# xiaopangxia/TCM-Ancient-Books

中医药古籍文本，近700项

Repository: https://github.com/xiaopangxia/TCM-Ancient-Books
Canonical: https://ross.abutalabs.com/products/tcm-ancient-books
License Family: other
Last push: 2023-09-27T12:58:32+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": 3109, "days_push": 1071, "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 1401, forks 535 (observed 2026-08-28T04:04:37.245157+00:00)

## What it is
A text corpus of nearly 700 Traditional Chinese Medicine (TCM) ancient books. It provides digitized classical Chinese medical texts for research and study.

## Use cases
- train NLP models on classical Chinese medical texts
- build a searchable database of TCM ancient books
- research historical Chinese medicine terminology
- digitize and preserve traditional medicine literature
- fine-tune language models on TCM classics
- build a reading app for ancient Chinese medical texts

## When to choose
- you need a large corpus of classical TCM texts for NLP or research
- you are building tools for searching or studying traditional Chinese medicine literature

## When to avoid
- you need structured or annotated data rather than raw text
- you need modern (non-classical) Chinese medical content
- you require a maintained library with an explicit license for commercial use

## Facets
- artifact type: dataset
- maturity: maintenance
- function: nlp, data-science
- domain: healthcare, education
- platform: cross-platform
- tags: traditional-chinese-medicine, ancient-books, chinese-text-corpus, text-dataset, natural-language-processing

## Member repositories
- xiaopangxia/TCM-Ancient-Books (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:37.245157+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 2026-08-30T04:39:04.848161+00:00, confidence not recorded.
  - readme: https://github.com/xiaopangxia/TCM-Ancient-Books (fetched 2026-08-28T04:04:37.245157+00:00, sha 8bd39e995729)
- Data as of 2026-08-30T08:39:29.467469+00:00.