yhua219/EduRABSA_SLM_v1_Test_data

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Licence and Copyright

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Copyright (c) 2025 Authors of Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis.

Both the original, and the formatted versions of the EduRABSA dataset presented in this repository are under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa] (CC BY-NC-SA 4.0).

Attribution is required. You may not use this dataset for commercial purposes. Any derivatives must be shared under CC BY-NC-SA 4.0.

EduRABSASLMv1Testdata

This dataset contains the dev and test sets used for developing the [EduRABSA_SLM_v1](https://huggingface.co/collections/yhua219/edurabsa-slm) models.

This dataset was developed on a subset of the test split of the EduRABSA dataset on the OE, AOPE, AOC, ASTE, and ASQE tasks, where:

• —each dev set contains 200 entries per task, and was used for post-training hyperparameter tuning.
• —each test set contains 300 entries per task, and was used for final model evaluation.
• —Each dev / test dataset has two versions: with 0-shot prompt and 4-shot prompt.

Full details about the dataset are available in the original paper EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks.

To cite this dataset:

@misc{hua2025dataefficientadaptationnovelevaluation,
      title={Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis}, 
      author={Yan Cathy Hua and Paul Denny and Jörg Wicker and Katerina Taškova},
      year={2025},
      eprint={2511.03034},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.03034}, 
}