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* 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* Remove outdated MDX handling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>
540 lines
24 KiB
Plaintext
540 lines
24 KiB
Plaintext
---
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title: TrainablePipe
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tag: class
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teaser: Base class for trainable pipeline components
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---
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This class is a base class and **not instantiated directly**. Trainable pipeline
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components like the [`EntityRecognizer`](/api/entityrecognizer) or
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[`TextCategorizer`](/api/textcategorizer) inherit from it and it defines the
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interface that components should follow to function as trainable components in a
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spaCy pipeline. See the docs on
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[writing trainable components](/usage/processing-pipelines#trainable-components)
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for how to use the `TrainablePipe` base class to implement custom components.
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{/* TODO: Pipe vs TrainablePipe, check methods below (all renamed to TrainablePipe for now) */}
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> #### Why is it implemented in Cython?
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>
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> The `TrainablePipe` class is implemented in a `.pyx` module, the extension
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> used by [Cython](/api/cython). This is needed so that **other** Cython
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> classes, like the [`EntityRecognizer`](/api/entityrecognizer) can inherit from
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> it. But it doesn't mean you have to implement trainable components in Cython –
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> pure Python components like the [`TextCategorizer`](/api/textcategorizer) can
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> also inherit from `TrainablePipe`.
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```python
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%%GITHUB_SPACY/spacy/pipeline/trainable_pipe.pyx
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```
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## TrainablePipe.\_\_init\_\_ {id="init",tag="method"}
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> #### Example
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>
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> ```python
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> from spacy.pipeline import TrainablePipe
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> from spacy.language import Language
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>
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> class CustomPipe(TrainablePipe):
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> ...
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>
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> @Language.factory("your_custom_pipe", default_config={"model": MODEL})
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> def make_custom_pipe(nlp, name, model):
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> return CustomPipe(nlp.vocab, model, name)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#create_pipe).
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| Name | Description |
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| ------- | -------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], Any]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| `**cfg` | Additional config parameters and settings. Will be available as the dictionary `cfg` and is serialized with the component. |
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## TrainablePipe.\_\_call\_\_ {id="call",tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/pipe#call) and [`pipe`](/api/pipe#pipe) delegate to the
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[`predict`](/api/pipe#predict) and
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[`set_annotations`](/api/pipe#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> pipe = nlp.add_pipe("your_custom_pipe")
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> # This usually happens under the hood
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> processed = pipe(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## TrainablePipe.pipe {id="pipe",tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/pipe#call) and
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[`pipe`](/api/pipe#pipe) delegate to the [`predict`](/api/pipe#predict) and
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[`set_annotations`](/api/pipe#set_annotations) methods.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> for doc in pipe.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## TrainablePipe.set_error_handler {id="set_error_handler",tag="method",version="3"}
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Define a callback that will be invoked when an error is thrown during processing
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of one or more documents with either [`__call__`](/api/pipe#call) or
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[`pipe`](/api/pipe#pipe). The error handler will be invoked with the original
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component's name, the component itself, the list of documents that was being
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processed, and the original error.
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> #### Example
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>
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> ```python
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> def warn_error(proc_name, proc, docs, e):
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> print(f"An error occurred when applying component {proc_name}.")
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>
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> pipe = nlp.add_pipe("ner")
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> pipe.set_error_handler(warn_error)
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> ```
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| Name | Description |
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| --------------- | -------------------------------------------------------------------------------------------------------------- |
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| `error_handler` | A function that performs custom error handling. ~~Callable[[str, Callable[[Doc], Doc], List[Doc], Exception]~~ |
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## TrainablePipe.get_error_handler {id="get_error_handler",tag="method",version="3"}
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Retrieve the callback that performs error handling for this component's
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[`__call__`](/api/pipe#call) and [`pipe`](/api/pipe#pipe) methods. If no custom
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function was previously defined with
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[`set_error_handler`](/api/pipe#set_error_handler), a default function is
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returned that simply reraises the exception.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("ner")
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> error_handler = pipe.get_error_handler()
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------------------------------------------- |
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| **RETURNS** | The function that performs custom error handling. ~~Callable[[str, Callable[[Doc], Doc], List[Doc], Exception]~~ |
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## TrainablePipe.initialize {id="initialize",tag="method",version="3"}
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Initialize the component for training. `get_examples` should be a function that
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returns an iterable of [`Example`](/api/example) objects. The data examples are
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used to **initialize the model** of the component and can either be the full
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training data or a representative sample. Initialization includes validating the
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network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize).
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<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
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This method was previously called `begin_training`.
