metadata

language:
  - en
license: mit
size_categories:
  - 10K<n<100K
pretty_name: race_high
tags:
  - multiple-choice
  - benchmark
  - evaluation
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: choices
      sequence: string
    - name: answerID
      dtype: int32
  splits:
    - name: eval
      num_bytes: 6941680
      num_examples: 3498
    - name: train
      num_bytes: 125397639
      num_examples: 62445
  download_size: 32290996
  dataset_size: 132339319
configs:
  - config_name: default
    data_files:
      - split: eval
        path: data/eval-*
      - split: train
        path: data/train-*

race_high Dataset

Dataset Information

Original Hugging Face Dataset: race
Subset: high
Evaluation Split: test
Training Split: train
Task Type: multiple_choice_with_context
Processing Function: process_race

Processing Function

The following function was used to process the dataset from its original source:

def process_race(example: Dict) -> Tuple[str, List[str], int]:
    """Process RACE dataset example."""
    context = example["article"]
    query = example["question"]
    choices = example["options"]
    answer_index = "ABCD".index(example["answer"])
    return context, query, choices, answer_index

Overview

This repository contains the processed version of the race_high dataset. The dataset is formatted as a collection of multiple-choice questions.

Dataset Structure

Each example in the dataset contains the following fields:

{
  "id": 0,
  "context": "The rain had continued for a week and the flood had created a big river which were running by Nancy Brown's farm. As she tried to gather her cows to a higher ground, she slipped and hit her head on a fallen tree trunk. The fall made her unconscious for a moment or two. When she came to, Lizzie, one of her oldest and favorite cows, was licking her face. \nAt that time, the water level on the farm was still rising. Nancy gathered all her strength to get up and began walking slowly with Lizzie. The rain had become much heavier, and the water in the field was now waist high. Nancy's pace got slower and slower because she felt a great pain in her head. Finally, all she could do was to throw her arm around Lizzie's neck and try to hang on. About 20 minutes later, Lizzie managed to pull herself and Nancy out of the rising water and onto a bit of high land, which seemed like a small island in the middle of a lake of white water. \nEven though it was about noon, the sky was so dark and the rain and lightning was so bad that it took rescuers more than two hours to discover Nancy. A man from a helicopter  lowered a rope, but Nancy couldn't catch it. A moment later, two men landed on the small island from a ladder in the helicopter. They raised her into the helicopter and took her to the school gym, where the Red Cross had set up an emergency shelter. \nWhen the flood disappeared two days later, Nancy immediately went back to the \"island.\" Lizzie was gone. She was one of 19 cows that Nancy had lost in the flood. \"I owe my life to her,\" said Nancy with tears.",
  "question": "What did Nancy try to do before she fell over?",
  "choices": [
    "Measure the depth of the river",
    "Look for a fallen tree trunk",
    "Protect her cows from being drowned",
    "Run away from the flooded farm"
  ],
  "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:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("DatologyAI/race_high")

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

Example Usage

# Load the dataset
dataset = load_dataset("DatologyAI/race_high")

# 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']])