Datasetpublic

SahmBenchmark/Sentiment_Analysis_MCQ_eval

Sentiment Analysis MCQ Evaluation Dataset Validation and test splits for financial sentiment analysis in MCQ format. Dataset Structure Format: Multiple choice questions Language: Arabic Domain: Financial reports Task: Sentiment classification Validation: 20 examples Test: 20 examples Fields id: Unique identifier query: Full MCQ prompt answer: Correct answer letter text: Question text choices: Answer options [a, b, c] gold: Correct answer index… See the full description on the dataset page: https://huggingface.co/datasets/SahmBenchmark/Sentiment_Analysis_MCQ_eval.

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face

0likes24downloads

Dataset Card

Sentiment Analysis MCQ Evaluation Dataset

Validation and test splits for financial sentiment analysis in MCQ format.

Dataset Structure

—Format: Multiple choice questions
—Language: Arabic
—Domain: Financial reports
—Task: Sentiment classification
—Validation: 20 examples
—Test: 20 examples

Fields

—id: Unique identifier
—query: Full MCQ prompt
—answer: Correct answer letter
—text: Question text
—choices: Answer options [a, b, c]
—gold: Correct answer index
—category: Report category
—original_sentiment: Ground truth sentiment

Answer Mapping

—a) positive - gold: 0
—b) negative - gold: 1
—c) neutral - gold: 2

Usage

python

from datasets import load_dataset

dataset = load_dataset("SahmBenchmark/Sentiment_Analysis_MCQ_eval")
test_data = dataset['test']

for example in test_data:
    print(f"Question: {example['text']}")
    print(f"Choices: {example['choices']}")
    print(f"Correct: {example['answer']} (index: {example['gold']})")