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77 lines
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77 lines
3.9 KiB
Plaintext
//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > SUMMARY
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p
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| We're very excited to finally introduce spaCy v2.0! On this page, you'll
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| find a summary of the new features, information on the backwards
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| incompatibilities, including a handy overview of what's been renamed or
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| deprecated. To help you make the most of v2.0, we also
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| #[strong re-wrote almost all of the usage guides and API docs], and added
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| more #[+a("/usage/examples") real-world examples]. If you're new to
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| spaCy, or just want to brush up on some NLP basics and the details of
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| the library, check out the
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| #[+a("/usage/spacy-101") spaCy 101 guide] that explains the most
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| important concepts with examples and illustrations.
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+legacy
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+h(2, "summary") Summary
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+grid.o-no-block
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+grid-col("half")
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p
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| This release features entirely new
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| #[strong deep learning-powered models] for spaCy's tagger,
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| parser and entity recognizer. The new models are
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| #[strong 10× smaller], #[strong 20% more accurate] and
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| #[strong even cheaper to run] than the previous generation.
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p
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| We've also made several usability improvements that are
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| particularly helpful for #[strong production deployments].
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| spaCy v2 now fully supports the Pickle protocol, making it
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| easy to use spaCy with
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| #[+a("https://spark.apache.org/") Apache Spark]. The
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| string-to-integer mapping is #[strong no longer stateful],
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| making it easy to reconcile annotations made in different
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| processes. Models are smaller and use less memory, and the
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| APIs for serialization are now much more consistent. Custom
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| pipeline components let you modify the #[code Doc] at any
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| stage in the pipeline. You can now also add your own
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| custom attributes, properties and methods to the #[code Doc],
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| #[code Token] and #[code Span].
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+table-of-contents
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+item #[+a("#summary") Summary]
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+item #[+a("#features") New features]
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+item #[+a("#features-models") Neural network models]
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+item #[+a("#features-pipelines") Improved processing pipelines]
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+item #[+a("#features-text-classification") Text classification]
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+item #[+a("#features-hash-ids") Hash values as IDs]
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+item #[+a("#features-vectors") Improved word vectors support]
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+item #[+a("#features-serializer") Saving, loading and serialization]
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+item #[+a("#features-displacy") displaCy visualizer]
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+item #[+a("#features-language") Language data and lazy loading]
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+item #[+a("#features-matcher") Revised matcher API and phrase matcher]
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+item #[+a("#incompat") Backwards incompatibilities]
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+item #[+a("#migrating") Migrating from spaCy v1.x]
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+item #[+a("#benchmarks") Benchmarks]
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p
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| The main usability improvements you'll notice in spaCy v2.0 are around
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| #[strong defining, training and loading your own models] and components.
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| The new neural network models make it much easier to train a model from
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| scratch, or update an existing model with a few examples. In v1.x, the
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| statistical models depended on the state of the #[code Vocab]. If you
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| taught the model a new word, you would have to save and load a lot of
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| data — otherwise the model wouldn't correctly recall the features of your
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| new example. That's no longer the case.
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p
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| Due to some clever use of hashing, the statistical models
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| #[strong never change size], even as they learn new vocabulary items.
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| The whole pipeline is also now fully differentiable. Even if you don't
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| have explicitly annotated data, you can update spaCy using all the
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| #[strong latest deep learning tricks] like adversarial training, noise
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| contrastive estimation or reinforcement learning.
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