Add dataset card with YAML frontmatter, fields, splits, and citation

README.md

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+---
+language:
+- en
+license: apache-2.0
+pretty_name: MedMCQA (AIIMS & NEET PG Medical Entrance MCQs)
+size_categories:
+- 100K<n<1M
+task_categories:
+- question-answering
+language_creators:
+- expert-generated
+annotations_creators:
+- expert-generated
+multilinguality:
+- monolingual
+tags:
+- medical
+- neet-pg
+- aiims
+- multiple-choice
+- benchmark
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: train.json
+  - split: validation
+    path: dev.json
+  - split: test
+    path: test.json
+---
+
+# MedMCQA (AIIMS & NEET PG Medical Entrance MCQs)
+
+## Dataset Summary
+
+This dataset is a re-upload of the [MedMCQA](https://github.com/medmcqa/medmcqa) dataset introduced by Pal et al. in *[MedMCQA: A Large-Scale Multi-Subject Multi-Choice Dataset for Medical Domain Question Answering](https://arxiv.org/abs/2203.14371)* (ACL 2022).
+
+MedMCQA is a large-scale multiple-choice question answering dataset sourced from Indian medical entrance examinations (AIIMS PG and NEET PG). It covers **20 medical subjects** and contains over **194,000** questions with four answer options each.
+
+This repository includes:
+- the **train** split (182,822 questions from mock tests and online test series),
+- the **dev** split (4,183 questions from NEET PG exams, 2001–present),
+- the **test** split (6,150 questions from AIIMS PG exams, 1991–present).
+
+> **Note**: The test split has **no ground-truth labels**. The standard community practice is to evaluate on the **dev** split. To evaluate on the test set, predictions must be submitted via the [official submission form](https://forms.gle/xLJHNbuvaRa2FXbD8).
+
+**Original resources**
+| Resource | Link |
+|---|---|
+| Original repository | https://github.com/medmcqa/medmcqa |
+| Published paper (ACL 2022) | https://aclanthology.org/2022.findings-acl.56 |
+| arXiv preprint | https://arxiv.org/abs/2203.14371 |
+
+## Supported Tasks
+
+- **Multiple-choice question answering**: given a medical question and four answer options, predict the correct option.
+- **Medical QA benchmarking**: evaluate domain-specific language models on multi-subject clinical and preclinical reasoning across 20 medical subjects.
+
+## Languages
+
+English (`en`)
+
+## Dataset Structure
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("awinml/medmcqa")
+```
+
+### Data Splits
+
+| Split | Examples | Has Labels (`cop`) | Has Explanations (`exp`) |
+|---|---:|:---:|:---:|
+| train | 182,822 | ✅ | ✅ (88% non-null) |
+| validation | 4,183 | ✅ | ✅ (53% non-null) |
+| test | 6,150 | ❌ | ❌ |
+| **total** | **193,155** | | |
+
+The splits are **separated by exam source**, not randomly:
+
+| Split | Source Exam | Period |
+|---|---|---|
+| train | Mock tests & online test series | Various |
+| validation (dev) | NEET PG exam | 2001–present |
+| test | AIIMS PG exam | 1991–present |
+
+### Data Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | `string` | Unique question ID. |
+| `question` | `string` | The question text. |
+| `opa` | `string` | Option A text. |
+| `opb` | `string` | Option B text. |
+| `opc` | `string` | Option C text. |
+| `opd` | `string` | Option D text. |
+| `cop` | `int` | Correct option index (1–4). Present in train and validation only. |
+| `exp` | `string` | Detailed medical explanation. Present in train and validation only (many null). |
+| `subject_name` | `string` | Medical subject category (20 subjects). |
+| `topic_name` | `string` | Topic within the subject. |
+| `choice_type` | `string` | `"single"` or `"multi"` answer type. |
+
+### Example
+
+```json
+{
+  "id": "f469cb22-2b04-4af1-8685-ad2831060a54",
+  "question": "Which of the following is not true about glelidings?",
+  "opa": "Gliding joints allow movement in a single plane",
+  "opb": "Gliding joints allow sliding movements",
+  "opc": "Gliding joints are also called plane joints",
+  "opd": "Gliding joints are found in the carpal bones of the wrist",
+  "cop": 1,
+  "exp": "Gliding joints allow sliding or gliding movements and are also called plane joints. They are found in the carpal bones of the wrist. They do not restrict movement to a single plane.",
+  "subject_name": "Anatomy",
+  "topic_name": "General anatomy",
+  "choice_type": "single"
+}
+```
+
+### Subject Categories (20 total)
+
+Anaesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine, Gynaecology & Obstetrics, Medicine, Microbiology, Ophthalmology, Pathology, Pediatrics, Pharmacology, Physiology, Psychiatry, Radiology, Skin, Social & Preventive Medicine, Surgery, Unknown.
+
+## Dataset Creation
+
+### Source Data
+
+This dataset is derived from the original [medmcqa/medmcqa](https://github.com/medmcqa/medmcqa) release. The questions are sourced from Indian medical entrance examinations (AIIMS PG and NEET PG), covering a broad range of medical subjects.
+
+This re-upload preserves the original train/dev/test split and all fields from the original authors.
+
+### Personal and Sensitive Information
+
+The dataset does not contain real patient records or direct personal identifiers. Questions are written as medical knowledge MCQs and may reference clinical scenarios with demographic or health-related attributes.
+
+## Considerations for Using the Data
+
+### Out-of-Scope Use
+
+This dataset should **not** be used for clinical diagnosis, treatment recommendations, or as a substitute for licensed medical expertise. Performance on multiple-choice exam questions does not reflect clinical safety.
+
+### Limitations
+
+- **India-centric**: sourced from Indian medical entrance exams (AIIMS PG, NEET PG), which may reflect curricula and terminology specific to the Indian medical education system.
+- **Exam-style format**: multiple-choice exam performance does not necessarily reflect clinical usefulness.
+- **No test labels**: the test split has no publicly available ground-truth labels; evaluation requires submission to the official form.
+- **Incomplete explanations**: 12% of train and 47% of dev entries have null explanation fields.
+
+## Licensing Information
+
+The original [medmcqa/medmcqa](https://github.com/medmcqa/medmcqa) repository is distributed under the [Apache License 2.0](https://github.com/medmcqa/medmcqa/blob/main/LICENSE). This re-upload follows that license.
+
+## Citation
+
+If you use this dataset, please cite the original MedMCQA paper:
+
+```bibtex
+@inproceedings{pal2022medmcqa,
+  title     = {MedMCQA: A Large-Scale Multi-Subject Multi-Choice Dataset for Medical Domain Question Answering},
+  author    = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
+  booktitle = {Proceedings of the Conference on Health, Inference, and Learning (CHIL)},
+  series    = {Proceedings of Machine Learning Research},
+  year      = {2022},
+  url       = {https://arxiv.org/abs/2203.14371}
+}
+```
+
+## Dataset Curators
+
+- **Original dataset authors**: Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu
+- **Hugging Face re-upload**: [awinml](https://huggingface.co/awinml)