WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction"

Last update: Dec 27, 2022

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Deep Learning BiRTE

Overview

BiRTE

WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction"

Requirements

The main requirements are:

python 3.6
torch 1.4.0
tqdm
transformers == 2.8.0

Usage

Train and select the model

python run.py --dataset=WebNLG --train=train --batch_size=6

python run.py --dataset=WebNLG_star --train=train --batch_size=6

python run.py --dataset=NYT --train=train --batch_size=18

python run.py --dataset=NYT_star --train=train --batch_size=18

python run.py --dataset=NYT10 --train=train --batch_size=18

python run.py --dataset=NYT11 --train=train --batch_size=18

Evaluate on the test set

python run.py --dataset=WebNLG --train=test

python run.py --dataset=WebNLG_star --train=test

python run.py --dataset=NYT --train=test

python run.py --dataset=NYT_star --train=test

python run.py --dataset=NYT10 --train=test

python run.py --dataset=NYT11 --train=test

Owner

GitHub Repository

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WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction"

Related tags

Overview

BiRTE

Requirements

Usage

Owner

Semi-supervised Learning for Sentiment Analysis

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

Python implementation of ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images, AAAI2022.

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This repository is dedicated to developing and maintaining code for experiments with wide neural networks.

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