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context manager with space (for consistency)
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@ -265,7 +265,7 @@ for doc in nlp.pipe(texts, disable=["tagger", "parser"]):
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If you need to **execute more code** with components disabled – e.g. to reset
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the weights or update only some components during training – you can use the
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[`nlp.select_pipes`](/api/language#select_pipes) contextmanager. At the end of
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[`nlp.select_pipes`](/api/language#select_pipes) context manager. At the end of
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the `with` block, the disabled pipeline components will be restored
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automatically. Alternatively, `select_pipes` returns an object that lets you
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call its `restore()` method to restore the disabled components when needed. This
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@ -274,7 +274,7 @@ blocks.
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```python
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### Disable for block
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# 1. Use as a contextmanager
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# 1. Use as a context manager
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with nlp.select_pipes(disable=["tagger", "parser"]):
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doc = nlp("I won't be tagged and parsed")
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doc = nlp("I will be tagged and parsed")
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@ -249,7 +249,7 @@ The following methods, attributes and commands are new in spaCy v3.0.
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| ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). |
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| [`Token.morph`](/api/token#attributes) [`Token.morph_`](/api/token#attributes) | Access a token's morphological analysis. |
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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.select_pipes`](/api/language#select_pipes) | Context manager 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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@ -336,13 +336,13 @@ Note that spaCy v3.0 now requires **Python 3.6+**.
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[training config](/usage/training#config).
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- [`Language.add_pipe`](/api/language#add_pipe) now takes the **string name** of
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the component factory instead of the component function.
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- **Custom pipeline components** now needs to be decorated with the
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- **Custom pipeline components** now need to be decorated with the
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[`@Language.component`](/api/language#component) or
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[`@Language.factory`](/api/language#factory) decorator.
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- [`Language.update`](/api/language#update) now takes a batch of
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[`Example`](/api/example) objects instead of raw texts and annotations, or
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`Doc` and `GoldParse` objects.
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- The `Language.disable_pipes` contextmanager has been replaced by
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- The `Language.disable_pipes` context manager has been replaced by
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[`Language.select_pipes`](/api/language#select_pipes), which can explicitly
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disable or enable components.
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- The [`Language.update`](/api/language#update),
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