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Datasets:
gplsi
/
discriminative_counterfeit_en

Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
1M - 10M
Libraries:
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pandas
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xet
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🔍 DISCRIMINATIVE COUNTERFEIT_EN Dataset

The DISCRIMINATIVE COUNTERFEIT_EN 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 English:

"The trademark {name} is involved in {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": "The trademark \"SHIPPING TIMES SINGAPORE\" is involved in \"Compact discs [read-only memory] (CD-ROMs); compact discs [audiovisual]; computer software; computer memories; data and information recorded in the electronic media; animated cartoons; intercommunication apparatus; magnetic data media; magnetic disks; all goods in this class.\".",
  "label": "expired",
}

📂 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 English.
  • 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{discriminative2025counterfeiten,
  author       = {Consuegra-Ayala, Juan Pablo and Muñoz Guillena, Rafael},
  title        = {DISCRIMINATIVE COUNTERFEIT_EN 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_en}}
}

⚠️ 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)

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