# duoergun0729/nlp

兜哥出品 <一本开源的NLP入门书籍>

Repository: https://github.com/duoergun0729/nlp
Canonical: https://ross.abutalabs.com/products/nlp
Language: Python
License Family: other
Topics: nlp, ai, security, word2vec, fasttext
Last push: 2025-01-06T03:32:49+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": 3075, "days_push": 604, "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 2492, forks 561 (observed 2026-08-28T04:06:56.442460+00:00)

## What it is
An open-source introductory book on NLP written in Chinese, hosted on GitHub and covering fundamentals like TF-IDF, Word2Vec, fastText, topic modeling, and text classification. It emphasizes NLP applications in security, such as spam filtering and abusive content detection.

## Use cases
- learn nlp from scratch
- train word2vec embeddings
- classify documents with fasttext
- detect spam and abusive comments with machine learning
- extract keywords with textrank and tfidf
- do sentiment analysis on reviews
- learn chinese text processing with jieba

## When to choose
- you want a free, beginner-friendly NLP book with a security focus
- you prefer learning through runnable Python examples
- you need Chinese-language NLP tutorials

## When to avoid
- you need a production NLP library or tool
- you want comprehensive deep-learning NLP coverage like transformers
- you require a formally licensed open-source codebase for commercial use

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, documentation
- domain: machine-learning, security, tutorials
- platform: python
- tags: open-book, word2vec, fasttext, chinese-nlp, text-classification, security-applications, natural-language-processing

## Member repositories
- duoergun0729/nlp (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.442460+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 2026-08-30T02:27:45.600075+00:00, confidence not recorded.
  - readme: https://github.com/duoergun0729/nlp (fetched 2026-08-28T04:06:56.442460+00:00, sha 348b9dde332a)
- Data as of 2026-08-30T08:39:29.467469+00:00.