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

Last update: Dec 19, 2022

Related tags

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}
}

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

Related tags

Overview

Prompt-Tuning

Setup

Data

Custom CSV Files

Custom JSONLINES Files

Train

Test

Citation

Owner

Andrew Zeng

Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Implementation of PersonaGPT Dialog Model

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

Toward Spatially Unbiased Generative Models (ICCV 2021)

Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

Datasets for new state-of-the-art challenge in disentanglement learning

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

A model to classify a piece of news as REAL or FAKE

MPI-IS Mesh Processing Library

TICC is a python solver for efficiently segmenting and clustering a multivariate time series

Human head pose estimation using Keras over TensorFlow.

A working implementation of the Categorical DQN (Distributional RL).

Agile SVG maker for python

Using some basic methods to show linkages and transformations of robotic arms

OBBDetection is a oriented object detection library, which is based on MMdetection.

Video Matting Refinement For Python

Pytorch based library to rank predicted bounding boxes using text/image user's prompts.

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.