Spark NLP

What it is

Natural language processing library built on top of Apache Spark ML to provide simple, performant, and accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment.

Spark NLP stands out as the only NLP library that integrates seamlessly with Apache Spark ML, offering unparalleled scalability and performance for large-scale text data processing.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited direct support for Python users

   While there is a Python wrapper, it might not cover all features available in the Scala API, leading to inconsistencies and limitations.

2. 02

   Performance overhead due to Apache Spark integration

   Apache Spark's distributed computing model can introduce significant performance overhead for smaller datasets or tasks that do not benefit from parallel processing.

3. 03

   Resource-intensive setup and maintenance

   Setting up a Spark cluster requires substantial resources, both in terms of hardware and expertise, which may be prohibitive for small teams or projects.

What it does well

• Distributed processing with Apache Spark ML

• High performance and accuracy in NLP tasks

• Scalability for large datasets

• Pre-trained models available out-of-the-box

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.