EduRABSA_SLM_v1_SLERP_phi4mini: Education-Domain Opinion Mining

The EduRABSA_SLM_v1_SLERP_phi4mini model is part of the EduRABSA_SLM family, developed by Yan Cathy Hua and colleagues. This model is a fine-tuned multi-task small language model (SLM) specifically engineered for resource-efficient opinion mining within education-domain reviews, such as student course or teaching evaluations and open-ended survey responses. It is built upon the Phi4-mini-instruct base model.

Key Capabilities

This model is adept at various fine-grained Aspect-based Sentiment Analysis (ABSA) tasks, extracting detailed opinion information from review entries. Its capabilities include:

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

Model Development

The EduRABSA_SLM_v1_SLERP_phi4mini model was created by weight-merging two LoRA fine-tuned models using the SLERP algorithm on the Phi4-mini-instruct base. The LoRA adaptors were fine-tuned using the specialized EduRABSA dataset. Full details on its development and performance are available in the paper "Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis".

Good For

Analyzing student feedback and evaluations.
Extracting specific opinions and sentiments related to courses, teaching staff, and universities.
Researchers and developers working on sentiment analysis in educational contexts.
Applications requiring fine-grained ABSA on domain-specific text with resource efficiency.