Prompt Tuning with Rules

Related tags

Deep LearningPTR
Overview

PTR

Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

If you use the code, please cite the following paper:

@article{han2021ptr,
  title={PTR: Prompt Tuning with Rules for Text Classification},
  author={Han, Xu and Zhao, Weilin and Ding, Ning and Liu, Zhiyuan and Sun, Maosong},
  journal={arXiv preprint arXiv:2105.11259},
  year={2021}
}

Requirements

The model is implemented using PyTorch. The versions of packages used are shown below.

  • numpy>=1.18.0

  • scikit-learn>=0.22.1

  • scipy>=1.4.1

  • torch>=1.3.0

  • tqdm>=4.41.1

  • transformers>=4.0.0

Baselines

Some baselines, especially the baselines using entity markers, come from the project [RE_improved_baseline].

Datasets

We provide all the datasets and prompts used in our experiments.

Run the experiments

(1) For TACRED

mkdir results
cd results
mkdir tacred
cd tacred
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_tacred.sh

(2) For TACREV

mkdir results
cd results
mkdir tacrev
cd tacrev
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_tacrev.sh

(3) For RETACRED

mkdir results
cd results
mkdir retacred
cd retacred
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_retacred.sh
Owner
THUNLP
Natural Language Processing Lab at Tsinghua University
THUNLP
SWA Object Detection

SWA Object Detection This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA

237 Nov 28, 2022
Code release for "Making a Bird AI Expert Work for You and Me".

Making-a-Bird-AI-Expert-Work-for-You-and-Me Code release for "Making a Bird AI Expert Work for You and Me". arxiv (Coming soon...) Changelog 2021/12/6

PRIS-CV: Computer Vision Group 11 Dec 11, 2022
A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning.

Open3DSOT A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning. The official code release of BAT an

Kangel Zenn 172 Dec 23, 2022
Agent-based model simulator for air quality and pandemic risk assessment in architectural spaces

Agent-based model simulation for air quality and pandemic risk assessment in architectural spaces. User Guide archABM is a fast and open source agent-

Vicomtech 10 Dec 05, 2022
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Ren Yurui 261 Jan 09, 2023
A Novel Plug-in Module for Fine-grained Visual Classification

Pytorch implementation for A Novel Plug-in Module for Fine-Grained Visual Classification. fine-grained visual classification task.

ChouPoYung 109 Dec 20, 2022
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

Jaeho Lee 41 Nov 10, 2022
Malware Analysis Neural Network project.

MalanaNeuralNetwork Description Malware Analysis Neural Network project. Table of Contents Getting Started Requirements Installation Clone Set-Up VENV

2 Nov 13, 2021
Implementation for ACProp ( Momentum centering and asynchronous update for adaptive gradient methdos, NeurIPS 2021)

This repository contains code to reproduce results for submission NeurIPS 2021, "Momentum Centering and Asynchronous Update for Adaptive Gradient Meth

Juntang Zhuang 15 Jun 11, 2022
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
MohammadReza Sharifi 27 Dec 13, 2022
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [BCNet, CVPR 2021] This is the official pytorch implementation of BCNet built on

Lei Ke 434 Dec 01, 2022
Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

Omar D. Domingues 1 Dec 02, 2021
GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras Overview This project provides a general environment for stoc

Kim, Ki Hyun 769 Dec 25, 2022
Implementation of our paper 'RESA: Recurrent Feature-Shift Aggregator for Lane Detection' in AAAI2021.

RESA PyTorch implementation of the paper "RESA: Recurrent Feature-Shift Aggregator for Lane Detection". Our paper has been accepted by AAAI2021. Intro

137 Jan 02, 2023
Meta-meta-learning with evolution and plasticity

Evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks

5 Jun 28, 2022
Pytorch implementation of DeePSiM

Pytorch implementation of DeePSiM

1 Nov 05, 2021
Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

GraphMask This repository contains an implementation of GraphMask, the interpretability technique for graph neural networks presented in our ICLR 2021

Michael Schlichtkrull 29 Sep 02, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022