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# Laptop-T5-Base The Laptop-T5-Base model was introduced in [A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis (SIGIR'23)](https://doi.org/10.1145/3539618.3591940) by Zengzhi Wang, Qiming Xie, and Rui Xia. The details are available at [Github:FS-ABSA](https://github.com/nustm/fs-absa) and [SIGIR'23 paper](https://doi.org/10.1145/3539618.3591940). # Model Description To bridge the domain gap between general pre-training and the task of interest in a specific domain (i.e., `laptop` in this repo), we conducted *domain-adaptive pre-training*, i.e., continuing pre-training the language model (i.e., T5) on the unlabeled corpus of the domain of interest (i.e., `laptop`) with the *text-infilling objective* (corruption rate of 15% and average span length of 1). We collect relevant 100k unlabeled reviews from Yelp for the aptop domain, respectively. For pre-training, we employ the [Adafactor](https://arxiv.org/abs/1804.04235) optimizer with a batch size of 80 …b4bb4e2 -
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