# nlpinaction/learning-nlp

nlp in action

Repository: https://github.com/nlpinaction/learning-nlp
Canonical: https://ross.abutalabs.com/products/learning-nlp
Language: Python
License Family: other
Topics: python, machine-learning
Last push: 2020-12-10T07:57:12+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3149, "days_push": 2092, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1043, forks 813 (observed 2026-08-28T04:03:20.991351+00:00)

## What it is
Companion code repository for a Chinese-language NLP book aimed at beginners, covering word segmentation, POS tagging, NER, keyword extraction, parsing, text vectorization, sentiment analysis, and ML/DL-based NLP. It provides hands-on Python examples organized by book chapter.

## Use cases
- learn nlp basics with python
- chinese word segmentation examples
- practice named entity recognition
- learn text vectorization techniques
- sentiment analysis tutorial code
- deep learning for nlp examples

## When to choose
- you are a beginner learning NLP fundamentals in Python
- you want practical code exercises accompanying an NLP book
- you focus on Chinese-language NLP tasks

## When to avoid
- you need a production-ready NLP library
- you require maintained software with a license and active releases
- you need English-language NLP resources specifically

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning
- domain: machine-learning, tutorials
- platform: python
- tags: chinese-nlp, book-code, beginner-friendly, text-processing, natural-language-processing

## Member repositories
- nlpinaction/learning-nlp (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.991351+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 2026-08-30T07:02:31.291090+00:00, confidence not recorded.
  - readme: https://github.com/nlpinaction/learning-nlp (fetched 2026-08-28T04:03:20.991351+00:00, sha 6c3fefce5ae4)
- Data as of 2026-08-30T08:39:29.467469+00:00.