What is Transformers used for?

Loading, running, and fine-tuning thousands of pretrained models from the Hugging Face Hub.

Should I use pipeline() or load models manually?

Start with pipeline() — one line gives you sensible pre/post-processing for dozens of tasks. Drop down to AutoModel and AutoTokenizer when you need control: custom batching, device placement, logits access, or serving at scale.

When should I switch from Transformers to a serving library?

For batch or production inference, use vLLM, TGI, or ONNX Runtime — Transformers is built for flexibility, not raw throughput. Reserve it for research, prototyping, and fine-tuning.

GuideIntermediate

Hugging Face Transformers Tutorial

Item: Hugging Face Transformers Tutorial
Rating: 4.7
Author: AI Resource Hub

The official tutorial for Hugging Face Transformers, the industry-standard library for NLP and computer vision. It covers text classification, tokenisation, translation, image segmentation, and more, with code examples for both PyTorch and TensorFlow backends. Aimed at practitioners who want to leverage pre-trained models or fine-tune them on custom datasets for production use.

Overview

"Hugging Face Transformers Tutorial" is a "Guide" resource curated by AI Resource Hub, filed under the Frameworks category and suited to Intermediate-level learners. It is provided by Hugging Face, was last updated on 2026-07-06, and holds an editorial score of 4.7/5 from our team. Click "Visit Resource" on the right to open the original page.