mirror of https://github.com/explosion/spaCy.git
* Add docs.jade file
This commit is contained in:
Matthew Honnibal
parent
8a8da6118e
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- var unicode_type = '<a class="reference" href="http://docs.python.org/library/functions.html#unicode"><em>unicode</em></a>'
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- var bool_type = '<a class="reference" href="http://docs.python.org/library/functions.html#bool"><em>bool</em></a>'
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- var int_type = ""
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- var Token_type = ""
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- var Span_type = ""
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- var Vocab_type = ""
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- var generator_type = ""
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mixin declare_class(name)
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details(open="true")
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summary
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span.declaration
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span.label class
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code #{name}
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block
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mixin method(name, parameters)
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details(open=attributes.open)
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summary
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span.declaration
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span.label #{name}
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span.parameters
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| self, #{parameters}
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block
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mixin params
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ul
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block
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mixin param(name, type, value)
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li
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if type
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<strong>#{name}</strong> (!{type}) –
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else
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<strong>#{name}</strong> –
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block
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mixin attribute(name, type, value)
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details(open=attributes.open)
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summary
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span.declaration
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span.label #{name}
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block
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mixin returns(name, type, value)
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li
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if type
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<strong>#{name}</strong> (!{type}) –
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else
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<strong>#{name}</strong> –
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block
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mixin returns(type)
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| tmp
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doctype html
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html(lang="en")
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head
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meta(charset="utf-8")
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title!= tag_line
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meta(name="description" content="")
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meta(name="author" content="Matthew Honnibal")
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link(rel="stylesheet" href="css/style.css")
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<!--[if lt IE 9]>
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script(src="http://html5shiv.googlecode.com/svn/trunk/html5.js")
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<![endif]-->
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body(id="docs")
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header
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h1.logo!= tag_line
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div.slogan!= slogan
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nav(role="navigation")
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ul
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li: a(href="#") Home
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li.active: a(href="#") Docs
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li: a(href="#") License
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li: a(href="#") Blog
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main.docs#content
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section.intro
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| Tmp
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article
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h3: a(href="#") Header
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+declare_class("spacy.en.English")
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+method("__init__", "data_dir=True, Tagger=True, Parser=True, Entity=True, Matcher=True, Packer=None, load_vectors=True")
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+params
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+param("data_dir")
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| The data directory. May be #{None}, to disable any data loading
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| (including the vocabulary).
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+param("Tokenizer")
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| A class/function that creates the tokenizer.
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+param("Tagger")
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| A class/function that creates the part-of-speech tagger.
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+param("Parser")
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| A class/function that creates the dependency parser.
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+param("Entity")
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| A class/function that creates the named entity recogniser.
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+param("load_vectors")
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| A boolean value to control whether the word vectors are loaded.
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+method("__call__", "text, tag=True, parse=True, entity=True")(open)
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+params
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+param("text", unicode_type)
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| The text to be processed. No pre-processing needs to be applied,
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| and any length of text can be submitted. Usually you will submit
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| a whole document. Text may be zero-length. An exception is raised
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| if byte strings are supplied.
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+param("tag", bool_type)
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| Whether to apply the part-of-speech tagger. Required for parsing
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| and entity recognition.
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+param("parse", bool_type)
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| Whether to apply the syntactic dependency parser.
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+param("entity", bool_type)
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| Whether to apply the named entity recognizer.
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pre.language-python
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code
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| from spacy.en import English
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| nlp = English()
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| doc = nlp(u'Some text.) # Applies tagger, parser, entity
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| doc = nlp(u'Some text.', parse=False) # Applies tagger and entity, not parser
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| doc = nlp(u'Some text.', entity=False) # Applies tagger and parser, not entity
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| doc = nlp(u'Some text.', tag=False) # Does not apply tagger, entity or parser
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| doc = nlp(u'') # Zero-length tokens, not an error
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| # doc = nlp(b'Some text') <-- Error: need unicode
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| doc = nlp(b'Some text'.decode('utf8')) # Encode to unicode first.
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+declare_class("spacy.tokens.doc.Doc")
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+method("__init__", "vocab")
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+params
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+param("vocab", vocab_type)
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| A vocabulary object
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+method("__getitem__", "i", int_type)
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+returns(Token_type)
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+method("__getitem__", "start_end", slice_type)
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+returns(Span_type)
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+method("__iter__")
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| Iterate over tokens
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+method("__len__")
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| Number of tokens in the document.
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+attribute("sents", generator_type)
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| Iterate over sentences in the document.
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+attribute("ents", generator_type)
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| Iterate over named entities in the document.
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+attribute("noun_chunks", generator_type)
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+method("to_array", "attr_ids")
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| Given a list of M attribute IDs, export the tokens to a numpy ndarray
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| of shape N*M, where N is the length of the sentence.
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+params
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+param("attr_ids", "list[int]")
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| A list of attribute ID ints.
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+returns("feat_array")
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| A feature matrix, with one row per word, and one column per attribute
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| indicated in the input attr_ids.
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+method("count_by", "attr_id")
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| Produce a dict of {attribute (int): count (ints)} frequencies, keyed
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| by the values of the given attribute ID.
