Datasets:

ekacare
/

indian_drug_mcqa

license: mit
task_categories:
  - question-answering
  - multiple-choice
language:
  - en
tags:
  - medical
  - healthcare
  - pharmacology
  - indian-drugs
  - mcqa
  - branded-medications
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: question_id
      dtype: string
    - name: question
      dtype: string
    - name: option_a
      dtype: string
    - name: option_b
      dtype: string
    - name: option_c
      dtype: string
    - name: option_d
      dtype: string
    - name: option_e
      dtype: string
    - name: correct_answer
      dtype: string
    - name: medication_name
      dtype: string
    - name: generic_name
      dtype: string
    - name: therapeutic_class
      dtype: string
    - name: action_class_name
      dtype: string
    - name: stem_format
      dtype: string
    - name: phrasing_style
      dtype: string
    - name: distractor_strategy
      dtype: string
    - name: difficulty_tier
      dtype: string
    - name: num_options
      dtype: int64
  splits:
    - name: test
      num_bytes: 654557
      num_examples: 1512
  download_size: 220199
  dataset_size: 654557

Indian Drug MCQA Dataset

A multiple-choice question answering dataset for evaluating knowledge of Indian branded medications — specifically the ability to identify generic names / salt compositions from brand names.

Dataset Description

This dataset contains 1512 questions covering Indian pharmaceutical brand names across 20+ therapeutic classes. Each question presents a branded medication and asks the model to identify its correct generic composition from 3-5 options. Includes variant questions for hard-difficulty drugs using different distractor strategies and phrasing styles to increase evaluation robustness.

Use Case

Evaluate medical language models on their knowledge of the Indian pharmaceutical market, including:

Brand name → generic name mapping
Salt composition identification
Therapeutic class awareness

Dataset Structure

Column	Type	Description
`question_id`	string	Unique identifier (e.g., "Q001")
`system_prompt`	string	Instruction prompt for the model
`question`	string	The MCQ question stem
`option_a`	string	Option A
`option_b`	string	Option B
`option_c`	string	Option C
`option_d`	string	Option D
`option_e`	string	Option E (may be empty for 4-option questions)
`correct_answer`	string	Correct answer letter (A-E)
`source_ekaid`	string	Source medication identifier
`medication_name`	string	Branded medication name
`generic_name`	string	Generic name / salt composition
`manufacturer_name`	string	Manufacturer
`therapeutic_class`	string	Therapeutic category
`action_class_name`	string	Pharmacological action class
`stem_format`	string	Question stem format (e.g., "brand_only", "brand_form")
`phrasing_style`	string	Phrasing style (e.g., "pharmacist", "clinical", "direct")
`distractor_strategy`	string	How distractors were chosen (e.g., "close_combo")
`difficulty_tier`	string	Difficulty level ("easy", "medium", "hard")
`num_options`	int	Number of answer options (4 or 5)

Usage

With KARMA Evaluation Framework

karma eval \
  --model "Qwen/Qwen3-0.6B" \
  --datasets "ekacare/indian_drug_mcqa" \
  --format table

Direct Loading

from datasets import load_dataset

ds = load_dataset("ekacare/indian_drug_mcqa", split="test")

# Example question
print(ds[0]["question"])
print(ds[0]["correct_answer"])

Metrics

Recommended evaluation metric: Exact Match on the predicted answer letter (A-E).

Citation

If you use this dataset, please cite:

@dataset{indian_drug_mcqa_2025,
  title={Indian Drug MCQA Dataset},
  author={Eka.Care},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/ekacare/indian_drug_mcqa}
}

License

This dataset is released under the MIT License.