SpaCy

Unlike NLTK, which is widely used for teaching and research, spaCy focuses on providing software for production usage. spaCy also supports deep learning workflows that allow connecting statistical models trained by popular machine learning libraries like TensorFlow, PyTorch or MXNet through its own machine learning library Thinc. Using Thinc as its backend, spaCy features convolutional neural network models for part-of-speech tagging, dependency parsing, text categorization and named entity recognition (NER). Prebuilt statistical neural network models to perform these tasks are available for 23 languages, including English, Portuguese, Spanish, Russian and Chinese, and there is also a multi-language NER model. Additional support for tokenization for more than 65 languages allows users to train custom models on their own datasets as well.

History

• 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.