//- 💫 DOCS > MODELS

include ../_includes/_mixins

+section("quickstart")
    p
        |  spaCy v2.0 features new neural models for #[strong tagging],
        |  #[strong parsing] and #[strong entity recognition]. The models have
        |  been designed and implemented from scratch specifically for spaCy, to
        |  give you an unmatched balance of speed, size and accuracy. A novel
        |  bloom embedding strategy with subword features is used to support
        |  huge vocabularies in tiny tables. Convolutional layers with residual
        |  connections, layer normalization and maxout non-linearity are used,
        |  giving much better efficiency than the standard BiLSTM solution. For
        |  more details, see the notes on the
        |  #[+a("/api/#nn-models") model architecture].

    p
        |  The parser and NER use an imitation learning objective to
        |  deliver #[strong accuracy in-line with the latest research systems],
        |  even when  evaluated from raw text. With these innovations, spaCy
        |  v2.0's models are #[strong 10× smaller],
        |  #[strong 20% more accurate], and #[strong just as fast] as the
        |  previous generation.

    include ../usage/_models/_quickstart

+section("install")
    +h(2, "install") Installation & Usage

    include ../usage/_models/_install-basics

    +infobox
        |  For more details on how to use models with spaCy, see the
        |  #[+a("/usage/models") usage guide on models].

+section("conventions")
    +h(2, "model-naming") Model naming conventions

    p
        |  In general, spaCy expects all model packages to follow the naming
        |  convention of #[code [lang]_[name]]. For spaCy's models, we also
        |  chose to divide the name into three components:

    +table
        +row
            +cell #[+label Type]
            +cell
                |  Model capabilities (e.g. #[code core] for general-purpose
                |  model with vocabulary, syntax, entities and word vectors, or
                |  #[code depent] for only vocab, syntax and entities).
        +row
            +cell #[+label Genre]
            +cell
                |  Type of text the model is trained on, e.g. #[code web] or
                |  #[code news].
        +row
            +cell #[+label Size]
            +cell Model size indicator, #[code sm], #[code md] or #[code lg].

    p
        |  For example, #[code en_core_web_sm] is a small English model trained
        |  on written web text (blogs, news, comments), that includes
        |  vocabulary, vectors, syntax and entities.

    +h(3, "model-versioning") Model versioning

    p
        |  Additionally, the model versioning reflects both the compatibility
        |  with spaCy, as well as the major and minor model version. A model
        |  version #[code a.b.c] translates to:

    +table
        +row
            +cell #[code a]
            +cell
                |  #[strong spaCy major version]. For example, #[code 2] for
                |  spaCy v2.x.
        +row
            +cell #[code b]
            +cell
                |  #[strong Model major version]. Models with a different major
                |  version can't be loaded by the same code. For example,
                |  changing the width of the model, adding hidden layers or
                |  changing the activation changes the model major version.
        +row
            +cell #[code c]
            +cell
                |  #[strong Model minor version]. Same model structure, but
                |  different parameter values, e.g. from being trained on
                |  different data, for different numbers of iterations, etc.

    p
        |  For a detailed compatibility overview, see the
        |  #[+a(gh("spacy-models", "compatibility.json")) #[code compatibility.json]]
        |  in the models repository. This is also the source of spaCy's internal
        |  compatibility check, performed when you run the
        |  #[+api("cli#download") #[code download]] command.