mirror of https://github.com/explosion/spaCy.git
482 lines
19 KiB
Markdown
482 lines
19 KiB
Markdown
---
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title: Models & Languages
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next: usage/facts-figures
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menu:
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- ['Quickstart', 'quickstart']
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- ['Language Support', 'languages']
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- ['Installation & Usage', 'download']
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- ['Production Use', 'production']
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---
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spaCy's trained pipelines can be installed as **Python packages**. This means
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that they're a component of your application, just like any other module.
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They're versioned and can be defined as a dependency in your `requirements.txt`.
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Trained pipelines can be installed from a download URL or a local directory,
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manually or via [pip](https://pypi.python.org/pypi/pip). Their data can be
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located anywhere on your file system.
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> #### Important note
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>
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> If you're upgrading to spaCy v3.x, you need to **download the new pipeline
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> packages**. If you've trained your own pipelines, you need to **retrain** them
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> after updating spaCy.
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## Quickstart {hidden="true"}
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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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## Language support {#languages}
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spaCy currently provides support for the following languages. You can help by
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improving the existing [language data](/usage/linguistic-features#language-data)
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and extending the tokenization patterns.
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[See here](https://github.com/explosion/spaCy/issues/3056) for details on how to
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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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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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<Languages />
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### Multi-language support {#multi-language new="2"}
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> ```python
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> # Standard import
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> from spacy.lang.xx import MultiLanguage
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> nlp = MultiLanguage()
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>
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> # With lazy-loading
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> nlp = spacy.blank("xx")
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> ```
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spaCy also supports pipelines trained on more than one language. This is
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especially useful for named entity recognition. The language ID used for
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multi-language or language-neutral pipelines is `xx`. The language class, a
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generic subclass containing only the base language data, can be found in
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[`lang/xx`](%%GITHUB_SPACY/spacy/lang/xx).
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To train a pipeline using the neutral multi-language class, you can set
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`lang = "xx"` in your [training config](/usage/training#config). You can also
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import the `MultiLanguage` class directly, or call
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[`spacy.blank("xx")`](/api/top-level#spacy.blank) for lazy-loading.
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### Chinese language support {#chinese new="2.3"}
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The Chinese language class supports three word segmentation options, `char`,
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`jieba` and `pkuseg`.
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> #### Manual setup
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>
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> ```python
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> from spacy.lang.zh import Chinese
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>
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> # Character segmentation (default)
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> nlp = Chinese()
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> # Jieba
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> cfg = {"segmenter": "jieba"}
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> nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
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> # PKUSeg with "mixed" model provided by pkuseg
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> cfg = {"segmenter": "pkuseg"}
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> nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
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> nlp.tokenizer.initialize(pkuseg_model="mixed")
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> ```
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```ini
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### config.cfg
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[nlp.tokenizer]
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@tokenizers = "spacy.zh.ChineseTokenizer"
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segmenter = "char"
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```
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| Segmenter | Description |
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| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `char` | **Character segmentation:** Character segmentation is the default segmentation option. It's enabled when you create a new `Chinese` language class or call `spacy.blank("zh")`. |
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| `jieba` | **Jieba:** to use [Jieba](https://github.com/fxsjy/jieba) for word segmentation, you can set the option `segmenter` to `"jieba"`. |
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| `pkuseg` | **PKUSeg**: As of spaCy v2.3.0, support for [PKUSeg](https://github.com/explosion/spacy-pkuseg) has been added to support better segmentation for Chinese OntoNotes and the provided [Chinese pipelines](/models/zh). Enable PKUSeg by setting tokenizer option `segmenter` to `"pkuseg"`. |
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<Infobox title="Changed in v3.0" variant="warning">
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In v3.0, the default word segmenter has switched from Jieba to character
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segmentation. Because the `pkuseg` segmenter depends on a model that can be
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loaded from a file, the model is loaded on
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[initialization](/usage/training#config-lifecycle) (typically before training).
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This ensures that your packaged Chinese model doesn't depend on a local path at
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runtime.
