"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021)

Related tags

Deep LearningStAR_KGC
Overview

STAR_KGC

This repo contains the source code of the paper accepted by WWW'2021. "Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion"(WWW 2021).

1. Thanks

The repository is partially based on huggingface transformers, KG-BERT and RotatE.

2. Installing requirement packages

  • conda create -n StAR python=3.6
  • source activate StAR
  • pip install numpy torch tensorboardX tqdm boto3 requests regex sacremoses sentencepiece matplotlib
2.1 Optional package (for mixed float Computation)

3. Dataset

  • WN18RR, FB15k-237, UMLS

    • Train and test set in ./data
    • As validation on original dev set is costly, we validated the model on dev subset during training.
    • The dev subset of WN18RR is provided in ./data/WN18RR called new_dev.dict. Use below commands to get the dev subset for WN18RR (FB15k-237 is similar without the --do_lower_case) used in training process.
     CUDA_VISIBLE_DEVICES=0 \
      python get_new_dev_dict.py \
     	--model_class bert \
     	--weight_decay 0.01 \
     	--learning_rate 5e-5 \
     	--adam_epsilon 1e-6 \
     	--max_grad_norm 0. \
     	--warmup_proportion 0.05 \
     	--do_train \
     	--num_train_epochs 7 \
     	--dataset WN18RR \
     	--max_seq_length 128 \
     	--gradient_accumulation_steps 4 \
     	--train_batch_size 16 \
     	--eval_batch_size 128 \
     	--logging_steps 100 \
     	--eval_steps -1 \
     	--save_steps 2000 \
     	--model_name_or_path bert-base-uncased \
     	--do_lower_case \
     	--output_dir ./result/WN18RR_get_dev \
     	--num_worker 12 \
     	--seed 42 \
    
     CUDA_VISIBLE_DEVICES=0 \
      python get_new_dev_dict.py \
     	--model_class bert \
     	--weight_decay 0.01 \
     	--learning_rate 5e-5 \
     	--adam_epsilon 1e-6 \
     	--max_grad_norm 0. \
     	--warmup_proportion 0.05 \
     	--do_eval \
     	--num_train_epochs 7 \
     	--dataset WN18RR \
     	--max_seq_length 128 \
     	--gradient_accumulation_steps 4 \
     	--train_batch_size 16 \
     	--eval_batch_size 128 \
     	--logging_steps 100 \
     	--eval_steps 1000 \
     	--save_steps 2000 \
     	--model_name_or_path ./result/WN18RR_get_dev \
     	--do_lower_case \
     	--output_dir ./result/WN18RR_get_dev \
     	--num_worker 12 \
     	--seed 42 \
    
  • NELL-One

    • We reformat original NELL-One as the three benchmarks above.
    • Please run the below command to get the reformatted data.
     python reformat_nell_one.py --data_dir path_to_downloaded --output_dir ./data/NELL_standard
    

4. Training and Test (StAR)

Run the below commands for reproducing results in paper. Note, all the eval_steps is set to -1 to train w/o validation and save the last checkpoint, because standard dev is very time-consuming. This can get similar results as in the paper.

4.1 WN18RR

CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
    --model_class roberta \
    --weight_decay 0.01 \
    --learning_rate 1e-5 \
    --adam_betas 0.9,0.98 \
    --adam_epsilon 1e-6 \
    --max_grad_norm 0. \
    --warmup_proportion 0.05 \
    --do_train --do_eval \
    --do_prediction \
    --num_train_epochs 7 \
    --dataset WN18RR \
    --max_seq_length 128 \
    --gradient_accumulation_steps 4 \
    --train_batch_size 16 \
    --eval_batch_size 128 \
    --logging_steps 100 \
    --eval_steps 4000 \
    --save_steps 2000 \
    --model_name_or_path roberta-large \
    --output_dir ./result/WN18RR_roberta-large \
    --num_worker 12 \
    --seed 42 \
    --cls_method cls \
    --distance_metric euclidean \
CUDA_VISIBLE_DEVICES=2 \
python run_link_prediction.py \
    --model_class bert \
    --weight_decay 0.01 \
    --learning_rate 5e-5 \
    --adam_betas 0.9,0.98 \
    --adam_epsilon 1e-6 \
    --max_grad_norm 0. \
    --warmup_proportion 0.05 \
    --do_train --do_eval \
    --do_prediction \
    --num_train_epochs 7 \
    --dataset WN18RR \
    --max_seq_length 128 \
    --gradient_accumulation_steps 4 \
    --train_batch_size 16 \
    --eval_batch_size 128 \
    --logging_steps 100 \
    --eval_steps 4000 \
    --save_steps 2000 \
    --model_name_or_path bert-base-uncased \
    --do_lower_case \
    --output_dir ./result/WN18RR_bert \
    --num_worker 12 \
    --seed 42 \
    --cls_method cls \
    --distance_metric euclidean \