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pre.language-python
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code
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| >>> from spacy.en import English, attrs
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| >>> nlp = English()
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| >>> tokens = nlp(u'apple apple orange banana')
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| >>> tokens.count_by(attrs.ORTH)
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| {12800L: 1, 11880L: 2, 7561L: 1}
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| >>> tokens.to_array([attrs.ORTH])
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| array([[11880],
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| [11880],
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| [7561],
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| [12800]])
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+method("from_array", "attrs, array")
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| Load from array
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+method("to_bytes")
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| Serialize
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+method("from_bytes")
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| Deserialize, loading from bytes
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+method("read_bytes")
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| classmethod
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+method("merge", "int start_idx, int end_idx, unicode tag, unicode lemma, unicode ent_type")
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| Merge a multi-word expression into a single token. Currently
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| experimental; API is likely to change.
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+declare_class("spacy.tokens.Token")
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+method("__init__", "vocab, doc, offset")
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+params
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+param("vocab", Vocab_type)
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p A Vocab object
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+param("doc", Doc_type)
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p The parent sequence
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+param("offset", Int_type)
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p The index of the token within the document
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details
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summary: h4 String Views
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+attribute("orth / orth_")
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| The form of the word with no string normalization or processing, as
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| it appears in the string, without trailing whitespace.
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+attribute("lemma / lemma_")
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| The "base" of the word, with no inflectional suffixes, e.g. the lemma of
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| "developing" is "develop", the lemma of "geese" is "goose", etc. Note that
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| <em>derivational</em> suffixes are not stripped, e.g. the lemma of
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| "instutitions" is "institution", not "institute". Lemmatization is
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| performed using the WordNet data, but extended to also cover closed-class
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| words such as pronouns. By default, the WN lemmatizer returns "hi"
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| as the lemma of "his". We assign pronouns the lemma -PRON-.
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+attribute("lower / lower_")
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| The form of the word, but forced to lower-case, i.e.
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pre.language-python: code lower = word.orth\_.lower()
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//+attribute("norm / norm_")
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// | The form of the word, after language-specific normalizations has been
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// | applied.
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+attribute("shape / shape_")
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| A transform of the word's string, to show orthographic features.
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| The characters a-z are mapped to x, A-Z is mapped to X, 0-9 is mapped
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| to d. After these mappings, sequences of 4 or more of the same character
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| are truncated to length 4. Examples: C3Po --> XdXx, favorite --> xxxx,
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| :) --> :)
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+attribute("prefix / prefix_")
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| A length-N substring from the start of the word. Length may vary by
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| language; currently for English n=1, i.e.
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pre.language-python: code prefix = word.orth\_[:1]
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+attribute("suffix / suffix_")
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| A length-N substring from the end of the word. Length may vary by
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| language; currently for English n=3, i.e.
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pre.language-python: code suffix = word.orth\_[-3:]
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//+attribute("lex_id")
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// | lex_id
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details
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summary: h4 Alignment and Output
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+attribute("idx")
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p Start index of the token in the string
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+method("__len__", "")
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p Length of the token's orth string, in unicode code-points.
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+method("__unicode__", "")
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p Same as token.orth_
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+method("__str__", "")
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p Varies between Python 2 and Python 3
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+attribute("string")
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p
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| The form of the word as it appears in the string, <strong>including
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| trailing whitespace</strong>. This is useful when you need to use
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| linguistic features to add inline mark-up to the string.
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+method("nbor, i=1")
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+params
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+param("i")
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p Offset relative to token
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details
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summary: h4 Distributional Features
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+attribute("repvec")
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p
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| A "word embedding" representation: a dense real-valued vector that supports
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| similarity queries between words. By default, spaCy currently loads
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| vectors produced by the Levy and Goldberg (2014) dependency-based word2vec
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| model.
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+attribute("cluster")
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p
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| The Brown cluster ID of the word. These are often useful features for
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| linear models. If you're using a non-linear model, particularly a
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| neural net or random forest, consider using the real-valued word
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| representation vector, in Token.repvec, instead.
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+attribute("prob")
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p
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| The unigram log-probability of the word, estimated from counts from a
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| large corpus, smoothed using Simple Good Turing estimation.
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details
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summary: h4 Syntactic Tags
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+attribute("pos / pos_")
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| A part-of-speech tag, from the Google Universal Tag Set, e.g.
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| code>NOUN</code>, <code>VERB</code>, <code>ADV</code>. Constants for
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| the 17 tag values are provided in <code>spacy.parts_of_speech.</code>
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+attribute("tag / tag_")
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| A morphosyntactic tag, e.g. <code>NN</code>, <code>VBZ</code>,
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| <code>DT</code>, etc. These tags are language/corpus specific, and
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| typically describe part-of-speech and some amount of morphological
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| information. For instance, in the Penn Treebank tag set, <code>VBZ</code>
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| is assigned to a present-tense singular verb.
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+attribute("dep / dep_")
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| The type of syntactic dependency relation between the word and its
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| syntactic head.
