Xiao9905 / P-tuning-v2

An optimized prompt tuning strategy comparable to fine-tuning across model scales and tasks.

P-tuning v2

P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

An optimized prompt tuning strategy achieving comparable performance to fine-tuning on small/medium-sized models and sequence tagging challenges.

P-tuning v2 leverages deep prompt tuning, which is to apply continuous prompts for every layer input of the pretrained transformer. Deep prompt tuning increases the capacity of continuous prompts and closes the gap to fine-tuning across various settings, especially for small models and hard tasks.

Reproduce Tips

We reimplement P-tuning v2's results on BERT-large/RoBERTa-large with:

• Ubuntu servers with NVIDIA GeForce RTX 3090 (24G) GPUs
• cuda 11.1
• packages with certain versions (provided below)

We notice that the best hyper-parameters can be sensitive to your server environment and package version. If you do not have the exact same environment, we highly recommend you to run hyper-parameter search in your environment based on our example hyper-parameter search script in search_script and result collection scripts search.py.

Setup

We conduct our experiment with Anaconda3. If you have installed Anaconda3, then create the environment for P-tuning v2:

conda create -n pt2 python=3.8.5
conda activate pt2

After we setup basic conda environment, install pytorch related packages via:

conda install -n pt2 pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch

Finally, install other python packages we need:

pip install -r requirements.txt

Data

For SuperGLUE and SQuAD datasets, we download them from the Huggingface Datasets APIs (embedded in our codes).