mirror of https://github.com/explosion/spaCy.git
182 lines
7.5 KiB
Plaintext
182 lines
7.5 KiB
Plaintext
//- 💫 DOCS > USAGE > EXAMPLES
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include ../_includes/_mixins
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+section("information-extraction")
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+h(3, "phrase-matcher") Using spaCy's phrase matcher
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+tag-new(2)
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p
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| This example shows how to use the new
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| #[+api("phrasematcher") #[code PhraseMatcher]] to efficiently find
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| entities from a large terminology list.
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+github("spacy", "examples/information_extraction/phrase_matcher.py")
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+h(3, "entity-relations") Extracting entity relations
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p
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| A simple example of extracting relations between phrases and
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| entities using spaCy's named entity recognizer and the dependency
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| parse. Here, we extract money and currency values (entities labelled
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| as #[code MONEY]) and then check the dependency tree to find the
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| noun phrase they are referring to – for example: "$9.4 million"
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| → "Net income".
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+github("spacy", "examples/information_extraction/entity_relations.py")
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+h(3, "subtrees") Navigating the parse tree and subtrees
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p
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| This example shows how to navigate the parse tree including subtrees
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| attached to a word.
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+github("spacy", "examples/information_extraction/parse_subtrees.py")
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+section("pipeline")
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+h(3, "custom-components-entities") Custom pipeline components and attribute extensions
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+tag-new(2)
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p
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| This example shows the implementation of a pipeline component
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| that sets entity annotations based on a list of single or
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| multiple-word company names, merges entities into one token and
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| sets custom attributes on the #[code Doc], #[code Span] and
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| #[code Token].
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+github("spacy", "examples/pipeline/custom_component_entities.py")
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+h(3, "custom-components-api")
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| Custom pipeline components and attribute extensions via a REST API
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+tag-new(2)
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p
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| This example shows the implementation of a pipeline component
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| that fetches country meta data via the
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| #[+a("https://restcountries.eu") REST Countries API] sets entity
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| annotations for countries, merges entities into one token and
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| sets custom attributes on the #[code Doc], #[code Span] and
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| #[code Token] – for example, the capital, latitude/longitude
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| coordinates and the country flag.
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+github("spacy", "examples/pipeline/custom_component_countries_api.py")
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+h(3, "custom-components-attr-methods") Custom method extensions
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+tag-new(2)
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p
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| A collection of snippets showing examples of extensions adding
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| custom methods to the #[code Doc], #[code Token] and
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| #[code Span].
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+github("spacy", "examples/pipeline/custom_attr_methods.py")
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+h(3, "multi-processing") Multi-processing with Joblib
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p
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| This example shows how to use multiple cores to process text using
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| spaCy and #[+a("https://pythonhosted.org/joblib/") Joblib]. We're
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| exporting part-of-speech-tagged, true-cased, (very roughly)
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| sentence-separated text, with each "sentence" on a newline, and
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| spaces between tokens. Data is loaded from the IMDB movie reviews
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| dataset and will be loaded automatically via Thinc's built-in dataset
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| loader.
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+github("spacy", "examples/pipeline/multi_processing.py")
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+section("training")
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+h(3, "training-ner") Training spaCy's Named Entity Recognizer
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p
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| This example shows how to update spaCy's entity recognizer
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| with your own examples, starting off with an existing, pre-trained
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| model, or from scratch using a blank #[code Language] class.
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+github("spacy", "examples/training/train_ner.py")
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+h(3, "new-entity-type") Training an additional entity type
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p
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| This script shows how to add a new entity type to an existing
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| pre-trained NER model. To keep the example short and simple, only
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| four sentences are provided as examples. In practice, you'll need
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| many more — a few hundred would be a good start.
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+github("spacy", "examples/training/train_new_entity_type.py")
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+h(3, "parser") Training spaCy's Dependency Parser
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p
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| This example shows how to update spaCy's dependency parser,
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| starting off with an existing, pre-trained model, or from scratch
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| using a blank #[code Language] class.
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+github("spacy", "examples/training/train_parser.py")
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+h(3, "tagger") Training spaCy's Part-of-speech Tagger
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p
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| In this example, we're training spaCy's part-of-speech tagger with a
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| custom tag map, mapping our own tags to the mapping those tags to the
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| #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme].
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+github("spacy", "examples/training/train_tagger.py")
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+h(3, "intent-parser") Training a custom parser for chat intent semantics
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p
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| spaCy's parser component can be used to trained to predict any type
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| of tree structure over your input text. You can also predict trees
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| over whole documents or chat logs, with connections between the
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| sentence-roots used to annotate discourse structure. In this example,
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| we'll build a message parser for a common "chat intent": finding
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| local businesses. Our message semantics will have the following types
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| of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
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| #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].
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+github("spacy", "examples/training/train_intent_parser.py")
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+h(3, "textcat") Training spaCy's text classifier
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+tag-new(2)
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p
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| This example shows how to train a multi-label convolutional neural
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| network text classifier on IMDB movie reviews, using spaCy's new
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| #[+api("textcategorizer") #[code TextCategorizer]] component. The
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| dataset will be loaded automatically via Thinc's built-in dataset
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| loader. Predictions are available via
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| #[+api("doc#attributes") #[code Doc.cats]].
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+github("spacy", "examples/training/train_textcat.py")
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+section("vectors")
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+h(3, "tensorboard") Visualizing spaCy vectors in TensorBoard
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p
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| These two scripts let you load any spaCy model containing word vectors
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| into #[+a("https://projector.tensorflow.org/") TensorBoard] to create
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| an #[+a("https://www.tensorflow.org/versions/r1.1/get_started/embedding_viz") embedding visualization].
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| The first example uses TensorBoard, the second example TensorBoard's
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| standalone embedding projector.
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+github("spacy", "examples/vectors_tensorboard.py")
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+github("spacy", "examples/vectors_tensorboard_standalone.py")
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+section("deep-learning")
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+h(3, "keras") Text classification with Keras
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p
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| This example shows how to use a #[+a("https://keras.io") Keras]
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| LSTM sentiment classification model in spaCy. spaCy splits
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| the document into sentences, and each sentence is classified using
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| the LSTM. The scores for the sentences are then aggregated to give
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| the document score. This kind of hierarchical model is quite
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| difficult in "pure" Keras or Tensorflow, but it's very effective.
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| The Keras example on this dataset performs quite poorly, because it
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| cuts off the documents so that they're a fixed size. This hurts
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| review accuracy a lot, because people often summarise their rating
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| in the final sentence.
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+github("spacy", "examples/deep_learning_keras.py")
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