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Update docs [ci skip]
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Ines Montani
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@ -140,22 +140,20 @@ in your config and see validation errors if the argument values don't match.
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The following methods, attributes and commands are new in spaCy v3.0.
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| Name | Description |
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| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). |
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| [`Language.select_pipes`](/api/language#select_pipes) | Contextmanager for enabling or disabling specific pipeline components for a block. |
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| [`Language.analyze_pipes`](/api/language#analyze_pipes) | [Analyze](/usage/processing-pipelines#analysis) components and their interdependencies. |
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| [`Language.resume_training`](/api/language#resume_training) | Experimental: continue training a pretrained model and initialize "rehearsal" for components that implement a `rehearse` method to prevent catastrophic forgetting. |
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| [`@Language.factory`](/api/language#factory) [`@Language.component`](/api/language#component) | Decorators for [registering](/usage/processing-pipelines#custom-components) pipeline component factories and simple stateless component functions. |
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| [`Language.has_factory`](/api/language#has_factory) | Check whether a component factory is registered on a language class.s |
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| [`Language.get_factory_meta`](/api/language#get_factory_meta) [`Language.get_pipe_meta`](/api/language#get_factory_meta) | Get the [`FactoryMeta`](/api/language#factorymeta) with component metadata for a factory or instance name. |
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| [`Language.config`](/api/language#config) | The [config](/usage/training#config) used to create the current `nlp` object. An instance of [`Config`](https://thinc.ai/docs/api-config#config) and can be saved to disk and used for training. |
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| [`Pipe.score`](/api/pipe#score) | Method on trainable pipeline components that returns a dictionary of evaluation scores. |
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| [`registry`](/api/top-level#registry) | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config). |
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| [`init config`](/api/cli#init-config) | CLI command for initializing a [training config](/usage/training) file with the recommended settings. |
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| [`init fill-config`](/api/cli#init-fill-config) | CLI command for auto-filling a partial config with all defaults and missing values. |
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| [`debug config`](/api/cli#debug-config) | CLI command for debugging a [training config](/usage/training) file and showing validation errors. |
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| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |
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| Name | Description |
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| ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). |
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| [`Language.select_pipes`](/api/language#select_pipes) | Contextmanager for enabling or disabling specific pipeline components for a block. |
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| [`Language.analyze_pipes`](/api/language#analyze_pipes) | [Analyze](/usage/processing-pipelines#analysis) components and their interdependencies. |
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| [`Language.resume_training`](/api/language#resume_training) | Experimental: continue training a pretrained model and initialize "rehearsal" for components that implement a `rehearse` method to prevent catastrophic forgetting. |
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| [`@Language.factory`](/api/language#factory) [`@Language.component`](/api/language#component) | Decorators for [registering](/usage/processing-pipelines#custom-components) pipeline component factories and simple stateless component functions. |
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| [`Language.has_factory`](/api/language#has_factory) | Check whether a component factory is registered on a language class.s |
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| [`Language.get_factory_meta`](/api/language#get_factory_meta) [`Language.get_pipe_meta`](/api/language#get_factory_meta) | Get the [`FactoryMeta`](/api/language#factorymeta) with component metadata for a factory or instance name. |
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| [`Language.config`](/api/language#config) | The [config](/usage/training#config) used to create the current `nlp` object. An instance of [`Config`](https://thinc.ai/docs/api-config#config) and can be saved to disk and used for training. |
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| [`Pipe.score`](/api/pipe#score) | Method on trainable pipeline components that returns a dictionary of evaluation scores. |
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| [`registry`](/api/top-level#registry) | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config). |
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| [`init config`](/api/cli#init-config) [`init fill-config`](/api/cli#init-fill-config) [`debug config`](/api/cli#debug-config) | CLI commands for initializing, auto-filling and debugging [training configs](/usage/training). |
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| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |
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## Backwards Incompatibilities {#incompat}
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@ -420,15 +418,20 @@ $ python -m spacy convert ./training.json ./output
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#### Training config {#migrating-training-config}
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The easiest way to get started with a training config is to use the
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[`init config`](/api/cli#init-config) command. You can start off with a blank
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config for a new model, copy the config from an existing model, or auto-fill a
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partial config like a starter config generated by our
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[quickstart widget](/usage/training#quickstart).
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[`init config`](/api/cli#init-config) command or the
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[quickstart widget](/usage/training#quickstart). You can define your
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requirements, and it will auto-generate a starter config with the best-matching
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default settings.
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```bash
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python -m spacy init-config ./config.cfg --lang en --pipeline tagger,parser
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$ python -m spacy init config ./config.cfg --lang en --pipeline tagger,parser
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```
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If you've exported a starter config from our
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[quickstart widget](/usage/training#quickstart), you can use the
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[`init fill-config`](/api/cli#init-fill-config) to fill it with all default
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values. You can then use the auto-generated `config.cfg` for training:
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```diff
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### {wrap="true"}
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- python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0
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