spaCy

For production NLP pipelines in Python, spaCy remains one of the standard choices. Built by Explosion and released under the MIT license, it provides fast tokenization, part-of-speech tagging, named entity recognition, dependency parsing, lemmatization, and text classification through a single pipeline API, along with built-in visualizers for syntax trees and entities. Pretrained pipelines are available for more than 70 languages and install as ordinary Python packages, while spaCy's training system supports fine-tuning on custom data, multi-task learning with pretrained transformers such as BERT, and plugging in custom PyTorch or TensorFlow models. The library is implemented with performance in mind, with a Cython core, and runs on macOS, Linux, and Windows. Where research toolkits emphasize flexibility for experimentation, spaCy emphasizes stable APIs, speed, and reproducible training for shipping software, which is why it appears so often in information extraction, document processing, and text preprocessing stages of larger ML systems.