Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Overview

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

This repository provides a pipeline to create a knowledge graph from raw texts. The pipeline concatenate major steps including:

  • Data processing: transform labeled text data to the Subject-Predicate-Object (SPO) format
  • Training: use a RNN-based algorithm to train an AI model to predict SPOs from given texts
  • Create a Neptune database: if the training metric (F1-Score) passes the threshold, create a Neptune database
  • Batch Transform: use the model trained in the Training step to do inferences on the test data
  • Bulk load: transform the inference results to the format which can be recognized by the bulkload function of Neptune, and load the transformed data to the Neptune database.

Prerequisites

  • Create an AWS account or use an existing AWS account.
  • Create a SageMaker Notebook instance. When you set up the notebook instance, you need to pay attention to following configurations:
    1. IAM role: you should attach policies of AmazonSageMakerFullAccess, IAMFullAccess, AmazonS3FullAccess, AmazonSNSFullAccess and NeptuneFullAccess to the IAM role.
    2. Network: in order to access the Neptune database created in the pipeline, a VPC is required to run the notebook.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.

Owner
AWS Samples
AWS Samples
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

OPTML Group 2 Oct 05, 2022
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a

4 Apr 15, 2022
Predicting future trajectories of people in cameras of novel scenarios and views.

Pedestrian Trajectory Prediction Predicting future trajectories of pedestrians in cameras of novel scenarios and views. This repository contains the c

8 Sep 03, 2022
Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of bra

Angad Bajwa 8 Oct 27, 2022
The Unsupervised Reinforcement Learning Benchmark (URLB)

The Unsupervised Reinforcement Learning Benchmark (URLB) URLB provides a set of leading algorithms for unsupervised reinforcement learning where agent

259 Dec 26, 2022
EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness

EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness Improving GAN Equilibrium by Raising Spatial Awareness Jianyuan Wang, Ceyuan Yang, Ying

GenForce: May Generative Force Be with You 149 Dec 19, 2022
[AAAI-2021] Visual Boundary Knowledge Translation for Foreground Segmentation

Trans-Net Code for (Visual Boundary Knowledge Translation for Foreground Segmentation, AAAI2021). [https://ojs.aaai.org/index.php/AAAI/article/view/16

ZJU-VIPA 2 Mar 04, 2022
Deep-learning-roadmap - All You Need to Know About Deep Learning - A kick-starter

Deep Learning - All You Need to Know Sponsorship To support maintaining and upgrading this project, please kindly consider Sponsoring the project deve

Instill AI 4.4k Dec 26, 2022
Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

TensorFlow White Paper Notes Features Notes broken down section by section, as well as subsection by subsection Relevant links to documentation, resou

Sam Abrahams 437 Oct 09, 2022
Unofficial PyTorch implementation of SimCLR by Google Brain

Unofficial PyTorch implementation of SimCLR by Google Brain

Rishabh Anand 2 Oct 13, 2021
[TNNLS 2021] The official code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement"

CSDNet-CSDGAN this is the code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement" Environment Preparing pyt

Jiaao Zhang 17 Nov 05, 2022
[ICCV'2021] "SSH: A Self-Supervised Framework for Image Harmonization", Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalyan Sunkavalli, Simon Chen, Sohrab Amirghodsi, Sarah Kong, Zhangyang Wang

SSH: A Self-Supervised Framework for Image Harmonization (ICCV 2021) code for SSH Representative Examples Main Pipeline RealHM DataSet Google Drive Pr

VITA 86 Dec 02, 2022
PyTorchMemTracer - Depict GPU memory footprint during DNN training of PyTorch

A Memory Tracer For PyTorch OOM is a nightmare for PyTorch users. However, most

Jiarui Fang 9 Nov 14, 2022
Jittor implementation of PCT:Point Cloud Transformer

PCT: Point Cloud Transformer This is a Jittor implementation of PCT: Point Cloud Transformer.

MenghaoGuo 547 Jan 03, 2023
Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise NeurIPS 2021: This repository is the official implementation of ODNL. Require

Hongxin Wei 12 Dec 07, 2022
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 26, 2022
The official implementation of Theme Transformer

Theme Transformer This is the official implementation of Theme Transformer. Checkout our demo and paper : Demo | arXiv Environment: using python versi

Ian Shih 85 Dec 08, 2022
ReAct: Out-of-distribution Detection With Rectified Activations

ReAct: Out-of-distribution Detection With Rectified Activations This is the source code for paper ReAct: Out-of-distribution Detection With Rectified

38 Dec 05, 2022
Generate high quality pictures. GAN. Generative Adversarial Networks

ESRGAN generate high quality pictures. GAN. Generative Adversarial Networks """ Super-resolution of CelebA using Generative Adversarial Networks. The

Lieon 1 Dec 14, 2021