spaCy/website/docs/api/legacy.mdx
Sofie Van Landeghem 554df9ef20
Website migration from Gatsby to Next (#12058)
* 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* Remove outdated MDX handling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>
2023-01-11 17:30:07 +01:00

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---
title: Legacy functions and architectures
teaser: Archived implementations available through spacy-legacy
source: spacy/legacy
---
The [`spacy-legacy`](https://github.com/explosion/spacy-legacy) package includes
outdated registered functions and architectures. It is installed automatically
as a dependency of spaCy, and provides backwards compatibility for archived
functions that may still be used in projects.
You can find the detailed documentation of each such legacy function on this
page.
## Architectures {id="architectures"}
These functions are available from `@spacy.registry.architectures`.
### spacy.Tok2Vec.v1 {id="Tok2Vec_v1"}
The `spacy.Tok2Vec.v1` architecture was expecting an `encode` model of type
`Model[Floats2D, Floats2D]` such as `spacy.MaxoutWindowEncoder.v1` or
`spacy.MishWindowEncoder.v1`.
> #### Example config
>
> ```ini
> [model]
> @architectures = "spacy.Tok2Vec.v1"
>
> [model.embed]
> @architectures = "spacy.CharacterEmbed.v1"
> # ...
>
> [model.encode]
> @architectures = "spacy.MaxoutWindowEncoder.v1"
> # ...
> ```
Construct a tok2vec model out of two subnetworks: one for embedding and one for
encoding. See the
["Embed, Encode, Attend, Predict"](https://explosion.ai/blog/deep-learning-formula-nlp)
blog post for background.
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `embed` | Embed tokens into context-independent word vector representations. For example, [CharacterEmbed](/api/architectures#CharacterEmbed) or [MultiHashEmbed](/api/architectures#MultiHashEmbed). ~~Model[List[Doc], List[Floats2d]]~~ |
| `encode` | Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder.v1](/api/legacy#MaxoutWindowEncoder_v1). ~~Model[Floats2d, Floats2d]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.MaxoutWindowEncoder.v1 {id="MaxoutWindowEncoder_v1"}
The `spacy.MaxoutWindowEncoder.v1` architecture was producing a model of type
`Model[Floats2D, Floats2D]`. Since `spacy.MaxoutWindowEncoder.v2`, this has been
changed to output type `Model[List[Floats2d], List[Floats2d]]`.
> #### Example config
>
> ```ini
> [model]
> @architectures = "spacy.MaxoutWindowEncoder.v1"
> width = 128
> window_size = 1
> maxout_pieces = 3
> depth = 4
> ```
Encode context using convolutions with maxout activation, layer normalization
and residual connections.
| Name | Description |
| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
| `maxout_pieces` | The number of maxout pieces to use. Recommended values are `2` or `3`. ~~int~~ |
| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
### spacy.MishWindowEncoder.v1 {id="MishWindowEncoder_v1"}
The `spacy.MishWindowEncoder.v1` architecture was producing a model of type
`Model[Floats2D, Floats2D]`. Since `spacy.MishWindowEncoder.v2`, this has been
changed to output type `Model[List[Floats2d], List[Floats2d]]`.
> #### Example config
>
> ```ini
> [model]
> @architectures = "spacy.MishWindowEncoder.v1"
> width = 64
> window_size = 1
> depth = 4
> ```
Encode context using convolutions with
[`Mish`](https://thinc.ai/docs/api-layers#mish) activation, layer normalization
and residual connections.
| Name | Description |
| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between `64` and `300`. ~~int~~ |
| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
### spacy.HashEmbedCNN.v1 {id="HashEmbedCNN_v1"}
Identical to [`spacy.HashEmbedCNN.v2`](/api/architectures#HashEmbedCNN) except
using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are included.
### spacy.MultiHashEmbed.v1 {id="MultiHashEmbed_v1"}
Identical to [`spacy.MultiHashEmbed.v2`](/api/architectures#MultiHashEmbed)
except with [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
included.
### spacy.CharacterEmbed.v1 {id="CharacterEmbed_v1"}
Identical to [`spacy.CharacterEmbed.v2`](/api/architectures#CharacterEmbed)
except using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
included.
### spacy.TextCatEnsemble.v1 {id="TextCatEnsemble_v1"}
The `spacy.TextCatEnsemble.v1` architecture built an internal `tok2vec` and
`linear_model`. Since `spacy.TextCatEnsemble.v2`, this has been refactored so
that the `TextCatEnsemble` takes these two sublayers as input.
