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)
The "EduRABSA_SLM_v1_SLERP" models correspond to the following models described in the paper:
EduRABSA_SLM_v1_SLERP_phi4mini = merged_Phi4_SLERP, base model Phi4-mini-instruct.
EduRABSA_SLM_v1_SLERP_qw2.5-1.5B = merged_Qwen2.5_SLERP, base model Qwen2.5-1.5B-Instruct.
Each of these models was created via weight-merging (using the SLERP algorithm) two LoRA fine-tuned models on the same base model, and shares the multi-task capabilities described above. The EduRABSA_SLM LoRA adaptors were fine-tuned using the EduRABSA dataset.
The model follows its base model's hardware and environment requirements, unless the model name contains onnx (in which case, please refer to the onnx-model's README file).
