# 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.

task_categories:question-answering size_categories:100K<n<1M region:us
{"size_categories": ["100K<n<1M"], "task_categories": ["question-answering"]}
TAGS
#task_categories-question-answering #size_categories-100K<n<1M #region-us 

# Dataset Card for DUQA

## Table of Contents
- Dataset Description
  * Abstract
  * Languages
- Dataset Structure
  * Data Instances
  * Data Fields
- Data Statistics
- Dataset Creation
  * Curation Rationale
  * Source Data
  * Annotations
- Considerations for Using the Data
  * Discussion of Social Impact and Biases
  * Other Known Limitations
- Additional Information
  * Dataset Curators
  * Licensing 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.



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

# Dataset Card for DUQA ## Table of Contents
- Dataset Description
  * Abstract
  * Languages
- Dataset Structure
  * Data Instances
  * Data Fields
- Data Statistics
- Dataset Creation
  * Curation Rationale
  * Source Data
  * Annotations
- Considerations for Using the Data
  * Discussion of Social Impact and Biases
  * Other Known Limitations
- Additional Information
  * Dataset Curators
  * Licensing 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.



The citation information for this dataset is not provided. Please consult the dataset provider for more details.
TAGS
#task_categories-question-answering #size_categories-100K<n<1M #region-us 
 # Dataset Card for DUQA ## Table of Contents
- Dataset Description
  * Abstract
  * Languages
- Dataset Structure
  * Data Instances
  * Data Fields
- Data Statistics
- Dataset Creation
  * Curation Rationale
  * Source Data
  * Annotations
- Considerations for Using the Data
  * Discussion of Social Impact and Biases
  * Other Known Limitations
- Additional Information
  * Dataset Curators
  * Licensing 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.



The citation information for this dataset is not provided. Please consult the dataset provider for more details.
passage: TAGS
#task_categories-question-answering #size_categories-100K<n<1M #region-us 
# Dataset Card for DUQA## Table of Contents
- Dataset Description
  * Abstract
  * Languages
- Dataset Structure
  * Data Instances
  * Data Fields
- Data Statistics
- Dataset Creation
  * Curation Rationale
  * Source Data
  * Annotations
- Considerations for Using the Data
  * Discussion of Social Impact and Biases
  * Other Known Limitations
- Additional Information
  * Dataset Curators
  * Licensing 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