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy.TextCatEnsemble.v1"
> exclusive_classes = false
> pretrained_vectors = null
> width = 64
> embed_size = 2000
> conv_depth = 2
> window_size = 1
> ngram_size = 1
> dropout = null
> nO = null
> ```
Stacked ensemble of a bag-of-words model and a neural network model. The neural
network has an internal CNN Tok2Vec layer and uses attention.
| Name | Description |
| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `pretrained_vectors` | Whether or not pretrained vectors will be used in addition to the feature vectors. ~~bool~~ |
| `width` | Output dimension of the feature encoding step. ~~int~~ |
| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
| `window_size` | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. ~~int~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. ~~int~~ |
| `dropout` | The dropout rate. ~~float~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TextCatCNN.v1 {id="TextCatCNN_v1"}
Since `spacy.TextCatCNN.v2`, this architecture has become resizable, which means
that you can add labels to a previously trained textcat. `TextCatCNN` v1 did not
yet support that.
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy.TextCatCNN.v1"
> exclusive_classes = false
> nO = null
>
> [model.tok2vec]
> @architectures = "spacy.HashEmbedCNN.v1"
> pretrained_vectors = null
> width = 96
> depth = 4
> embed_size = 2000
> window_size = 1
> maxout_pieces = 3
> subword_features = true
> ```
A neural network model where token vectors are calculated using a CNN. The
vectors are mean pooled and used as features in a feed-forward network. This
architecture is usually less accurate than the ensemble, but runs faster.
| Name | Description |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TextCatBOW.v1 {id="TextCatBOW_v1"}
Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
that you can add labels to a previously trained textcat. `TextCatBOW` v1 did not
yet support that.
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy.TextCatBOW.v1"
> exclusive_classes = false
> ngram_size = 1
> no_output_layer = false
> nO = null
> ```
An n-gram "bag-of-words" model. This architecture should run much faster than
the others, but may not be as accurate, especially if texts are short.
| Name | Description |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~ |
| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TransitionBasedParser.v1 {id="TransitionBasedParser_v1"}
Identical to
[`spacy.TransitionBasedParser.v2`](/api/architectures#TransitionBasedParser)
except the `use_upper` was set to `True` by default.
## Layers {id="layers"}
These functions are available from `@spacy.registry.layers`.
### spacy.StaticVectors.v1 {id="StaticVectors_v1"}
Identical to [`spacy.StaticVectors.v2`](/api/architectures#StaticVectors) except
for the handling of tokens without vectors.
<Infobox title="Bugs for tokens without vectors" variant="warning">
`spacy.StaticVectors.v1` maps tokens without vectors to the final row in the
vectors table, which causes the model predictions to change if new vectors are
added to an existing vectors table. See more details in
[issue #7662](https://github.com/explosion/spaCy/issues/7662#issuecomment-813925655).
</Infobox>
## Loggers {id="loggers"}
These functions are available from `@spacy.registry.loggers`.
### spacy.ConsoleLogger.v1 {id="ConsoleLogger_v1"}
> #### Example config
>
> ```ini
> [training.logger]
> @loggers = "spacy.ConsoleLogger.v1"
> progress_bar = true
> ```
Writes the results of a training step to the console in a tabular format.
<Accordion title="Example console output" spaced>
```bash
$ python -m spacy train config.cfg
```
```
Using CPU
Loading config and nlp from: config.cfg
Pipeline: ['tok2vec', 'tagger']
Start training
Training. Initial learn rate: 0.0
E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
--- ------ ------------ ----------- ------- ------
0 0 0.00 86.20 0.22 0.00
0 200 3.08 18968.78 34.00 0.34
0 400 31.81 22539.06 33.64 0.34
0 600 92.13 22794.91 43.80 0.44
0 800 183.62 21541.39 56.05 0.56
0 1000 352.49 25461.82 65.15 0.65
0 1200 422.87 23708.82 71.84 0.72
0 1400 601.92 24994.79 76.57 0.77
0 1600 662.57 22268.02 80.20 0.80
0 1800 1101.50 28413.77 82.56 0.83
0 2000 1253.43 28736.36 85.00 0.85
0 2200 1411.02 28237.53 87.42 0.87
0 2400 1605.35 28439.95 88.70 0.89
```
Note that the cumulative loss keeps increasing within one epoch, but should
start decreasing across epochs.
</Accordion>
| Name | Description |
| -------------- | --------------------------------------------------------- |
| `progress_bar` | Whether the logger should print the progress bar ~~bool~~ |
Logging utilities for spaCy are implemented in the
[`spacy-loggers`](https://github.com/explosion/spacy-loggers) repo, and the
functions are typically available from `@spacy.registry.loggers`.
More documentation can be found in that repo's
[readme](https://github.com/explosion/spacy-loggers/blob/main/README.md) file.