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Most Widely Used in
the Enterprise
the Enterprise
Widely deployed production-grade codebase.
New releases every 2 weeks since 2017.
Growing community.
State of the Art Python NLP
John Snow Labs' NLP is an open source text processing library for Python, Java, and Scala. It provides production-grade, scalable, and trainable versions of the latest research in natural language processing.
State of the Art
Accuracy
Accuracy
First production-grade versions of novel deep learning NLP research.
Use pre-trained models to train to fit your data.
Unmatched
Speed & Scale
Speed & Scale
Spark NLP was 80x faster than spaCy to train locally on 2.6MB of data.
Scale to a Spark cluster with zero code changes.
The most widely used NLP library in the Enterprise, by far
Gradient Flow NLP Survey, 2021
Why JohnSnowLab`s Natural Language Processing?
Accuracy
Spark NLP delivered the best performing accuracy on multiple public academic benchmarks.
To the left are F1 scores for the Named Entity Recognition task on the CoNLL 2003 dataset.
Scalability
Zero code changes are needed to scale a pipeline to any spark cluster.
Speed
Optimized builds for the latest chips from Intel, (CPU) Nvidia (GPU), Apple (M1/M2), and AWS (Graviton) enable the fastest training & inference of state-of-the-art models.
This benchmark compares the speed of image transformers inference on the 34k ImageNet dataset on a single machine. Spark NLP is 34% faster than Hugging Face when running on a single CPU, and 51% faster than Hugging Face on a single GPU.
Out Of The Box Functionality
Trainable to understand your language
Spark NLP is optimized for training domain-specific NLP models, so you can adapt it to learn the nuances of jargon and documents you must support.
Speed
Optimized builds for the latest chips from Intel, (CPU) Nvidia (GPU), Apple (M1/M2), and AWS (Graviton) enable the fastest training & inference of state-of-the-art models.
This benchmark compares the speed of image transformers inference on the 34k ImageNet dataset on a single machine. Spark NLP is 34% faster than Hugging Face when running on a single CPU, and 51% faster than Hugging Face on a single GPU.
