//- 💫 DOCS > USAGE > EXAMPLES

include ../_includes/_mixins

+section("pipeline")
    +h(3, "custom-components-entities") Custom pipeline components and attribute extensions
        +tag-new(2)

    p
        |  This example shows the implementation of a pipeline component
        |  that sets entity annotations based on a list of single or
        |  multiple-word company names, merges entities into one token and
        |  sets custom attributes on the #[code Doc], #[code Span] and
        |  #[code Token].

    +github("spacy", "examples/pipeline/custom_component_entities.py")

    +h(3, "custom-components-api")
        |  Custom pipeline components and attribute extensions via a REST API
        +tag-new(2)

    p
        |  This example shows the implementation of a pipeline component
        |  that fetches country meta data via the
        |  #[+a("https://restcountries.eu") REST Countries API] sets entity
        |  annotations for countries, merges entities into one token and
        |  sets custom attributes on the #[code Doc], #[code Span] and
        |  #[code Token] – for example, the capital, latitude/longitude
        |  coordinates and the country flag.

    +github("spacy", "examples/pipeline/custom_component_countries_api.py")

    +h(3, "custom-components-attr-methods") Custom method extensions
        +tag-new(2)

    p
        |  A collection of snippets showing examples of extensions adding
        |  custom methods to the #[code Doc], #[code Token] and
        |  #[code Span].

    +github("spacy", "examples/pipeline/custom_attr_methods.py")

+section("matching")
    +h(3, "matcher") Using spaCy's rule-based matcher

    p
        |  This example shows how to use spaCy's rule-based
        |  #[+api("matcher") #[code Matcher]] to find and label entities across
        |  documents.

    +github("spacy", "examples/matcher_example.py")

    +h(3, "phrase-matcher") Using spaCy's phrase matcher
        +tag-new(2)

    p
        |  This example shows how to use the new
        |  #[+api("phrasematcher") #[code PhraseMatcher]] to efficiently find
        |  entities from a large terminology list.

    +github("spacy", "examples/phrase_matcher.py")

+section("training")
    +h(3, "training-ner") Training spaCy's Named Entity Recognizer

    p
        |  This example shows how to update spaCy's entity recognizer
        |  with your own examples, starting off with an existing, pre-trained
        |  model, or from scratch using a blank #[code Language] class.

    +github("spacy", "examples/training/train_ner.py")

    +h(3, "new-entity-type") Training an additional entity type

    p
        |  This script shows how to add a new entity type to an existing
        |  pre-trained NER model. To keep the example short and simple, only
        |  four sentences are provided as examples. In practice, you'll need
        |  many more — a few hundred would be a good start.

    +github("spacy", "examples/training/train_new_entity_type.py")

    +h(3, "parser") Training spaCy's parser

    p
        |  This example shows how to update spaCy's dependency parser,
        |  starting off with an existing, pre-trained model, or from scratch
        |  using a blank #[code Language] class.

    +github("spacy", "examples/training/train_parser.py")

    +h(3, "textcat") Training spaCy's text classifier
        +tag-new(2)

    p
        |  This example shows how to use and train spaCy's new
        |  #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
        |  on IMDB movie reviews.

    +github("spacy", "examples/training/train_textcat.py")

+section("deep-learning")
    +h(3, "keras") Text classification with Keras

    p
        |  In this example, we're using spaCy to pre-process text for use with
        |  a #[+a("https://keras.io") Keras] text classification model.

    +github("spacy", "examples/deep_learning_keras.py")

    +h(3, "keras-parikh-entailment") A decomposable attention model for Natural Language Inference

    p
        |  This example contains an implementation of the entailment prediction
        |  model described by #[+a("https://arxiv.org/pdf/1606.01933.pdf") Parikh et al. (2016)].
        |  The model is notable for its competitive performance with very few
        |  parameters, and was implemented using #[+a("https://keras.io") Keras]
        |  and spaCy.

    +github("spacy", "examples/keras_parikh_entailment/__main__.py", "examples/keras_parikh_entailment")