multifit/results/logs/ru/wide2.md

                                        3         2.812496    2.877055    0.468569

4 2.705551 2.792535 0.479420

5 2.649598 2.726415 0.487439

6 2.599835 2.635610 0.499679

7 2.574639 2.554657 0.512358

8 2.489573 2.475936 0.523280

9 2.396540 2.415555 0.534089

10 2.374290 2.401968 0.536601

Total time: 15:49:20 data/wiki/ru-100/models/sp15k Saving info data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/info.json Fire trace:

1. Initial component
2. Accessed property "lm" (/home/test/workspace/ulmfit-multilingual/ulmfit/__main__.py:32)
3. Called routine "LMHyperParams" (/home/test/workspace/ulmfit-multilingual/ulmfit/__main__.py:32)
4. Accessed property "train" (/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py:174)
5. Called routine "train_lm" (/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py:174)
6. ('Could not consume arg:', '--nh')

Type: NoneType String form: None

Usage: _main_.py lm --dataset-path data/wiki/ru-100 --tokenizer=sp --nl 4 --name nl4-wide2 --max-vocab 15000 --lang ru --qrnn=True - train 10 --bs=100 --drop_mult=0 - (multifit) test@test:/workspace/ulmfit-multilingual$ less data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/info.json (multifit) test@test:/workspace/ulmfit-multilingual$ CUDA_VISIBLE_DEVICES=1 python -m ulmfit lm --dataset-path data/wiki/${LANG}-100 --tokenizer='sp' --nl 4 --name 'nl4-wide2' --max-vocab 15000 --lang ${LANG} --qrnn=True - train 10 --bs=100 --drop_mult=0 ^C100 -- (multifit) test@test:/workspace/ulmfit-multilingual$ mv data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/ data/wiki/ru-100/models/sp15k/qrnn_nl4-2.m/ (multifit) test@test:/workspace/ulmfit-multilingual$ less data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/info.json^C (multifit) test@test:/workspace/ulmfit-multilingual$ CUDA_VISIBLE_DEVICES=1 python -m ulmfit lm --dataset-path data/wiki/${LANG}-100 --tokenizer='sp' --nl 4 --name 'nl4-wid e2' --max-vocab 15000 --lang ${LANG} --qrnn=True --nh 3100 - train 10 --bs=100 --drop_mult=0 Max vocab: 15000 Cache dir: data/wiki/ru-100/models/sp15k Model dir: data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m ^CTraceback (most recent call last): File "/home/test/anaconda3/envs/multifit/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/test/anaconda3/envs/multifit/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/test/workspace/ulmfit-multilingual/ulmfit/__main__.py", line 119, in fire.Fire(ULMFiT()) File "/home/test/anaconda3/envs/multifit/lib/python3.7/site-packages/fire/core.py", line 127, in Fire component_trace = _Fire(component, args, context, name) File "/home/test/anaconda3/envs/multifit/lib/python3.7/site-packages/fire/core.py", line 366, in _Fire component, remaining_args) File "/home/test/anaconda3/envs/multifit/lib/python3.7/site-packages/fire/core.py", line 542, in _CallCallable result = fn(*varargs, *kwargs) File "/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py", line 176, in train_lm data_lm = self.load_wiki_data(bs=bs) if data_lm is None else data_lm File "/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py", line 253, in load_wiki_data train_df=read_wiki_articles(trn_path), File "/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py", line 48, in read_wiki_articles if i < len(lines)-2 and lines[i+1].strip() == "" and istitle(lines[i+2]): File "/home/test/workspace/ulmfit-multilingual/ulmfit/pretrain_lm.py", line 39, in istitle return len(re.findall(r'^ ?= [^=] = ?$', line)) != 0 File "/home/test/anaconda3/envs/multifit/lib/python3.7/re.py", line 223, in findall return _compile(pattern, flags).findall(string) KeyboardInterrupt ^C (multifit) test@test:/workspace/ulmfit-multilingual$ CUDA_VISIBLE_DEVICES=1 python -m ulmfit lm --dataset-path data/wiki/${LANG}-100 --tokenizer='sp' --nl 4 --name 'nl4-wide2' --max-vocab 15000 --lang ${LANG} --qrnn=True --nh 3100 - train 10 --bs=100 --drop_mult=0 --label-smoothing-eps=0.1 Max vocab: 15000 Cache dir: data/wiki/ru-100/models/sp15k Model dir: data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m Wiki text was split to 193047 articles Wiki text was split to 460 articles Data lm, trn: 193047, val: 460 Size of vocabulary: 15000 