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hamzaaouadi
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MoroccanMedMCQA-FR

Tasks:
Question Answering
Modalities:
Text
Formats:
json
Sub-tasks:
multiple-choice-qa
Languages:
French
Size:
1K - 10K
Tags:
medical
french
morocco
benchmark
mcq
multiple-choice
DOI:
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MoroccanMedMCQA-FR is available exclusively for non-commercial academic research purposes. By requesting access you agree to the MoroccanMedMCQA-FR Data Usage Agreement, which prohibits redistribution, commercial use, and use for training commercial AI systems. Recipients must cite the original paper in any resulting publication. Requests are reviewed manually within 14 business days. For questions contact: hamza.aouadi@usmba.ac.ma

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MoroccanMedMCQA-FR: A French-Language Moroccan Medical Multiple-Choice QA Benchmark

Language: French Questions: 6,771 Specialties: 33 License: CC-BY-NC-4.0 Task: Medical MCQ

Dataset Description

MoroccanMedMCQA-FR is the first French-language medical multiple-choice question answering (MCQ) benchmark grounded in the Moroccan medical faculty curriculum. It comprises 6,771 officially sourced MCQs drawn from past examinations of the Faculty of Medicine and Pharmacy of Fès (FMPF), Université Sidi Mohammed Ben Abdellah, Morocco, covering the full 5-year medical curriculum across 33 medical specialties and spanning exam sessions from 2015 to 2023.

Unlike most existing medical QA benchmarks — which focus on English-language, US/UK-centric licensing exams with single-answer questions — MoroccanMedMCQA-FR features 77.9% multi-answer questions (where examinees must select all correct options), official past exam questions with authoritative corrections, and rich metadata including specialty, curriculum year, and exam session.

  • Paper: [PLACEHOLDER — Title TBD] (link will be added upon publication)
  • Authors:
    • AOUADI Hamza — Université Sidi Mohammed Ben Abdellah, Fès, Morocco
    • ELGAROUANI Said — Université Sidi Mohammed Ben Abdellah, Fès, Morocco
    • NFAOUI El Habib — Université Sidi Mohammed Ben Abdellah, Fès, Morocco
  • Contact: hamza.aouadi@usmba.ac.ma
  • License: CC BY-NC 4.0

Key Features

Feature MoroccanMedMCQA-FR FrenchMedMCQA Typical English Benchmarks
Language French French English
Region Morocco / North Africa France US / UK
Medical domain General medicine (33 specialties) Pharmacy only General medicine
Questions 6,771 3,105 Varies
Answer format 77.9% multi-answer 65.2% multi-answer Mostly single-answer
Options per question 4–5 (A–E) 5 (A–E) 4–5
Vocabulary size 29,284 13,000 Varies
Curriculum scope Full 5-year medical journey Pharmacy diploma Licensing exam only
Source Official past exams Official past exams Varies
Ground truth Official corrections Official corrections Varies
Clinical context field Yes (13.5%) No Varies
Split ratio 70/10/20 70/10/20 Varies
Publicly available Gated ✅ Fully open Varies

Dataset Statistics

MoroccanMedMCQA-FR comprises 6,771 questions drawn from official past exams of the Faculty of Medicine and Pharmacy of Fès, spanning 9 years of exam sessions (2015–2023) across 33 medical specialties and 5 curriculum years. The dataset is predominantly multi-answer: 77.9% of questions require selecting multiple correct options, with an average of 2.58 correct answers per question — making it significantly more challenging than most existing medical MCQ benchmarks. The distribution of correct answers per question is as follows: 1 correct option (21.6%), 2 (26.1%), 3 (29.2%), 4 (16.6%), and 5 (6.0%). A total of 914 questions (13.5%) include a clinical context vignette, with an average context length of 102.7 tokens (ranging from 11 to 1,049 tokens). The average question length is 11.9 tokens and the average answer option length is 6.6 tokens. The dataset vocabulary spans 29,284 unique words. Most questions offer five answer options (91.5%), while 8.5% have four options. Exam sessions are split between Normale (57.5%), Rattrapage (42.2%), and Exceptionnelle (0.3%) sessions.

