mirror of https://github.com/explosion/spaCy.git
Document different ways to create a pipeline (#10762)
* Document different ways to create a pipeline: moved up/slightly modified paragraph on pipeline creation. * Document different ways to create a pipeline: changed Finnish to Ukrainian in example for language without trained pipeline. * Document different ways to create a pipeline: added explanation of blank pipeline. * Document different ways to create a pipeline: exchanged Ukrainian with Yoruba.
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@ -27,6 +27,35 @@ import QuickstartModels from 'widgets/quickstart-models.js'
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<QuickstartModels title="Quickstart" id="quickstart" description="Install a default trained pipeline package, get the code to load it from within spaCy and an example to test it. For more options, see the section on available packages below." />
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<QuickstartModels title="Quickstart" id="quickstart" description="Install a default trained pipeline package, get the code to load it from within spaCy and an example to test it. For more options, see the section on available packages below." />
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### Usage note
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> If lemmatization rules are available for your language, make sure to install
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> spaCy with the `lookups` option, or install
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> [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
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> separately in the same environment:
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>
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> ```bash
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> $ pip install -U %%SPACY_PKG_NAME[lookups]%%SPACY_PKG_FLAGS
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> ```
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If a trained pipeline is available for a language, you can download it using the
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[`spacy download`](/api/cli#download) command as shown above. In order to use
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languages that don't yet come with a trained pipeline, you have to import them
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directly, or use [`spacy.blank`](/api/top-level#spacy.blank):
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```python
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from spacy.lang.yo import Yoruba
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nlp = Yoruba() # use directly
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nlp = spacy.blank("yo") # blank instance
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```
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A blank pipeline is typically just a tokenizer. You might want to create a blank
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pipeline when you only need a tokenizer, when you want to add more components
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from scratch, or for testing purposes. Initializing the language object directly
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yields the same result as generating it using `spacy.blank()`. In both cases the
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default configuration for the chosen language is loaded, and no pretrained
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components will be available.
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## Language support {#languages}
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## Language support {#languages}
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spaCy currently provides support for the following languages. You can help by
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spaCy currently provides support for the following languages. You can help by
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@ -37,28 +66,6 @@ contribute to development. Also see the
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[training documentation](/usage/training) for how to train your own pipelines on
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[training documentation](/usage/training) for how to train your own pipelines on
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your data.
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your data.
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> #### Usage note
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>
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> If a trained pipeline is available for a language, you can download it using
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> the [`spacy download`](/api/cli#download) command. In order to use languages
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> that don't yet come with a trained pipeline, you have to import them directly,
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> or use [`spacy.blank`](/api/top-level#spacy.blank):
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>
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> ```python
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> from spacy.lang.fi import Finnish
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> nlp = Finnish() # use directly
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> nlp = spacy.blank("fi") # blank instance
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> ```
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>
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> If lemmatization rules are available for your language, make sure to install
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> spaCy with the `lookups` option, or install
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> [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
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> separately in the same environment:
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>
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> ```bash
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> $ pip install -U %%SPACY_PKG_NAME[lookups]%%SPACY_PKG_FLAGS
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> ```
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import Languages from 'widgets/languages.js'
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import Languages from 'widgets/languages.js'
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<Languages />
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<Languages />
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