//- 💫 DOCS > USAGE > EXAMPLES

include ../_includes/_mixins

+section("information-extraction")
    +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/information_extraction/phrase_matcher.py")

    +h(3, "entity-relations") Extracting entity relations

    p
        |  A simple example of extracting relations between phrases and
        |  entities using spaCy's named entity recognizer and the dependency
        |  parse. Here, we extract money and currency values (entities labelled
        |  as #[code MONEY]) and then check the dependency tree to find the
        |  noun phrase they are referring to – for example: "$9.4 million"
        |  → "Net income".

    +github("spacy", "examples/information_extraction/entity_relations.py")

    +h(3, "subtrees") Navigating the parse tree and subtrees

    p
        |  This example shows how to navigate the parse tree including subtrees
        |  attached to a word.

    +github("spacy", "examples/information_extraction/parse_subtrees.py")

+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")

    +h(3, "multi-processing") Multi-processing with Joblib

    p
        |  This example shows how to use multiple cores to process text using
        |  spaCy and #[+a("https://pythonhosted.org/joblib/") Joblib]. We're
        |  exporting part-of-speech-tagged, true-cased, (very roughly)
        |  sentence-separated text, with each "sentence" on a newline, and
        |  spaces between tokens. Data is loaded from the IMDB movie reviews
        |  dataset and will be loaded automatically via Thinc's built-in dataset
        |  loader.

    +github("spacy", "examples/pipeline/multi_processing.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 Dependency 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, "tagger") Training spaCy's Part-of-speech Tagger

    p
        |  In this example, we're training spaCy's part-of-speech tagger with a
        |  custom tag map, mapping our own tags to the mapping those tags to the
        |  #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme].

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

    +h(3, "intent-parser") Training a custom parser for chat intent semantics

    p
        |  spaCy's parser component can be used to trained to predict any type
        |  of tree structure over your input text. You can also predict trees
        |  over whole documents or chat logs, with connections between the
        |  sentence-roots used to annotate discourse structure. In this example,
        |  we'll build a message parser for a common "chat intent": finding
        |  local businesses. Our message semantics will have the following types
        |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
        |  #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].

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

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

    p
        |  This example shows how to train a multi-label convolutional neural
        |  network text classifier on IMDB movie reviews, using spaCy's new
        |  #[+api("textcategorizer") #[code TextCategorizer]] component. The
        |  dataset will be loaded automatically via Thinc's built-in dataset
        |  loader. Predictions are available via
        |  #[+api("doc#attributes") #[code Doc.cats]].

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

+section("vectors")
    +h(3, "fasttext") Loading pre-trained fastText vectors

    p
        |  This simple snippet is all you need to be able to use the Facebook's
        |  #[+a("https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md") fastText vectors]
        |  (294 languages, pre-trained on Wikipedia) with spaCy.  Once they're
        |  loaded, the vectors will be available via spaCy's built-in
        |  #[code similarity()] methods.

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

    +h(3, "tensorboard") Visualizing spaCy vectors in TensorBoard

    p
        |  These two scripts let you load any spaCy model containing word vectors
        |  into #[+a("https://projector.tensorflow.org/") TensorBoard] to create
        |  an #[+a("https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz") embedding visualization].
        |  The first example uses TensorBoard, the second example TensorBoard's
        |  standalone embedding projector.

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

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

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

    p
        |  This example shows how to use a #[+a("https://keras.io") Keras]
        |  LSTM sentiment classification model in spaCy. spaCy splits
        |  the document into sentences, and each sentence is classified using
        |  the LSTM. The scores for the sentences are then aggregated to give
        |  the document score. This kind of hierarchical model is quite
        |  difficult in "pure" Keras or Tensorflow, but it's very effective.
        |  The Keras example on this dataset performs quite poorly, because it
        |  cuts off the documents so that they're a fixed size. This hurts
        |  review accuracy a lot, because people often summarise their rating
        |  in the final sentence.

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