# nlp-with-transformers/notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

Repository: https://github.com/nlp-with-transformers/notebooks
Canonical: https://ross.abutalabs.com/products/nlp-with-transformers-notebooks
Homepage: https://transformersbook.com/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2026-05-29T12:54:09+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 84, release rhythm 35, longevity 100
- inputs: {"age_days": 1770, "days_push": 96, "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 4800, forks 1477 (observed 2026-08-28T04:09:00.181439+00:00)

## What it is
A collection of Jupyter notebooks containing the example code from the O'Reilly book 'Natural Language Processing with Transformers'. The notebooks cover transformer-based NLP tasks and are designed to run on GPU-enabled cloud platforms like Colab or locally.

## Use cases
- learn how to fine-tune transformer models for NLP tasks
- follow along with the NLP with Transformers book exercises
- get runnable examples of Hugging Face Transformers in PyTorch
- study named entity recognition, question answering, and text classification with transformers
- practice NLP on Google Colab with GPU support

## When to choose
- you are reading the book and want the accompanying code
- you want hands-on, chapter-by-chapter transformer tutorials
- you prefer PyTorch-based examples with cloud notebook support

## When to avoid
- you need the TensorFlow code paths, which are no longer tested
- you want a maintained question-answering chapter, which relies on deprecated Haystack 0.9/1.4 and Elasticsearch 7.x
- you need production-ready code rather than educational examples

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, llm-training, rag
- domain: deep-learning, tutorials, large-language-models
- platform: python, cross-platform
- tags: jupyter-notebooks, huggingface-transformers, pytorch, book-examples, education, natural-language-processing, gpu

## Member repositories
- nlp-with-transformers/notebooks (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.181439+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 2026-08-29T18:18:38.083646+00:00, confidence not recorded.
  - readme: https://github.com/nlp-with-transformers/notebooks (fetched 2026-08-28T04:09:00.181439+00:00, sha 177eb6496713)
- Data as of 2026-08-30T08:39:29.467469+00:00.