SpaCy

• Version 1.0 was released on October 19, 2016, and included preliminary support for deep learning workflows by supporting custom processing pipelines. It further included a rule matcher that supported entity annotations, and an officially documented training API. • Version 2.0 was released on November 7, 2017, and introduced convolutional neural network models for 7 different languages. It also supported custom processing pipeline components and extension attributes, and featured a built-in trainable text classification component. • Version 3.0 was released on February 1, 2021, and introduced state-of-the-art transformer-based pipelines. It also introduced a new configuration system and training workflow, as well as type hints and project templates. This version dropped support for Python 2. == Main features ==

• Non-destructive tokenization • "Alpha tokenization" support for over 65 languages • Built-in support for trainable pipeline components such as Named entity recognition, Part-of-speech tagging, dependency parsing, Text classification, Entity Linking and more • Statistical models for 19 languages • Multi-task learning with pretrained transformers like BERT • Support for custom models in PyTorch, TensorFlow and other frameworks • State-of-the-art speed and accuracy • Production-ready training system • Built-in visualizers for syntax and named entities • Easy model packaging, deployment and workflow management == Extensions and visualizers ==

visualization generated with the displaCy visualizer spaCy comes with several extensions and visualizations that are available as free, open-source libraries: • : A machine learning library optimized for CPU usage and deep learning with text input. • : A library for computing word similarities, based on Word2vec. • : An open-source dependency parse tree visualizer built with JavaScript, CSS and SVG. • : An open-source named entity visualizer built with JavaScript and CSS. == References ==