Metric Value
Total questions 6,771
Medical specialties 33
Exam years covered 2015 – 2023 (9 years)
Curriculum years 1st – 5th year
Language French
Vocabulary size 29,284 words
Questions with clinical context 914 (13.5%)
Avg. context length 102.7 tokens (range: 11–1,049)
5-option questions (A–E) 6,198 (91.5%)
4-option questions (A–D) 573 (8.5%)
Single-answer questions 1,465 (21.6%)
Multi-answer questions 5,273 (77.9%)
Average correct options 2.58
Avg. question length 11.9 tokens
Avg. answer option length 6.6 tokens
Normale sessions 3,891 (57.5%)
Rattrapage sessions 2,860 (42.2%)
Exceptionnelle sessions 20 (0.3%)

Distribution by Curriculum Year

Curriculum Year Questions % of Total
1st Year 795 11.7%
2nd Year 484 7.1%
3rd Year 548 8.1%
4th Year 1,842 27.2%
5th Year 3,102 45.8%

Top 10 Medical Specialties

Specialty Questions
Synthèse thérapeutique et raisonnement clinique 577
Biochimie clinique 484
Gynécologie Obstétrique 477
Maladies de l'enfant 447
Ophtalmologie 399
Santé mentale 397
Maladies du système nerveux 386
Immunopathologie 366
Médecine Sociale et Santé Publique 311
Maladies de l'appareil digestive 302

Dataset Structure

Files

File Format Records Description
MoroccanMedMCQA-FR.json JSON 6,771 Full dataset
MoroccanMedMCQA-FR.csv CSV 6,771 Full dataset
MoroccanMedMCQA-FR_train.json/csv JSON/CSV 4,739 (70%) Train split
MoroccanMedMCQA-FR_val.json/csv JSON/CSV 677 (10%) Validation split
MoroccanMedMCQA-FR_test_no_answers.json/csv JSON/CSV 1,355 (20%) Test split (answers withheld)

Splits

The dataset is split using stratified sampling by specialty to ensure all 33 specialties are proportionally represented in each split:

Split Records Percentage
Train 4,739 70%
Validation 677 10%
Test 1,355 20%

Note: The test split is provided without correct answers to preserve benchmark integrity. Researchers wishing to evaluate on the test set should submit their predictions to hamza.aouadi@usmba.ac.ma.

Data Fields

Example 1 — Question with clinical context and 5 options:

{
    "id": 26,
    "curriculum_year": "1er année",
    "exam_session": {
        "fmp": "Fez",
        "year": 2015,
        "session_type": "Rattrapage"
    },
    "speciality": "Biologie",
    "context": "La culture des cellules tumorales a été réalisée dans un milieu nutritif et riche en CO2. Après quelques jours de culture, les cellules reçoivent le gène P53 et sont séparées en 2 groupes. Le groupe 1 reçoit par la suite du ca2+ et le produit Arf (il bloque la formation du complexe MDM2-P53) et on constate que les cellules de ce groupe ne dépassent pas la phase G2. Le groupe 2 reçoit les facteurs de croissance et l'anticorps antiP53 et on constate que les cellules de ce groupe prolifèrent.",
    "question": "La P53",
    "answers": {
        "a": "Agit négativement sur la croissance cellulaire, lorsqu'elle est libre",
        "b": "Agit sur les cellules par ubiquitinilation des petites protéines P21 ,et P27 (inhibitrices de la phase G1)",
        "c": "Bloque le cycle cellulaire par action sur le complexe cycline B-CDK1 (activateur de la phase G2)",
        "d": "Le MDM2 est inhibiteur direct de la P53",
        "e": "Le produit Arf entraîne un endommagement de l'ADN parce qu'il bloque le complexe MDM2-P53"
    },
    "correct_answers": ["a", "c", "d"]
}

Example 2 — Question without context and without option E:

{
    "id": 1,
    "curriculum_year": "1er année",
    "exam_session": {
        "fmp": "Fez",
        "year": 2015,
        "session_type": "Normale"
    },
    "speciality": "Biologie",
    "context": null,
    "question": "Les connexines sont des protéines de communication des cellules de l'oreille interne. Chaque jonction est composée de connexons qui sont constitués de connexines formant ainsi un canal, dont la nécessité est capitale dans de nombreux processus physiologiques",
    "answers": {
        "a": "Les connexines sont des molécules de jonction serrée (tight)",
        "b": "Les connexines sont des molécules de jonction communicante (gap)",
        "c": "Les connexines sont des molécules de communication intercellulaire paracrine",
        "d": "Les connexines permettent une communication intracytoplasmique des cellules adjacentes",
        "e": null
    },
    "correct_answers": ["b", "d"]
}
Field Type Description
id integer Unique question identifier
curriculum_year string Medical curriculum year (1st–5th)
exam_session.fmp string Faculty of Medicine location ("Fez")
exam_session.year integer Exam year (2015–2023)
exam_session.session_type string Session type (Normale / Rattrapage / Exceptionnelle)
speciality string Medical specialty
context string or null Clinical context/vignette (null if absent)
question string Question text
answers.a–e string or null Answer options (null if option not present)
correct_answers list List of correct option letters in lowercase ([] if none)

