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README.md

Examples of using opendelta together with 🤗 transformers.

In this repo, we construct a very general pipeline to train and test a PLM using 🤗 transformers.

The pipeline was constructed together with openpromptu, which is a light and model-agnostic version of openprompt.

Pool of PLMs

We are going to adapt most of the models in 🤗 transformers in the repos. The different pipeline, processing, or configurations are specified in ./backbones/. You can add your own model in this file to support customized models.

A example script to run the repo in offline mode

conda activate [YOURENV]
PATHBASE=[YOURPATH]

JOBNAME="adapter_t5-base"
DATASET="superglue-cb"

cd $PATHBASE/OpenDelta/examples/examples_prompt/
python configs/gen_t5.py --job $JOBNAME

export TRANSFORMERS_OFFLINE=1
export HF_DATASETS_OFFLINE=1
python src/run.py configs/$JOBNAME/$DATASET.json \
--model_name_or_path [YOURPATH_TO_T5_BASE] \
--tokenizer_name [YOURPATH_TO_T5_BASE] \
--datasets_saved_path [YOURPATH_TO_CB_DATASETS] \
--finetuned_delta_path ${PATHBASE}/delta_checkpoints/ \
--num_train_epochs 20 \
--bottleneck_dim 24 \
--delay_push True

A example of quick testing the repo.

conda activate [YOURENV]
PATHBASE=[YOURPATH]

JOBNAME="adapter_t5-base"
DATASET="superglue-cb"

cd $PATHBASE/OpenDelta/examples/examples_prompt/

export TRANSFORMERS_OFFLINE=1
export HF_DATASETS_OFFLINE=1
export DELTACENTER_OFFLINE=0
python src/test.py configs/$JOBNAME/$DATASET.json \
--model_name_or_path [YOURPATH_TO_T5_BASE] \
--tokenizer_name [YOURPATH_TO_T5_BASE] \
--datasets_saved_path [YOURPATH_TO_CB_DATASETS] \
--finetuned_delta_path thunlp/t5-base_adapter_superglue-cb_20220701171436c80 \
--delta_cache_dir "./delta_checkpoints/" \
--force_download True