FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing
This project contains experiment code for the respective publication: training task adapters by progressively unfreezing them during the training. This is built on a Fork of the Hugging Face Transformers library and the adapter-transformer library.
https://www.ukp.tu-darmstadt.de/
This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication. If you encounter any issues, please do not hesitate to email us at: chen.liu AT tu-darmstadt DOT de
Please see the original Adapter_README.md for adapter-transformers. From source by cloning the repository:
git clone
cd naccl2024-fun
pip install -e .
The scripts for running our experiments are:
examples/pytorch/question-answering/run_qa.py- XQuAD/MLQAexamples/pytorch/multiple-choice/run_copa.py- XCOPAexamples/pytorch/text-classification/run_xnli.py- XNLI
Note: Please comment out the import wandb if you don't wish to send results to wandb.
Alternatively, to enable logging to wandb, you need to add something like this
wandb.init(project="myproject", entity="abcde")
to the scripts.
Other than the args from the original adapter transformers, there are several important args:
-
--freeze_all[bool] This option will freeze all layers for "roberta" or "bert" based-model. This is required to use all scheduled unfreezing. -
--use_gu[bool] This option allows you to run gradual unfreezing on predetermined intervals. -
--use_schedule[bool] This option allows you to turn on other scheduled unfreezing method. -
--unfreeze_interval[str] Unfreezing interval. Available ones are:50-12,100-12,800-12,1000-12. -
--schedule_style[str] This option allows you to choose from one of the schduled unfreezing: "lpft", "one", "rand" or a pre-setted schedule (e.g. "schedule-1"). Note, you don't need this to run gradual_unfreezing. -
--exp_name[str] Experiment name to report to wandb.
Assume you want to run on qa dataset, with gradual unfreezing:
python run_qa.py \
--model_name_or_path xlm-roberta-base \
--dataset_name squad \
--seed $seed \
--do_train \
--do_eval \
--freeze_all \ # feeze all the adapters initially
--use_gu \ # use gradual unfreezing
--train_adapter \
--adapter_config "pfeiffer+inv" \
--load_lang_adapter en/wiki@ukp \
--language en \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 32 \
--learning_rate 5e-4 \
--num_train_epochs 15 \
--overwrite_output_dir \
--save_total_limit 2 \
--load_best_model_at_end \
--evaluation_strategy steps \
--metric_for_best_model eval_f1 \
--greater_is_better True \
--max_seq_length 384 \
--doc_stride 128 \
--exp_name xlmr_squad_gu_$seed \
--output_dir squad_gu_$seed
If you want to run on QA dataset, with FUN, unfreeze at every 50 steps:
python run_qa.py \
--model_name_or_path xlm-roberta-base \
--dataset_name squad \
--seed $seed \
--do_train \
--do_eval \
--freeze_all \ # feeze all the adapters initially
--use_schedule \ # use a schedule other than gu
--schedule_style one \ # unfreeze one adapter at a time using tr(F)
--unfreeze_interval 50-12 \ # unfreeze an adapter every 50 steps
--train_adapter \
--adapter_config "pfeiffer+inv" \
--load_lang_adapter en/wiki@ukp \
--language en \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 32 \
--learning_rate 5e-4 \
--num_train_epochs 15 \
--overwrite_output_dir \
--save_total_limit 2 \
--load_best_model_at_end \
--evaluation_strategy steps \
--metric_for_best_model eval_f1 \
--greater_is_better True \
--max_seq_length 384 \
--doc_stride 128 \
--exp_name xlmr_squad_fun_$seed \
--output_dir squad_fun_$seed
If you use this for your work, please consider citing our paper, as well as the AdapterHub.
@inproceedings{liu-etal-2023-fun,
title = {Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing},
author = "Chen Cecilia Liu and Jonas Pfeiffer and Ivan Vuli\'{c} and Iryna Gurevych"
publisher = {arXiv},
year = {2023},
}