language: en
CPT
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.
Requirements
First, install PyTorch by following the instructions from the official website. To faithfully reproduce our results, please use the correct 1.7.0 version corresponding to your platforms/CUDA versions. PyTorch version higher than 1.7.0 should also work. For example, if you use Linux and CUDA11 (how to check CUDA version), install PyTorch by the following command,
pip install torch==1.7.0+cu110 -f https://download.pytorch.org/whl/torch_stable.html
If you instead use CUDA <11 or CPU, install PyTorch by the following command,
pip install torch==1.7.0
Then run the following script to install the remaining dependencies,
pip install -r requirements.txt
Attention: Our model is based on transformers==4.11.3 and adapter-transformers==2.2.0. Using them from other versions may cause some unexpected bugs.
Use CPT with Huggingface
You can easily import our continually post-trained model with HuggingFace's transformers:
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Import our model. The package will take care of downloading the models automatically
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = AutoModelForSequenceClassification.from_pretrained("UIC-Liu-Lab/CPT", trust_remote_code=True)
# Tokenize input texts
texts = [
"There's a kid on a skateboard.",
"A kid is skateboarding.",
"A kid is inside the house."
]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
# Task id and smax
t = torch.LongTensor([0]).to(model.device) # using task 0's CL-plugin, choose from {0, 1, 2, 3}
smax = 400
# Get the model output!
res = model(**inputs, return_dict=True, t=t, s=smax)
If you encounter any problem when directly loading the models by HuggingFace's API, you can also download the models manually from the repo and use model = AutoModel.from_pretrained({PATH TO THE DOWNLOAD MODEL}).
Note: The post-trained weights you load contain un-trained classification heads. The post-training sequence is Restaurant -> AI -> ACL -> AGNews, you can use the downloaded weights to fine-tune the corresponding end-task. The results (MF1/Acc) will be consistent with follows.
| Restaurant | AI | ACL | AGNews | Avg. | |
|---|---|---|---|---|---|
| UIC-Liu-Lab/CPT | 53.90 / 75.13 | 30.42 / 30.89 | 37.56 / 38.53 | 63.77 / 65.79 | 46.41 / 52.59 |
Citation
Please cite our paper if you use CPT in your work:
@inproceedings{ke2022continual,
title={Continual Training of Language Models for Few-Shot Learning},
author={Ke, Zixuan and Lin, Haowei and Shao, Yijia and Xu, Hu and Shu, Lei, and Liu, Bing},
booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
year={2022}
}