spaCy

Why we included this project

Most Python teams reach for spaCy when text processing has to work in a real product, not just a research notebook. It ships pretrained pipelines for 70+ languages covering tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and text classification, so you can get useful results without training a model yourself. When the off-the-shelf models aren't enough, its config-driven training system lets you fine-tune components on your own data and package the result for deployment. It also works with transformer models like BERT and has a large plugin ecosystem, so it fits into an existing ML stack instead of forcing you to rebuild around it. If you're building search, document processing, or anything that needs structured linguistic output, this is the library to build on.