GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

Last update: Dec 24, 2022

Overview

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

This repository implements a capsule model IntentCapsNet-ZSL on the SNIPS-NLU dataset in Python 3 with PyTorch, first introduced in the paper Zero-shot User Intent Detection via Capsule Neural Networks.

The code aims to follow PyTorch best practices, using torch instead of numpy where possible, and using .cuda() for GPU computation. Feel free to contribute via pull requests.

Requirements

python 3.6+

torch 1.0.1

numpy

gensim

scikit-learn

Usage and Modification

To run the training-validation loop: python run.py.
The custom Dataset class is implemented in dataset.py.

Acknowledgements

Original repository (TensorFlow, Python 2): https://github.com/congyingxia/ZeroShotCapsule
Re-implementation (PyTorch, Python 2): https://github.com/nhhoang96/ZeroShotCapsule-PyTorch-

Please see the following paper for the details:

Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu. Zero-shot User Intent Detection via Capsule Neural Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.

https://arxiv.org/abs/1809.00385

@article{xia2018zero,
  title={Zero-shot User Intent Detection via Capsule Neural Networks},
  author={Xia, Congying and Zhang, Chenwei and Yan, Xiaohui and Chang, Yi and Yu, Philip S},
  journal={arXiv preprint arXiv:1809.00385},  
  year={2018}
}

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

Related tags

Overview

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

Requirements

Usage and Modification

Acknowledgements

References

Owner

Joel Huang

Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

An implementation of Performer, a linear attention-based transformer, in Pytorch

PyTorch wrappers for using your model in audacity!

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

Fast, general, and tested differentiable structured prediction in PyTorch

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

Distiller is an open-source Python package for neural network compression research.

Code snippets created for the PyTorch discussion board

Pytorch implementation of Distributed Proximal Policy Optimization

This is an differentiable pytorch implementation of SIFT patch descriptor.

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

ocaml-torch provides some ocaml bindings for the PyTorch tensor library.

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

High-fidelity performance metrics for generative models in PyTorch

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

On the Variance of the Adaptive Learning Rate and Beyond

PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

Training PyTorch models with differential privacy