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Traditional-Chinese-Medicine-Dataset-Pretrain

Verified by Mdexa · 2026-11-14Checked against the source text under public rules.How we check

Commercial use: Yes
Derivatives: Yes
Redistribution: Yes
File on Mdexa: NoLink only

first-hand data-scope statement(s) allow commercial use and redistribution

PermissiveLink onlyApache-2.0Save

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<a href="https://mdexa.com/datasets/traditional-chinese-medicine-dataset-pretrain/"><img src="https://mcp.mdexa.com/v1/badge/datasets/traditional-chinese-medicine-dataset-pretrain.svg" alt="Mdexa verified · Permissive · 2026-11" height="20"></a>

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Cite this pageMdexa, Traditional-Chinese-Medicine-Dataset-Pretrain, https://mdexa.com/datasets/traditional-chinese-medicine-dataset-pretrain/, verified 2026-11-14.

Source riskReal patient textScraped

Key facts

Domain: Traditional Chinese medicine · Classical texts · Case records · Chinese materia medica · Formulas · Acupuncture, moxibustion and tuina · Diagnostic methods · Clinical medicine Traditional medicine · Modern medicine
Content: Clinical cases · Classical texts · Databases · Terminologies and coding systemsTCM pretraining corpus from non-Internet internal sources: ~1GB of clinical cases, classics, encyclopedias and terminology entries, 99% Simplified Chinese; for pretraining only.
Size: —As stated: 约1GB
Language / modality: Chinese / Text
Publisher: SylvanL
Year: —
Expert review: —
Verification: Verified: license text seen

Commercial use of Traditional-Chinese-Medicine-Dataset-Pretrain

Commercial use: Yes. License status: Permissive.

Conditions: Notice kept

Quoted statements and their sources are in Traditional-Chinese-Medicine-Dataset-Pretrain license evidence.

Verdicts record what sources state and are not legal advice.

Tags

Intended use: Pretraining
Quoted from the source注意：该数据集仅适用于预训练或继续预训练用途，针对SFT/IFT的QA数据集详见：SylvanL/Traditional-Chinese-Medicine-Dataset-SFTSnapshot s01
Specialty: Multi-specialty
Quoted from the source包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质内容，涵盖全面，配比均衡。Snapshot s01
Source type: Classical texts
Quoted from the source包含来自688本中医领域常用教材、著作、典籍的146244条书本内容。Snapshot s01
Textbooks and reference books
Quoted from the source包含来自688本中医领域常用教材、著作、典籍的146244条书本内容。Snapshot s01
Built from a knowledge base
Quoted from the source涵盖疾病、症状、医疗美容、药品、中药材、保健品、方剂、药膳食疗、针灸穴位、术语等的百科词条Snapshot s01

Author-statedInferredHuman-confirmed

Traditional-Chinese-Medicine-Dataset-Pretrain license evidence 2

Hugging Face card metadataPage opened

Apache-2.0
Third-party text · Hugging Face card metadatalicense: apache-2.0
Statement found in snapshot s01 (readme)

huggingface.co2026-11-12 · s01
READMEPage opened

Third-party text · README注意：该数据集仅适用于预训练或继续预训练用途，针对SFT/IFT的QA数据集详见：SylvanL/Traditional-Chinese-Medicine-Dataset-SFT
Statement found in snapshot s01 (readme)

huggingface.co2026-11-12 · s01

Source and lineage

No upstream or downstream datasets recorded.

Known issues with Traditional-Chinese-Medicine-Dataset-Pretrain

The 230,000-record EMR-derived portion is assembled by rule-based table joining and the authors describe its quality as only moderate.

Preflight

Not run Runs only on ingested data; says nothing about quality.

