//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0 > SUMMARY

p
    |  We're very excited to finally introduce spaCy v2.0! On this page, you'll
    |  find a summary of the new features, information on the backwards
    |  incompatibilities, including a handy overview of what's been renamed or
    |  deprecated. To help you make the most of v2.0, we also
    |  #[strong re-wrote almost all of the usage guides and API docs], and added
    |  more #[+a("/usage/examples") real-world examples]. If you're new to
    |  spaCy, or just want to brush up on some NLP basics and the details of
    |  the library, check out the
    |  #[+a("/usage/spacy-101") spaCy 101 guide] that explains the most
    |  important concepts with examples and illustrations.

+h(2, "summary") Summary

+grid.o-no-block
    +grid-col("half")

        p
            |  This release features entirely new
            |  #[strong deep learning-powered models] for spaCy's tagger,
            |  parser and entity recognizer. The new models are
            |  #[strong 10× smaller], #[strong 20% more accurate] and
            |  #[strong even cheaper to run] than the previous generation.

        p
            |  We've also made several usability improvements that are
            |  particularly helpful for #[strong production deployments].
            |  spaCy v2 now fully supports the Pickle protocol, making it
            |  easy to use spaCy with
            |  #[+a("https://spark.apache.org/") Apache Spark]. The
            |  string-to-integer mapping is #[strong no longer stateful],
            |  making it easy to reconcile annotations made in different
            |  processes. Models are smaller and use less memory, and the
            |  APIs for serialization are now much more consistent. Custom
            |  pipeline components let you modify the #[code Doc] at any
            |  stage in the pipeline. You can now also add your own
            |  custom attributes, properties and methods to the #[code Doc],
            |  #[code Token] and #[code Span].

    +table-of-contents
        +item #[+a("#summary") Summary]
        +item #[+a("#features") New features]
        +item #[+a("#features-models") Neural network models]
        +item #[+a("#features-pipelines") Improved processing pipelines]
        +item #[+a("#features-text-classification") Text classification]
        +item #[+a("#features-hash-ids") Hash values as IDs]
        +item #[+a("#features-vectors") Improved word vectors support]
        +item #[+a("#features-serializer") Saving, loading and serialization]
        +item #[+a("#features-displacy") displaCy visualizer]
        +item #[+a("#features-language") Language data and lazy loading]
        +item #[+a("#features-matcher") Revised matcher API and phrase matcher]
        +item #[+a("#incompat") Backwards incompatibilities]
        +item #[+a("#migrating") Migrating from spaCy v1.x]
        +item #[+a("#benchmarks") Benchmarks]

p
    |  The main usability improvements you'll notice in spaCy v2.0 are around
    |  #[strong defining, training and loading your own models] and components.
    |  The new neural network models make it much easier to train a model from
    |  scratch, or update an existing model with a few examples. In v1.x, the
    |  statistical models depended on the state of the #[code Vocab]. If you
    |  taught the model a new word, you would have to save and load a lot of
    |  data — otherwise the model wouldn't correctly recall the features of your
    |  new example. That's no longer the case.

p
    |  Due to some clever use of hashing, the statistical models
    |  #[strong never change size], even as they learn new vocabulary items.
    |  The whole pipeline is also now fully differentiable. Even if you don't
    |  have explicitly annotated data, you can update spaCy using all the
    |  #[strong latest deep learning tricks] like adversarial training, noise
    |  contrastive estimation or reinforcement learning.