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</Infobox>
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<Accordion title="Details on spaCy's Chinese API">
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The `initialize` method for the Chinese tokenizer class supports the following
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config settings for loading `pkuseg` models:
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| Name | Description |
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| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `pkuseg_model` | Name of a model provided by `spacy-pkuseg` or the path to a local model directory. ~~str~~ |
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| `pkuseg_user_dict` | Optional path to a file with one word per line which overrides the default `pkuseg` user dictionary. Defaults to `"default"`, the default provided dictionary. ~~str~~ |
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The initialization settings are typically provided in the
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[training config](/usage/training#config) and the data is loaded in before
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training and serialized with the model. This allows you to load the data from a
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local path and save out your pipeline and config, without requiring the same
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local path at runtime. See the usage guide on the
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[config lifecycle](/usage/training#config-lifecycle) for more background on
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this.
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```ini
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### config.cfg
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[initialize]
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[initialize.tokenizer]
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pkuseg_model = "/path/to/model"
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pkuseg_user_dict = "default"
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```
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You can also initialize the tokenizer for a blank language class by calling its
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`initialize` method:
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```python
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### Examples
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# Initialize the pkuseg tokenizer
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cfg = {"segmenter": "pkuseg"}
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nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
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# Load spaCy's OntoNotes model
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nlp.tokenizer.initialize(pkuseg_model="spacy_ontonotes")
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# Load pkuseg's "news" model
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nlp.tokenizer.initialize(pkuseg_model="news")
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# Load local model
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nlp.tokenizer.initialize(pkuseg_model="/path/to/pkuseg_model")
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# Override the user directory
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nlp.tokenizer.initialize(pkuseg_model="spacy_ontonotes", pkuseg_user_dict="/path/to/user_dict")
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```
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You can also modify the user dictionary on-the-fly:
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```python
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# Append words to user dict
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nlp.tokenizer.pkuseg_update_user_dict(["中国", "ABC"])
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# Remove all words from user dict and replace with new words
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nlp.tokenizer.pkuseg_update_user_dict(["中国"], reset=True)
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# Remove all words from user dict
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nlp.tokenizer.pkuseg_update_user_dict([], reset=True)
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```
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</Accordion>
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<Accordion title="Details on trained and custom Chinese pipelines" spaced>
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The [Chinese pipelines](/models/zh) provided by spaCy include a custom `pkuseg`
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model trained only on
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[Chinese OntoNotes 5.0](https://catalog.ldc.upenn.edu/LDC2013T19), since the
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models provided by `pkuseg` include data restricted to research use. For
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research use, `pkuseg` provides models for several different domains (`"mixed"`
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(equivalent to `"default"` from `pkuseg` packages), `"news"` `"web"`,
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`"medicine"`, `"tourism"`) and for other uses, `pkuseg` provides a simple
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[training API](https://github.com/explosion/spacy-pkuseg/blob/master/readme/readme_english.md#usage):
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```python
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import spacy_pkuseg as pkuseg
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from spacy.lang.zh import Chinese
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# Train pkuseg model
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pkuseg.train("train.utf8", "test.utf8", "/path/to/pkuseg_model")
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# Load pkuseg model in spaCy Chinese tokenizer
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cfg = {"segmenter": "pkuseg"}
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nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
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nlp.tokenizer.initialize(pkuseg_model="/path/to/pkuseg_model")
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```
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</Accordion>
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### Japanese language support {#japanese new=2.3}
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> #### Manual setup
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>
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> ```python
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> from spacy.lang.ja import Japanese
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>
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> # Load SudachiPy with split mode A (default)
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> nlp = Japanese()
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> # Load SudachiPy with split mode B
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> cfg = {"split_mode": "B"}
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> nlp = Japanese.from_config({"nlp": {"tokenizer": cfg}})
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> ```
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The Japanese language class uses
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[SudachiPy](https://github.com/WorksApplications/SudachiPy) for word
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segmentation and part-of-speech tagging. The default Japanese language class and
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the provided Japanese pipelines use SudachiPy split mode `A`. The tokenizer
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config can be used to configure the split mode to `A`, `B` or `C`.