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</Infobox>
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> pipe.initialize(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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## TrainablePipe.predict {id="predict",tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
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modifying them.
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<Infobox variant="danger">
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This method needs to be overwritten with your own custom `predict` method.
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</Infobox>
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> scores = pipe.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | The model's prediction for each document. |
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## TrainablePipe.set_annotations {id="set_annotations",tag="method"}
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Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
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<Infobox variant="danger">
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This method needs to be overwritten with your own custom `set_annotations`
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method.
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</Infobox>
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> scores = pipe.predict(docs)
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> pipe.set_annotations(docs, scores)
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> ```
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| Name | Description |
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| -------- | ------------------------------------------------ |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `scores` | The scores to set, produced by `Tagger.predict`. |
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## TrainablePipe.update {id="update",tag="method"}
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Learn from a batch of [`Example`](/api/example) objects containing the
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predictions and gold-standard annotations, and update the component's model.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> optimizer = nlp.initialize()
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> losses = pipe.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## TrainablePipe.rehearse {id="rehearse",tag="method,experimental",version="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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the "catastrophic forgetting" problem. This feature is experimental.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> optimizer = nlp.resume_training()
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> losses = pipe.rehearse(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## TrainablePipe.get_loss {id="get_loss",tag="method"}
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Find the loss and gradient of loss for the batch of documents and their
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predicted scores.
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<Infobox variant="danger">
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This method needs to be overwritten with your own custom `get_loss` method.
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</Infobox>
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> scores = ner.predict([eg.predicted for eg in examples])
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> loss, d_loss = ner.get_loss(examples, scores)
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> ```
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| Name | Description |
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| ----------- | --------------------------------------------------------------------------- |
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| `examples` | The batch of examples. ~~Iterable[Example]~~ |
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| `scores` | Scores representing the model's predictions. |
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| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
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## TrainablePipe.score {id="score",tag="method",version="3"}
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Score a batch of examples.
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> #### Example
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>
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> ```python
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> scores = pipe.score(examples)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------- |
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| `examples` | The examples to score. ~~Iterable[Example]~~ |
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| _keyword-only_ |
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| `\*\*kwargs` | Any additional settings to pass on to the scorer. ~~Any~~ |
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| **RETURNS** | The scores, e.g. produced by the [`Scorer`](/api/scorer). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## TrainablePipe.create_optimizer {id="create_optimizer",tag="method"}
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Create an optimizer for the pipeline component. Defaults to
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[`Adam`](https://thinc.ai/docs/api-optimizers#adam) with default settings.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> optimizer = pipe.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## TrainablePipe.use_params {id="use_params",tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> with pipe.use_params(optimizer.averages):
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> pipe.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## TrainablePipe.finish_update {id="finish_update",tag="method"}
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Update parameters using the current parameter gradients. Defaults to calling
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[`self.model.finish_update`](https://thinc.ai/docs/api-model#finish_update).
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> optimizer = nlp.initialize()
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> losses = pipe.update(examples, sgd=None)
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> pipe.finish_update(sgd)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ----- | ------------------------------------- |
|
||
| `sgd` | An optimizer. ~~Optional[Optimizer]~~ |
|
||
|
||
## TrainablePipe.add_label {id="add_label",tag="method"}
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> pipe = nlp.add_pipe("your_custom_pipe")
|
||
> pipe.add_label("MY_LABEL")
|
||
> ```
|
||
|
||
Add a new label to the pipe, to be predicted by the model. The actual
|
||
implementation depends on the specific component, but in general `add_label`
|
||
shouldn't be called if the output dimension is already set, or if the model has
|
||
already been fully [initialized](#initialize). If these conditions are violated,
|
||
the function will raise an Error. The exception to this rule is when the
|
||
component is [resizable](#is_resizable), in which case
|
||
[`set_output`](#set_output) should be called to ensure that the model is
|
||
properly resized.
|
||
|
||
<Infobox variant="danger">
|
||
|
||
This method needs to be overwritten with your own custom `add_label` method.
|
||
|
||
</Infobox>
|
||
|
||
| Name | Description |
|
||
| ----------- | ------------------------------------------------------- |
|
||
| `label` | The label to add. ~~str~~ |
|
||
| **RETURNS** | 0 if the label is already present, otherwise 1. ~~int~~ |
|
||
|
||
Note that in general, you don't have to call `pipe.add_label` if you provide a
|
||
representative data sample to the [`initialize`](#initialize) method. In this
|
||
case, all labels found in the sample will be automatically added to the model,
|
||
and the output dimension will be
|
||
[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
|
||
|
||
## TrainablePipe.is_resizable {id="is_resizable",tag="property"}
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> can_resize = pipe.is_resizable
|
||
> ```
|
||
>
|
||
> With custom resizing implemented by a component:
|
||
>
|
||
> ```python
|
||
> def custom_resize(model, new_nO):
|
||
> # adjust model
|
||
> return model
|
||
>
|
||
> custom_model.attrs["resize_output"] = custom_resize
|
||
> ```
|
||
|
||
Check whether or not the output dimension of the component's model can be
|
||
resized. If this method returns `True`, [`set_output`](#set_output) can be
|
||
called to change the model's output dimension.
