task_categories:text-classification task_categories:token-classification task_categories:table-question-answering task_categories:question-answering language:en license:apache-2.0 size_categories:n<1K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us chemistry biology code medical
---
license: apache-2.0
task_categories:
- text-classification
- token-classification
- table-question-answering
- question-answering
language:
- en
tags:
- chemistry
- biology
- code
- medical
pretty_name: Aurora
size_categories:
- 10K<n<100K
---
# My Awesome Dataset

A comprehensive description of my awesome dataset.

## Table of Contents

- [Dataset Description](#dataset-description)
- [Data Fields](#data-fields)
- [Splits](#splits)
- [Citation](#citation)
- [License](#license)
- [Homepage](#homepage)
- [How to Use](#how-to-use)
- [Downloading and Loading](#downloading-and-loading)
- [Further Information](#further-information)

## Dataset Description

This dataset contains images of cats and dogs.  The images were collected from [mention data source(s), e.g., a specific website, scraped from the internet]. It is intended for use in image classification tasks.  The dataset consists of [number] images, with approximately [percentage]% allocated to the training set and [percentage]% to the test set. [Add more details about the dataset, class distribution, challenges, etc.]

## Data Fields

*   `image`: The image data (can be file paths or the actual image data).  Images are in [format, e.g., JPEG, PNG].
*   `label`: The label of the image (0 for cat, 1 for dog).
*   `text_description`: (Optional) A short text description of the image.

## Splits

The dataset is divided into the following splits:

*   `train`: [Number] examples.
*   `test`: [Number] examples.
*   `validation`: (Optional) [Number] examples.

## Citation

```bibtex
@article{my_dataset_citation,
  title={My Awesome Dataset},
  author={Your Name(s)},
  year={2024},
  journal={Journal Name},
  volume={1},
  number={1},
  pages={1-10}
}