Using pretrained language models for biomedical knowledge graph completion.

Overview

LMs for biomedical KG completion

This repository contains code to run the experiments described in:

Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study (arXiv link)
Rahul Nadkarni, David Wadden, Iz Beltagy, Noah A. Smith, Hannaneh Hajishirzi, Tom Hope

Data

The edge splits we used for our experiments can be downloaded using the following links:

Link File size
RepoDB, transductive split 11 MB
RepoDB, inductive split 11 MB
Hetionet, transductive split 49 MB
Hetionet, inductive split 49 MB
MSI, transductive split 813 MB
MSI, inductive split 813 MB

Each of these filees should be placed in the subgraph directory before running any of the experiment scripts. Please see the README.md file in the subgraph directory for more information on the edge split files. If you would like to recreate the edge splits yourself or construct new edge splits, use the scripts titled script/create_*_dataset.py.

Environment

The environment.yml file contains all of the necessary packages to use this code. We recommend using Anaconda/Miniconda to set up an environment, which you can do with the command

conda-env create -f environment.yml

Entity names and descriptions

The files that contain entity names and descriptions for all of the datasets can be found in data/processed directory. If you would like to recreate these files yourself, you will need to use the scripts for each dataset found in data/script.

Pre-tokenization

The main training script for the LMs src/lm/run.py can take in pre-tokenized entity names and descriptions as input, and several of the training scripts in script/training are set up to do so. If you would like to pre-tokenize text before fine-tuning, follow the instructions in script/pretokenize.py. You can also pass in one of the .tsv files found in data/processed for the argument --info_filename instead of a file with pre-tokenized text.

Training

All of the scripts for training models can be found in the src directory. The script for training all KGE models is src/kge/run.py, while the script for training LMs is src/lm/run.py. Our code for training KGE models is heavily based on this code from the Open Graph Benchmark Github repository. Examples of how to use each of these scripts, including training with Slurm, can be found in the script/training directory. This directory includes all of the scripts we used to run the experiments for the results in the paper.

Owner
Rahul Nadkarni
Computer Science Ph.D. student
Rahul Nadkarni
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street This is

ShotaDEGUCHI 2 Apr 18, 2022
make ASCII Art by Deep Learning

DeepAA This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by NIPS 2017 Workshop,

OsciiArt 1.4k Dec 28, 2022
2D&3D human pose estimation

Human Pose Estimation Papers [CVPR 2016] - 201511 [IJCAI 2016] - 201602 Other Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors

133 Jan 02, 2023
Few-Shot Object Detection via Association and DIscrimination

Few-Shot Object Detection via Association and DIscrimination Code release of our NeurIPS 2021 paper: Few-Shot Object Detection via Association and DIs

Cao Yuhang 49 Dec 18, 2022
Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Soubhik Sanyal 689 Dec 25, 2022
Breaching - Breaching privacy in federated learning scenarios for vision and text

Breaching - A Framework for Attacks against Privacy in Federated Learning This P

Jonas Geiping 139 Jan 03, 2023
Shared Attention for Multi-label Zero-shot Learning

Shared Attention for Multi-label Zero-shot Learning Overview This repository contains the implementation of Shared Attention for Multi-label Zero-shot

dathuynh 26 Dec 14, 2022
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

DECAF (DEbiasing CAusal Fairness) Code Author: Trent Kyono This repository contains the code used for the "DECAF: Generating Fair Synthetic Data Using

van_der_Schaar \LAB 7 Nov 24, 2022
SIR model parameter estimation using a novel algorithm for differentiated uniformization.

TenSIR Parameter estimation on epidemic data under the SIR model using a novel algorithm for differentiated uniformization of Markov transition rate m

The Spang Lab 4 Nov 30, 2022
Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection

CP-Cluster Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection, Instance Segme

Yichun Shen 41 Dec 08, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 01, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
In Search of Probeable Generalization Measures

In Search of Probeable Generalization Measures Exciting News! In Search of Probeable Generalization Measures has been accepted to the International Co

Mahdi S. Hosseini 6 Sep 11, 2022
Constrained Logistic Regression - How to apply specific constraints to logistic regression's coefficients

Constrained Logistic Regression Sample implementation of constructing a logistic regression with given ranges on each of the feature's coefficients (v

1 Dec 29, 2021
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
An End-to-End Machine Learning Library to Optimize AUC (AUROC, AUPRC).

Logo by Zhuoning Yuan LibAUC: A Machine Learning Library for AUC Optimization Website | Updates | Installation | Tutorial | Research | Github LibAUC a

Optimization for AI 176 Jan 07, 2023