4.3 KiB
python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --base-lm-path data/wiki/${LANG}-100/models/sp15k/qrnn_${NAME}.m --lang=${LANG} --name ${NAME}-2 - train 20 --bs 18 --num-cls-epochs=4 --lr_sched=1cycle --label-smoothing-eps=0.1 Max vocab: 15000 Cache dir: /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Model dir: /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl8-2.m Loading validation /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/ru.dev.csv Data lm, trn: 9195, val: 1021 Data cls, trn: 1000, val: 1000 Data tst, trn: 1000, val: 4000 Size of vocabulary: 15000 First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '', '▁', '▁,', '▁.', '▁в', 'а', 'и', 'е', '▁и', 'й', '▁на', 'х'] Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15} Loading pretrained model Unknown tokens 0, first 100: [] Training lm from: [PosixPath('/home/n-waves/workspace/ulmfit-multilingual/data/wiki/ru-100/models/sp15k/qrnn_nl8.m/lm_best'), PosixPath('/home/n-waves/workspace/ulmfit-multilingual/data/wiki/ru-100/models/sp15k/qrnn_nl8.m/../itos')] epoch train_loss valid_loss accuracy 1 3.966292 3.450758 0.527065 Total time: 02:58 epoch train_loss valid_loss accuracy 1 3.495761 3.276329 0.560047 2 3.319947 3.102911 0.593742 3 3.137904 2.955317 0.620171 4 3.040286 2.839161 0.642270 5 2.869962 2.753622 0.658331 6 2.905739 2.680881 0.672860 7 2.836454 2.620925 0.685026 8 2.857271 2.569716 0.695722 9 2.702872 2.520050 0.705589 10 2.701559 2.473591 0.715346 11 2.740815 2.429558 0.725597 12 2.646513 2.389550 0.735010 13 2.587685 2.349614 0.744885 14 2.546527 2.311087 0.754463 15 2.568136 2.278581 0.762980 16 2.492115 2.252367 0.769275 17 2.338561 2.230529 0.775072 18 2.447506 2.218215 0.778437 19 2.364424 2.212115 0.780085 20 2.367132 2.210520 0.780424 Total time: 1:30:47 /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Saving info /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl8-2.m/info.json Single training schedule epoch train_loss valid_loss accuracy 1 0.969255 0.769736 0.843000 2 0.846340 0.813483 0.839000 3 0.718175 0.705339 0.867000 4 0.609513 0.726442 0.875000 Total time: 02:54 Saving models at /home/n-waves/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl8-2.m Loss and accuracy using (cls_best): [0.4056449, tensor(0.8698)] 0.40564489364624023
(fastaiv1) n-waves@GV100:~/workspace/ulmfit-multilingual$ CUDA_VISIBLE_DEVICES=0 python -m ulmfit lm --dataset-path data/wiki/${LANG}-100 --tokenizer='sp' --nl 4 --name 'nl4-merity-wide2' --max-vocab 15000 --lang ${LANG} --qrnn=True --bptt=140 --nh 3100 - train 10 --bs=50 --drop_mult=0 --label-smoothing-eps=0.1 Max vocab: 15000 Cache dir: data/wiki/ru-100/models/sp15k Model dir: data/wiki/ru-100/models/sp15k/qrnn_nl4-merity-wide2.m Wiki text was split to 193047 articles Wiki text was split to 460 articles Data lm, trn: 193047, val: 460 Size of vocabulary: 15000 First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '', '▁', '▁,', '▁.', '▁в', 'а', 'и', 'е', '▁и', 'й', '▁на', 'х'] Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15} Training lm from random weights epoch train_loss valid_loss accuracy 1 3.871650 3.908779 0.467000 2 3.834093 3.884629 0.467916 3 3.741005 3.870331 0.469612 4 3.785444 3.818511 0.476906 5 3.741888 3.752743 0.486148 6 3.678481 3.672177 0.499054 7 3.570398 3.581498 0.512801 8 3.455193 3.482614 0.530569 9 3.379779 3.409405 0.543477 10 3.384574 3.387195 0.548881 Total time: 27:24:33 data/wiki/ru-100/models/sp15k