README.md

metadata

license: apache-2.0
datasets:
  - jakartaresearch/semeval-absa
language:
  - en
metrics:
  - f1
  - exact_match
library_name: transformers

Restaurant-T5-Base

The Restaurant-T5-Base model was introduced in A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis (SIGIR'23) by Zengzhi Wang, Qiming Xie, and Rui Xia.

The details are available at Github:FS-ABSA and SIGIR'23 paper.

Model Description

To bridge the domain gap between general pre-training and the task of interest in a specific domain (i.e., restaurant in this repo), we conducted domain-adaptive pre-training, continuing pre-training the language model (i.e., T5) on the unlabeled corpus of the domain of interest (i.e., restaurant) with the text-infilling objective (corruption rate of 15% and average span length of 1).