{"adoption": {"forks": 402, "observed_at": "2026-08-28T04:07:05.176485+00:00", "stars": 2623}, "canonical_url": "https://ross.abutalabs.com/products/awesome_chinese_medical_nlp", "card": {"archived": false, "artifact_type": "learning-resource", "description": "中文医学NLP公开资源整理：术语集/语料库/词向量/预训练模型/知识图谱/命名实体识别/QA/信息抽取/模型/论文/etc", "domain": ["healthcare", "machine-learning", "awesome-lists"], "enriched": true, "function": ["nlp", "machine-learning", "search-engine", "parser"], "health_score": 20, "homepage": null, "language": null, "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["GanjinZero/awesome_Chinese_medical_NLP"], "name": "GanjinZero/awesome_Chinese_medical_NLP", "platform": ["cross-platform"], "pushed_at": "2024-01-17T06:47:55+00:00", "repo": "GanjinZero/awesome_Chinese_medical_NLP", "stars": 2623, "tags": ["chinese", "medical-nlp", "awesome-list", "datasets", "knowledge-graph", "pretrained-models", "resource-curation", "natural-language-processing"], "topics": ["nlp", "medical", "resource", "dictionary", "dataset", "model", "knowledge-graph"], "urls": [], "use_cases": ["find Chinese medical NLP datasets", "locate pretrained Chinese biomedical BERT models", "discover medical knowledge graphs in Chinese", "find corpora for Chinese clinical text mining", "research Chinese medical named entity recognition", "find benchmarks for Chinese medical language understanding"], "what_it_is": "A curated awesome-list of public Chinese medical NLP resources, including terminology sets, corpora, word vectors, pretrained models, knowledge graphs, and papers. It aggregates links to datasets, benchmarks like CBLUE, and tools for tasks such as NER, QA, and information extraction.", "when_to_avoid": ["you need ready-to-use software rather than a link collection", "you work with non-Chinese medical NLP", "you need guaranteed maintenance or licensing of the listed resources"], "when_to_choose": ["you need a starting point for Chinese medical NLP research", "you are looking for Chinese medical corpora or terminology resources", "you want to survey available Chinese biomedical pretrained models and knowledge graphs"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/awesome_chinese_medical_nlp", "repo": "GanjinZero/awesome_Chinese_medical_NLP", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:05.176485+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:19:53.773252+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "8f6fdd5379a29258445af23d3840845010e4bd1209ad7acb1d423367a977708d", "fetched_at": "2026-08-28T04:07:05.176485+00:00", "kind": "readme", "missing": false, "url": "https://github.com/GanjinZero/awesome_Chinese_medical_NLP"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2499, "days_push": 959, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}