P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

Last update: Dec 30, 2022

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Overview

P-tuning v2

P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

An optimized prompt tuning strategy for smaller models and hard natural language understanding (NLU) tasks (e.g., sequence tagging).

This repo is still under construction (2-4 weeks expected). Your kindly starring our repo could encourage us to work harder :)

Owner

THUDM

Data Mining Research Group at Tsinghua University

GitHub Repository

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P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

Related tags

Overview

P-tuning v2

Owner

THUDM

Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

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Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space

Storage-optimizer - Identify potintial optimizations on the cloud storage accounts

CVPR 2021 Challenge on Super-Resolution Space

Adaptation through prediction: multisensory active inference torque control

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

Alternatives to Deep Neural Networks for Function Approximations in Finance

Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

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