be-great 0.0.2

Generating Realistic Tabular Data using Large Language Models

Generation of Realistic Tabular data
with pretrained Transformer-based language models

     

Our GReaT framework utilizes the capabilities of pretrained large language Transformer models to synthesize realistic tabular data. New samples are generated with just a few lines of code, following an easy-to-use API.

GReaT Installation

The GReaT framework can be easily installed using with pip:

pip install be-great

GReaT Quickstart

In the example below, we show how the GReaT approach is used to generate synthetic tabular data for the California Housing dataset.

from be_great import GReaT
from sklearn.datasets import fetch_california_housing

data = fetch_california_housing(as_frame=True).frame

model = GReaT(llm='distilgpt2', epochs=50)
model.fit(data)
synthetic_data = model.sample(n_samples=100)

GReaT Citation

If you use GReaT, please link or cite our work:

@article{
}

GReaT Acknowledgements

We sincerely thank the HuggingFace :hugs: framework.