spaCy/website/index.jade

159 lines
6.7 KiB
Plaintext
Raw Normal View History

2016-10-03 21:19:13 +03:00
//- 💫 LANDING PAGE
2016-03-31 17:24:48 +03:00
2016-10-03 21:19:13 +03:00
include _includes/_mixins
2016-03-31 17:24:48 +03:00
2016-10-31 21:04:15 +03:00
+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
2017-06-06 13:49:25 +03:00
+landing-badge(gh("spaCy") + "/releases/tag/v2.0.0-alpha", "v2alpha", "Try spaCy v2.0.0 alpha!")
2017-04-08 11:09:39 +03:00
2016-10-31 21:04:15 +03:00
+grid.o-content
+grid-col("third").o-card
2016-10-31 21:04:15 +03:00
+h(2) Fastest in the world
p
| spaCy excels at large-scale information extraction tasks.
2016-10-31 21:04:15 +03:00
| 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")
2016-10-31 21:04:15 +03:00
| Facts & figures
+grid-col("third").o-card
2016-10-31 21:04:15 +03:00
+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")
2016-10-31 21:04:15 +03:00
| Get started
+grid-col("third").o-card
2016-10-31 21:04:15 +03:00
+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")
2016-10-31 21:04:15 +03:00
| 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}
if LATEST_NEWS
+a(LATEST_NEWS.url) #[+tag.o-icon New!] #{LATEST_NEWS.title}
2016-10-31 21:04:15 +03:00
.o-content
2016-03-31 17:24:48 +03:00
+grid
2016-10-31 21:04:15 +03:00
+grid-col("two-thirds")
+terminal("lightning_tour.py").
2017-03-18 17:24:42 +03:00
# Install: pip install spacy && python -m spacy download en
2016-10-31 21:04:15 +03:00
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
2016-11-02 12:46:14 +03:00
+item GIL-free #[strong multi-threading]
2016-10-31 21:04:15 +03:00
+item Efficient binary serialization
+item Easy #[strong deep learning] integration
+item Statistical models for #[strong English] and #[strong German]
2016-10-31 21:04:15 +03:00
+item State-of-the-art speed
+item Robust, rigorously evaluated accuracy
.o-inline-list
+button("/docs/usage/lightning-tour", true, "secondary")
2016-10-31 21:04:15 +03:00
| See examples
2016-10-03 21:19:13 +03:00
.o-block.u-text-center.u-padding
2016-10-31 21:04:15 +03:00
h3.u-text-label.u-color-subtle.o-block spaCy is trusted by
2016-10-03 21:19:13 +03:00
each row in logos
+grid("center").o-inline-list
each details, name in row
+a(details[0])
img(src="/assets/img/logos/#{name}.png" alt=name width=(details[1] || 150)).u-padding-small
2016-10-31 21:04:15 +03:00
.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("/docs/api/philosophy") mission] is to make
2016-10-31 21:04:15 +03:00
| 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