Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
English
Size:
100K - 1M
Tags:
advertising
License:
license: apache-2.0 | |
task_categories: | |
- text-classification | |
language: | |
- en | |
tags: | |
- advertising | |
pretty_name: DAC693k | |
size_categories: | |
- 100K<n<1M | |
# DAC693k | |
## Description | |
This dataset, named "DAC693k," is designed for ad targeting in a multi-class classification setting. It consists of two main columns: "domain" and "classes." The "domain" column contains a list of domains, representing various websites or online entities. The "classes" column contains an array representation of ad targeting multi-classes associated with each domain. | |
## Usage | |
### Hugging Face Datasets Library | |
The dataset is formatted to be seamlessly integrated with Hugging Face's datasets library. Users can easily load the dataset using the following code: | |
```python | |
from datasets import load_dataset | |
# Load the dataset | |
dataset = load_dataset("ansi-code/domain-advertising-classes-693k") | |
``` | |
## Columns | |
- domain: This column contains the domains of websites or online entities. | |
- classes: The "classes" column represents an array of multi-class labels associated with each domain for ad targeting. (see here mapping https://github.com/patcg-individual-drafts/topics/blob/main/taxonomy_v1.md) | |
## Data Format | |
- domain: String | |
- classes: List of strings representing multi-class labels | |
## License | |
This dataset is released under the Apache 2.0 license. | |
## Citation | |
If you use this dataset in your work, please cite it using the following BibTeX entry: | |
```bibtex | |
@dataset{silvi-2023-dac693k, | |
title = {domain-advertising-classes-693k}, | |
author = {Andrea Silvi}, | |
year = {2023}, | |
} | |
``` | |
## Acknowledgements | |
Additionally, we acknowledge the usage of the ad targeting taxonomy provided in [this GitHub repository](https://github.com/patcg-individual-drafts/topics/). The taxonomy has been instrumental in organizing and labeling the multi-class targets associated with each domain in the dataset. | |