Demos and visualizations aren't just eye candy – they're an essential part of explaining and exploring AI technologies, especially during development. A good visualisation lets you understand your model's behaviour and catch obvious problems early. Our demos include visualisations for spaCy's depency trees, entity recognition and similarity models, along with a word-sense explorer trained on Reddit comments.

displaCy Dependency Visualizer

Visualise spaCy's guess at the syntactic structure of a sentence. Arrows point from children to heads, and are labelled by their relation type.

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displaCy Named Entity Visualizer

Visualise spaCy's guess at the named entities in the document. You can filter the displayed types, to only show the annotations you're interested in.

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Rule-based Matcher Explorer

Test spaCy's rule-based Matcher by creating token patterns interactively and running them over your text. Explore how spaCy processes your text – and why your pattern matches, or doesn't.

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sense2vec: Semantic Analysis of the Reddit Hivemind

Our neural network read every comment posted to Reddit in 2015, and built a semantic map using word2vec and spaCy.

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GraphQL queries for spaCy

A simple and experimental app that lets you query spaCy's linguistic annotations using GraphQL, a powerful, strongly typed API query language

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Whether you're working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster.

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