scikit

Code Examples
Full code examples you can modify and run.

Using spaCy's phrase matcher
v2.0 This feature is new and was introduced in spaCy v2.0

This example shows how to use the new PhraseMatcher to efficiently find entities from a large terminology list.

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spacy/examples/information_extraction/phrase_matcher.py

Extracting entity relations

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 MONEY) and then check the dependency tree to find the noun phrase they are referring to – for example: "$9.4 million" → "Net income".

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spacy/examples/information_extraction/entity_relations.py

Navigating the parse tree and subtrees

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

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spacy/examples/information_extraction/parse_subtrees.py

Custom pipeline components and attribute extensions
v2.0 This feature is new and was introduced in spaCy v2.0

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 Doc, Span and Token.

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Custom pipeline components and attribute extensions via a REST API
v2.0 This feature is new and was introduced in spaCy v2.0

This example shows the implementation of a pipeline component that fetches country meta data via the REST Countries API sets entity annotations for countries, merges entities into one token and sets custom attributes on the Doc, Span and Token – for example, the capital, latitude/longitude coordinates and the country flag.

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spacy/examples/pipeline/custom_component_countries_api.py

Custom method extensions
v2.0 This feature is new and was introduced in spaCy v2.0

A collection of snippets showing examples of extensions adding custom methods to the Doc, Token and Span.

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Multi-processing with Joblib

This example shows how to use multiple cores to process text using spaCy and 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.

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Training spaCy's Named Entity Recognizer

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 Language class.

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Training an additional entity type

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.

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Training spaCy's Dependency Parser

This example shows how to update spaCy's dependency parser, starting off with an existing, pre-trained model, or from scratch using a blank Language class.

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Training spaCy's Part-of-speech Tagger

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 Universal Dependencies scheme.

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Training a custom parser for chat intent semantics

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: ROOT, PLACE, QUALITY, ATTRIBUTE, TIME and LOCATION.

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Training spaCy's text classifier
v2.0 This feature is new and was introduced in spaCy v2.0

This example shows how to train a multi-label convolutional neural network text classifier on IMDB movie reviews, using spaCy's new TextCategorizer component. The dataset will be loaded automatically via Thinc's built-in dataset loader. Predictions are available via Doc.cats .

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Loading pre-trained fastText vectors

This simple snippet is all you need to be able to use the Facebook's fastText vectors (294 languages, pre-trained on Wikipedia) with spaCy. Once they're loaded, the vectors will be available via spaCy's built-in similarity() methods.

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Text classification with Keras

This example shows how to use a 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.

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