Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"

Overview

Prompt-Tuning

Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning" Currently, we support the following huggigface models:

  • BartForConditionalGeneration

Setup

conda create -n prompt-tuning python==3.7.0
pip install -r requirements.txt

Data

The task of summarization supports custom CSV and JSONLINES formats.

You can use data2json.py to transformer data to JSONLINES formats.

Custom CSV Files

If it's a csv file the training and validation files should have a column for the inputs texts and a column for the summaries.

If the csv file has just two columns as in the following example:

text,summary
"I'm sitting here in a boring room. It's just another rainy Sunday afternoon. I'm wasting my time I got nothing to do. I'm hanging around I'm waiting for you. But nothing ever happens. And I wonder","I'm sitting in a room where I'm waiting for something to happen"
"I see trees so green, red roses too. I see them bloom for me and you. And I think to myself what a wonderful world. I see skies so blue and clouds so white. The bright blessed day, the dark sacred night. And I think to myself what a wonderful world.","I'm a gardener and I'm a big fan of flowers."
"Christmas time is here. Happiness and cheer. Fun for all that children call. Their favorite time of the year. Snowflakes in the air. Carols everywhere. Olden times and ancient rhymes. Of love and dreams to share","It's that time of year again."

The first column is assumed to be for text and the second is for summary.

If the csv file has multiple columns, you can then specify the names of the columns to use:

    --text_column text_column_name \
    --summary_column summary_column_name \

For example if the columns were:

id,date,text,summary

and you wanted to select only text and summary, then you'd pass these additional arguments:

    --text_column text \
    --summary_column summary \

Custom JSONLINES Files

The second supported format is jsonlines. Here is an example of a jsonlines custom data file.

{"text": "I'm sitting here in a boring room. It's just another rainy Sunday afternoon. I'm wasting my time I got nothing to do. I'm hanging around I'm waiting for you. But nothing ever happens. And I wonder", "summary": "I'm sitting in a room where I'm waiting for something to happen"}
{"text": "I see trees so green, red roses too. I see them bloom for me and you. And I think to myself what a wonderful world. I see skies so blue and clouds so white. The bright blessed day, the dark sacred night. And I think to myself what a wonderful world.", "summary": "I'm a gardener and I'm a big fan of flowers."}
{"text": "Christmas time is here. Happiness and cheer. Fun for all that children call. Their favorite time of the year. Snowflakes in the air. Carols everywhere. Olden times and ancient rhymes. Of love and dreams to share", "summary": "It's that time of year again."}

Same as with the CSV files, by default the first value will be used as the text record and the second as the summary record. Therefore you can use any key names for the entries, in this example text and summary were used.

And as with the CSV files, you can specify which values to select from the file, by explicitly specifying the corresponding key names. In our example this again would be:

    --text_column text \
    --summary_column summary \

Train

bash run_train.sh

You can adjust the values for the arguments --train_file, --validation_file in run_train.sh

To control the prompt length, you can adjust the values for the arguments --pre_seq_len in run_train.sh.

Other setting, such as learning rate, batch_size, you can also adjust in run_train.sh.

Test

bash run_test.sh

You can adjust the values for the arguments --test_file in run_test.sh

Other setting, you can also adjust in run_test.sh. The generated summary is in output_dir/generated_predictions.txt

Citation

@misc{lester2021power,
      title={The Power of Scale for Parameter-Efficient Prompt Tuning}, 
      author={Brian Lester and Rami Al-Rfou and Noah Constant},
      year={2021},
      eprint={2104.08691},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Owner
Andrew Zeng
Andrew Zeng
Andrew Zeng
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Regression Transformer Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression . Development se

International Business Machines 27 Jan 05, 2023
Demonstrates iterative FGSM on Apple's NeuralHash model.

apple-neuralhash-attack Demonstrates iterative FGSM on Apple's NeuralHash model. TL;DR: It is possible to apply noise to CSAM images and make them loo

Lim Swee Kiat 11 Jun 23, 2022
Code, Models and Datasets for OpenViDial Dataset

OpenViDial This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Vis

119 Dec 08, 2022
🌳 A Python-inspired implementation of the Optimum-Path Forest classifier.

OPFython: A Python-Inspired Optimum-Path Forest Classifier Welcome to OPFython. Note that this implementation relies purely on the standard LibOPF. Th

Gustavo Rosa 30 Jan 04, 2023
Official implementation of the ICLR 2021 paper

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

Bosch Research 272 Dec 28, 2022
A scikit-learn-compatible module for estimating prediction intervals.

|Anaconda|_ MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals using your favourite sklearn

SimAI 584 Dec 27, 2022
A unified 3D Transformer Pipeline for visual synthesis

Overview This is the official repo for the paper: NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion. NÜWA is a unified multimodal p

Microsoft 2.6k Jan 06, 2023
Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21

MonoFlex Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21. Work in progress. Installation This repo is tested w

Yunpeng 169 Dec 06, 2022
Python library to receive live stream events like comments and gifts in realtime from TikTok LIVE.

TikTokLive A python library to connect to and read events from TikTok's LIVE service A python library to receive and decode livestream events such as

Isaac Kogan 277 Dec 23, 2022
Portfolio asset allocation strategies: from Markowitz to RNNs

Portfolio asset allocation strategies: from Markowitz to RNNs Research project to explore different approaches for optimal portfolio allocation starti

Luigi Filippo Chiara 1 Feb 05, 2022
(CVPR2021) Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain Mingchen Zhuge*, Dehong Gao*, Deng-Ping Fan#, Linbo Jin, Ben Chen, Haoming Zhou, Minghui

248 Dec 04, 2022
Calibrate your listeners! Robust communication-based training for pragmatic speakers. Findings of EMNLP 2021.

Calibrate your listeners! Robust communication-based training for pragmatic speakers Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman Findings of

Rose E. Wang 3 Apr 02, 2022
Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

StyleGAR TODO: add arxiv link Implementation of Inverting Generative Adversarial Renderer for Face Reconstruction TODO: for test Currently, some model

155 Oct 27, 2022
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments [Project website] [Paper] This project is a PyTorch

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 49 Nov 28, 2022
Gesture Volume Control v.2

Gesture volume control v.2 In this project I am going to learn how to use Gesture Control to change the volume of a computer. I first look into hand t

Pavel Dat 23 Dec 26, 2022
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring

Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas

YunzhuangS 2 May 02, 2022
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

Sanghyun Son 2.1k Dec 27, 2022
SimDeblur is a simple framework for image and video deblurring, implemented by PyTorch

SimDeblur (Simple Deblurring) is an open source framework for image and video deblurring toolbox based on PyTorch, which contains most deep-learning based state-of-the-art deblurring algorithms. It i

220 Jan 07, 2023
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

40 Dec 13, 2022