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83
website/docs/usage/_spacy-101/_architecture.jade Normal file
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@ -0,0 +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
| #[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.

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website/docs/usage/index.jade
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@ -21,13 +21,8 @@ p
+qs({config: 'venv', os: 'linux'}) source .env/bin/activate
+qs({config: 'venv', os: 'windows'}) .env\Scripts\activate
+qs({config: 'gpu', os: 'mac'}) export CUDA_HOME=/usr/local/cuda-8.0
+qs({config: 'gpu', os: 'mac'}) export PATH=$PATH:$CUDA_HOME/bin
+qs({config: 'gpu', os: 'linux'}) export CUDA_HOME=/usr/local/cuda-8.0
+qs({config: 'gpu', os: 'linux'}) export PATH=$PATH:$CUDA_HOME/bin
+qs({config: 'gpu', package: 'pip'}) pip install -U chainer
+qs({config: 'gpu', package: 'source'}) pip install -U chainer
+qs({config: 'gpu', package: 'conda'}) conda install -c anaconda chainer
+qs({config: 'gpu', os: 'mac'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
+qs({config: 'gpu', os: 'linux'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
+qs({package: 'pip'}) pip install -U spacy
+qs({package: 'conda'}) conda install -c conda-forge spacy
@ -96,27 +91,15 @@ p
| #[+a("http://chainer.org") Chainer]'s CuPy module, which provides
| a NumPy-compatible interface for GPU arrays.
+aside("Why is this so complicated?")
| Installing Chainer when no GPU is available currently causes an
| error. We therefore do not specify Chainer as a dependency. However,
| CuPy will be split out into
| #[+a("https://www.slideshare.net/beam2d/chainer-v2-alpha/7") its own package]
| in Chainer v2.0. We'll have a smoother installation process for this
| in an upcoming version.
p
| First, install follows the normal CUDA installation procedure. Next, set
| your environment variables so that the installation will be able to find
| CUDA. Next, install Chainer, and check that CuPy can be imported
| correctly. Finally, install spaCy.
| CUDA. Finally, install spaCy.
+code(false, "bash").
export CUDA_HOME=/usr/local/cuda-8.0 # Or wherever your CUDA is
export PATH=$PATH:$CUDA_HOME/bin
pip install chainer
python -c "import cupy; assert cupy" # Check it installed
pip install spacy
python -c "import thinc.neural.gpu_ops" # Check the GPU ops were built

86
website/docs/usage/spacy-101.jade
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@ -65,13 +65,15 @@ p
| not designed specifically for chat bots, and only provides the
| underlying text processing capabilities.
+item #[strong spaCy is not research software].
| It's is built on the latest research, but unlike
| #[+a("https://github./nltk/nltk") NLTK], which is intended for
| teaching and research, spaCy follows a more opinionated approach and
| focuses on production usage. Its aim is to provide you with the best
| possible general-purpose solution for text processing and machine learning
| with text input – but this also means that there's only one implementation
| of each component.
| It's is built on the latest research, but it's designed to get
| things done. This leads to fairly different design decisions than
| #[+a("https://github./nltk/nltk") NLTK]
| or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
| created as platforms for teaching and research. The main difference
| is that spaCy is integrated and opinionated. spaCy tries to avoid asking
| the user to choose between multiple algorithms that deliver equivalent
| functionality. Keeping the menu small lets spaCy deliver generally better
| performance and developer experience.
+item #[strong spaCy is not a company].
| It's an open-source library. Our company publishing spaCy and other
| software is called #[+a(COMPANY_URL, true) Explosion AI].
@ -79,7 +81,7 @@ p
+h(2, "features") Features
p
| Across the documentations, you'll come across mentions of spaCy's
| Across the documentation, you'll come across mentions of spaCy's
| features and capabilities. Some of them refer to linguistic concepts,
| while others are related to more general machine learning functionality.
@ -171,7 +173,9 @@ p
p
| Even though a #[code Doc] is processed – e.g. split into individual words
| and annotated – it still holds #[strong all information of the original text],
| like whitespace characters. This way, you'll never lose any information
| like whitespace characters. You can always get the offset of a token into the
| original string, or reconstruct the original by joining the tokens and their
| trailing whitespace. This way, you'll never lose any information
| when processing text with spaCy.
+h(3, "annotations-token") Tokenization
@ -268,69 +272,7 @@ include _spacy-101/_language-data
+h(2, "architecture") Architecture
+under-construction
+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.
include _spacy-101/_architecture.jade
+h(3, "architecture-pipeline") Pipeline components