Add dataset card

README.md

@@ -1,32 +1,65 @@
 ---
-dataset_info:
-  features:
-  - name: question
-    dtype: string
-  - name: choices
-    sequence: string
-  - name: answer_index
-    dtype: int64
-  - name: answer_text
-    dtype: string
-  - name: source
-    dtype: string
-  - name: explanation
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 133475088
-    num_examples: 182822
-  - name: validation
-    num_bytes: 2255614
-    num_examples: 4183
-  download_size: 84134828
-  dataset_size: 135730702
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+license: apache-2.0
+task_categories:
+- question-answering
+- multiple-choice
+language:
+- en
+tags:
+- medical
+- healthcare
+- mcqa
+- entrance-exam
+size_categories:
+- 100K<n<1M
 ---
+
+# MedMCQA MCQA Dataset
+
+This dataset contains the MedMCQA dataset converted to Multiple Choice Question Answering (MCQA) format.
+
+## Dataset Description
+
+MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. It covers various medical subjects and topics, making it ideal for evaluating AI systems on medical knowledge.
+
+## Dataset Structure
+
+Each example contains:
+- `question`: The medical question
+- `choices`: List of 4 possible answers (A, B, C, D)
+- `answer_index`: Index of the correct answer (0-3)
+- `answer_text`: Text of the correct answer
+- `source`: Dataset source ("medmcqa")
+- `explanation`: Detailed explanation including subject and topic information
+
+ ## Data Splits
+ 
+ - Train: 182822 examples
+ - Validation: 4183 examples (Test split skipped - no labels available)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("RikoteMaster/medmcqa-mcqa")
+```
+
+## Original Dataset
+
+This dataset is based on the MedMCQA dataset:
+- Paper: https://arxiv.org/abs/2203.14371
+- Original repository: https://huggingface.co/datasets/openlifescienceai/medmcqa
+
+## Citation
+
+```bibtex
+@misc{pal2022medmcqa,
+    title={MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering}, 
+    author={Ankit Pal and Logesh Kumar Umapathi and Malaikannan Sankarasubbu},
+    year={2022},
+    eprint={2203.14371},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```