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---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: choices
sequence: string
- name: answerID
dtype: int64
splits:
- name: eval
num_bytes: 396224
num_examples: 1954
- name: few_shot_prompts
num_bytes: 4077
num_examples: 20
download_size: 222012
dataset_size: 400301
configs:
- config_name: default
data_files:
- split: eval
path: data/eval-*
- split: few_shot_prompts
path: data/few_shot_prompts-*
---
# social_i_qa Dataset
## Overview
This repository contains the processed version of the social_i_qa 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/social_i_qa")
# Access the data
for example in dataset['train']:
print(example)
```
## Example Usage
```python
# Load the dataset
dataset = load_dataset("DatologyAI/social_i_qa")
# Get a sample question
sample = dataset['train'][0]
# Print the question
print("Question:", sample['question'])
print("Choices:")
for i, choice in enumerate(sample['choices']):
print(f"{{i}}. {{choice}}")
print("Correct Answer:", sample['choices'][sample['answerID']])
```
## Dataset Creation
This dataset was processed to ensure:
- All entries are sorted by ID
- All string values have been stripped of extra whitespace
- Consistent JSON formatting
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