README.md

metadata

language:
  - ar
license: apache-2.0
task_categories:
  - multiple-choice
  - question-answering
pretty_name: Arabic Accounting MCQ Training Dataset
tags:
  - accounting
  - mcq
  - arabic
  - training
  - education
dataset_info:
  features:
    - name: id
      dtype: string
    - name: query
      dtype: string
    - name: answer
      dtype: string
    - name: text
      dtype: string
    - name: choices
      list: string
    - name: gold
      dtype: int64
    - name: conversations
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
  splits:
    - name: train
      num_bytes: 853344
      num_examples: 249
  download_size: 251153
  dataset_size: 853344
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Arabic Accounting MCQ Training Dataset

Training dataset for Arabic accounting multiple choice questions with English letter choices.

Dataset Structure

• Format: Multiple choice questions (4 options)
• Language: Arabic questions with English letter choices
• Domain: Accounting and finance
• Size: ~80% of total dataset

Fields

• id: Unique identifier
• query: Full MCQ prompt with instructions
• answer: Correct answer letter (a, b, c, d)
• text: Question text without instructions
• choices: List of options ['a', 'b', 'c', 'd']
• gold: Zero-based index of correct answer (0-3)

Example

{
  "id": "accounting_mcq_00001",
  "query": "اقرأ السؤال التالي بعناية واختر الإجابة الصحيحة...",
  "answer": "d",
  "text": "السؤال: [accounting question]...",
  "choices": ["a", "b", "c", "d"],
  "gold": 3
}

Usage

from datasets import load_dataset

dataset = load_dataset("SahmBenchmark/arabic-accounting-mcq_train")
train_data = dataset['train']

for example in train_data:
    print(f"Question: {example['text']}")
    print(f"Choices: {example['choices']}")
    print(f"Answer: {example['answer']}")

For evaluation data, see: SahmBenchmark/arabic-accounting-mcq_eval