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details
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summary: h4 Navigating the Parse Tree
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+attribute("head")
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p
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| The Token that is the immediate syntactic head of the word. If the
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| word is the root of the dependency tree, the same word is returned.
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+attribute("lefts")
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p
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| An iterator for the immediate leftward syntactic children of the
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| word.
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+attribute("rights")
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p
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| An iterator for the immediate rightward syntactic children of the
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| word.
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+attribute("n_lefts")
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p
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| The number of immediate syntactic children preceding the word in
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| the string.
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+attribute("n_rights")
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p
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| The number of immediate syntactic children following the word in
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| the string.
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+attribute("children")
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p
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| An iterator that yields from lefts, and then yields from rights.
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+attribute("subtree")
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p
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| An iterator for the part of the sentence syntactically governed by
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| the word, including the word itself.
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+attribute("left_edge")
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p The leftmost edge of the token's subtree
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+attribute("right_edge")
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p The rightmost edge of the token's subtree
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details
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summary: h4 Named Entities
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+attribute("ent_type")
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p If the token is part of an entity, its entity type.
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+attribute("ent_iob")
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p The IOB (inside, outside, begin) entity recognition tag for the token.
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details
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summary: h4 Lexeme Flags
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+method("check_flag", "flag_id")
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+params
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+param("flag_id")
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| flag ID
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+attribute("is_oov")
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+attribute("is_alpha")
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+attribute("is_ascii")
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+attribute("is_digit")
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+attribute("is_lower")
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+attribute("is_title")
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+attribute("is_punct")
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+attribute("is_space")
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+attribute("like_url")
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+attribute("like_num")
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+attribute("like_email")
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//+attribute("conjuncts")
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// | Conjuncts
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+declare_class("spacy.tokens.span.Span")
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+params
|
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+method("__getitem__")
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p Get item
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+method("__iter__")
|
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p Iter
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+method("__len__")
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p Len
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+attribute("root")
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p Syntactic head
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+attribute("lefts")
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p Tokens that are:
|
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ol
|
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li To the left of the span;
|
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li Syntactic children of words within the span
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p i.e.
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pre.language-python
|
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code
|
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| lefts = [span.doc[i] for i in range(0, span.start)
|
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| if span.doc[i].head in span]
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+attribute("rights")
|
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p Tokens that are:
|
||||
ol
|
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li To the right of the span;
|
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li Syntactic children of words within the span
|
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p i.e.
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pre.language-python
|
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code
|
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| rights = [span.doc[i] for i in range(span.end, len(span.doc))
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| if span.doc[i].head in span]
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+attribute("string")
|
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p String
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|
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+attribute("lemma / lemma_")
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p String
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+attribute("label / label_")
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p String
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+attribute("subtree")
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p String
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+declare_class("spacy.vocab.Vocab", "data_dir=None, lex_props_getter=None")
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+method("__len__")
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+returns
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p Number of words in the vocabulary.
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|
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+method("__getitem__", "key_int")
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+params
|
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+param("key")
|
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p Integer ID
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+returns: p A Lexeme object
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+method("__getitem__", "key_str")
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+params
|
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+param("key_str", unicode_type)
|
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p A string in the vocabulary
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+returns("Lexeme")
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|
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+method("__setitem__", "orth_str", "props")
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+params
|
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+param("orth_str", unicode_type)
|
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p The orth key
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|
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+param("props", dict_type)
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p A props dictionary
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+returns("None")
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|
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+method("dump", "loc")
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+params
|
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+param("loc", unicode_type)
|
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p Path where the vocabulary should be saved
|
||||
|
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+method("load_lexemes", "loc")
|
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+params
|
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+param("loc", unicode_type)
|
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p Path to load the lexemes.bin file from
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|
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+method("load_vectors", "loc")
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+params
|
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+param("loc", unicode_type)
|
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p Path to load the vectors.bin from
|
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|
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|
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+declare_class("spacy.strings.StringStore")
|
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+method("__len__")
|
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+returns("int")
|
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p Number of strings in the string-store
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|
||||
+method("__getitem__", "key_int")
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+params
|
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+param("key_int")
|
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p An integer key
|
||||
|
||||
+returns(unicode_type)
|
||||
p The string that the integer key maps to
|
||||
|
||||
+method("__getitem__", "key_unicode")
|
||||
+params
|
||||
+param("key_unicode")
|
||||
p A key, as a unicode string
|
||||
|
||||
+returns(int_type)
|
||||
p The integer ID of the string.
|
||||
|
||||
+method("__getitem__", "key_utf8_bytes")
|
||||
+params
|
||||
+param("key_utf8_bytes", bytes_type)
|
||||
p p A key, as a UTF-8 encoded byte-string
|
||||
|
||||
+returns(int_type)
|
||||
p The integer ID of the string.
|
||||
|
||||
+method("dump", "loc")
|
||||
+params
|
||||
+param("loc")
|
||||
p File path to save the strings.txt to.
|
||||
|
||||
+method("load")
|
||||
+params
|
||||
+param("loc")
|
||||
p File path to load the strings.txt from.
|
||||
|
||||
script(src="js/prism.js")
|
||||
Loading…
Reference in New Issue