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State of the Art Natural Language Understanding in Python

This talk introduces Spark NLP: the most widely used NLP library in the enterprise, thanks to implementing production-grade, trainable, and scalable versions of state-of-the-art deep learning & transfer learning NLP research, as a permissive open-source library in Python, Java, and Scala – backed by a highly active community and team. Spark NLP library implements core NLP algorithms including lemmatization, part of speech tagging, dependency parsing, named entity recognition, spell checking, multi-class and multi-label text classification, sentiment analysis, emotion detection, unsupervised keyword extraction, and state-of-the-art Transformers such as BERT, ELMO, ALBERT, XLNet, and Universal Sentence Encoder.

The latest release of Spark NLP 2.6.0 comes with over 330+ pretrained models, pipelines, and Transformers in 46 languages.

The talk will demonstrate using these features to solve common NLP use cases, scale computationally expensive Transformers such as BERT, and train state-of-the-art models with a few lines of code using Spark NLP in Python.

How To Train BERT 15x Faster

While state-of-the-art NLP models are very powerful, they also require massive computational resources to train. Access to GPUs is increasingly necessary for...