First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '', '▁', '▁,', '▁.', '▁в', 'а', 'и', 'е', '▁и', 'й', '▁на', 'х'] Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'output_p': 0.25, 'hidden_p': 0.1, 'input_p': 0.2, 'embed_p': 0.02, 'weight_p': 0.15} Training lm from random weights epoch train_loss valid_loss accuracy 1 3.908233 3.950865 0.463469 2 3.738863 3.815026 0.477703 3 3.696502 3.779513 0.483625 4 3.692592 3.720908 0.490143 5 3.600519 3.652444 0.501671 6 3.564568 3.582584 0.511550 7 3.472859 3.493226 0.525943 8 3.390483 3.407970 0.541749 9 3.351620 3.344207 0.552758 10 3.329683 3.330087 0.556380 Total time: 51:05:43 data/wiki/ru-100/models/sp15k Saving info data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/info.json (multifit) test@test:/workspace/ulmfit-multilingual$ export CUDA_VISIBLE_DEVICES=1 (multifit) test@test:/workspace/ulmfit-multilingual$ LANG=ru (multifit) test@test:/workspace/ulmfit-multilingual$ NAME=nl4-wide2 (multifit) test@test:/workspace/ulmfit-multilingual$ 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} - train 20 --bs 18 --num-cls-epochs=4 --lr_sched=1cycle --label-smoothing-eps=0.1 Max vocab: 15000 Cache dir: /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Model dir: /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2.m Loading validation /home/test/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/test/workspace/ulmfit-multilingual/data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/lm_best'), PosixPath('/home/test/workspace/ulmfit-multilingual/data/wiki/ru-100/models/sp15k/qrnn_nl4-wide2.m/../itos')] epoch train_loss valid_loss accuracy 1 3.777423 3.222261 0.564503 Total time: 04:35 epoch train_loss valid_loss accuracy 1 3.292465 3.029143 0.602257 2 3.034045 2.858176 0.634576 3 2.943366 2.710314 0.665116 4 2.722069 2.596702 0.687515 5 2.819853 2.508158 0.705020 6 2.734984 2.417240 0.724748 7 2.674353 2.332395 0.743694 8 2.527344 2.251373 0.762892 9 2.473972 2.168185 0.784043 10 2.359504 2.093983 0.803255 11 2.287590 2.019540 0.823566 12 2.254421 1.943832 0.845138 13 2.203321 1.884380 0.863381 14 2.142532 1.824186 0.881509 15 2.121573 1.777664 0.894901 16 2.013238 1.740772 0.905824 17 2.026189 1.715271 0.913569 18 1.904322 1.700163 0.917917 19 1.889113 1.692539 0.919811 20 1.903118 1.691033 0.920319 Total time: 3:10:09 /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Saving info /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2.m/info.json Single training schedule epoch train_loss valid_loss accuracy 1 1.006922 0.880313 0.788000 2 0.823572 0.782953 0.860000 3 0.679078 0.749164 0.872000 4 0.579215 0.707200 0.872000 Total time: 06:30 Saving models at /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2.m Loss and accuracy using (cls_best): [0.3935929, tensor(0.8708)] 0.393592894077301 0.8707500100135803 (multifit) test@test:~/workspace/ulmfit-multilingual$ python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --base-lm-path data/mldoc/${LANG}-1/models/sp15k/qrnn${NAME}.m --lang=${LANG} --name ${NAME}-16 - train 0 --bs 18 --num-cls-epochs=16 --lr_sched=1cycle --label-smoothing-eps=0.1 Max vocab: 15000 Cache dir: /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Model dir: /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2-16.m Loading validation /home/test/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: [] /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k Saving info /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2-16.m/info.json Single training schedule epoch train_loss valid_loss accuracy 1 1.067446 0.822965 0.824000 2 0.897088 0.845636 0.826000 3 0.778055 0.828693 0.847000 4 0.685080 0.893327 0.823000 5 0.620457 0.929057 0.800000 6 0.587644 0.802154 0.859000 7 0.570255 0.713434 0.872000 8 0.543071 0.705259 0.871000 9 0.517465 0.715090 0.867000 10 0.498291 0.695459 0.876000 11 0.497857 0.698052 0.862000 12 0.486924 0.681911 0.878000 13 0.479041 0.676714 0.874000 14 0.475131 0.677843 0.878000 15 0.467238 0.672065 0.876000 16 0.476889 0.680850 0.875000 Total time: 23:47 Saving models at /home/test/workspace/ulmfit-multilingual/data/mldoc/ru-1/models/sp15k/qrnn_nl4-wide2-16.m Loss and accuracy using (cls_best): [0.41155785, tensor(0.8700)] 0.4115578532218933 0.8700000047683716