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Update architecture blurb and move other info

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ines 2017-06-04 13:28:58 +02:00
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92
website/docs/usage/_spacy-101/_architecture.jade
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@ -1,15 +1,83 @@
//- 💫 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 sequence of tokens and all their annotations.
| the #[code vocab] owns a set of 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 single source of truth. 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 views that point into it. The #[code Doc]
| object is constructed by the #[code Tokenizer], and then 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 annotated document. It also orchestrates training and serialisation.
| 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.
+image
include ../../../assets/img/docs/architecture.svg
.u-text-right
+button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic
+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("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.
+row
+cell #[+api("vocab") #[code Vocab]]
+cell
| A lookup table for the vocabulary that allows you to access
| #[code Lexeme] objects.
+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("stringstore") #[code StringStore]]
+cell Map strings to and from hash values.
+row
+row
+cell #[+api("tokenizer") #[code Tokenizer]]
+cell
| Segment text, and create #[code Doc] objects with the discovered
| segment boundaries.
+row
+cell #[+api("matcher") #[code Matcher]]
+cell
| Match sequences of tokens, based on pattern rules, similar to
| regular expressions.

62
website/docs/usage/spacy-101.jade
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@ -274,68 +274,6 @@ include _spacy-101/_language-data
include _spacy-101/_architecture.jade
+image
include ../../assets/img/docs/architecture.svg
.u-text-right
+button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic
+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("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.
+row
+cell #[+api("vocab") #[code Vocab]]
+cell
| A lookup table for the vocabulary that allows you to access
| #[code Lexeme] objects.
+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("stringstore") #[code StringStore]]
+cell Map strings to and from hash values.
+row
+row
+cell #[+api("tokenizer") #[code Tokenizer]]
+cell
| Segment text, and create #[code Doc] objects with the discovered
| segment boundaries.
+row
+cell #[+api("matcher") #[code Matcher]]
+cell
| Match sequences of tokens, based on pattern rules, similar to
| regular expressions.
+h(3, "architecture-pipeline") Pipeline components
+table(["Name", "Description"])