MARE - Multi-Attribute Relation Extraction

Related tags

Deep Learningmare
Overview

MARE - Multi-Attribute Relation Extraction

Repository for the paper submission: #TODO: insert link, when available

Environment

Tested with Ubuntu 18.04, Anaconda 2020.11 and NVIDIA driver version 450.102.04 If you have a lower driver version and you don't need to train own models, we recommend to install the environment without the cuda requirement.

If you have no coda-compatible GPU, delete the cudatoolkit dependency from the environment.yml file. If you do not have a cuda GPU, remove the line

- cudatoolkit=10.2.89

from environment.yml.

To install the conda environment execute

conda env create -f environment.yml

Install mare (the local directory) via pip

pip install -e .

This may take several minutes.

Reproduction of results

To reproduce the values from the Paper, download the corresponding models from https://fh-aachen.sciebo.de/s/D5FLVN7qk2UTCmX and put the .tar.gz files in the models folder or execute the following shell commands.

wget -c https://fh-aachen.sciebo.de/s/D5FLVN7qk2UTCmX/download -O models.zip

unzip models.zip

rm models.zip

The following instructions can be used to reproduce the results in the paper. All evaluations create a subfolder in evaluations.

Sequence Tagging

The values for AR, Cl, MRE, CRE und BRE correspond to the values of MARE Seq. Tag. in Table 2.

The F1 scores for AR_no_trigger und MRE_no_trigger correspond to the values of Seq. Tag. with Trigger un Table 3

sh scripts/evaluate_model.sh models/sequence.tar.gz evaluations/seq_tag seq_lab_elmo_pred mare

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.42957746478873204
recall_micro: 0.4765625
f1_micro: 0.45185185185185106

EVALUATION RESULTS FOR Cl

precision_micro: 0.725352112676056
recall_micro: 0.8046875
f1_micro: 0.7629629629629631

EVALUATION RESULTS FOR CRE

precision_micro: 0.28169014084507005
recall_micro: 0.3125
f1_micro: 0.296296296296296

EVALUATION RESULTS FOR AR

precision_micro: 0.660412757973733
recall_micro: 0.6591760299625461
f1_micro: 0.659793814432989

EVALUATION RESULTS FOR BRE

precision_micro: 0.439252336448598
recall_micro: 0.49473684210526303
f1_micro: 0.46534653465346504

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.464788732394366
recall_micro: 0.515625
f1_micro: 0.48888888888888804

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.6410891089108911
recall_micro: 0.6301703163017031
f1_micro: 0.635582822085889

Span Labeling

The values for AR, Cl, MRE, CRE und BRE correspond to the values of MARE Span Lab. in Table 2.

The F1 scores for AR_no_trigger und MRE_no_trigger correspond to the values of Span Lab. with Trigger un Table 3

sh scripts/evaluate_model.sh models/span_based.tar.gz evaluations/span_lab mare.span_based_precidtor.SpanBasedPredictor mare

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.47244094488188904
recall_micro: 0.46875000000000006
f1_micro: 0.47058823529411703

EVALUATION RESULTS FOR Cl

precision_micro: 0.8031496062992121
recall_micro: 0.796875
f1_micro: 0.8

EVALUATION RESULTS FOR CRE

precision_micro: 0.291338582677165
recall_micro: 0.2890625
f1_micro: 0.290196078431372

EVALUATION RESULTS FOR AR

precision_micro: 0.751619870410367
recall_micro: 0.651685393258427
f1_micro: 0.698094282848545

EVALUATION RESULTS FOR BRE

precision_micro: 0.49473684210526303
recall_micro: 0.49473684210526303
f1_micro: 0.49473684210526303

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.519685039370078
recall_micro: 0.515625
f1_micro: 0.517647058823529

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.7298850574712641
recall_micro: 0.618004866180048
f1_micro: 0.6693017127799731

Dygie ++

The values for AR, Cl, MRE, CRE und BRE correspond to the values of Dygie++ in Table 2.

The F1 scores for AR_no_trigger und MRE_no_trigger correspond to the values of Dygie++ with Trigger in Table 3

sh scripts/evaluate_model.sh models/dygiepp.tar.gz evaluations/dygiepp mare.evaluation.mock_model.DygieppMockModel mare

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.47154471544715404
recall_micro: 0.453125
f1_micro: 0.46215139442231

EVALUATION RESULTS FOR Cl

precision_micro: 0.7723577235772351
recall_micro: 0.7421875
f1_micro: 0.7569721115537841

EVALUATION RESULTS FOR CRE

precision_micro: 0.260162601626016
recall_micro: 0.25
f1_micro: 0.254980079681274

