mirror of https://github.com/explosion/spaCy.git
157 lines
6.8 KiB
Plaintext
157 lines
6.8 KiB
Plaintext
//- 💫 LANDING PAGE
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include _includes/_mixins
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+landing-header
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h1.c-landing__title.u-heading-0
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| Industrial-Strength#[br]
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| Natural Language#[br]
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| Processing
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h2.c-landing__title.o-block.u-heading-3
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span.u-text-label.u-text-label--light in Python
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+grid.o-content.c-landing__blocks
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+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
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+h(3) Fastest in the world
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p
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| spaCy excels at large-scale information extraction tasks.
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| It's written from the ground up in carefully memory-managed
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| Cython. Independent research has confirmed that spaCy is
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| the fastest in the world. If your application needs to
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| process entire web dumps, spaCy is the library you want to
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| be using.
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+button("/usage/facts-figures", true, "primary")
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| Facts & figures
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+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
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+h(3) Get things done
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p
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| spaCy is designed to help you do real work — to build real
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| products, or gather real insights. The library respects
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| your time, and tries to avoid wasting it. It's easy to
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| install, and its API is simple and productive. We like to
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| think of spaCy as the Ruby on Rails of Natural Language
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| Processing.
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+button("/usage", true, "primary")
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| Get started
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+grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
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+h(3) Deep learning
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p
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| spaCy is the best way to prepare text for deep learning.
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| It interoperates seamlessly with TensorFlow, PyTorch,
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| scikit-learn, Gensim and the rest of Python's awesome AI
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| ecosystem. With spaCy, you can easily construct linguistically
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| sophisticated statistical models for a variety of NLP problems.
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+button("/usage/training", true, "primary")
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| Read more
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.o-content
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+grid
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+grid-col("two-thirds")
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+terminal("lightning_tour.py", "More examples", "/usage/spacy-101#lightning-tour").
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# Install: pip install spacy && spacy download en
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import spacy
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# Load English tokenizer, tagger, parser, NER and word vectors
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nlp = spacy.load('en')
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# Process a document, of any size
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text = open('war_and_peace.txt').read()
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doc = nlp(text)
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# Find named entities, phrases and concepts
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for entity in doc.ents:
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print(entity.text, entity.label_)
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# Determine semantic similarities
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doc1 = nlp(u'the fries were gross')
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doc2 = nlp(u'worst fries ever')
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doc1.similarity(doc2)
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# Hook in your own deep learning models
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nlp.add_pipe(load_my_model(), before='parser')
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+grid-col("third")
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+h(2) Features
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+list
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+item Non-destructive #[strong tokenization]
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+item #[strong Named entity] recognition
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+item Support for #[strong #{LANG_COUNT}+ languages]
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+item #[strong #{MODEL_COUNT} statistical models] for #{MODEL_LANG_COUNT} languages
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+item Pre-trained #[strong word vectors]
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+item Easy #[strong deep learning] integration
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+item Part-of-speech tagging
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+item Labelled dependency parsing
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+item Syntax-driven sentence segmentation
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+item Built in #[strong visualizers] for syntax and NER
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+item Convenient string-to-hash mapping
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+item Export to numpy data arrays
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+item Efficient binary serialization
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+item Easy #[strong model packaging] and deployment
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+item State-of-the-art speed
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+item Robust, rigorously evaluated accuracy
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+landing-banner("Convolutional neural network models", "New in v2.0")
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p
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| spaCy v2.0 features new neural models for #[strong tagging],
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| #[strong parsing] and #[strong entity recognition]. The models have
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| been designed and implemented from scratch specifically for spaCy, to
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| give you an unmatched balance of speed, size and accuracy. A novel
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| bloom embedding strategy with subword features is used to support
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| huge vocabularies in tiny tables. Convolutional layers with residual
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| connections, layer normalization and maxout non-linearity are used,
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| giving much better efficiency than the standard BiLSTM solution.
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| Finally, the parser and NER use an imitation learning objective to
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| deliver accuracy in-line with the latest research systems,
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| even when evaluated from raw text. With these innovations, spaCy
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| v2.0's models are #[strong 10× smaller],
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| #[strong 20% more accurate], and #[strong even cheaper to run] than
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| the previous generation.
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.o-block-small.u-text-right
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+button("/models", true, "secondary-light") Download models
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+landing-logos("spaCy is trusted by", logos)
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+button(gh("spacy") + "/stargazers", false, "secondary", "small")
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| and many more
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+landing-logos("Featured on", features).o-block-small
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+landing-banner("Prodigy: Radically efficient machine teaching", "From the makers of spaCy")
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p
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| Prodigy is an #[strong annotation tool] so efficient that data scientists can
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| do the annotation themselves, enabling a new level of rapid
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| iteration. Whether you're working on entity recognition, intent
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| detection or image classification, Prodigy can help you
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| #[strong train and evaluate] your models faster. Stream in your own examples or
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| real-world data from live APIs, update your model in real-time and
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| chain models together to build more complex systems.
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.o-block-small.u-text-right
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+button("https://prodi.gy", true, "secondary-light") Try it out
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.o-content
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+grid
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+grid-col("half")
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+h(2) Benchmarks
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p
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| In 2015, independent researchers from Emory University and
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| Yahoo! Labs showed that spaCy offered the
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| #[strong fastest syntactic parser in the world] and that its
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| accuracy was #[strong within 1% of the best] available
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| (#[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al., 2015]).
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| spaCy v2.0, released in 2017, is more accurate than any of
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| the systems Choi et al. evaluated.
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.o-inline-list
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+button("/usage/facts-figures#benchmarks", true, "secondary") See details
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+grid-col("half")
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include usage/_facts-figures/_benchmarks-choi-2015
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