Pytorch-version BERT-flow: One can apply BERT-flow to any PLM within Pytorch framework.

Last update: Dec 01, 2022

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Overview

Pytorch-bertflow

This is an re-implemented version of BERT-flow using Pytorch framework, which can reproduce the results from the original repo. This code is used to reproduce the results in the TSDAE paper.

Usage

Please refer to the simple example ./example.py

python example.py

Note

Please shuffle your training data, which makes a huge difference.
The pooling function makes a huge difference in some datasets (especially for the ones used in the paper). To reproduce the results, please use 'first-last-avg'.

Contact

Contact person and main contributor: Kexin Wang, [email protected]

https://www.ukp.tu-darmstadt.de/

https://www.tu-darmstadt.de/

Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

Owner

Ubiquitous Knowledge Processing Lab

GitHub Repository

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