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

Source code for the NeurIPS 2021 paper "On the Second-order Convergence Properties of Random Search Methods"

[ICLR'19] Trellis Networks for Sequence Modeling

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Python Classes: Medical Insurance Project using Object Oriented Programming Concepts

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COVINS -- A Framework for Collaborative Visual-Inertial SLAM and Multi-Agent 3D Mapping

the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

The VeriNet toolkit for verification of neural networks

🗣️ Microsoft Edge TTS for Home Assistant, no need for app_key

Code for "Multi-Compound Transformer for Accurate Biomedical Image Segmentation"

VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging, ICCV2021 [PyTorch Code]

Implementations of polygamma, lgamma, and beta functions for PyTorch

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