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

[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

Predicting the duration of arrival delays for commercial flights.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

DROPO: Sim-to-Real Transfer with Offline Domain Randomization

Simple-Neural-Network From Scratch in Python

Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

PyTorch-Geometric Implementation of MarkovGNN: Graph Neural Networks on Markov Diffusion

This repository contains the code used for the implementation of the paper "Probabilistic Regression with HuberDistributions"

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