yhua219/lora_adaptor_for_qw2.5-1.5B_Q31.2_train1000_R8_multitask_OE-AOPE-AOC-ASTE-ASQE_fewshot

Licence and Copyright

 

• The "EduRABSA_SLM" LoRA adaptors and any merged models derived from them are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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

• The two pre-trained base models ("the original models") Phi4-mini-instruct and Qwen2.5-1.5B-Instruct were used for inference and training LoRA adaptors. No modifications were made to the original models. The original models’ licences and copyright notices (where provided) are included in the corresponding subdirectories of this repository.

The EduRABSA_SLM Model Family

The "EduRABSA_SLM" model family consists of fine-tuned multi-task small LLMs (SLMs) designed for resource-efficient opinion mining on education-domain reviews of courses, teaching staff, and universities (e.g. student course or teaching evaluations, and open-ended survey responses).

To cite the LoRA adaptors or merged models in this family:

@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}, 
}

The EduRABSA_SLM multi-task models can perform opinion mining across the following fine-grained Aspect-based Sentiment Analysis (ABSA) tasks, extracting outputs for and across each review entry as illustrated in the image below:

• Opinion Extraction (OE)
• Aspect-Opinion Pair-Extraction (AOPE)
• Aspect-opinion Categorisation (AOC; ASC with opinion term)
• Aspect-(opinion)-Sentiment Triplet Extraction (ASTE)
• Aspect-(opinion-category)-Sentiment Quadruplet Extraction (ASQE)

  

Adaptor Info

This LoRA adaptor is a part of the "EduRABSA_SLM" family and correspond to LoRA_Qwen2.5_1000_R8_MT_4S described in the paper.

It is fine-tuned with the base model Qwen2.5-1.5B-Instruct using the EduRABSA dataset.

Full details regarding the development and performance of the EduRABSA_SLM models are available in our paper Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis.

Please refer to the base model's hardware and environment requirements for using this adaptor.

How to Use

• Please visit the project's GitHub repository for adaptor merging script and education review ABSA task prompts.