![]() device) # using task 0's CL-plugin, choose from ). Inputs = tokenizer( texts, padding = True, truncation = True, return_tensors = "pt") from_pretrained( "UIC-Liu-Lab/CPT", trust_remote_code = True) Model = AutoModelForSequenceClassification. The package will take care of downloading the models automatically tokenizer = AutoTokenizer. ![]() Import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification # Import our model. For example, if you use Linux and CUDA11 ( how to check CUDA version), install PyTorch by the following command, PyTorch version higher than 1.7.0 should also work. To faithfully reproduce our results, please use the correct 1.7.0 version corresponding to your platforms/CUDA versions. And the following figure is an illustration of our model.įirst, install PyTorch by following the instructions from the official website. Experimental results verify its effectiveness. Under the goal of improving few-shot end-task learning in these domains, we propose a system called CPT (Continual Post-Training), which to our knowledge, is the first continual post-training system. We propose the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. This repository contains the code and pre-trained models for our EMNLP'22 paper Continual Training of Language Models for Few-Shot Learning by Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, and Bing Liu. ![]() Continual Training of Language Models for Few-Shot Learning ![]()
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