spaCy/website/index.jade

include _includes/_mixins

- var logos = [ ['chartbeat', 'https://chartbeat.com'], ['socrata', 'https://www.socrata.com'], ['keyreply', 'https://keyreply.com/'], [ 'kip', 'http://kipthis.com'], ['cytora', 'http://www.cytora.com'], ['signaln', 'http://signaln.com'] ]


//-  Landing Page
//- ============================================================================

header.header
    .header-body
        +h1.header-title.header-text
            | Industrial-strength#[br]
            | Natural Language#[br]
            | Processing

        +lead.header-text
            strong
                | Thousands of researchers are trying to make#[br]
                | computers understand text. They're succeeding.#[br]
                | We help you get their work out of papers and#[br]
                | into production.#[br]

        +button('secondary')(href='/docs/#install') Install spaCy

+divider('bar') 
    | #[a(href='https://github.com/' + profiles.github + '/spaCy/releases' target='_blank'): #[strong Latest Release:] v#{spacy_version}] 
    | #[a(href='https://github.com/' + profiles.github + '/spaCy' target='_blank'): +icon('github', 'secondary') #[strong #{spacy_stars}+ stars] on GitHub] 
    | #[a(href='https://www.reddit.com/r/' + profiles.reddit target='_blank'): +icon('reddit', 'secondary') #[strong User Group] on Reddit]

main.main

    +grid('padding', 'space-between')
        +grid-col('half')
            +h2 Built for Production
            
            p.text-big.
                Most AI software is built for research.  Over the last ten years,
                we've used a lot of that software, and built some of it ourselves, 
                especially for natural language processing (NLP).
                But the faster the research has moved, the more impatient we've
                become. We want to see advanced NLP technologies get out into 
                great products, as the basis of great businesses. We built spaCy 
                to make that happen.
                
            +h2 Easy and Powerful
                
            p.text-big.
                For any NLP task, there are always lots of competing algorithms. 
                We don't believe in implementing them all and letting you choose. 
                Instead, we just implement one – the best one.   
                When better algorithms are developed, we can update the library without 
                breaking your code or bloating the API. This approach makes spaCy
                both #[strong easier] and #[strong more powerful] than a pluggable
                architecture. spaCy also features a #[strong unique whole-document design].
                Where other NLP libraries rely on sentence detection as
                a pre-process, spaCy reads the whole document at once, making it much more
                robust to informal and poorly formatted text.  
               
            +h2 Permissive open-source license (MIT)
            p.text-big.
                We think spaCy is valuable software, so we made it free, to raise
                its value even higher.
                Making spaCy open-source puts us on the same side –
                we can tell you everything about how it works, and let
                you run it however you like. We think the software would be much
                less valuable as a service, which could disappear at any point.
            
        +grid-col('half', 'valign-bottom')
            +code-demo('lightning_tour.py').block.
                # 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)
                
                from spacy.attrs import *
                # All strings mapped to integers, for easy export to numpy
                np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])

                from reddit_corpus import RedditComments
                reddit = RedditComments('/path/to/reddit/corpus')
                # Parse a stream of documents, with multi-threading (no GIL!)
                # Processes over 100,000 tokens per second.
                for doc in nlp.pipe(reddit.texts, batch_size=10000, n_threads=4):
                    # Multi-word expressions, such as names, dates etc
                    # can be merged into single tokens
                    for ent in doc.ents:
                        ent.merge(ent.root.tag_, ent.text, ent.ent_type_)
                    # Efficient, lossless serialization --- all annotations
                    # saved, same size as uncompressed text
                    byte_string = doc.to_bytes()

    +h2.text-center
        +label('strong') spaCy is trusted by

    +grid('space-around', 'valign-center', 'padding')
        each logo in logos
            a(href=logo[1] target='_blank')
                img(src='assets/img/logos/' + logo[0] + '.png').logo--small

    +divider

    +grid
        +grid-col('half', 'valign-center', 'align-center')
            .image-container
                img(src='assets/img/spacy_screen.png' style='display: block')

        +grid-col('half')
            +h2 
                +label('strong') About spaCy
                .h2 What we do

            p.text-big spaCy helps you write programs that do clever things with text.  You give it a string of characters, it gives you an object that provides multiple useful views of its meaning and linguistic structure.  Specifically, spaCy features a high performance tokenizer, part-of-speech tagger, named entity recognizer and syntactic dependency parser, with built-in support for word vectors. All of the functionality is united behind a clean high-level Python API, that makes it easy to use the different annotations together.
            
            p.text-big To make spaCy as fast and easy to install as we could, we built it #[strong from the ground up] from custom components, with #[strong custom implementations], and sometimes #[strong custom algorithms]. It's written in clean but efficient Cython code, which allows us to manage both low level details and the high-level Python API in a single codebase.
 
