The Top 10 NLP Frameworks Available

How to select the best NLP framework for your data science and artificial intelligence (AI) project.

What is NLP and Why is it Important?

Natural Language Processing (NLP) is an incredibly popular subfield of machine learning, which combines computer science, artificial intelligence (AI) and linguistics. NLP analyzes large amounts of natural language data using a collection of methods and algorithms aimed to understand human languages. Through these techniques, language rules including semantics and syntax are learned and analyzed to extract meaning. Common NLP applications in the present world include Google Translate, automated telephone calls, automated word processors such as Grammarly and mobile predictive text, and personal assistant applications such as Siri (Apple) and Alexa (Amazon). Other examples of applications that leverage NLP can be seen in TAUS Matching Data and the cleaning library for TAUS Data Marketplace.

Top 10 NLP Frameworks

  1. Tensorflow

Keras vs Tensorflow vs PyTorch

If you are looking to get your feet wet with NLP in a framework, Keras, Tensorflow, and PyTorch are perhaps the three most popular options. The chart below shows a comparison of the three frameworks for your given application. If you are looking to train a highly complex model with a large volume of data, Tensorflow or PyTorch may be good fits. If you have a smaller dataset with a simple model, Keras would be a better option.

keras, python, data, tensorflow, nlp, pytorch
keras, python, data, tensorflow, nlp, pytorch


As the field of NLP continues to grow and improve, technologies continue to advance in parallel. Tensorflow, Keras, and PyTorch are the most widely used general deep learning frameworks that provide NLP support. Frameworks built specific to NLP include SpaCy, CoreNLP, and XLM/mBERT. Depending on the application, the above ten frameworks are great to explore when beginning development of an NLP-based project.

Gateway to the World of Language Data