spaCy/website/usage/examples.jade
2017-10-10 04:26:06 +02:00

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//- 💫 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, "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, "ner-standalone") Training an NER system from scratch
p
| This example is written to be self-contained and reasonably
| transparent. To achieve that, it duplicates some of spaCy's internal
| functionality.
+github("spacy", "examples/training/train_ner_standalone.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")