Datasets:
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---
language:
- en
license: mit
size_categories:
- 10K<n<100K
pretty_name: siqa
tags:
- multiple-choice
- benchmark
- evaluation
configs:
- config_name: default
data_files:
- split: eval
path: data/eval-*
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int32
- name: question
dtype: string
- name: choices
sequence: string
- name: answerID
dtype: int32
splits:
- name: eval
num_bytes: 380631
num_examples: 1954
- name: train
num_bytes: 6460849
num_examples: 33410
download_size: 3900341
dataset_size: 6841480
---
# siqa Dataset
## Dataset Information
- **Original Hugging Face Dataset**: `lighteval/siqa`
- **Subset**: `default`
- **Evaluation Split**: `validation`
- **Training Split**: `train`
- **Task Type**: `multiple_choice`
- **Processing Function**: `process_siqa`
## Processing Function
The following function was used to process the dataset from its original source:
```python
def process_siqa(example: Dict) -> Tuple[str, List[str], int]:
"""Process SocialIQA dataset example."""
query = f"{example['context']} {example['question']}"
# Get the original choices
original_choices = [example['answerA'], example['answerB'], example['answerC']]
correct_answer = original_choices[int(example["label"]) - 1] # Convert 1-based index to 0-based
# Find the new index of the correct answer after shuffling
answer_index = original_choices.index(correct_answer)
return query, original_choices, answer_index
```
## Overview
This repository contains the processed version of the siqa dataset. The dataset is formatted as a collection of multiple-choice questions.
## Dataset Structure
Each example in the dataset contains the following fields:
```json
{
"id": 0,
"question": "Tracy didn't go home that evening and resisted Riley's attacks. What does Tracy need to do before this?",
"choices": [
"make a new plan",
"Go home and see Riley",
"Find somewhere to go"
],
"answerID": 2
}
```
## Fields Description
- `id`: Unique identifier for each example
- `question`: The question or prompt text
- `choices`: List of possible answers
- `answerID`: Index of the correct answer in the choices list (0-based)
## Loading the Dataset
You can load this dataset using the Hugging Face datasets library:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("DatologyAI/siqa")
# Access the data
for example in dataset['train']:
print(example)
```
## Example Usage
```python
# Load the dataset
dataset = load_dataset("DatologyAI/siqa")
# Get a sample question
sample = dataset['train'][0]
# Print the question
print("Question:", sample['question'])
print("Choices:")
for idx, choice in enumerate(sample['choices']):
print(f"{idx}. {choice}")
print("Correct Answer:", sample['choices'][sample['answerID']])
```
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