README.md

metadata

task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
config_names:
  - corpus
tags:
  - text-retrieval
dataset_info:
  - config_name: default
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: float64
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
configs:
  - config_name: default
    data_files:
      - split: test
        path: relevance.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: corpus.jsonl
  - config_name: queries
    data_files:
      - split: queries
        path: queries.jsonl

The ChatDoctor-HealthCareMagic-100k dataset comprises 112,000 real-world medical question-and-answer pairs, providing a substantial and diverse collection of authentic medical dialogues. There is a slight risk to this dataset since there are grammatical inconsistencies in many of the questions and answers, but this can potentially help separate strong healthcare retrieval models from weak ones.

Usage

import datasets

# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "queries")
documents = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "default")