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* Update docs
This commit is contained in:
Matthew Honnibal
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@ -23,6 +23,10 @@ under spacy.en.defs.
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.. autommodule:: spacy.en.pos
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:members:
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.. automodule:: spacy.en.attrs
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:members:
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:undoc-members:
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The Tokens Class
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----------------
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@ -1,8 +0,0 @@
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API
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===
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.. toctree::
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:maxdepth: 2
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tokenizers/index.rst
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lexicon.rst
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@ -1,6 +0,0 @@
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spacy.word.Lexeme
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=================
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.. autoclass:: spacy.word.Lexeme
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:members:
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@ -1,94 +0,0 @@
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spacy.en.EN
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============
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.. automodule:: spacy.en
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Tokenizer API
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-------------
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.. automethod:: spacy.en.EN.tokenize
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:noindex:
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.. automethod:: spacy.en.EN.lookup
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:noindex:
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Lexeme Features Flag IDs
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------------------------
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A number of boolean features are computed for English Lexemes. To access a feature,
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pass its ID to the :py:meth:`spacy.word.Lexeme.check_flag` function.
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Orthographic Features
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---------------------
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These features describe the `orthographic` (lettering) type of the word. The
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function used to compute the value is listed along with the flag.
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.. data:: IS_ALPHA
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:py:func:`spacy.orth.is_alpha`
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.. data:: IS_DIGIT
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:py:func:`spacy.orth.is_digit`
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.. data:: IS_UPPER
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:py:func:`spacy.orth.is_upper`
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.. data:: IS_PUNCT
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:py:func:`spacy.orth.is_punct`
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.. data:: IS_SPACE
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:py:func:`spacy.orth.is_space`
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.. data:: IS_ASCII
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:py:func:`spacy.orth.is_ascii`
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.. data:: IS_TITLE
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:py:func:`spacy.orth.is_title`
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.. data:: IS_LOWER
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:py:func:`spacy.orth.is_lower`
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.. data:: IS_UPPER
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:py:func:`spacy.orth.is_upper`
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Distributional Orthographic Features
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------------------------------------
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These features describe how often the lower-cased form of the word appears
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in various case-styles in a large sample of English text. See :py:func:`spacy.orth.oft_case`
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.. data:: OFT_UPPER
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.. data:: OFT_LOWER
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.. data:: OFT_TITLE
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Tag Dictionary Features
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-----------------------
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These features describe whether the word commonly occurs with a given
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part-of-speech, in a large text corpus, using a part-of-speech tagger designed
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to reduce the tag-dictionary bias of its training corpus. See
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:py:func:`spacy.orth.can_tag`.
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.. data:: CAN_PUNCT
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.. data:: CAN_CONJ
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.. data:: CAN_NUM
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.. data:: CAN_DET
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.. data:: CAN_ADP
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.. data:: CAN_ADJ
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.. data:: CAN_ADV
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.. data:: CAN_VERB
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.. data:: CAN_NOUN
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.. data:: CAN_PDT
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.. data:: CAN_POS
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.. data:: CAN_PRON
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.. data:: CAN_PRT
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@ -1,8 +0,0 @@
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Tokenizers
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===================================
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Each module listed here implements a different tokenization scheme, usually
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intended for a specific language.
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.. toctree::
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en.rst
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@ -1,13 +0,0 @@
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How
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===
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Tutorial
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--------
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Installation
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------------
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API
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---
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@ -79,9 +79,11 @@ you'll find NLTK etc much more expensive, because what you save on license
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cost, you'll lose many times over in lost productivity. $5000 does not buy you
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much developer time.
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.. toctree::
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:hidden:
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:maxdepth: 3
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features.rst
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license_stories.rst
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api.rst
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@ -1,31 +0,0 @@
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What
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====
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Overview
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--------
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Feature List
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------------
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License (for the code)
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-------
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+------------------+------+
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| Non-commercial | $0 |
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+------------------+------+
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| Trial commercial | $0 |
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+------------------+------+
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| Full commercial | $500 |
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+------------------+------+
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spaCy is non-free software. Its source is published, but the copyright is
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retained by the author (Matthew Honnibal). Licenses are currently under preparation.
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There is currently a gap between the output of academic NLP researchers, and
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the needs of a small software companiess. I left academia to try to correct this.
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My idea is that non-commercial and trial commercial use should "feel" just like
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free software. But, if you do use the code in a commercial product, a small
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fixed license-fee will apply, in order to fund development.
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Pricing (for the data)
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----------------------
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@ -1,28 +0,0 @@
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Why
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===
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Benchmarks
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----------
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Efficiency
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----------
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+--------+-------+--------------+--------------+
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| System | Time | Words/second | Speed Factor |
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+--------+-------+--------------+--------------+
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| NLTK | 6m4s | 89,000 | 1.00 |
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+--------+-------+--------------+--------------+
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| spaCy | 9.5s | 3,093,000 | 38.30 |
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+--------+-------+--------------+--------------+
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Accuracy
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--------
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The comparison refers to 30 million words from the English Gigaword, on
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a Maxbook Air. For context, calling string.split() on the data completes in
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about 5s.
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Pros and Cons
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-------------
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