README.md

---
language:
- en
license: mit
tags:
  - natural-language-processing
  - text-classification
  - travel
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
---

# Dataset Name: Descriptive and Categorized Travel Snippets

## Description
This dataset is a synthetic collection created from 160 different travel-related categories, providing detailed descriptions of accessible adventures and activities. It aims to inspire and inform individuals about opportunities accommodating diverse needs, each entry pairing a detailed description with a category label reflecting the nature of the activity.

## Dataset Structure
- **Descriptions**: Each row contains a description enclosed in double quotes, detailing various accessible activities, highlighting key features and attractions.
- **Category**: Accompanying each description is a category label that identifies the type of activity or location described.

## Use Cases
This dataset is ideal for:
- Researchers and organizations interested in accessible tourism and activity planning.
- Developers creating applications or services that recommend activities based on accessibility needs.
- Data scientists and students looking for real-world datasets for training natural language processing models.

## Source
The descriptions were synthesized based on the specified travel categories.

## Acknowledgments
Special thanks to the organizations and communities that promote accessibility and have contributed to the inspiration behind this dataset.

## License
This dataset is provided under the [mit license]. Users are encouraged to use and share this dataset while adhering to the terms of the license.


## Citation
If you use this dataset in your research, please cite it using the following BibTeX entry:
```bibtex
@dataset{descriptive_categorized_travel_snippets,
  title = {Descriptive and Categorized Travel Snippets}, 
  year = {2024}, 
  publisher = {Path Pal Team}
}