EVALUATION RESULTS FOR AR

precision_micro: 0.630434782608695
recall_micro: 0.651685393258427
f1_micro: 0.6408839779005521

EVALUATION RESULTS FOR BRE

precision_micro: 0.550561797752809
recall_micro: 0.51578947368421
f1_micro: 0.5326086956521741

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.536585365853658
recall_micro: 0.515625
f1_micro: 0.525896414342629

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.596810933940774
recall_micro: 0.6374695863746951
f1_micro: 0.616470588235294

SpERT (SpART = SpERT with AllenNLP)

The value for BRE corresponds to the values of SpERT in Table 2.

sh scripts/evaluate_model.sh models/spart.tar.gz evaluations/spert spart spart

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.43269230769230704
recall_micro: 0.3515625
f1_micro: 0.387931034482758

EVALUATION RESULTS FOR Cl

precision_micro: 0.596153846153846
recall_micro: 0.484375
f1_micro: 0.5344827586206891

EVALUATION RESULTS FOR CRE

precision_micro: 0.08653846153846101
recall_micro: 0.0703125
f1_micro: 0.077586206896551

EVALUATION RESULTS FOR AR

precision_micro: 0.519230769230769
recall_micro: 0.202247191011235
f1_micro: 0.2911051212938

EVALUATION RESULTS FOR BRE

precision_micro: 0.573333333333333
recall_micro: 0.45263157894736805
f1_micro: 0.505882352941176

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.48076923076923006
recall_micro: 0.390625
f1_micro: 0.43103448275862005

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.528
recall_micro: 0.16058394160583903
f1_micro: 0.246268656716417

Sequence Tagging Baseline

The values for AR, Cl, MRE, CRE und BRE correspond to the values of MARE Baseline in Table 2.

sh scripts/evaluate_model.sh models/sequence_tagging_baseline.tar.gz evaluations/seq_tag_baseline seq_lab_elmo_pred mare

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.396825396825396
recall_micro: 0.390625
f1_micro: 0.39370078740157405

EVALUATION RESULTS FOR Cl

precision_micro: 0.682539682539682
recall_micro: 0.671875
f1_micro: 0.677165354330708

EVALUATION RESULTS FOR CRE

precision_micro: 0.26190476190476103
recall_micro: 0.2578125
f1_micro: 0.259842519685039

EVALUATION RESULTS FOR AR

precision_micro: 0.6591422121896161
recall_micro: 0.5468164794007491
f1_micro: 0.597748208802456

EVALUATION RESULTS FOR BRE

precision_micro: 0.40206185567010305
recall_micro: 0.410526315789473
f1_micro: 0.40625000000000006

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.42857142857142805
recall_micro: 0.421875
f1_micro: 0.42519685039370003

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.6296296296296291
recall_micro: 0.49635036496350304
f1_micro: 0.5551020408163261

Sequence Tagging No Trigger

The F1 scores for AR_no_trigger und MRE_no_trigger correspond to the values of Seq. Tag. without Trigger in Table 3

Change the include_trigger Parameter in mare/seq_lab_elmo_pred.py to False.

sh scripts/evaluate_model.sh models/sequence_no_trigger.tar.gz evaluations/seq_tag_no_trig seq_lab_elmo_pred_no_trig mare

The result shoud be


EVALUATION RESULTS FOR MRE

precision_micro: 0.056
recall_micro: 0.0546875
f1_micro: 0.055335968379446

EVALUATION RESULTS FOR Cl

precision_micro: 0.728
recall_micro: 0.7109375
f1_micro: 0.7193675889328061

EVALUATION RESULTS FOR CRE

precision_micro: 0.048
recall_micro: 0.046875
f1_micro: 0.047430830039525

EVALUATION RESULTS FOR AR

precision_micro: 0.662337662337662
recall_micro: 0.47752808988764006
f1_micro: 0.554951033732317

EVALUATION RESULTS FOR BRE

precision_micro: 0.07865168539325801
recall_micro: 0.073684210526315
f1_micro: 0.07608695652173901

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.512
recall_micro: 0.5
f1_micro: 0.50592885375494

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.662337662337662
recall_micro: 0.620437956204379
f1_micro: 0.6407035175879391

Span Labeling No Trigger

The F1 scores for AR_no_trigger und MRE_no_trigger correspond to the values of Span Lab. without Trigger in Table 3

sh scripts/evaluate_model.sh models/span_based_no_trigger_local.tar.gz evaluations/span_lab_no_trig mare.span_based_precidtor.SpanBasedPredictor mare

The result shoud be

EVALUATION RESULTS FOR MRE

precision_micro: 0.07563025210084001
recall_micro: 0.0703125
f1_micro: 0.072874493927125

