//- 💫 DOCS > USAGE > SPACY 101 > PIPELINES

p
    |  When you call #[code nlp] on a text, spaCy first tokenizes the text to
    |  produce a #[code Doc] object. The #[code Doc] is then processed in several
    |  different steps – this is also referred to as the
    |  #[strong processing pipeline]. The pipeline used by the
    |  #[+a("/docs/usage/models") default models] consists of a
    |  vectorizer, a tagger, a parser and an entity recognizer. Each pipeline
    |  component returns the processed #[code Doc], which is then passed on to
    |  the next component.

+image
    include ../../../assets/img/docs/pipeline.svg
    .u-text-right
        +button("/assets/img/docs/pipeline.svg", false, "secondary").u-text-tag View large graphic

+aside
    |  #[strong Name:] ID of the pipeline component.#[br]
    |  #[strong Component:] spaCy's implementation of the component.#[br]
    |  #[strong Creates:] Objects, attributes and properties modified and set by
    |  the component.

+table(["Name", "Component", "Creates"])
    +row
        +cell tokenizer
        +cell #[+api("tokenizer") #[code Tokenizer]]
        +cell #[code Doc]

    +row("divider")
        +cell vectorizer
        +cell #[code Vectorizer]
        +cell #[code Doc.tensor]

    +row
        +cell tagger
        +cell #[+api("tagger") #[code Tagger]]
        +cell #[code Doc[i].tag]

    +row
        +cell parser
        +cell #[+api("dependencyparser") #[code DependencyParser]]
        +cell
            |  #[code Doc[i].head], #[code Doc[i].dep], #[code Doc.sents],
            |  #[code Doc.noun_chunks]

    +row
        +cell ner
        +cell #[+api("entityrecognizer") #[code EntityRecognizer]]
        +cell #[code Doc.ents], #[code Doc[i].ent_iob], #[code Doc[i].ent_type]