4.2 FB15k-237

CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
    --model_class roberta \
    --weight_decay 0.01 \
    --learning_rate 1e-5 \
    --adam_betas 0.9,0.98 \
    --adam_epsilon 1e-6 \
    --max_grad_norm 0. \
    --warmup_proportion 0.05 \
    --do_train --do_eval \
    --do_prediction \
    --num_train_epochs 7. \
    --dataset FB15k-237 \
    --max_seq_length 100 \
    --gradient_accumulation_steps 4 \
    --train_batch_size 16 \
    --eval_batch_size 128 \
    --logging_steps 100 \
    --eval_steps -1 \
    --save_steps 2000 \
    --model_name_or_path roberta-large \
    --output_dir ./result/FB15k-237_roberta-large \
    --num_worker 12 \
    --seed 42 \
    --fp16 \
    --cls_method cls \
    --distance_metric euclidean \

4.3 UMLS

CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
    --model_class roberta \
    --weight_decay 0.01 \
    --learning_rate 1e-5 \
    --adam_betas 0.9,0.98 \
    --adam_epsilon 1e-6 \
    --max_grad_norm 0. \
    --warmup_proportion 0.05 \
    --do_train --do_eval \
    --do_prediction \
    --num_train_epochs 20 \
    --dataset UMLS \
    --max_seq_length 16 \
    --gradient_accumulation_steps 1 \
    --train_batch_size 16 \
    --eval_batch_size 128 \
    --logging_steps 100 \
    --eval_steps -1 \
    --save_steps 200 \
    --model_name_or_path roberta-large \
    --output_dir ./result/UMLS_model \
    --num_worker 12 \
    --seed 42 \
    --cls_method cls \
    --distance_metric euclidean 

4.4 NELL-One

CUDA_VISIBLE_DEVICES=0 \
python run_link_prediction.py \
    --model_class bert \
    --do_train --do_eval \usepacka--do_prediction \
    --warmup_proportion 0.1 \
    --learning_rate 5e-5 \
    --num_train_epochs 8. \
    --dataset NELL_standard \
    --max_seq_length 32 \
    --gradient_accumulation_steps 1 \
    --train_batch_size 16 \
    --eval_batch_size 128 \
    --logging_steps 100 \
    --eval_steps -1 \
    --save_steps 2000 \
    --model_name_or_path bert-base-uncased \
    --do_lower_case \
    --output_dir ./result/NELL_model \
    --num_worker 12 \
    --seed 42 \
    --fp16 \
    --cls_method cls \
    --distance_metric euclidean 

5. StAR_Self-Adp

5.1 Data preprocessing

  • Get the trained model of RotatE, more details please refer to RotatE.

  • Run the below commands sequentially to get the training dataset of StAR_Self-Adp.

    • Run the run_get_ensemble_data.py in ./StAR
     CUDA_VISIBLE_DEVICES=0 python run_get_ensemble_data.py \
     	--dataset WN18RR \
     	--model_class roberta \
     	--model_name_or_path ./result/WN18RR_roberta-large \
     	--output_dir ./result/WN18RR_roberta-large \
     	--seed 42 \
     	--fp16 
    
    • Run the ./codes/run.py in rotate. (please replace the TRAINED_MODEL_PATH with your own trained model's path)
     CUDA_VISIBLE_DEVICES=3 python ./codes/run.py \
     	--cuda --init ./models/RotatE_wn18rr_0 \
     	--test_batch_size 16 \
     	--star_info_path /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \
     	--get_scores --get_model_dataset 
    

