Hugging Face Transformers Pipeline Tokenizer Models Explained Step by Step

In this video, we explore the Hugging Face ecosystem and understand how to use Transformers, pipelines, tokenizers, and pre-trained models step by step. This is a very important video if you want to build real world NLP and Generative AI applications. Here is the GitHub repo link https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. Hugging Face Website https://huggingface.co Hugging Face Ecosystem Models Pre-trained models for NLP, computer vision, and audio tasks Datasets Large collection of ready to use datasets Spaces Deploy and share ML apps Inference API Run models without local setup Libraries Transformers Datasets Gradio Accelerate Bitsandbytes PEFT TRL Transformers Library Install and import pip install transformers This library provides easy access to thousands of pre-trained models and utilities. Pipeline Pipeline is a high level API that hides all intermediate steps such as preprocessing, tokenization, padding, model inference, and post processing. Flow Text → Preprocessing → Tokenization → Padding → Model → Output Label Example Sentiment Analysis You can directly use a pre-trained model sentiment analysis pipeline It can classify text like This is good product This is bad product Batch predictions are also supported You can pass multiple sentences at once and get predictions easily Text Generation and Summarization You can generate or summarize long text using models like T5 This allows you to convert long paragraphs into shorter meaningful summaries Zero Shot Classification You can classify text into categories without training Example Input my credit card is not working Labels loan, credit card, services, others The model will automatically pick the most relevant category AutoTokenizer Tokenizer converts text into the format expected by the model Example Input text is converted into input ids Each token gets a unique id It also generates attention ma