Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning
This repo is for Findings at EMNLP 2021 paper: Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning. Code clean-up is still in progress.
Data
Please extract the downloaded data and place it under PROJECT_DIR/datasets. Our training data stream and few-shot datasets are curated from https://github.com/iesl/leopard and https://github.com/INK-USC/CrossFit.
The directory structure is
PROJECT_DIR/datasets/crossfit_data/data/ + 55 classification tasks from the link above, e.g. PROJECT_DIR/datasets/crossfit_data/data/anli
PROJECT_DIR/datasets/leopard/ + 17 tasks from the link above, e.g. PROJECT_DIR/datasets/leopard/airline
Environment
Our code uses PyTorch 1.7.1. To allow fp16 training, you should also install apex.
Running Experiments
Training on CLIF-26
reg=0.01
lr=1e-4
seed=0
python run_model.py --tasks cola sst2 mrpc stsb qqp mnli qnli rte wnli \
--output_dir runs/glue_cfew_10k_choice_hnet_hardlong_sample_reg${reg}_s64_d256_limit/${lr}/${seed} \
--do_train --eval_period 100000 --eval_at_epoch_end --wait_step 3 --num_train_epochs 100 --seed ${seed} \
--train_batch_size 64 --gradient_accumulation_steps 2 --learning_rate ${lr} --max_output_length 8 \
--generator_hdim 32 --example_limit 100 --train_limit 10000 --cl_method hnet --h_l2reg ${reg} \
--adapter_dim 256 --adapter_dim_final 64 --hard_long_term --limit_label_vocab_space \
--sample_batch --scale_loss --stm_size 64
Few-shot evaluation on CLIF-26
python run_model.py --task_collection leopard --k_shot 16 --max_input_length 100 \
--output_dir /runs/glue_cfew_10k_choice_hnet_hardlong_sample_reg${reg}_s64_d256_limit/${lr}/${seed} \
--do_few_shot_predict --eval_period 100000 --eval_at_epoch_end --wait_step 3 --num_train_epochs 100 \
--seed ${seed} --train_batch_size 64 --predict_batch_size 16 --few_shot_train_batch_size 16 \
--few_shot_wait_step 100000 --few_shot_num_train_epochs 800 --wait_step 3 --gradient_accumulation_steps 4 \
--scale_by_accumulation --learning_rate ${lr} --max_output_length 8 --generator_hdim 32 \
--example_limit 100 --train_limit 10000 --cl_method naive --h_l2reg ${reg} --adapter_dim 256 \
--adapter_dim_final 64 --hard_long_term --limit_label_vocab_space --no_short_term --long_term_task_emb_num 9 \
--postfix "naive_16shot" --sample_batch --stm_size 64 --few_shot_eval_period 200
Training and evaluation on CLIF-55
reg=0.01
lr=1e-4
seed=0
python run_model.py --task_collection crossfit_cls_train --crossfit_k_shot 16 --ssd --output_dir runs/crossfit_hnet_merge_space_${reg}/${lr}/${seed} --skip_intermediate_ckpt --add_space --merge_split --split_id ${seed} --seed ${seed} --do_train --eval_every_k_tasks 5 --eval_period 100 --skip_intermediate_ckpt --train_batch_size 64 --wait_step 3 --num_train_epochs 10000000 --learning_rate ${lr} --max_output_length 64 --example_limit 100 --train_limit 10000 --cl_method hnet --h_l2reg ${reg} --adapter_dim 256 --generator_hdim 32 --adapter_dim_final 64 --sample_batch --hard_long_term --stm_size 64
python run_model.py --task_collection crossfit_cls_train --crossfit_k_shot 16 --ssd --output_dir runs/crossfit_hnet_merge_space${reg}/${lr}/${seed} --skip_intermediate_ckpt --add_space --merge_split --split_id ${seed} --seed ${seed} --do_predict --eval_every_k_tasks 5 --eval_period 100 --skip_intermediate_ckpt --train_batch_size 64 --wait_step 3 --num_train_epochs 10000000 --learning_rate ${lr} --max_output_length 64 --example_limit 100 --train_limit 10000 --cl_method hnet --h_l2reg ${reg} --adapter_dim 256 --generator_hdim 32 --adapter_dim_final 64 --sample_batch --hard_long_term --stm_size 64
for split_id in 0 1 2 3 4
do
python run_model.py --task_collection crossfit_cls_test --crossfit_k_shot 16 --ssd --postfix "split${split_id}" --long_term_task_emb_num 45 --do_few_shot_predict --few_shot_eval_period 200 --few_shot_num_train_epochs 800 --few_shot_train_batch_size 64 --few_shot_wait_step 100 --mtl_task_num 45 --output_dir runs/crossfit_hnet_merge_space_${reg}/${lr}/${seed} --add_space --limit_label_vocab_space --split_id ${split_id} --seed ${seed} --eval_period 100 --train_batch_size 64 --gradient_accumulation_steps 1 --wait_step 6 --num_train_epochs 10000 --learning_rate ${lr} --max_output_length 64 --example_limit 100 --train_limit 10000 --cl_method naive --adapter_dim 256 --generator_hdim 32 --adapter_dim_final 64 --sample_batch --hard_long_term
done
Here are mapping between command line arguments and implemented methods.
- BART-Single without adapter:
--cl_method naive --no_param_gen --skip_adapter --train_all - BART-Single-MTL:
--cl_method naive --no_param_gen --skip_mtl --mtl --train_all - BiHNET-Vanilla:
--cl_method naive --hard_long_term - BiHNET with trained task embeddings:
--cl_method hnet --no_short_term --train_task_embs --hard_long_term - BART-Adapter-Single:
--cl_method naive --no_param_gen --lr 3e-4
116 Nov 09, 2022
819 Jan 03, 2023
22 Dec 19, 2022
46 Nov 23, 2022
577 Jan 07, 2023
1.7k Dec 28, 2022
1 Jan 04, 2022
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16 Dec 12, 2022
60 Dec 25, 2022
11 Jul 21, 2022
44 Dec 31, 2022
72 Dec 09, 2022
847 Dec 19, 2022
130 Jan 04, 2023
72 Dec 30, 2022
42 Dec 27, 2022
64 May 08, 2022
7 Nov 07, 2022
16 Dec 07, 2022