5.2 Train and Test

  • Run the run.py in ./StAR/ensemble. Note the --mode should be alternate in head and tail, and perform a average operation to get the final results.
  • Note: Please replace YOUR_OUTPUT_DIR, TRAINED_MODEL_PATH and StAR_FILE_PATH in ./StAR/peach/common.py with your own paths to run the command and code.
CUDA_VISIBLE_DEVICES=2 python run.py \
--do_train --do_eval --do_prediction --seen_feature \
--mode tail \
--learning_rate 1e-3 \
--feature_method mix \
--neg_times 5 \
--num_train_epochs 3 \
--hinge_loss_margin 0.6 \
--train_batch_size 32 \
--test_batch_size 64 \
--logging_steps 100 \
--save_steps 2000 \
--eval_steps -1 \
--warmup_proportion 0 \
--output_dir /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large_ensemble  \
--dataset_dir /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \
--context_score_path /home/wangbo/workspace/StAR_KGC-master/StAR/result/WN18RR_roberta-large \
--translation_score_path /home/wangbo/workspace/StAR_KGC-master/rotate/models/RotatE_wn18rr_0  \
--seed 42 
Owner
Bo Wang
Ph.D. student at the School of Artificial Intelligence, Jilin University.
Bo Wang
Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning

Understanding the Effects of Datasets Characteristics on Offline Reinforcement Learning Kajetan Schweighofer1, Markus Hofmarcher1, Marius-Constantin D

Institute for Machine Learning, Johannes Kepler University Linz 17 Dec 28, 2022
CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network - Pytorch This repo holds the pytorch-version codes of paper: "Temporal Context Aggregation Network for Temporal

Zhiwu Qing 63 Sep 27, 2022
FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS)

FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery by Ailong Ma, Junjue Wang*, Yanfei Zhon

Kingdrone 43 Jan 05, 2023
Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight

Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification Implementation of "Learning From Multiple Experts: Se

27 Nov 05, 2022
Tightness-aware Evaluation Protocol for Scene Text Detection

TIoU-metric Release on 27/03/2019. This repository is built on the ICDAR 2015 evaluation code. If you propose a better metric and require further eval

Yuliang Liu 206 Nov 18, 2022
A Framework for Encrypted Machine Learning in TensorFlow

TF Encrypted is a framework for encrypted machine learning in TensorFlow. It looks and feels like TensorFlow, taking advantage of the ease-of-use of t

TF Encrypted 0 Jul 06, 2022
Anonymize BLM Protest Images

Anonymize BLM Protest Images This repository automates @BLMPrivacyBot, a Twitter bot that shows the anonymized images to help keep protesters safe. Us

Stanford Machine Learning Group 40 Oct 13, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat

Microsoft 8.4k Dec 28, 2022
EssentialMC2 Video Understanding

EssentialMC2 Introduction EssentialMC2 is a complete system to solve video understanding tasks including MHRL(representation learning), MECR2( relatio

Alibaba 106 Dec 11, 2022
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
September-Assistant - Open-source Windows Voice Assistant

September - Windows Assistant September is an open-source Windows personal assis

The Nithin Balaji 9 Nov 22, 2022
Pre-trained BERT Models for Ancient and Medieval Greek, and associated code for LaTeCH 2021 paper titled - "A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek"

Ancient Greek BERT The first and only available Ancient Greek sub-word BERT model! State-of-the-art post fine-tuning on Part-of-Speech Tagging and Mor

Pranaydeep Singh 22 Dec 08, 2022
Source code for ZePHyR: Zero-shot Pose Hypothesis Rating @ ICRA 2021

ZePHyR: Zero-shot Pose Hypothesis Rating ZePHyR is a zero-shot 6D object pose estimation pipeline. The core is a learned scoring function that compare

R-Pad - Robots Perceiving and Doing 18 Aug 22, 2022
A tensorflow model that predicts if the image is of a cat or of a dog.

Quick intro Hello and thank you for your interest in my project! This is the backend part of a two-repo application. The other part can be found here

Tudor Matei 0 Mar 08, 2022
Collection of in-progress libraries for entity neural networks.

ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio

25 Dec 01, 2022
Vpw analyzer - A visual J1850 VPW analyzer written in Python

VPW Analyzer A visual J1850 VPW analyzer written in Python Requires Tkinter, Pan

7 May 01, 2022
A Domain-Agnostic Benchmark for Self-Supervised Learning

DABS: A Domain Agnostic Benchmark for Self-Supervised Learning This repository contains the code for DABS, a benchmark for domain-agnostic self-superv

Alex Tamkin 81 Dec 09, 2022
A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

Xiaohe Ma 14 Oct 18, 2022
Code for "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds", CVPR 2021

PV-RAFT This repository contains the PyTorch implementation for paper "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clou

Yi Wei 43 Dec 05, 2022
🤖 A Python library for learning and evaluating knowledge graph embeddings

PyKEEN PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-m

PyKEEN 1.1k Jan 09, 2023