spark-nlp

Why we included this project

Teams that already run data pipelines on Apache Spark will find this one of the few NLP libraries built natively for that environment rather than retrofitted onto it. Spark NLP ships more than 100,000 pretrained models and pipelines covering tasks like tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, machine translation, summarization, and question answering, all exposed as annotations that move through Spark's distributed engine. That means you can process very large corpora without pulling data out of your cluster, and since the core library is Apache-licensed, running it in production and fine-tuning models alongside the pretrained defaults is straightforward. If you mostly want quick experiments on a single machine, lighter tools will feel more comfortable; this one earns its keep when throughput and horizontal scaling across many nodes are what you're optimizing for.