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```ini
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### config.cfg
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[nlp.tokenizer]
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@tokenizers = "spacy.ja.JapaneseTokenizer"
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split_mode = "A"
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```
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<Infobox variant="warning">
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If you run into errors related to `sudachipy`, which is currently under active
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development, we suggest downgrading to `sudachipy==0.4.9`, which is the version
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used for training the current [Japanese pipelines](/models/ja).
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</Infobox>
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## Installing and using trained pipelines {#download}
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The easiest way to download a trained pipeline is via spaCy's
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[`download`](/api/cli#download) command. It takes care of finding the
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best-matching package compatible with your spaCy installation.
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> #### Important note for v3.0
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>
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> Note that as of spaCy v3.0, shortcut links like `en` that create (potentially
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> brittle) symlinks in your spaCy installation are **deprecated**. To download
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> and load an installed pipeline package, use its full name:
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>
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> ```diff
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> - python -m spacy download en
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> + python -m spacy download en_core_web_sm
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> ```
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>
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> ```diff
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> - nlp = spacy.load("en")
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> + nlp = spacy.load("en_core_web_sm")
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> ```
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```cli
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# Download best-matching version of a package for your spaCy installation
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$ python -m spacy download en_core_web_sm
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# Download exact package version
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$ python -m spacy download en_core_web_sm-3.0.0 --direct
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```
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The download command will [install the package](/usage/models#download-pip) via
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pip and place the package in your `site-packages` directory.
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```cli
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$ pip install -U %%SPACY_PKG_NAME%%SPACY_PKG_FLAGS
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$ python -m spacy download en_core_web_sm
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```
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```python
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import spacy
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("This is a sentence.")
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```
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### Installation via pip {#download-pip}
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To download a trained pipeline directly using
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[pip](https://pypi.python.org/pypi/pip), point `pip install` to the URL or local
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path of the wheel file or archive. Installing the wheel is usually more
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efficient. To find the direct link to a package, head over to the
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[releases](https://github.com/explosion/spacy-models/releases), right click on
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the archive link and copy it to your clipboard.
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```bash
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# With external URL
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$ pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl
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$ pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
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# With local file
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$ pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
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$ pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
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```
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By default, this will install the pipeline package into your `site-packages`
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directory. You can then use `spacy.load` to load it via its package name or
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[import it](#usage-import) explicitly as a module. If you need to download
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pipeline packages as part of an automated process, we recommend using pip with a
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direct link, instead of relying on spaCy's [`download`](/api/cli#download)
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command.
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You can also add the direct download link to your application's
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`requirements.txt`. For more details, see the section on
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[working with pipeline packages in production](#production).
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### Manual download and installation {#download-manual}
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In some cases, you might prefer downloading the data manually, for example to
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place it into a custom directory. You can download the package via your browser
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from the [latest releases](https://github.com/explosion/spacy-models/releases),
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or configure your own download script using the URL of the archive file. The
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archive consists of a package directory that contains another directory with the
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pipeline data.
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```yaml
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### Directory structure {highlight="6"}
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└── en_core_web_md-3.0.0.tar.gz # downloaded archive
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├── setup.py # setup file for pip installation
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├── meta.json # copy of pipeline meta
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└── en_core_web_md # 📦 pipeline package
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├── __init__.py # init for pip installation
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└── en_core_web_md-3.0.0 # pipeline data
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├── config.cfg # pipeline config
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├── meta.json # pipeline meta
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└── ... # directories with component data
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```
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You can place the **pipeline package directory** anywhere on your local file
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system.
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### Using trained pipelines with spaCy {#usage}
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To load a pipeline package, use [`spacy.load`](/api/top-level#spacy.load) with
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the package name or a path to the data directory:
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> #### Important note for v3.0
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>
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> Note that as of spaCy v3.0, shortcut links like `en` that create (potentially
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> brittle) symlinks in your spaCy installation are **deprecated**. To download
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> and load an installed pipeline package, use its full name:
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>
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> ```diff
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> - python -m spacy download en
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> + python -m spacy dowmload en_core_web_sm
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> ```
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```python
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import spacy
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nlp = spacy.load("en_core_web_sm") # load package "en_core_web_sm"
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nlp = spacy.load("/path/to/en_core_web_sm") # load package from a directory
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doc = nlp("This is a sentence.")
