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README.md

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 ---
-dataset_info:
-  features:
-  - name: dataset
-    dtype: string
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: choices
-    sequence: string
-  - name: answer
-    dtype: string
-  - name: support
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 12554162.663742885
-    num_examples: 25389
-  - name: validation
-    num_bytes: 2215731.336257114
-    num_examples: 4481
-  download_size: 8989707
-  dataset_size: 14769894.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+annotations_creators:
+- expert-generated
+language:
+- en
+license: mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- multiple-choice
+task_ids:
+- multiple-choice-qa
+pretty_name: MNLP M3 MCQA Dataset
 ---
+
+# MNLP M3 MCQA Dataset
+
+The **MNLP M3 MCQA Dataset** is a carefully curated collection of **Multiple-Choice Question Answering (MCQA)** examples, unified from several academic and benchmark datasets.
+
+Developed as part of the *CS-552: Modern NLP* course at EPFL (Spring 2025), this dataset is designed for training and evaluating models on multiple-choice QA tasks, particularly in the **STEM** and general knowledge domains.
+
+## Key Features
+
+- ~30,000 MCQA questions
+- 6 diverse sources: `SciQ`, `OpenBookQA`, `MathQA`, `ARC-Easy`, `ARC-Challenge`, and `MedMCQA`
+- Each question has exactly 4 options (A–D) and one correct answer
+- Covers a wide range of topics: science, technology, engineering, mathematics, and general knowledge
+
+## Dataset Structure
+
+Each example is a dictionary with the following fields:
+
+| Field     | Type     | Description                                       |
+|-----------|----------|---------------------------------------------------|
+| `dataset` | `string` | Source dataset (`sciq`, `openbookqa`, etc.)      |
+| `id`      | `string` | Unique identifier for the question                |
+| `question`| `string` | The question text                                 |
+| `choices` | `list`   | List of 4 answer options (corresponding to A–D)   |
+| `answer`  | `string` | The correct option, as a letter: `"A"`, `"B"`, `"C"`, or `"D"` |
+| `support` | `string` | A brief explanation or fact supporting the correct answer when available |
+
+```markdown
+Example:
+```json
+{
+  "dataset": "sciq",
+  "id": "sciq_01_00042",
+  "question": "What does a seismograph measure?",
+  "choices": ["Earthquakes", "Rainfall", "Sunlight", "Temperature"],
+  "answer": "A",
+  "support": "A seismograph is an instrument that detects and records earthquakes."
+}
+```
+
+## Source Datasets
+
+This dataset combines multiple high-quality MCQA sources to support research and fine-tuning in STEM education and reasoning. The full corpus contains **29,870 multiple-choice questions** from the following sources:
+
+| Source (Hugging Face)                       | Name                | Size   | Description & Role in the Dataset                                                                                                                                                                                             |
+| ------------------------------------------- | ------------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `allenai/sciq`                              | **SciQ**            | 11,679 | **Science questions** (Physics, Chemistry, Biology, Earth science). Crowdsourced with 4 answer choices and optional supporting evidence. Used to provide **well-balanced, factual STEM questions** at a middle/high-school level. |
+| `allenai/openbookqa`                        | **OpenBookQA**      | 4,957  | Science exam-style questions requiring **multi-step reasoning** and use of **commonsense or external knowledge**. Contributes more **challenging** and **inference-based** questions.                                         |
+| `allenai/math_qa`                           | **MathQA**          | 5,000  | Subsample of quantitative math word problems derived from AQuA-RAT, annotated with structured answer options. Introduces **numerical reasoning** and **problem-solving** components into the dataset.                         |
+| `allenai/ai2_arc` (config: `ARC-Easy`)      | **ARC-Easy**        | 2,140  | Science questions at the middle school level. Useful for testing **basic STEM understanding** and **factual recall**. Filtered to retain only valid 4-choice entries.                                                         |
+| `allenai/ai2_arc` (config: `ARC-Challenge`) | **ARC-Challenge**   | 1,094  | More difficult science questions requiring **reasoning and inference**. Widely used as a benchmark for evaluating LLMs. Also filtered for clean MCQA format compatibility.                                                    |
+| `openlifescienceai/medmcqa`                | **MedMCQA**         | 5,000  | A subsample of multiple-choice questions on **medical topics** from various exams, filtered for a single-choice format. Contains real-world and domain-specific **clinical reasoning** questions covering various medical disciplines.         |
+
+## Intended Applications and Structure
+
+This dataset is split into three parts: 
+
+- `train` (~70%) — for training MCQA models
+- `validation` (~15%) — for tuning and monitoring performance during training
+- `test` (~15%) — for final evaluation on unseen questions
+
+It is suitable for multiple-choice question answering tasks, especially in the **STEM** domain (Science, Technology, Engineering, Mathematics).  
+
+## Author
+
+This dataset was created and published by [Youssef Belghmi](https://huggingface.co/youssefbelghmi) as part of the *CS-552: Modern NLP* course at EPFL (Spring 2025).