p-tuning_NLU

Overview

这个小项目是受乐于分享的苏剑林大佬这篇p-tuning 文章启发，也实现了个使用P-tuning进行NLU分类的任务，思路是一样的，prompt实现方式有不同，这里是将[unused*]的embeddings参数抽取出用于初始化prompt_embed后，再接一个lstm和mlp用于关联各prompt，与最初p-tuning提出《GPT Understands, Too》的实现一样，结果显示在few-shot上p-tuning非常接近finetune效果。

Dataset

数据是情感分类，下载地址百度网盘提取码：osja

Evaluation

1. finetune

python few_shot_finetune.py

测试集效果：

epoch: 0 - acc: 0.897679 - best_test_acc: 0.8976788252013264
epoch: 1 - acc: 0.876362 - best_test_acc: 0.8976788252013264
epoch: 2 - acc: 0.884889 - best_test_acc: 0.8976788252013264
epoch: 3 - acc: 0.884415 - best_test_acc: 0.8976788252013264
epoch: 4 - acc: 0.884415 - best_test_acc: 0.8976788252013264

全量参数对小样本进行finetune，仅1个epoch就收敛了

2. p-tuning

python few_shot_ptuning.py

测试集效果：

epoch: 0 - acc: 0.546660 - best_test_acc: 0.5466603505447655
epoch: 1 - acc: 0.687826 - best_test_acc: 0.6878256750355282
epoch: 2 - acc: 0.737091 - best_test_acc: 0.7370914258645191
epoch: 3 - acc: 0.722406 - best_test_acc: 0.7370914258645191
epoch: 4 - acc: 0.776883 - best_test_acc: 0.7768829938417812
epoch: 5 - acc: 0.805306 - best_test_acc: 0.8053055423969683
epoch: 6 - acc: 0.833254 - best_test_acc: 0.8332543818095689
epoch: 7 - acc: 0.837991 - best_test_acc: 0.8379914732354334
epoch: 8 - acc: 0.854571 - best_test_acc: 0.8545712932259593
epoch: 9 - acc: 0.858361 - best_test_acc: 0.8583609663666508
epoch: 10 - acc: 0.856466 - best_test_acc: 0.8583609663666508
epoch: 11 - acc: 0.853150 - best_test_acc: 0.8583609663666508
epoch: 12 - acc: 0.868783 - best_test_acc: 0.8687825675035529
epoch: 13 - acc: 0.877309 - best_test_acc: 0.877309332070109
epoch: 14 - acc: 0.873993 - best_test_acc: 0.877309332070109
epoch: 15 - acc: 0.877783 - best_test_acc: 0.8777830412126955
epoch: 16 - acc: 0.882994 - best_test_acc: 0.8829938417811464
epoch: 17 - acc: 0.881573 - best_test_acc: 0.8829938417811464
epoch: 18 - acc: 0.889626 - best_test_acc: 0.8896257697773567
epoch: 19 - acc: 0.877783 - best_test_acc: 0.8896257697773567

仅prompt_embed和lstm及mlp去做p-tuning，20个epoch后接近收敛，acc=0.8896，略小于finetun的acc 0.8977

附上苏神结果对比：

p-tuning for few-shot NLU task

Related tags

Overview

p-tuning_NLU

Overview

Dataset

Evaluation

Owner

This repository collects together basic linguistic processing data for using dataset dumps from the Common Voice project

Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/

Automatic privilege escalation for misconfigured capabilities, sudo and suid binaries

🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.

This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

FewCLUE: 为中文NLP定制的小样本学习测评基准

KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)

Script to download some free japanese lessons in portuguse from NHK

Common Voice Dataset explorer

Predict an emoji that is associated with a text

Installation, test and evaluation of Scribosermo speech-to-text engine

👄 The most accurate natural language detection library for Python, suitable for long and short text alike

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

This is a simple item2vec implementation using gensim for recbole

Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

Chinese Named Entity Recognization (BiLSTM with PyTorch)

PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Simple Speech to Text, Text to Speech

SAVI2I: Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

Mkdocs + material + cool stuff