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

The source codes for TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation.

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

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Code for the paper: Hierarchical Reinforcement Learning With Timed Subgoals, published at NeurIPS 2021

ViDT: An Efficient and Effective Fully Transformer-based Object Detector

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CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image.

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A deep learning library that makes face recognition efficient and effective

TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

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