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