spaCy/website/index.jade
2016-10-31 19:04:15 +01:00

154 lines
6.6 KiB
Plaintext

//- 💫 LANDING PAGE
include _includes/_mixins
+landing-header
h1.c-landing__title.u-heading-0
| Industrial-Strength#[br]
| Natural Language#[br]
| Processing
h2.c-landing__title.o-block.u-heading-1
| in Python
+grid.o-content
+grid-col("third").o-card.u-padding-medium
+h(2) Fastest in the world
p
| spaCy excells at large-scale information extraction tasks.
| It's written from the ground up in carefully memory-managed
| Cython. Independent research has confirmed that spaCy is
| the fastest in the world. If your application needs to
| process entire web dumps, spaCy is the library you want to
| be using.
+button("/docs/api", true, "primary")(target="_self")
| Facts & figures
+grid-col("third").o-card.u-padding-medium
+h(2) Get things done
p
| spaCy is designed to help you do real work — to build real
| products, or gather real insights. The library respects
| your time, and tries to avoid wasting it. It's easy to
| install, and its API is simple and productive. I like to
| think of spaCy as the Ruby on Rails of Natural Language
| Processing.
+button("/docs/usage", true, "primary")(target="_self")
| Get started
+grid-col("third").o-card.u-padding-medium
+h(2) Deep learning
p
| spaCy is the best way to prepare text for deep learning.
| It interoperates seamlessly with
| #[+a("https://www.tensorflow.org") TensorFlow],
| #[+a("https://keras.io") Keras],
| #[+a("http://scikit-learn.org") Scikit-Learn],
| #[+a("https://radimrehurek.com/gensim") Gensim] and the
| rest of Python's awesome AI ecosystem. spaCy helps you
| connect the statistical models trained by these libraries
| to the rest of your application.
+button("/docs/usage/deep-learning", true, "primary")(target="_self")
| Read more
.o-inline-list.o-block.u-border-bottom.u-text-small.u-text-center.u-padding-small
+a(gh("spaCy") + "/releases")
strong.u-text-label.u-color-subtle #[+icon("code", 18)] Latest release:
| v#{SPACY_VERSION}
.o-content
+grid
+grid-col("two-thirds")
+terminal("lightning_tour.py").
# Install: pip install spacy && python -m spacy.en.download
import spacy
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load('en')
# Process a document, of any size
text = open('war_and_peace.txt').read()
doc = nlp(text)
# Hook in your own deep learning models
similarity_model = load_my_neural_network()
def install_similarity(doc):
doc.user_hooks['similarity'] = similarity_model
nlp.pipeline.append(install_similarity)
doc1 = nlp(u'the fries were gross')
doc2 = nlp(u'worst fries ever')
doc1.similarity(doc2)
+grid-col("third")
+h(2) Features
+list
+item Non-destructive #[strong tokenization]
+item Syntax-driven sentence segmentation
+item Pre-trained #[strong word vectors]
+item Part-of-speech tagging
+item #[strong Named entity] recognition
+item Labelled dependency parsing
+item Convenient string-to-int mapping
+item Export to numpy data arrays
+item Efficient #[strong multi-threading]
+item Efficient binary serialization
+item Easy #[strong deep learning] integration
+item State-of-the-art speed
+item Robust, rigorously evaluated accuracy
.o-inline-list
+button("/docs/usage/lightning-tour", true, "secondary")(target="_self")
| See examples
.o-block.u-text-center.u-padding
h3.u-text-label.u-color-subtle.o-block spaCy is trusted by
.o-block
each row in logos
each url, name in row
+a(url)
img(src="/assets/img/logos/#{name}.png" alt="name" width="150").u-padding-medium.u-grayscale
br
.u-pattern.u-padding
+grid.o-card.o-content
+grid-col("quarter")
img(src="/assets/img/profile_matt.png" width="280")
+grid-col("three-quarters")
+h(2) What's spaCy all about?
p
| By 2014, I'd been publishing NLP research for about 10
| years. During that time, I saw a huge gap open between the
| technology that Google-sized companies could take to market,
| and what was available to everyone else. This was especially
| clear when companies started trying to use my research. Like
| most researchers, my work was free to read, but expensive to
| apply. You could run my code, but its requirements were
| narrow. My code's mission in life was to print results
| tables for my papers — it was good at this job, and bad at
| all others.
p
| spaCy's #[a(href="/docs/api/philosophy") mission] is to make
| cutting-edge NLP practical and commonly available. That's
| why I left academia in 2014, to build a production-quality
| open-source NLP library. It's why
| #[+a("https://twitter.com/_inesmontani") Ines] joined the
| project in 2015, to build visualisations, demos and
| annotation tools that make NLP technologies less abstract
| and easier to use. Together, we've founded
| #[+a(COMPANY_URL, true) Explosion AI], to develop data packs
| you can drop into spaCy to extend its capabilities. If
| you're processing Hindi insurance claims, you need a model
| for that. We can build it for you.
.o-block
+a("https://twitter.com/honnibal")
+svg("graphics", "matt-signature", 60, 45).u-color-theme