publish dataset

README.md

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+---
+language:
+- en
+license: mit
+pretty_name: "BenchBase: MedMCQA"
+tags:
+- medical
+- question-answering
+- multiple-choice
+- benchmark
+- clinical-knowledge
+- medical-education
+- benchbase
+task_categories:
+- question-answering
+- multiple-choice
+size_categories:
+- 100K<n<1M
+---
+
+<h1 align="center">BenchBase: MedMCQA</h1>
+
+<h3 align="center">193,000+ multiple-choice medical questions from Indian postgraduate medical entrance exams, spanning 2,400 healthcare topics.</h3>
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/Layered-Labs/benchbase-medmcqa">
+    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md.svg" alt="Dataset on HuggingFace"/>
+  </a>
+  &nbsp;
+  <img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"/>
+</p>
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/Layered-Labs/benchbase-medmcqa/resolve/main/banner.svg" alt="BenchBase: MedMCQA" width="100%"/>
+</p>
+
+---
+
+## Overview
+
+BenchBase: MedMCQA is a repackaging of the MedMCQA dataset for the Layered Labs BenchBase
+benchmark suite. The source dataset contains 193,155 multiple-choice questions drawn from
+AIIMS and PGMR medical entrance examinations, covering 2,400+ healthcare topics across
+clinical medicine, pharmacology, pathology, anatomy, biochemistry, and more. Each question
+includes four answer options, a correct answer label, subject and topic annotations, and
+optional expert explanations. The train split (182,822 questions) includes correct answer
+labels; the test split (6,150 questions) withholds them for evaluation. This repackaging
+standardizes column names and encoding to match the BenchBase schema.
+
+
+---
+
+## Statement of Need
+
+Evaluating medical language models requires standardized, clinically relevant benchmarks.
+MedMCQA is one of the largest and most topic-diverse medical QA datasets available, but
+the original release uses inconsistent column names and encoding that complicate integration
+into multi-benchmark evaluation pipelines. This BenchBase repackaging normalizes the schema,
+documents all fields explicitly, and makes the dataset drop-in compatible with other BenchBase
+medical benchmarks for unified evaluation.
+
+
+---
+
+## Intended Use
+
+This dataset is intended for researchers and engineers evaluating or fine-tuning language
+models on medical question answering. It is appropriate for benchmarking general medical
+knowledge, subject-specific clinical reasoning, and model calibration across 2,400+ topics.
+It is not intended as a clinical decision support tool or as a substitute for professional
+medical judgment.
+
+
+---
+
+## Limitations
+
+Questions are drawn from Indian postgraduate medical entrance exams and may reflect regional
+clinical guidelines, drug names, and diagnostic conventions that differ from US or European
+practice. The test split withholds correct answer labels, so evaluation requires submission
+to the original leaderboard for held-out accuracy. Expert explanations are available for only
+a subset of training questions. Some questions have ambiguous or disputed correct answers
+as noted in the research literature.
+
+
+---
+
+## Dataset Structure
+
+### Splits
+
+| Split | Rows |
+|-------|------|
+| train | 182,822 |
+| validation | 4,183 |
+| test | 6,150 |
+
+### Features
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | `string` | Unique question identifier. |
+| `question` | `string` | The medical question text. |
+| `opa` | `string` | Answer option A. |
+| `opb` | `string` | Answer option B. |
+| `opc` | `string` | Answer option C. |
+| `opd` | `string` | Answer option D. |
+| `answer` | `int8` | Index of the correct option (0=A, 1=B, 2=C, 3=D). Null in test split. |
+| `explanation` | `string` | Expert explanation for the correct answer, where available. |
+| `subject` | `string` | Medical subject area (e.g., Pharmacology, Anatomy). |
+| `topic` | `string` | Specific topic within the subject. |
+| `choice_type` | `string` | Whether the question is single-answer or multi-answer. |
+
+---
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Layered-Labs/benchbase-medmcqa")
+print(ds)
+```
+
+### Example
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Layered-Labs/benchbase-medmcqa")
+
+# Inspect the training split
+train = ds["train"]
+print(train[0])
+
+# Filter to pharmacology questions
+pharma = train.filter(lambda x: x["subject"] == "Pharmacology")
+print(f"Pharmacology questions: {len(pharma)}")
+
+```
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{layeredlabs_benchbase_medmcqa,
+  title     = {BenchBase: MedMCQA},
+  author    = {Ridwan, Abdullah and Hossain, Radhyyah},
+  year      = {2025},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/Layered-Labs/benchbase-medmcqa}
+}
+```
+
+---
+
+## License
+
+Released under the [MIT License](LICENSE).
+Maintained by [Layered Labs](https://layeredlabs.ai).

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