Downloading Traditional-Chinese-Medicine-Dataset-Pretrain and API access

No file on Mdexa (see the verdict). Get it from the source and confirm the terms there:

huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain

10 files · 575.2 MB

curl -L --create-dirs -o ".gitattributes" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/.gitattributes"
curl -L --create-dirs -o "CPT_medicalRecord_source1_61486.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_medicalRecord_source1_61486.json"
echo "4e43ac20d8063de20f61773473480fa248525781092d8d02008f5741e8f43f38  CPT_medicalRecord_source1_61486.json" | sha256sum -c
curl -L --create-dirs -o "CPT_medicalRecord_source2_15307.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_medicalRecord_source2_15307.json"
echo "8d1a83f65d0cd27f7727d9c1e793560da87a3d10f6aa50af104fa3ec86efe4f5  CPT_medicalRecord_source2_15307.json" | sha256sum -c
curl -L --create-dirs -o "CPT_medicalRecord_source3_230000.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_medicalRecord_source3_230000.json"
echo "a6a3cf4d696fb3307afdfd0918341d13c49ad83db307c1e3390e5f488feadbf4  CPT_medicalRecord_source3_230000.json" | sha256sum -c
curl -L --create-dirs -o "CPT_medicalRecord_source4_48665.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_medicalRecord_source4_48665.json"
echo "337b89722abb775814aff4fd77698b6a0280115773407ac1a7f86b30c678b8c2  CPT_medicalRecord_source4_48665.json" | sha256sum -c
curl -L --create-dirs -o "CPT_tcmBooks_source1_146244.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_tcmBooks_source1_146244.json"
echo "81714c013a09ffcbc7a32bcdf1b6a9dc22772d0c2485f9b77598c3f8c98c713c  CPT_tcmBooks_source1_146244.json" | sha256sum -c
curl -L --create-dirs -o "CPT_tcmKnowledge_source1_17921.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_tcmKnowledge_source1_17921.json"
echo "5ea806912de0bc24f8b1fc8adb65f219191903938116169b7b2e00f45d001880  CPT_tcmKnowledge_source1_17921.json" | sha256sum -c
curl -L --create-dirs -o "CPT_tcmKnowledge_source2_12889.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/CPT_tcmKnowledge_source2_12889.json"
echo "fd88c8db6598e888319a6c79c3374b926420b64a359f708ad6a9005cb5b6051d  CPT_tcmKnowledge_source2_12889.json" | sha256sum -c
curl -L --create-dirs -o "README.md" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/README.md"
curl -L --create-dirs -o "dataset_info.json" "https://huggingface.co/datasets/SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain/resolve/fd01649d8e019ed2b002119e0fd74076c5f7b73d/dataset_info.json"

Citing Traditional-Chinese-Medicine-Dataset-Pretrain

Mdexa, Traditional-Chinese-Medicine-Dataset-Pretrain, https://mdexa.com/datasets/traditional-chinese-medicine-dataset-pretrain/, verified 2026-11-14.

More citation details

The source gives no citation text; the line above cites this card.

Machine-readable

traditional-chinese-medicine-dataset-pretrain.jsontraditional-chinese-medicine-dataset-pretrain.mdcatalog.json

Change log 2

2026-11-14	Re-adjudicated under the current rules; all machine checks still pass.
2026-11-14	Added via the automated pipeline and passed all checks (gates, verbatim quotes, rule engine, two independent extractions).

Listed in

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LicensePermissiveRestrictedConflictUnstatedUnverifiable

MdexaHostedEligibleLink onlyPending

VerdictYesNoConditionalUnclear

Definitions

Permissive: Every statement agrees and allows commercial use and redistribution.
Restricted: A statement forbids commercial use or adaptation, limits use to research, or the rights belong to a third party or require payment.
Conflict: Statements in different places contradict each other.
Unstated: No data license statement was found.
Unverifiable: The page could not be opened, or only second-hand information was found.
Hosted: Mdexa serves the files. Needs a permissive license, no risk flags and a human sign-off.
Eligible: Permissive with no risk flags, waiting for a human sign-off.
Link only: Indexed and linked to the source: the license is restricted, or permissive with a risk flag.
Pending: License in conflict, unstated or unverifiable; linked to the source until resolved.
Exam-derived: Questions come from copyrighted examinations.
Scraped: Content was collected from websites.
Real patient text: Derived from real patient text; privacy authorisation needs checking.
Model-generated: Generated by a model; the model's output terms need checking.
Derived: Processed from other datasets, whose terms also apply.
Unknown provenance: The source is not stated, so its copyright cannot be judged.

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