Implementing Transformers in NLP Under 5 Lines Of Codes

This article was published as a part of the Data Science Blogathon


Today, we will see a gentle introduction to the transformers library for executing state-of-the-art models for complex NLP tasks.

Applying state-of-the-art Natural Language Processing models has never been more straightforward. Hugging Face has revealed a compelling library called transformers that allow us to perform and use a broad class of state-of-the-art NLP models in a specific way.

natural language processing with transformers

Today we are operating to install and use the transformers library for diverse tasks such as:

  • Text Classification
  • Question-Answering
  • Masked Language Modeling
  • Text Generation
  • Named Entity Recognition
  • Text Summarization

So before we start evaluating each of the implementations for the varying tasks, let’s fix the transformers library. In my case, I am operating on macOS; when attempting to install instantly with pip, I got an error which I did by previously connecting the Rust compiler as follows:

$ curl --proto '=https' --tlsv1.2




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