DatologyAI/commonsense_qa_rephrased

---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: prefix
dtype: string
- name: choices
sequence: string
- name: answerID
dtype: int64
splits:
- name: train
num_bytes: 209658
num_examples: 1221
download_size: 128995
dataset_size: 209658
---
# commonsense_qa Dataset

## Overview
This repository contains the rephrased version of the commonsense_qa dataset. The dataset is formatted as a collection of multiple-choice questions.

## Dataset Structure
Each example in the dataset contains the following fields:

```python
{
"id": 0,
"prefix": "A revolving door is convenient for two direction travel, but it also serves as a security measure at a",
"choices": [
"bank.",
"library.",
"department store.",
"mall.",
"New York."
],
"answerID": 0
}
```

## 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/{dataset_name}")

# Access the data
for example in dataset['train']:
print(example)
```

## Example Usage

```python
# Load the dataset
dataset = load_dataset("DatologyAI/{dataset_name}")

# 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:
1. All entries are sorted by ID
2. All string values have been stripped of extra whitespace
3. Consistent JSON formatting