task_categories:question-answering size_categories:100K<n<1M region:us
---
task_categories:
- question-answering
size_categories:
- 100K<n<1M
---
# Dataset Card for DUQA

## Table of Contents
- [Dataset Description](#dataset-description)
  * [Abstract](#abstract)
  * [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  * [Data Instances](#data-instances)
  * [Data Fields](#data-fields)
- [Data Statistics](#data-statistics)
- [Dataset Creation](#dataset-creation)
  * [Curation Rationale](#curation-rationale)
  * [Source Data](#source-data)
  * [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  * [Discussion of Social Impact and Biases](#discussion-of-social-impact-and-biases)
  * [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  * [Dataset Curators](#dataset-curators)
  * [Licensing Information](#licensing-information)
  * [Citation Information](#citation-information)

## Dataset Description

### Abstract

DUQA is a dataset for single-step unit conversion questions. It comes in three sizes, ”DUQA10k”, ”DUQA100k” and ”DUQA1M”, with 10,000, 100,000 and 1,000,000 entries respectively. Each size contains a mixture of basic and complex conversion questions, including simple conversion, multiple answer, max/min, argmax/argmin, and noisy/q-noisy questions. The complexity level varies based on the amount of information present in the sentence and the number of reasoning steps required to calculate a correct answer.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

A single instance in the dataset consists of a question related to a single-step unit conversion problem, along with its corresponding correct answer.

### Data Fields

The dataset contains fields for the question, answer, and additional context about the question, along with multiple choices answers. 

## Data Statistics

The dataset comes in three sizes, with 10,000, 100,000 and 1,000,000 entries respectively.

## Dataset Creation

### Curation Rationale

The dataset is curated to help machine learning models understand and perform single-step unit conversions. This ability is essential for many real-world applications, including but not limited to physical sciences, engineering, and data analysis tasks.

### Source Data

The source data for the dataset is generated using a Python library provided with the dataset, which can create new datasets from a list of templates. 

### Annotations

The dataset does not contain any annotations.

## Considerations for Using the Data

### Discussion of Social Impact and Biases

The dataset is neutral and does not contain any explicit biases or social implications as it deals primarily with mathematical conversion problems.

### Other Known Limitations

The complexity of the questions is limited to single-step unit conversions. It does not cover multi-step or more complex unit conversion problems.

## Additional Information

### Dataset Curators

The dataset was created by a team of researchers. More information might be needed to provide specific names or organizations.

### Licensing Information

The licensing information for this dataset is not provided. Please consult the dataset provider for more details.

### Citation Information

The citation information for this dataset is not provided. Please consult the dataset provider for more details.
