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SPARK NLP
Enterprise NLP at scale on Apache Spark with pre-trained pipelines
Apache-2.0
ABOUT
Processing natural language at enterprise scale typically means stitching together separate libraries for tokenization, NER, sentiment, and embeddings — none of which are designed for distributed execution on large datasets. Spark NLP provides a unified library that runs on Apache Spark clusters, offering 30,000+ pre-trained pipelines and models for tasks like named entity recognition, text classification, question answering, and text generation. It scales from a single node to hundreds of worker nodes without code changes, making it suitable for processing millions of documents in production.
INSTALL
pip install spark-nlpINTEGRATION GUIDE
1. Extract named entities like people, organizations, and locations from millions of documents on a Spark cluster
2. Build text classification pipelines for sentiment analysis, spam detection, or content moderation at scale
3. Deploy question-answering models over enterprise document repositories using distributed inference
4. Create multilingual NLP pipelines that process documents in 200+ languages with pre-trained embeddings
TAGS
nlpsparkbig-datanatural-languageentity-recognitionclassification