//- 💫 DOCS > USAGE > PROCESSING PIPELINES > PIPELINES

p
    |  spaCy makes it very easy to create your own pipelines consisting of
    |  reusable components – this includes spaCy's default tensorizer, tagger,
    |  parser and entity regcognizer, but also your own custom processing
    |  functions. A pipeline component can be added to an already existing
    |  #[code nlp] object, specified when initialising a #[code Language] class,
    |  or defined within a
    |  #[+a("/usage/saving-loading#models-generating") model package].

p
    |  When you load a model, spaCy first consults the model's
    |  #[+a("/usage/saving-loading#models-generating") meta.json]. The
    |  meta typically includes the model details, the ID of a language class,
    |  and an optional list of pipeline components. spaCy then does the
    |  following:

+aside-code("meta.json (excerpt)", "json").
    {
        "name": "example_model",
        "lang": "en"
        "description": "Example model for spaCy",
        "pipeline": ["tensorizer", "tagger"]
    }

+list("numbers")
    +item
        |  Look up #[strong pipeline IDs] in the available
        |  #[strong pipeline factories].
    +item
        |  Initialise the #[strong pipeline components] by calling their
        |  factories with the #[code Vocab] as an argument. This gives each
        |  factory and component access to the pipeline's shared data, like
        |  strings, morphology and annotation scheme.
    +item
        |  Load the #[strong language class and data] for the given ID via
        |  #[+api("util.get_lang_class") #[code get_lang_class]].
    +item
        |  Pass the path to the #[strong model data] to the #[code Language]
        |  class and return it.

p
    |  So when you call this...

+code.
    nlp = spacy.load('en')

p
    | ... the model tells spaCy to use the pipeline
    |  #[code.u-break ["tensorizer", "tagger", "parser", "ner"]]. spaCy will
    |  then look up each string in its internal factories registry and
    |  initialise the individual components. It'll then load
    |  #[code spacy.lang.en.English], pass it the path to the model's data
    |  directory, and return it for you to use as the #[code nlp] object.

p
    |  Fundamentally, a #[+a("/models") spaCy model] consists of three
    |  components: #[strong the weights], i.e. binary data loaded in from a
    |  directory, a #[strong pipeline] of functions called in order,
    |  and #[strong language data] like the tokenization rules and annotation
    |  scheme. All of this is specific to each model, and defined in the
    |  model's #[code meta.json] – for example, a Spanish NER model requires
    |  different weights, language data and pipeline components than an English
    |  parsing and tagging model. This is also why the pipeline state is always
    |  held by the #[code Language] class.
    |  #[+api("spacy#load") #[code spacy.load]] puts this all together and
    |  returns an instance of #[code Language]  with a pipeline set and access
    |  to the binary data:

+code("spacy.load under the hood").
    lang = 'en'
    pipeline = ['tensorizer', 'tagger', 'parser', 'ner']
    data_path = 'path/to/en_core_web_sm/en_core_web_sm-2.0.0'

    cls = spacy.util.get_lang_class(lang)  # 1. get Language instance, e.g. English()
    nlp = cls(pipeline=pipeline)           # 2. initialise it with the pipeline
    nlp.from_disk(model_data_path)         # 3. load in the binary data

p
    |  When you call #[code nlp] on a text, spaCy will #[strong tokenize] it and
    |  then #[strong call each component] on the #[code Doc], in order.
    |  Since the model data is loaded, the components can access it to assign
    |  annotations to the #[code Doc] object, and subsequently to the
    |  #[code Token] and #[code Span] which are only views of the #[code Doc],
    |  and don't own any data themselves. All components return the modified
    |  document, which is then processed by the component next in the pipeline.

+code("The pipeline under the hood").
    doc = nlp.make_doc(u'This is a sentence')
    for proc in nlp.pipeline:
        doc = proc(doc)

+h(3, "creating") Creating pipeline components and factories

p
    |  spaCy lets you customise the pipeline with your own components. Components
    |  are functions that receive a #[code Doc] object, modify and return it.
    |  If your component is stateful, you'll want to create a new one for each
    |  pipeline. You can do that by defining and registering a factory which
    |  receives the shared #[code Vocab] object and returns a component.

+h(4, "creating-component") Creating a  component

p
    |  A component receives a #[code Doc] object and
    |  #[strong performs the actual processing] – for example, using the current
    |  weights to make a prediction and set some annotation on the document. By
    |  adding a component to the pipeline, you'll get access to the #[code Doc]
    |  at any point #[strong during] processing – instead of only being able to
    |  modify it afterwards.

+aside-code("Example").
    def my_component(doc):
        # do something to the doc here
        return doc

+table(["Argument", "Type", "Description"])
    +row
        +cell #[code doc]
        +cell #[code Doc]
        +cell The #[code Doc] object processed by the previous component.

