Gentle Introduction to Hugging Face Transformers Library

Boost your NLP Skills

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Understanding and leveraging NLP continues to be an incredibly valuable skill for Data Scientists.

And more so now that it is hugely in demand across multiple industries.

But this is 2025, and plotting word clouds won’t get you far, the expectations are much higher!

Transformers, the neural network architecture introduced in the Attention is All You Need paper in 2017, are now the backbone of state-of-the-art NLP solutions.

And the best way to get hands-on experience with Transformers models (such as LLama 3.3 or GPT-2) is by using Hugging Face’s Transformers library.

With it, you’ll have access to pre-trained models that you can deploy locally or fine-tune for NLP tasks like classification, summarization, text generation, and others.

So let’s get started!

Here is what we’ll cover:

1. What is the Hugging Face Transformers library?
2. Method 1: Using the pipeline API
3. Method 2: Using AutoTokenizer and AutoModelForCausalLM
4. Demo: Access to a Google Colab so you can try it out yourself

What is the Hugging Face…