This repo is to present various code demos on how to use our Graph4NLP library.

Related tags

Deep Learninggraph4nlp_demo
Overview

Deep Learning on Graphs for Natural Language Processing Demo

The repository contains code examples for DLG4NLP tutorials at NAACL 2021, SIGIR 2021, KDD 2021, IJCAI 2021, AAAI 2022 and TheWebConf 2022.

Slides can be downloaded from here.

Get Started

You will need to install our graph4nlp library in order to run the demo code. Please follow the following environment setup instructions. Please also refer to the graph4nlp repository page for more details on how to use the library.

Environment setup

  1. Create virtual environment
conda create --name graph4nlp python=3.8
conda activate graph4nlp
  1. Install graph4nlp library
  • Clone the github repo
git clone -b [branch_version] https://github.com/graph4ai/graph4nlp.git
cd graph4nlp

Please choose the branch version corresponding to the demo version as shown in the table below.

demo version library branch version
[email protected] 2022 v0.5.5
TheWebConf 2022 v0.5.5
AAAI 2022 v0.5.5
CLIQ-ai 2021 stable_nov2021b
IJCAI 2021 stable_202108
KDD 2021 stable_202108
SIGIR 2021 stable
NAACL 2021 stable
  • Then run ./configure (or ./configure.bat if you are using Windows 10) to config your installation. The configuration program will ask you to specify your CUDA version. If you do not have a GPU, please choose 'cpu'.
./configure
  • Finally, install the package
python setup.py install
  1. Install other packages
pip install torchtext
pip install notebook
  1. Set up StanfordCoreNLP (for static graph construction only, unnecessary for this demo because preprocessed data is provided)
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

Start Jupyter notebook and run the demo

After complete the above steps, you can start the jupyter notebook server to run the demo:

cd graph4nlp_demo/XYZ
jupyter notebook

Note that you will need to change XYZ to the specific folder name.

Additional Resources:

Owner
Graph4AI
Graph4AI
GitHub Repository
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

This repository contains the code accompanying the paper " FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation" Paper link: R

20 Jun 29, 2022
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l

Microsoft 244 Jan 06, 2023
Simple and understandable swin-transformer OCR project

swin-transformer-ocr ocr with swin-transformer Overview Simple and understandable swin-transformer OCR project. The model in this repository heavily r

Ha YongWook 67 Dec 31, 2022
Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python

deepface Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid

Kushal Shingote 2 Feb 10, 2022
Control-Robot-Arm-using-PS4-Controller - A Robotic Arm based on Raspberry Pi and Arduino that controlled by PS4 Controller

Control-Robot-Arm-using-PS4-Controller You can see all details about this Robot

MohammadReza Sharifi 5 Jan 01, 2022
alfred-py: A deep learning utility library for **human**

Alfred Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then a

JinTian 800 Jan 03, 2023
Spatial Sparse Convolution Library

SpConv: Spatially Sparse Convolution Library PyPI Install Downloads CPU (Linux Only) pip install spconv CUDA 10.2 pip install spconv-cu102 CUDA 11.1 p

Yan Yan 1.2k Jan 07, 2023
A collection of papers about Transformer in the field of medical image analysis.

A collection of papers about Transformer in the field of medical image analysis.

Junyu Chen 377 Jan 05, 2023
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data arXiv This is the code base for weakly supervised NER. We provide a

Amazon 92 Jan 04, 2023
Public implementation of "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression" from CoRL'21

Self-Supervised Reward Regression (SSRR) Codebase for CoRL 2021 paper "Learning from Suboptimal Demonstration via Self-Supervised Reward Regression "

19 Dec 12, 2022
Tutorial to set up TensorFlow Object Detection API on the Raspberry Pi

A tutorial showing how to set up TensorFlow's Object Detection API on the Raspberry Pi

Evan 1.1k Dec 26, 2022
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020)

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020) Official implementation of: Forest R-CNN: Large-Vo

Jialian Wu 54 Jan 06, 2023
Perturb-and-max-product: Sampling and learning in discrete energy-based models

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

Vicarious 2 Mar 14, 2022
PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our paper

Flow Gaussian Mixture Model (FlowGMM) This repository contains a PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our pa

Pavel Izmailov 124 Nov 06, 2022
A programming language written with python

Kaoft A programming language written with python How to use A simple Hello World: c="Hello World" c Output: "Hello World" Operators: a=12

1 Jan 24, 2022
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

Unofficial pytorch implementation of the paper "Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective"

16 Nov 21, 2022
Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification

Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification (ACDNE) This is a pytorch implementation of the Adv

陈志豪 8 Oct 13, 2022
Efficient 3D Backbone Network for Temporal Modeling

VoV3D is an efficient and effective 3D backbone network for temporal modeling implemented on top of PySlowFast. Diverse Temporal Aggregation and

102 Dec 06, 2022
Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

DeepStock Technical experimentations to beat the stock market using deep learning. Experimentations Deep Learning Stock Prediction with Daily News Hea

Keon 449 Dec 29, 2022