Usage

Repository File Structure 

hamzaaouadi/MoroccanMedMCQA-FR/
├── data/
│   ├── full/
│   │   ├── MoroccanMedMCQA-FR.json
│   │   └── MoroccanMedMCQA-FR.csv
│   ├── train/
│   │   ├── MoroccanMedMCQA-FR_train.json
│   │   └── MoroccanMedMCQA-FR_train.csv
│   ├── val/
│   │   ├── MoroccanMedMCQA-FR_val.json
│   │   └── MoroccanMedMCQA-FR_val.csv
│   └── test/
│       ├── MoroccanMedMCQA-FR_test_no_answers.json
│       └── MoroccanMedMCQA-FR_test_no_answers.csv
└── README.md

Loading with 🤗 Datasets 

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("hamzaaouadi/MoroccanMedMCQA-FR",
                       data_files="data/full/MoroccanMedMCQA-FR.json")

# Load train split
train = load_dataset("hamzaaouadi/MoroccanMedMCQA-FR",
                     data_files="data/train/MoroccanMedMCQA-FR_train.json")

# Load validation split
val = load_dataset("hamzaaouadi/MoroccanMedMCQA-FR",
                   data_files="data/val/MoroccanMedMCQA-FR_val.json")

# Load test split (no answers)
test = load_dataset("hamzaaouadi/MoroccanMedMCQA-FR",
                    data_files="data/test/MoroccanMedMCQA-FR_test_no_answers.json")

Loading with pandas (CSV) 

import pandas as pd

# Load full dataset
df = pd.read_csv("data/full/MoroccanMedMCQA-FR.csv", sep=";", encoding="utf-8-sig")

# Load train split
train_df = pd.read_csv("data/train/MoroccanMedMCQA-FR_train.csv", sep=";", encoding="utf-8-sig")

# Filter by specialty
ophtalmologie = df[df["speciality"] == "Ophtalmologie"]

# Filter by curriculum year
year5 = df[df["curriculum_year"] == "5eme année"]

# Filter by session type
normale = df[df["session_type"] == "Normale"]

Loading JSON directly 

import json

with open("data/full/MoroccanMedMCQA-FR.json", "r", encoding="utf-8") as f:
    data = json.load(f)

# Example: get all multi-answer questions
multi_answer = [q for q in data if len(q["correct_answers"]) > 1]
print(f"Multi-answer questions: {len(multi_answer)}")

# Example: get questions with clinical context
with_context = [q for q in data if q["context"] is not None]
print(f"Questions with context: {len(with_context)}")

Data Collection and Quality

Source

All questions were collected from official past examinations of the Faculty of Medicine and Pharmacy of Fès (FMPF), Morocco, with official corrections provided by the faculty. No crowdsourcing or AI generation was used.

Quality Control

  • Duplicate questions removed (22 duplicates identified and removed)
  • Answer options normalized and validated
  • Correct answers verified against official faculty corrections
  • Session types and specialty labels standardized

Limitations

  • Dataset covers only one Moroccan faculty (FMPF, Fès) — results may not generalize to other Moroccan or Francophone medical curricula
  • Exam years 2015–2023 only — older content not included
  • The specialty "Oncologie médicale" has only 2 questions due to limited exam coverage

Access and License

License

This dataset is released under CC BY-NC 4.0. It is available exclusively for non-commercial academic research.

Gated Access

Access to MoroccanMedMCQA-FR is gated. To request access:

  1. Click the "Access repository" button above
  2. Fill in your name, institutional affiliation, and intended use
  3. Agree to the Data Usage Agreement
  4. Requests are reviewed manually within 14 business days

For questions or issues, contact: hamza.aouadi@usmba.ac.ma

Data Usage Agreement

By accessing this dataset, you agree to:

  • Use the dataset solely for non-commercial academic research
  • Not redistribute, share, or sublicense the dataset
  • Cite the original paper in any resulting publication
  • Not use the dataset to train or evaluate commercial AI systems

Citation

If you use MoroccanMedMCQA-FR in your research, please cite:

@dataset{aouadi2025moroccanmedmcqa-fr,
  author    = {AOUADI, Hamza and ELGAROUANI, Said and NFAOUI, El Habib},
  title     = {MoroccanMedMCQA-FR: A French-Language Moroccan Medical Multiple-Choice QA Benchmark},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/hamzaaouadi/MoroccanMedMCQA-FR},
  note      = {Paper: [PLACEHOLDER — to be updated upon publication]}
}

The BibTeX entry will be updated with the full paper reference upon publication.

Acknowledgements

This dataset was compiled from official past examinations of the Faculty of Medicine and Pharmacy of Fès (FMPF), Université Sidi Mohammed Ben Abdellah (USMBA), Morocco. We thank the faculty for making these resources available for academic research.
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