    +divider

    +h2.text-center
        +label('strong') What our users say...

    +grid('padding')
        +grid-col('third', 'valign-center')
            <blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">&quot;Dead Code Should be Buried&quot; <a href="http://t.co/AxfZRRz8nB">http://t.co/AxfZRRz8nB</a> by <a href="https://twitter.com/honnibal">@honnibal</a> on NLP tools &amp; new Python library spaCy <a href="http://t.co/C9f798R3aO">http://t.co/C9f798R3aO</a> looks nice!</p>&mdash; Andrej Karpathy (@karpathy) <a href="https://twitter.com/karpathy/status/640098689894232064">September 5, 2015</a></blockquote>

        +grid-col('third', 'valign-center')
            <blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">spaCy seems pretty exciting to me - and it is clear that NLTK has not kept up with <a href="https://twitter.com/hashtag/NLP?src=hash">#NLP</a>. <a href="http://t.co/mUPFUMLrbo">http://t.co/mUPFUMLrbo</a> <a href="https://twitter.com/hashtag/python?src=hash">#python</a> <a href="https://twitter.com/hashtag/datascience?src=hash">#datascience</a></p>&mdash; Alex Engler (@AlexCEngler) <a href="https://twitter.com/AlexCEngler/status/648537133544833025">September 28, 2015</a></blockquote>

        +grid-col('third', 'valign-center')
            <blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Explore what NLP can do these days with Sense2vec and Spacy <a href="https://t.co/MHZEP2yLo4">https://t.co/MHZEP2yLo4</a></p>&mdash; Chattermill.io (@chatter_mill) <a href="https://twitter.com/chatter_mill/status/699660272907059200">February 16, 2016</a></blockquote>

    +divider

    +grid('padding', 'valign-center')
        +grid-col('half')
            +h2
                .label-strong Benchmarks
                .h2 State-of-the-art speed and accuracy

            p.text-big spaCy is committed to rigorous evaluation under standard methodology.  Two peer-reviewed papers in 2015 confirm that it offers the #[strong fastest syntactic parser in the world] and that #[strong its accuracy is within 1% of the best] available. The few systems that are more accurate are 20&times; slower or more.
            
            p.text-big The first of the evaluations was published by #[strong Yahoo! Labs] and #[strong Emory University], as part of a survey of current parsing technologies #[a(href="http://aclweb.org/anthology/P/P15/P15-1038.pdf" target="_blank") (Choi et al., 2015)].  Their results and subsequent discussions helped us develop a novel psychologically-motivated technique to improve spaCy's accuracy, which we published in joint work with Macquarie University #[a(href="https://aclweb.org/anthology/D/D15/D15-1162.pdf" target="_blank") (Honnibal and Johnson, 2015)].
        
        +grid-col('half')
            +table(["System", "Language", "Accuracy", "Speed (WPS)"])
               +row
                   +cell #[+logo('tiny')]
                   +cell #[strong Cython]
                   +cell #[strong 91.8]
                   +cell #[strong 13,963]
               +row
                   +cell ClearNLP
                   +cell Java
                   +cell 91.7
                   +cell 10,271
               +row
                   +cell CoreNLP
                   +cell Java
                   +cell 89.6
                   +cell 8,602
               +row
                   +cell MATE
                   +cell Java
                   +cell 92.5
                   +cell 550
               +row
                   +cell Turbo
                   +cell C++
                   +cell 92.4
                   +cell 349

            
    +divider

    +h2.text-center
        .label-strong Latest Blog Posts
        .h3 Read more about NLP

    !=partial('_includes/_latest-posts', { max: 3 } )

    !=partial('_includes/_newsletter', { divider: 'top' } )

    script(async src="https://platform.twitter.com/widgets.js" charset="utf-8")