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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. The following methods, attributes and commands are new in spaCy v3.0.
| Name | Description | | Name | Description |
| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). | | [`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.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. | | [`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. | | [`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. | | [`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). | | [`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 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). |
| [`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. |
| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). | | [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |
## Backwards Incompatibilities {#incompat} ## Backwards Incompatibilities {#incompat}
@ -420,15 +418,20 @@ $ python -m spacy convert ./training.json ./output
#### Training config {#migrating-training-config} #### Training config {#migrating-training-config}
The easiest way to get started with a training config is to use the 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 [`init config`](/api/cli#init-config) command or the
config for a new model, copy the config from an existing model, or auto-fill a [quickstart widget](/usage/training#quickstart). You can define your
partial config like a starter config generated by our requirements, and it will auto-generate a starter config with the best-matching
[quickstart widget](/usage/training#quickstart). default settings.
```bash ```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 ```diff
### {wrap="true"} ### {wrap="true"}
- python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0 - python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0