You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
By requesting access to this dataset, you agree to the following terms:
- License Acceptance: You confirm that you have read, understood, and accept the dataset's license: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Log in or Sign Up to review the conditions and access this dataset content.
🔍 DISCRIMINATIVE COUNTERFEIT_ES Dataset
The DISCRIMINATIVE COUNTERFEIT_ES dataset is a discriminative corpus for counterfeit and validity checking of trademark-related claims.
The task consists of classifying claims into one of two categories:
- fake
- not-fake
Claims follow a standardized textual pattern in Spanish:
"La marca {name} se dedica a {description}."
This dataset enables training and evaluating models for fine-grained legal-status classification, supporting research in trademark verification, counterfeit detection, and natural language understanding in legal/administrative contexts.
🧾 Example
{
"claim": "La marca \"SHIPPING TIMES SINGAPORE\" se dedica a \"Discos compactos (solo para su lectura) (CD-Rom); discos compactos (audiovisuales); software de ordenadores; memorias para ordenadores; datos e información registrados en soportes electrónicos; dibujos animados; aparatos de intercomunicación; soportes de datos magnéticos; discos magnéticos; todos comprendidos en esta clase.\".",
"label": "not-fake",
}
📂 Dataset Structure
The dataset contains individual claim–label entries, each representing a short, legally structured statement. Every instance includes:
| Column | Type | Description |
|---|---|---|
| claim | string |
Trademark-oriented statement following the fixed linguistic template. |
| label | string |
One of: fake, not-fake. |
🧪 Task Description
This dataset is designed for supervised text classification, addressing:
- Claim authenticity detection
- Trademark status verification
- Discriminative modelling for legal text snippets
Models trained on this corpus learn to identify legal validity signals within short, formulaic descriptions.
⚠️ Notes
- The dataset focuses exclusively on Spanish.
- Claims follow a fixed syntactic pattern, simplifying structural parsing while keeping semantic complexity.
💰 Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública, co-financed by the EU – NextGenerationEU, within the framework of the project Desarrollo de Modelos ALIA.
📚 Reference
Please cite the dataset using the following BibTeX entry:
@misc{discriminative2025counterfeites,
author = {Consuegra-Ayala, Juan Pablo and Muñoz Guillena, Rafael},
title = {DISCRIMINATIVE COUNTERFEIT_ES Dataset},
year = {2025},
institution = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
howpublished = {\url{https://huggingface.co/datasets/gplsi/discriminative_counterfeit_es}}
}
⚠️ Disclaimer
Be aware that the data may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this data , or use the data themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence.
The University of Alicante, as the owner and creator of the dataset, shall not be held liable for any outcomes resulting from third-party use.
📜 License
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
- Downloads last month
- 7