    +row("foot")
        +cell returns
        +cell #[code Doc]
        +cell The #[code Doc] object processed by this pipeline component.

p
    |  When creating a new #[code Language] class, you can pass it a list of
    |  pipeline component functions to execute in that order. You can also
    |  add it to an existing pipeline by modifying #[code nlp.pipeline] – just
    |  be careful not to overwrite a pipeline or its components by accident!

+code.
    # Create a new Language object with a pipeline
    from spacy.language import Language
    nlp = Language(pipeline=[my_component])

    # Modify an existing pipeline
    nlp = spacy.load('en')
    nlp.pipeline.append(my_component)

+h(4, "creating-factory") Creating a factory

p
    |  A factory is a #[strong function that returns a pipeline component].
    |  It's called with the #[code Vocab] object, to give it access to the
    |  shared data between components – for example, the strings, morphology,
    |  vectors or annotation scheme. Factories are useful for creating
    |  #[strong stateful components], especially ones which
    |  #[strong depend on shared data].

+aside-code("Example").
    def my_factory(vocab):
        # load some state
        def my_component(doc):
            # process the doc
            return doc
        return my_component

+table(["Argument", "Type", "Description"])
    +row
        +cell #[code vocab]
        +cell #[code Vocab]
        +cell
            |  Shared data between components, including strings, morphology,
            |  vectors etc.

    +row("foot")
        +cell returns
        +cell callable
        +cell The pipeline component.

p
    |  By creating a factory, you're essentially telling spaCy how to get the
    |  pipeline component #[strong once the vocab is available]. Factories need to
    |  be registered via #[+api("spacy#set_factory") #[code set_factory()]] and
    |  by assigning them a unique ID. This ID can be added to the pipeline as a
    |  string. When creating a pipeline, you're free to mix strings and
    |  callable components:

+code.
    spacy.set_factory('my_factory', my_factory)
    nlp = Language(pipeline=['my_factory', my_other_component])

p
    |  If spaCy comes across a string in the pipeline, it will try to resolve it
    |  by looking it up in the available factories. The factory will then be
    |  initialised with the #[code Vocab]. Providing factory names instead of
    |  callables also makes it easy to specify them in the model's
    |  #[+a("/usage/saving-loading#models-generating") meta.json]. If you're
    |  training your own model and want to use one of spaCy's default components,
    |  you won't have to worry about finding and implementing it either – to use
    |  the default tagger, simply add #[code "tagger"] to the pipeline, and
    |  #[strong spaCy will know what to do].

+infobox("Important note")
    |  Because factories are #[strong resolved on initialisation] of the
    |  #[code Language] class, it's #[strong not possible] to add them to the
    |  pipeline afterwards, e.g. by modifying #[code nlp.pipeline]. This only
    |  works with individual component functions. To use factories, you need to
    |  create a new #[code Language] object, or generate a
    |  #[+a("/usage/training#models-generating") model package] with
    |  a custom pipeline.

+h(3, "disabling") Disabling pipeline components

p
    |  If you don't need a particular component of the pipeline – for
    |  example, the tagger or the parser, you can disable loading it. This can
    |  sometimes make a big difference and improve loading speed. Disabled
    |  component names can be provided to #[+api("spacy#load") #[code spacy.load()]],
    |  #[+api("language#from_disk") #[code Language.from_disk()]] or the
    |  #[code nlp] object itself as a list:

+code.
    nlp = spacy.load('en', disable['parser', 'tagger'])
    nlp = English().from_disk('/model', disable=['tensorizer', 'ner'])
    doc = nlp(u"I don't want parsed", disable=['parser'])

p
    |  Note that you can't write directly to #[code nlp.pipeline], as this list
    |  holds the #[em actual components], not the IDs. However, if you know the
    |  order of the components, you can still slice the list:

+code.
    nlp = spacy.load('en')
    nlp.pipeline = nlp.pipeline[:2] # only use the first two components

+infobox("Important note: disabling pipeline components")
    .o-block
        |  Since spaCy v2.0 comes with better support for customising the
        |  processing pipeline components, the #[code parser], #[code tagger]
        |  and #[code entity] keyword arguments have been replaced with
        |  #[code disable], which takes a list of pipeline component names.
        |  This lets you disable both default and custom components when loading
        |  a model, or initialising a Language class via
        |  #[+api("language-from_disk") #[code from_disk]].
    +code-new.
        nlp = spacy.load('en', disable=['tagger', 'ner'])
        doc = nlp(u"I don't want parsed", disable=['parser'])
    +code-old.
        nlp = spacy.load('en', tagger=False, entity=False)
        doc = nlp(u"I don't want parsed", parse=False)