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.
Please refer to the simple example ./example.py
python example.py- 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 person and main contributor: Kexin Wang, kexin.wang.2049@gmail.com
https://www.ukp.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.