# NielsRogge/Transformers-Tutorials

This repository contains demos I made with the Transformers library by HuggingFace.

Repository: https://github.com/NielsRogge/Transformers-Tutorials
Canonical: https://ross.abutalabs.com/products/transformers-tutorials
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: transformers, pytorch, bert, vision-transformer, layoutlm, gpt-2
Last push: 2026-04-20T12:36:38+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 35, longevity 100
- inputs: {"age_days": 2193, "days_push": 135, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11748, forks 1736 (observed 2026-08-28T04:10:49.647715+00:00)

## What it is
A collection of Jupyter notebook tutorials demonstrating how to use the HuggingFace Transformers library with PyTorch. It covers fine-tuning and inference for many transformer models across NLP, vision, and audio tasks.

## Use cases
- learn how to fine-tune BERT for named entity recognition
- tutorial for multi-label text classification with transformers
- run inference with vision transformer models
- fine-tune GPT-2 on custom text
- understand HuggingFace transformers library with examples
- classify audio with the audio spectrogram transformer
- learn document understanding with LayoutLM

## When to choose
- you want hands-on notebook examples for HuggingFace Transformers models
- you need reference code for fine-tuning transformer models in PyTorch
- you are learning NLP, vision, or audio tasks with pretrained transformers

## When to avoid
- you need a production-ready library rather than educational notebooks
- you use TensorFlow instead of PyTorch
- you want a structured course rather than standalone demos

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, computer-vision, transformers
- domain: deep-learning, computer-vision, tutorials, large-language-models
- platform: python, jvm
- tags: huggingface, jupyter-notebooks, pytorch, transformers, tutorials, fine-tuning, bert, gpt-2, vision-transformer, layoutlm, natural-language-processing

## Member repositories
- NielsRogge/Transformers-Tutorials (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:49.647715+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:15:21.325304+00:00, confidence not recorded.
  - readme: https://github.com/NielsRogge/Transformers-Tutorials (fetched 2026-08-28T04:10:49.647715+00:00, sha 745d4143ba9c)
- Data as of 2026-08-30T08:39:29.467469+00:00.