DART
Implementation for ICLR2022 paper Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners.
❗ NOTE: The code has been reorganized and we also provide a paper-list at PromptKG.
Environment
- [email protected]
- Use
pip install -r requirements.txtto install dependencies. wandbaccount is required if the user wants to search for best hyper-parameter combinations.
Data source
- 16-shot GLUE dataset from LM-BFF.
- Generated data consists of 5 random splits (13/21/42/87/100) for a task, each has 16 samples.
- The generation process follows LM-BFF here.
How to run
- To train / test on a data split from a single task with specific parameters, use
run.py.- For customized training & evaluation, you can modify based on the sample configuration file
config/sample.yml.
- For customized training & evaluation, you can modify based on the sample configuration file
$ python run.py -h
usage: run.py [-h] [--config CONFIG] [--do_train] [--do_test]
optional arguments:
-h, --help show this help message and exit
--config CONFIG, -c CONFIG
Configuration file storing all parameters
--do_train
--do_test- To search optimal hyper-parameters for each task and reproduce our result, please use
sweep.py:
$ python sweep.py -h
usage: sweep.py [-h] [--project_name PROJECT_NAME] --task_name TASK_NAME
[--data_split {13,21,42,87,100}]
[--pretrain_model PRETRAIN_MODEL] [--pet_method {pet,diffpet}]
[--random_seed RANDOM_SEED] [--max_run MAX_RUN]
optional arguments:
-h, --help show this help message and exit
--project_name PROJECT_NAME
project name for sweep
--task_name TASK_NAME
--data_split {13,21,42,87,100}
few-shot split-id for GLUE dataset
--pretrain_model PRETRAIN_MODEL
name or path for pretrained model
--pet_method {pet,diffpet}
prompt encoding method
--random_seed RANDOM_SEED
random seed for training
--max_run MAX_RUN maximum tries for sweepHow to Cite
@inproceedings{
zhang2022differentiable,
title={Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners},
author={Ningyu Zhang and Luoqiu Li and Xiang Chen and Shumin Deng and Zhen Bi and Chuanqi Tan and Fei Huang and Huajun Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=ek9a0qIafW}
}