EVALUATION RESULTS FOR Cl

precision_micro: 0.789915966386554
recall_micro: 0.734375
f1_micro: 0.761133603238866

EVALUATION RESULTS FOR CRE

precision_micro: 0.067226890756302
recall_micro: 0.0625
f1_micro: 0.064777327935222

EVALUATION RESULTS FOR AR

precision_micro: 0.72
recall_micro: 0.47191011235955005
f1_micro: 0.570135746606334

EVALUATION RESULTS FOR BRE

precision_micro: 0.103448275862068
recall_micro: 0.09473684210526301
f1_micro: 0.09890109890109801

EVALUATION RESULTS FOR MRE_no_trigger

precision_micro: 0.5630252100840331
recall_micro: 0.5234375
f1_micro: 0.542510121457489

EVALUATION RESULTS FOR AR_no_trigger

precision_micro: 0.72
recall_micro: 0.613138686131386
f1_micro: 0.6622864651773981
Owner
GitHub Repository
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe

D-X-Y 2k Dec 30, 2022
Faster Convex Lipschitz Regression

Faster Convex Lipschitz Regression This reepository provides a python implementation of our Faster Convex Lipschitz Regression algorithm with GPU and

Ali Siahkamari 0 Nov 19, 2021
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 03, 2023
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca

Kevin Costa 73 Sep 11, 2022
GAN-STEM-Conv2MultiSlice - Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

GAN-STEM-Conv2MultiSlice GAN method to help covert lower resolution STEM images generated by convolution methods to higher resolution STEM images gene

UW-Madison Computational Materials Group 2 Feb 10, 2021
python debugger and anti-vm that checks if you're in a virtual machine or if someones trying to debug your file

Anti-Debug was made by Love ❌ code ✅ 🎉 ・What it checks for ・ Kills tools that can be used to debug your file ・ Exits if ran in vm (supports different

Rdimo 31 Aug 09, 2022
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Transformer-based models are widely used in natural language processi

Zhanpeng Zeng 12 Jan 01, 2023
Collection of TensorFlow2 implementations of Generative Adversarial Network varieties presented in research papers.

TensorFlow2-GAN Collection of tf2.0 implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will

41 Apr 28, 2022
A transformer which can randomly augment VOC format dataset (both image and bbox) online.

VocAug It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it i

Coder.AN 1 Mar 05, 2022
Reproducing Results from A Hybrid Approach to Targeting Social Assistance

title author date output Reproducing Results from A Hybrid Approach to Targeting Social Assistance Lendie Follett and Heath Henderson 12/28/2021 html_

Lendie Follett 0 Jan 06, 2022
Evaluating different engineering tricks that make RL work

Reinforcement Learning Tricks, Index This repository contains the code for the paper "Distilling Reinforcement Learning Tricks for Video Games". Short

Anssi 15 Dec 26, 2022
Code for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators"

Query Variation Generators This repository contains the code and annotation data for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelin

Gustavo Penha 12 Nov 20, 2022
Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

Geometry Processing with Neural Fields Pytorch implementation for the NeurIPS 2021 paper: Geometry Processing with Neural Fields Guandao Yang, Serge B

Guandao Yang 162 Dec 16, 2022
Weakly Supervised Text-to-SQL Parsing through Question Decomposition

Weakly Supervised Text-to-SQL Parsing through Question Decomposition The official repository for the paper "Weakly Supervised Text-to-SQL Parsing thro

14 Dec 19, 2022
Libtorch yolov3 deepsort

Overview It is for my undergrad thesis in Tsinghua University. There are four modules in the project: Detection: YOLOv3 Tracking: SORT and DeepSORT Pr

Xu Wei 226 Dec 13, 2022
Eye-Blink-Counter - Python based Computer Vision project which counts how many time a person blinks

Eye Blink Counter OpenCV and Mediapipe No Blink Blink

Rohit Ingole 2 Mar 24, 2022
Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020.

RegNet Pytorch Implementation of "Desigining Network Design Spaces", Radosavovic et al. CVPR 2020. Paper | Official Implementation RegNet offer a very

Vishal R 2 Feb 11, 2022
Check out the StyleGAN repo and place it in the same directory hierarchy as the present repo

Variational Model Inversion Attacks Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani Most commands are in run_scripts. W

Jackson Wang 15 Dec 26, 2022
Implementation of Stochastic Image-to-Video Synthesis using cINNs.

Stochastic Image-to-Video Synthesis using cINNs Official PyTorch implementation of Stochastic Image-to-Video Synthesis using cINNs accepted to CVPR202

CompVis Heidelberg 135 Dec 28, 2022
FS-Mol: A Few-Shot Learning Dataset of Molecules

FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation

Microsoft 114 Dec 15, 2022