spaCy/website/usage/_spacy-101/_architecture.jade

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

p
    |  The central data structures in spaCy are the #[code Doc] and the
    |  #[code Vocab]. The #[code Doc] object owns the
    |  #[strong sequence of tokens] and all their annotations. The #[code Vocab]
    |  object owns a set of #[strong look-up tables] that make common
    |  information available across documents. By centralising strings, word
    |  vectors and lexical attributes, we avoid storing multiple copies of this
    |  data. This saves memory, and ensures there's a
    |  #[strong single source of truth].

p
    |  Text annotations are also designed to allow a single source of truth: the
    |  #[code Doc] object owns the data, and #[code Span] and #[code Token] are
    |  #[strong views that point into it]. The #[code Doc] object is constructed
    |  by the #[code Tokenizer], and then #[strong modified in place] by the
    |  components of the pipeline. The #[code Language] object coordinates these
    |  components. It takes raw text and sends it through the pipeline,
    |  returning an #[strong annotated document]. It also orchestrates training
    |  and serialization.

+graphic("/assets/img/architecture.svg")
    include ../../assets/img/architecture.svg

+h(3, "architecture-containers") Container objects

+table(["Name", "Description"])
    +row
        +cell #[+api("doc") #[code Doc]]
        +cell A container for accessing linguistic annotations.

    +row
        +cell #[+api("span") #[code Span]]
        +cell A slice from a #[code Doc] object.

    +row
        +cell #[+api("token") #[code Token]]
        +cell
            |  An individual token — i.e. a word, punctuation symbol, whitespace,
            |  etc.

    +row
        +cell #[+api("lexeme") #[code Lexeme]]
        +cell
            |  An entry in the vocabulary. It's a word type with no context, as
            |  opposed to a word token. It therefore has no part-of-speech tag,
            |  dependency parse etc.

+h(3, "architecture-pipeline") Processing pipeline

+table(["Name", "Description"])
    +row
        +cell #[+api("language") #[code Language]]
        +cell
            |  A text-processing pipeline. Usually you'll load this once per
            |  process as #[code nlp] and pass the instance around your application.

    +row
        +cell #[+api("pipe") #[code Pipe]]
        +cell Base class for processing pipeline components.

    +row
        +cell #[+api("tensorizer") #[code Tensorizer]]
        +cell
            |  Add tensors with position-sensitive meaning representations to
            |  #[code Doc] objects.

    +row
        +cell #[+api("tagger") #[code Tagger]]
        +cell Annotate part-of-speech tags on #[code Doc] objects.

    +row
        +cell #[+api("dependencyparser") #[code DependencyParser]]
        +cell Annotate syntactic dependencies on #[code Doc] objects.

    +row
        +cell #[+api("entityrecognizer") #[code EntityRecognizer]]
        +cell
            |  Annotate named entities, e.g. persons or products, on #[code Doc]
            |  objects.

    +row
        +cell #[+api("textcategorizer") #[code TextCategorizer]]
        +cell  Assigning categories or labels to #[code Doc] objects.

    +row
        +cell #[+api("tokenizer") #[code Tokenizer]]
        +cell
            |  Segment text, and create #[code Doc] objects with the discovered
            |  segment boundaries.

    +row
        +cell #[+api("lemmatizer") #[code Lemmatizer]]
        +cell
            |  Determine the base forms of words.

    +row
        +cell #[code Morphology]
        +cell
            |  Assign linguistic features like lemmas, noun case, verb tense etc.
            |  based on the word and its part-of-speech tag.

    +row
        +cell #[+api("matcher") #[code Matcher]]
        +cell
            |  Match sequences of tokens, based on pattern rules, similar to
            |  regular expressions.

    +row
        +cell #[+api("phrasematcher") #[code PhraseMatcher]]
        +cell Match sequences of tokens based on phrases.


+h(3, "architecture-other") Other classes

+table(["Name", "Description"])
    +row
        +cell #[+api("vocab") #[code Vocab]]
        +cell
            |  A lookup table for the vocabulary that allows you to access
            |  #[code Lexeme] objects.

    +row
        +cell #[+api("stringstore") #[code StringStore]]
        +cell Map strings to and from hash values.

    +row
        +cell #[+api("vectors") #[code Vectors]]
        +cell Container class for vector data keyed by string.

    +row
        +cell #[+api("goldparse") #[code GoldParse]]
        +cell Collection for training annotations.

    +row
        +cell #[+api("goldcorpus") #[code GoldCorpus]]
        +cell
            |  An annotated corpus, using the JSON file format. Manages
            |  annotations for tagging, dependency parsing and NER.