Training code for GPT-J with soft embedding:

Soft embedding code has been taken here: https://github.com/kipgparker/soft-prompt-tuning Some data classes based on same-named classes from this repo: https://github.com/XiangLi1999/PrefixTuning

Look train.sh for training prompts.

gptj_prompt_generation.ipynb - for text generation with trained prompt.

You will need the WebNLG dataset: https://gitlab.com/shimorina/webnlg-dataset Take any it's JSON version.

Install HuggingFace Transformers library from theit git repository:

pip install git+https://github.com/huggingface/transformers.git

Evaluation code hasn't been done, so it's a much to do here ;)

Orange line - train 1 prefix a time. Blue - train 2 prefixes a time.

The Power of Scale for Parameter-Efficient Prompt Tuning

Implementation of soft embeddings from <https://arxiv.org/abs/2104.08691v1> using Pytorch and Huggingface transformers

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}
}