Omkar2118/datasets huggingface dataset:
CMeEE
Full name: Chinese Medical Named Entity Recognition Dataset

Chinese Medical Named Entity Recognition, a dataset first released in CHIP20204, is used for CMeEE task. Given a pre-defined schema, the task is to identify and extract entities from the given sentence and classify them into nine categories: disease, clinical manifestations, drugs, medical equipment, medical procedures, body, medical examinations, microorganisms, and department.

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
CHIP-STS
Full name: Semantic Textual Similarity Dataset

CHIP Semantic Textual Similarity, a dataset for sentence similarity in the non-i.i.d.
(non-independent and identically distributed) setting, is used for the CHIP-STS task. Specifically, the
task aims to transfer learning between disease types on Chinese disease questions and answer data.
Given question pairs related to 5 different diseases (The disease types in the training and testing set
are different), the task intends to determine whether the semantics of the two sentences are similar.

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
CHIP-CDN
Full name: Clinical Diagnosis Normalization Dataset

CHIP Clinical Diagnosis Normalization, a dataset that aims to standardize the terms
from the final diagnoses of Chinese electronic medical records, is used for the CHIP-CDN task.
Given the original phrase, the task is required to normalize it to standard terminology based on the
International Classification of Diseases (ICD-10) standard for Beijing Clinical Edition v601.

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
CHIP-CTC

CHIP Clinical Trial Classification, a dataset aimed at classifying clinical trials eligibility criteria, which are fundamental guidelines of clinical trials defined to identify whether a subject
meets a clinical trial or not, is used for the CHIP-CTC task. All text data are collected from the
website of the Chinese Clinical Trial Registry (ChiCTR)
, and a total of 44 categories are defined.
The task is like text classification; although it is not a new task, studies and corpus for the Chinese
clinical trial criterion are still limited, and we hope to promote future researches for social benefits.
KUAKE-QIC
Full name: Query Intent Classification Dataset

KUAKE Query Intent Classification, a dataset for intent classification, is used for the
KUAKE-QIC task. Given the queries of search engines, the task requires to classify each of them into
one of 11 medical intent categories defined in KUAKE-QIC, including diagnosis, etiology analysis,
treatment plan, medical advice, test result analysis, disease description, consequence prediction,
precautions, intended effects, treatment fees, and others.

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
KUAKE-QTR
Full name: Query-Title Relevance Dataset

KUAKE Query Title Relevance, a dataset used to estimate the relevance of the title
of a query document, is used for the KUAKE-QTR task. Given a query (e.g., “Symptoms of vitamin
B deficiency”), the task aims to find the relevant title (e.g., “The main manifestations of vitamin B
deficiency”).

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
KUAKE-QQR
Full name: Query-Query Relevance Dataset

KUAKE Query-Query Relevance, a dataset used to evaluate the relevance of the
content expressed in two queries, is used for the KUAKE-QQR task. Similar to KUAKE-QTR, the
task aims to estimate query-query relevance, which is an essential and challenging task in real-world
search engines.

Introducing paper: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark