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//- 💫 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, "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")