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arvyz/plue-ppbert
plue-ppbert is a fill-mask model from arvyz. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.
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From the Hugging Face model README
This repository contains code for downloading data and implementations of baseline systems for PLUE.
<p align="center"> <a href="#setup">Setup</a> • <a href="#Usage">Usage</a> • <a href="#license">License</a> • <a href="#citation">Citation</a> </p> </div>This model is based on the PLUE effort cited below.
We have already provided all PLUE datasets and the correponding preprocessing scripts in the data folder. Except PolicyIE, we have also uploaded the all pre-processed datasets.
To pre-process policyIE, please run
cd data/policyie
bash run.sh
If you want to checkout how we preprocess PrivacyQA, PIExtract, APP-350, and OPP-115, please run
cd data
bash setup.sh
To download our pre-training corpus, please run
cd pretraining/data
bash download.sh
In each pre-trained model folder, please download all the required dependencies
pip install -r requirements.txt
Note that the dependencies are associated with each pre-trained models. After all dependencies are properly installed, please run
bash train.sh
All fine-tuning tasks share the same environment
cd finetuning
pip install -r requirements.txt
for each task, please run the run.sh in the corresponding folder. For example, if we want to run APP-350 with pp-roberta, we run
cd finetuning/classification/app350/
bash run.sh 0 policy_roberta # 0 indicate the gpu_id
Contents of this repository is under the MIT license. The license applies to the released model checkpoints as well.
@inproceedings{chi2023plue,
author = {Chi, Jianfeng and Ahmad, Wasi Uddin and Tian, Yuan and Chang, Kai-Wei},
title = {PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English},
booktitle = {ACL (short)},
year = {2023}
}