|
>
|
|
> [components]
|
|
>
|
|
> [components.tagger]
|
|
> source = "en_core_web_sm"
|
|
> replace_listeners = ["model.tok2vec"]
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `tok2vec_name` | Name of the token-to-vector component, typically `"tok2vec"` or `"transformer"`.~~str~~ |
|
|
| `pipe_name` | Name of pipeline component to replace listeners for. ~~str~~ |
|
|
| `listeners` | The paths to the listeners, relative to the component config, e.g. `["model.tok2vec"]`. Typically, implementations will only connect to one tok2vec component, `model.tok2vec`, but in theory, custom models can use multiple listeners. The value here can either be an empty list to not replace any listeners, or a _complete_ list of the paths to all listener layers used by the model that should be replaced.~~Iterable[str]~~ |
|
|
|
|
## Language.meta {id="meta",tag="property"}
|
|
|
|
Meta data for the `Language` class, including name, version, data sources,
|
|
license, author information and more. 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` when you save an `nlp` object to disk. See the
|
|
[meta data format](/api/data-formats#meta) for more details.
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0">
|
|
|
|
As of v3.0, the meta only contains **meta information** about the pipeline and
|
|
isn't used to construct the language class and pipeline components. This
|
|
information is expressed in the [`config.cfg`](/api/data-formats#config).
|
|
|
|
</Infobox>
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> print(nlp.meta)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------- |
|
|
| **RETURNS** | The meta data. ~~Dict[str, Any]~~ |
|
|
|
|
## Language.config {id="config",tag="property",version="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 {id="to_disk",tag="method",version="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 {id="from_disk",tag="method",version="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 the 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 {id="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 {id="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/linguistic-features#language-data)
|
|
available to the loaded object.
|
|
|
|
Note that if you want to serialize and reload a whole pipeline, using this alone
|
|
won't work, you also need to handle the config. See
|
|
["Serializing the pipeline"](https://spacy.io/usage/saving-loading#pipeline) for
|
|
details.
|
|
|
|
> #### 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 {id="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` | List of pipeline component names, in order. ~~List[str]~~ |
|
|
| `pipe_labels` | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~ |
|
|
| `pipe_factories` | 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` | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~ |
|
|
|
|
## Class attributes {id="class-attributes"}
|
|
|
|
| Name | Description |
|
|
| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `Defaults` | Settings, data and factory methods for creating the `nlp` object and processing pipeline. ~~Defaults~~ |
|
|
| `lang` | [IETF language tag](https://www.w3.org/International/articles/language-tags/), such as 'en' for English. ~~str~~ |
|
|
| `default_config` | Base [config](/usage/training#config) to use for [Language.config](/api/language#config). Defaults to [`default_config.cfg`](%%GITHUB_SPACY/spacy/default_config.cfg). ~~Config~~ |
|
|
|
|
## Defaults {id="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`](%%GITHUB_SPACY/spacy/lang/en/stop_words.py) ~~Set[str]~~ |
|
|
| `tokenizer_exceptions` | Tokenizer exception rules, string mapped to list of token attributes.<br />**Example:** [`de/tokenizer_exceptions.py`](%%GITHUB_SPACY/spacy/lang/de/tokenizer_exceptions.py) ~~Dict[str, List[dict]]~~ |
|
|
| `prefixes`, `suffixes`, `infixes` | Prefix, suffix and infix rules for the default tokenizer.<br />**Example:** [`puncutation.py`](%%GITHUB_SPACY/spacy/lang/punctuation.py) ~~Optional[Sequence[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`](%%GITHUB_SPACY/spacy/lang/fr/tokenizer_exceptions.py) ~~Optional[Callable]~~ |
|
|
| `url_match` | Regular expression for matching URLs. Prefixes and suffixes are removed before applying the match.<br />**Example:** [`tokenizer_exceptions.py`](%%GITHUB_SPACY/spacy/lang/tokenizer_exceptions.py) ~~Optional[Callable]~~ |
|
|
| `lex_attr_getters` | Custom functions for setting lexical attributes on tokens, e.g. `like_num`.<br />**Example:** [`lex_attrs.py`](%%GITHUB_SPACY/spacy/lang/en/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`](%%GITHUB_SPACY/spacy/lang/en/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`](%%GITHUB_SPACY/spacy/lang/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`](%%GITHUB_SPACY/spacy/lang/zh/__init__.py) ~~Config~~ |
|
|
|
|
## Serialization fields {id="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 {id="factorymeta",version="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~~ |
|
|
| `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. If a weight is set to `None`, the score will not be logged or weighted. ~~Dict[str, Optional[float]]~~ |
|
|
| `scores` | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Based on the `default_score_weights` and used for [pipe analysis](/usage/processing-pipelines#analysis). ~~Iterable[str]~~ |
|