# king-yyf/CMeKG_tools

Repository: https://github.com/king-yyf/CMeKG_tools
Canonical: https://ross.abutalabs.com/products/cmekg_tools
Language: Python
License: MIT
License Family: permissive
Last push: 2023-11-03T03:18:58+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": 1779, "days_push": 1034, "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 1493, forks 497 (observed 2026-08-28T04:04:52.782698+00:00)

## What it is
A collection of pretrained models and Python code for the Chinese Medical Knowledge Graph (CMeKG), providing medical text segmentation, medical named entity recognition, and medical relation extraction. Models are BERT-based and distributed via Baidu Netdisk with training and inference scripts included.

## Use cases
- extract medical entities from Chinese clinical text
- extract disease-symptom relation triples from Chinese medical text
- segment Chinese medical text
- train a medical NER model
- build a Chinese medical knowledge graph
- identify drugs and diseases in Chinese sentences

## When to choose
- you need Chinese-language medical NER, relation extraction, or word segmentation with pretrained BERT models
- you are building on or extending the CMeKG medical knowledge graph
- you want trainable PyTorch/transformers pipelines for medical Chinese NLP

## When to avoid
- you need English or multilingual medical NLP
- you need a production-ready service with an API rather than scripts and model files
- you cannot download large models from Baidu Netdisk
- you need actively maintained code with recent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, parser
- domain: healthcare, artificial-intelligence
- platform: python
- tags: chinese-medical-nlp, knowledge-graph, named-entity-recognition, relation-extraction, word-segmentation, bert, medical-text-mining, natural-language-processing

## Member repositories
- king-yyf/CMeKG_tools (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.782698+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 2026-08-30T04:33:27.531589+00:00, confidence not recorded.
  - readme: https://github.com/king-yyf/CMeKG_tools (fetched 2026-08-28T04:04:52.782698+00:00, sha b280d0fcdd46)
- Data as of 2026-08-30T08:39:29.467469+00:00.