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

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//- 💫 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.
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+graphic("/assets/img/architecture.svg")
include ../../assets/img/architecture.svg
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+h(3, "architecture-containers") Container objects
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+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.
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+h(3, "architecture-pipeline") Processing pipeline
+table(["Name", "Description"])
+row
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+cell #[+api("language") #[code Language]]
+cell
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| A text-processing pipeline. Usually you'll load this once per
| process as #[code nlp] and pass the instance around your application.
+row
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+cell #[+api("pipe") #[code Pipe]]
+cell Base class for processing pipeline components.
+row
+cell #[+api("tensorizer") #[code Tensorizer]]
+cell
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| Add tensors with position-sensitive meaning representations to
| #[code Doc] objects.
+row
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+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.
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+row
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+cell #[+api("lemmatizer") #[code Lemmatizer]]
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+cell
| Determine the base forms of words.
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+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.
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+row
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+cell #[+api("phrasematcher") #[code PhraseMatcher]]
+cell Match sequences of tokens based on phrases.
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+h(3, "architecture-other") Other classes
+table(["Name", "Description"])
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+row
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+cell #[+api("vocab") #[code Vocab]]
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+cell
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| A lookup table for the vocabulary that allows you to access
| #[code Lexeme] objects.
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+row
+cell #[+api("stringstore") #[code StringStore]]
+cell Map strings to and from hash values.
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+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.