|
||
|
||
For built-in components that are not resizable, you have to create and train a
|
||
new model from scratch with the appropriate architecture and output dimension.
|
||
For custom components, you can implement a `resize_output` function and add it
|
||
as an attribute to the component's model.
|
||
|
||
| Name | Description |
|
||
| ----------- | ---------------------------------------------------------------------------------------------- |
|
||
| **RETURNS** | Whether or not the output dimension of the model can be changed after initialization. ~~bool~~ |
|
||
|
||
## TrainablePipe.set_output {id="set_output",tag="method"}
|
||
|
||
Change the output dimension of the component's model. If the component is not
|
||
[resizable](#is_resizable), this method will raise a `NotImplementedError`. If a
|
||
component is resizable, the model's attribute `resize_output` will be called.
|
||
This is a function that takes the original model and the new output dimension
|
||
`nO`, and changes the model in place. When resizing an already trained model,
|
||
care should be taken to avoid the "catastrophic forgetting" problem.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> if pipe.is_resizable:
|
||
> pipe.set_output(512)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ---- | --------------------------------- |
|
||
| `nO` | The new output dimension. ~~int~~ |
|
||
|
||
## TrainablePipe.to_disk {id="to_disk",tag="method"}
|
||
|
||
Serialize the pipe to disk.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> pipe = nlp.add_pipe("your_custom_pipe")
|
||
> pipe.to_disk("/path/to/pipe")
|
||
> ```
|
||
|
||
| 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` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
|
||
## TrainablePipe.from_disk {id="from_disk",tag="method"}
|
||
|
||
Load the pipe from disk. Modifies the object in place and returns it.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> pipe = nlp.add_pipe("your_custom_pipe")
|
||
> pipe.from_disk("/path/to/pipe")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ----------------------------------------------------------------------------------------------- |
|
||
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
||
| _keyword-only_ | |
|
||
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
| **RETURNS** | The modified pipe. ~~TrainablePipe~~ |
|
||
|
||
## TrainablePipe.to_bytes {id="to_bytes",tag="method"}
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> pipe = nlp.add_pipe("your_custom_pipe")
|
||
> pipe_bytes = pipe.to_bytes()
|
||
> ```
|
||
|
||
Serialize the pipe to a bytestring.
|
||
|
||
| Name | Description |
|
||
| -------------- | ------------------------------------------------------------------------------------------- |
|
||
| _keyword-only_ | |
|
||
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
| **RETURNS** | The serialized form of the pipe. ~~bytes~~ |
|
||
|
||
## TrainablePipe.from_bytes {id="from_bytes",tag="method"}
|
||
|
||
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
||
|
||
> #### Example
|
||
>
|
||
> ```python
|
||
> pipe_bytes = pipe.to_bytes()
|
||
> pipe = nlp.add_pipe("your_custom_pipe")
|
||
> pipe.from_bytes(pipe_bytes)
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| -------------- | ------------------------------------------------------------------------------------------- |
|
||
| `bytes_data` | The data to load from. ~~bytes~~ |
|
||
| _keyword-only_ | |
|
||
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
||
| **RETURNS** | The pipe. ~~TrainablePipe~~ |
|
||
|
||
## Attributes {id="attributes"}
|
||
|
||
| Name | Description |
|
||
| ------- | --------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `vocab` | The shared vocabulary that's passed in on initialization. ~~Vocab~~ |
|
||
| `model` | The model powering the component. ~~Model[List[Doc], Any]~~ |
|
||
| `name` | The name of the component instance in the pipeline. Can be used in the losses. ~~str~~ |
|
||
| `cfg` | Keyword arguments passed to [`TrainablePipe.__init__`](/api/pipe#init). Will be serialized with the component. ~~Dict[str, Any]~~ |
|
||
|
||
## 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 = pipe.to_disk("/path")
|
||
> ```
|
||
|
||
| Name | Description |
|
||
| ------- | -------------------------------------------------------------- |
|
||
| `cfg` | The config file. You usually don't want to exclude this. |
|
||
| `model` | The binary model data. You usually don't want to exclude this. |
|