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```
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<Infobox title="Tip: Preview model info" emoji="💡">
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You can use the [`info`](/api/cli#info) command or
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[`spacy.info()`](/api/top-level#spacy.info) method to print a pipeline
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packages's meta data before loading it. Each `Language` object with a loaded
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pipeline also exposes the pipeline's meta data as the attribute `meta`. For
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example, `nlp.meta['version']` will return the package version.
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</Infobox>
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### Importing pipeline packages as modules {#usage-import}
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If you've installed a trained pipeline via [`spacy download`](/api/cli#download)
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or directly via pip, you can also `import` it and then call its `load()` method
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with no arguments:
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```python
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### {executable="true"}
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import en_core_web_sm
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nlp = en_core_web_sm.load()
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doc = nlp("This is a sentence.")
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```
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How you choose to load your trained pipelines ultimately depends on personal
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preference. However, **for larger code bases**, we usually recommend native
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imports, as this will make it easier to integrate pipeline packages with your
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existing build process, continuous integration workflow and testing framework.
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It'll also prevent you from ever trying to load a package that is not installed,
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as your code will raise an `ImportError` immediately, instead of failing
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somewhere down the line when calling `spacy.load()`. For more details, see the
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section on [working with pipeline packages in production](#production).
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## Using trained pipelines in production {#production}
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If your application depends on one or more trained pipeline packages, you'll
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usually want to integrate them into your continuous integration workflow and
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build process. While spaCy provides a range of useful helpers for downloading
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and loading pipeline packages, the underlying functionality is entirely based on
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native Python packaging. This allows your application to handle a spaCy pipeline
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like any other package dependency.
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### Downloading and requiring package dependencies {#models-download}
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spaCy's built-in [`download`](/api/cli#download) command is mostly intended as a
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convenient, interactive wrapper. It performs compatibility checks and prints
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detailed error messages and warnings. However, if you're downloading pipeline
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packages as part of an automated build process, this only adds an unnecessary
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layer of complexity. If you know which packages your application needs, you
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should be specifying them directly.
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Because pipeline packages are valid Python packages, you can add them to your
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application's `requirements.txt`. If you're running your own internal PyPi
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installation, you can upload the pipeline packages there. pip's
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[requirements file format](https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format)
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supports both package names to download via a PyPi server, as well as direct
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URLs.
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```text
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### requirements.txt
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spacy>=3.0.0,<4.0.0
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https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz#egg=en_core_web_sm
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```
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Specifying `#egg=` with the package name tells pip which package to expect from
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the download URL. This way, the package won't be re-downloaded and overwritten
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if it's already installed - just like when you're downloading a package from
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PyPi.
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All pipeline packages are versioned and specify their spaCy dependency. This
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ensures cross-compatibility and lets you specify exact version requirements for
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each pipeline. If you've [trained](/usage/training) your own pipeline, you can
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use the [`spacy package`](/api/cli#package) command to generate the required
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meta data and turn it into a loadable package.
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### Loading and testing pipeline packages {#models-loading}
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Pipeline packages are regular Python packages, so you can also import them as a
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package using Python's native `import` syntax, and then call the `load` method
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to load the data and return an `nlp` object:
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```python
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|
import en_core_web_sm
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|
nlp = en_core_web_sm.load()
|
|
```
|
|
|
|
In general, this approach is recommended for larger code bases, as it's more
|
|
"native", and doesn't rely on spaCy's loader to resolve string names to
|
|
packages. If a package can't be imported, Python will raise an `ImportError`
|
|
immediately. And if a package is imported but not used, any linter will catch
|
|
that.
|
|
|
|
Similarly, it'll give you more flexibility when writing tests that require
|
|
loading pipelines. For example, instead of writing your own `try` and `except`
|
|
logic around spaCy's loader, you can use
|
|
[pytest](http://pytest.readthedocs.io/en/latest/)'s
|
|
[`importorskip()`](https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip)
|
|
method to only run a test if a specific pipeline package or version is
|
|
installed. Each pipeline package package exposes a `__version__` attribute which
|
|
you can also use to perform your own version compatibility checks before loading
|
|
it.
|