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Update docs [ci skip]

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Ines Montani 2020-08-18 01:29:34 +02:00
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@ -141,7 +141,7 @@ in your config and see validation errors if the argument values don't match.
The following methods, attributes and commands are new in spaCy v3.0.
| Name | Description |
| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). |
| [`Language.select_pipes`](/api/language#select_pipes) | Contextmanager for enabling or disabling specific pipeline components for a block. |
| [`Language.analyze_pipes`](/api/language#analyze_pipes) | [Analyze](/usage/processing-pipelines#analysis) components and their interdependencies. |
@ -152,9 +152,7 @@ The following methods, attributes and commands are new in spaCy v3.0.
| [`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. |
| [`Pipe.score`](/api/pipe#score) | Method on trainable pipeline components that returns a dictionary of evaluation scores. |
| [`registry`](/api/top-level#registry) | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config). |
| [`init config`](/api/cli#init-config) | CLI command for initializing a [training config](/usage/training) file with the recommended settings. |
| [`init fill-config`](/api/cli#init-fill-config) | CLI command for auto-filling a partial config with all defaults and missing values. |
| [`debug config`](/api/cli#debug-config) | CLI command for debugging a [training config](/usage/training) file and showing validation errors. |
| [`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). |
| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |
## Backwards Incompatibilities {#incompat}
@ -420,15 +418,20 @@ $ python -m spacy convert ./training.json ./output
#### Training config {#migrating-training-config}
The easiest way to get started with a training config is to use the
[`init config`](/api/cli#init-config) command. You can start off with a blank
config for a new model, copy the config from an existing model, or auto-fill a
partial config like a starter config generated by our
[quickstart widget](/usage/training#quickstart).
[`init config`](/api/cli#init-config) command or the
[quickstart widget](/usage/training#quickstart). You can define your
requirements, and it will auto-generate a starter config with the best-matching
default settings.
```bash
python -m spacy init-config ./config.cfg --lang en --pipeline tagger,parser
$ python -m spacy init config ./config.cfg --lang en --pipeline tagger,parser
```
If you've exported a starter config from our
[quickstart widget](/usage/training#quickstart), you can use the
[`init fill-config`](/api/cli#init-fill-config) to fill it with all default
values. You can then use the auto-generated `config.cfg` for training:
```diff
### {wrap="true"}
- python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0