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

3D-RETR: End-to-End Single and Multi-View3D Reconstruction with Transformers

S3-plugin is a high performance PyTorch dataset library to efficiently access datasets stored in S3 buckets.

Over9000 optimizer

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

PyGCL: Graph Contrastive Learning Library for PyTorch

Pytorch implementation of Distributed Proximal Policy Optimization

Riemannian Adaptive Optimization Methods with pytorch optim

Implements pytorch code for the Accelerated SGD algorithm.

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

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

PyTorch extensions for fast R&D prototyping and Kaggle farming

Training PyTorch models with differential privacy

A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

Fast, general, and tested differentiable structured prediction in PyTorch

TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

torch-optimizer -- collection of optimizers for Pytorch

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

Tacotron 2 - PyTorch implementation with faster-than-realtime inference