//- 💫 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, "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")