# practical-nlp/practical-nlp-code

Official Repository for Code associated with 'Practical Natural Language Processing' book by O'Reilly Media

Repository: https://github.com/practical-nlp/practical-nlp-code
Canonical: https://ross.abutalabs.com/products/practical-nlp-code
Homepage: http://www.practicalnlp.ai/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: natural-language-processing, natural-language-understanding, oreilly-books
Last push: 2023-09-29T18:16:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2325, "days_push": 1069, "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 1461, forks 642 (observed 2026-08-28T04:04:47.542516+00:00)

## What it is
The official companion code repository for the O'Reilly book 'Practical Natural Language Processing', containing Jupyter notebooks organized chapter-by-chapter covering NLP pipelines, text representation, and text classification. It is a learning resource for practitioners building real-world NLP systems rather than a reusable software library.

## Use cases
- learn practical nlp from a book with runnable code
- jupyter notebooks for text classification examples
- understand how to build an nlp pipeline end to end
- study text representation techniques like embeddings and tf-idf
- course material for teaching applied natural language processing
- examples of adapting nlp to domains like healthcare and fake news detection

## When to choose
- you are reading the Practical NLP book and want to follow along with code
- you want hands-on notebooks for learning applied NLP concepts
- you are an educator looking for structured NLP course material

## When to avoid
- you need a production-ready NLP library to import into your project
- you need up-to-date code on modern TensorFlow or recent Python versions without refactoring
- you want a maintained tool rather than book companion notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, data-science
- domain: machine-learning, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, book-companion, oreilly, text-classification, text-representation, nlp-pipeline, natural-language-processing, linux

## Member repositories
- practical-nlp/practical-nlp-code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:47.542516+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 2026-08-30T04:35:21.896663+00:00, confidence not recorded.
  - readme: https://github.com/practical-nlp/practical-nlp-code (fetched 2026-08-28T04:04:47.542516+00:00, sha 9479b83d28dd)
  - homepage: http://www.practicalnlp.ai/ (fetched 2026-08-29T11:44:00.903094+00:00, sha 49cc73d25419)
- Data as of 2026-08-30T08:39:29.467469+00:00.