From ef6cf3b27646992233b3f2415fffad366a12ecdc Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Tue, 18 Aug 2020 01:29:34 +0200 Subject: [PATCH] Update docs [ci skip] --- website/docs/usage/v3.md | 45 +++++++++++++++++++++------------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/website/docs/usage/v3.md b/website/docs/usage/v3.md index fda5393a4..a72baa7fa 100644 --- a/website/docs/usage/v3.md +++ b/website/docs/usage/v3.md @@ -140,22 +140,20 @@ 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. | -| [`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. | -| [`@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. | -| [`Language.has_factory`](/api/language#has_factory) | Check whether a component factory is registered on a language class.s | -| [`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. | -| [`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. | -| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). | +| 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. | +| [`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. | +| [`@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. | +| [`Language.has_factory`](/api/language#has_factory) | Check whether a component factory is registered on a language class.s | +| [`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. | +| [`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) [`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