2023-10-16 13:43:46,897 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,898 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(32001, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-16 13:43:46,898 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,898 MultiCorpus: 7142 train + 698 dev + 2570 test sentences - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator 2023-10-16 13:43:46,898 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,898 Train: 7142 sentences 2023-10-16 13:43:46,898 (train_with_dev=False, train_with_test=False) 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 Training Params: 2023-10-16 13:43:46,899 - learning_rate: "3e-05" 2023-10-16 13:43:46,899 - mini_batch_size: "8" 2023-10-16 13:43:46,899 - max_epochs: "10" 2023-10-16 13:43:46,899 - shuffle: "True" 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 Plugins: 2023-10-16 13:43:46,899 - LinearScheduler | warmup_fraction: '0.1' 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 Final evaluation on model from best epoch (best-model.pt) 2023-10-16 13:43:46,899 - metric: "('micro avg', 'f1-score')" 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 Computation: 2023-10-16 13:43:46,899 - compute on device: cuda:0 2023-10-16 13:43:46,899 - embedding storage: none 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 Model training base path: "hmbench-newseye/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:43:53,652 epoch 1 - iter 89/893 - loss 2.49154230 - time (sec): 6.75 - samples/sec: 3658.17 - lr: 0.000003 - momentum: 0.000000 2023-10-16 13:44:00,646 epoch 1 - iter 178/893 - loss 1.62126567 - time (sec): 13.75 - samples/sec: 3614.22 - lr: 0.000006 - momentum: 0.000000 2023-10-16 13:44:07,488 epoch 1 - iter 267/893 - loss 1.22582402 - time (sec): 20.59 - samples/sec: 3653.03 - lr: 0.000009 - momentum: 0.000000 2023-10-16 13:44:14,523 epoch 1 - iter 356/893 - loss 0.99528059 - time (sec): 27.62 - samples/sec: 3661.62 - lr: 0.000012 - momentum: 0.000000 2023-10-16 13:44:21,361 epoch 1 - iter 445/893 - loss 0.85563832 - time (sec): 34.46 - samples/sec: 3639.09 - lr: 0.000015 - momentum: 0.000000 2023-10-16 13:44:27,857 epoch 1 - iter 534/893 - loss 0.75454568 - time (sec): 40.96 - samples/sec: 3635.00 - lr: 0.000018 - momentum: 0.000000 2023-10-16 13:44:34,891 epoch 1 - iter 623/893 - loss 0.67109967 - time (sec): 47.99 - samples/sec: 3647.33 - lr: 0.000021 - momentum: 0.000000 2023-10-16 13:44:41,532 epoch 1 - iter 712/893 - loss 0.61218176 - time (sec): 54.63 - samples/sec: 3661.46 - lr: 0.000024 - momentum: 0.000000 2023-10-16 13:44:48,148 epoch 1 - iter 801/893 - loss 0.56314889 - time (sec): 61.25 - samples/sec: 3652.60 - lr: 0.000027 - momentum: 0.000000 2023-10-16 13:44:55,012 epoch 1 - iter 890/893 - loss 0.52200864 - time (sec): 68.11 - samples/sec: 3640.49 - lr: 0.000030 - momentum: 0.000000 2023-10-16 13:44:55,226 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:44:55,226 EPOCH 1 done: loss 0.5208 - lr: 0.000030 2023-10-16 13:44:58,272 DEV : loss 0.12075067311525345 - f1-score (micro avg) 0.7066 2023-10-16 13:44:58,288 saving best model 2023-10-16 13:44:58,731 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:45:05,320 epoch 2 - iter 89/893 - loss 0.13241194 - time (sec): 6.59 - samples/sec: 3790.57 - lr: 0.000030 - momentum: 0.000000 2023-10-16 13:45:12,294 epoch 2 - iter 178/893 - loss 0.12021519 - time (sec): 13.56 - samples/sec: 3795.99 - lr: 0.000029 - momentum: 0.000000 2023-10-16 13:45:19,078 epoch 2 - iter 267/893 - loss 0.11750837 - time (sec): 20.35 - samples/sec: 3792.31 - lr: 0.000029 - momentum: 0.000000 2023-10-16 13:45:25,904 epoch 2 - iter 356/893 - loss 0.11841414 - time (sec): 27.17 - samples/sec: 3728.66 - lr: 0.000029 - momentum: 0.000000 2023-10-16 13:45:32,684 epoch 2 - iter 445/893 - loss 0.11544678 - time (sec): 33.95 - samples/sec: 3718.33 - lr: 0.000028 - momentum: 0.000000 2023-10-16 13:45:39,485 epoch 2 - iter 534/893 - loss 0.11398255 - time (sec): 40.75 - samples/sec: 3711.64 - lr: 0.000028 - momentum: 0.000000 2023-10-16 13:45:46,276 epoch 2 - iter 623/893 - loss 0.11078897 - time (sec): 47.54 - samples/sec: 3690.56 - lr: 0.000028 - momentum: 0.000000 2023-10-16 13:45:52,795 epoch 2 - iter 712/893 - loss 0.10867892 - time (sec): 54.06 - samples/sec: 3704.94 - lr: 0.000027 - momentum: 0.000000 2023-10-16 13:45:59,583 epoch 2 - iter 801/893 - loss 0.10557874 - time (sec): 60.85 - samples/sec: 3706.50 - lr: 0.000027 - momentum: 0.000000 2023-10-16 13:46:05,978 epoch 2 - iter 890/893 - loss 0.10564723 - time (sec): 67.25 - samples/sec: 3683.34 - lr: 0.000027 - momentum: 0.000000 2023-10-16 13:46:06,201 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:46:06,201 EPOCH 2 done: loss 0.1055 - lr: 0.000027 2023-10-16 13:46:10,199 DEV : loss 0.11806550621986389 - f1-score (micro avg) 0.7094 2023-10-16 13:46:10,215 saving best model 2023-10-16 13:46:10,792 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:46:17,508 epoch 3 - iter 89/893 - loss 0.06756403 - time (sec): 6.71 - samples/sec: 3546.43 - lr: 0.000026 - momentum: 0.000000 2023-10-16 13:46:24,311 epoch 3 - iter 178/893 - loss 0.06905887 - time (sec): 13.52 - samples/sec: 3559.14 - lr: 0.000026 - momentum: 0.000000 2023-10-16 13:46:31,239 epoch 3 - iter 267/893 - loss 0.06890371 - time (sec): 20.44 - samples/sec: 3556.52 - lr: 0.000026 - momentum: 0.000000 2023-10-16 13:46:38,289 epoch 3 - iter 356/893 - loss 0.06587791 - time (sec): 27.49 - samples/sec: 3546.44 - lr: 0.000025 - momentum: 0.000000 2023-10-16 13:46:45,243 epoch 3 - iter 445/893 - loss 0.06381783 - time (sec): 34.45 - samples/sec: 3560.17 - lr: 0.000025 - momentum: 0.000000 2023-10-16 13:46:51,806 epoch 3 - iter 534/893 - loss 0.06384950 - time (sec): 41.01 - samples/sec: 3583.39 - lr: 0.000025 - momentum: 0.000000 2023-10-16 13:46:59,175 epoch 3 - iter 623/893 - loss 0.06292702 - time (sec): 48.38 - samples/sec: 3566.95 - lr: 0.000024 - momentum: 0.000000 2023-10-16 13:47:06,251 epoch 3 - iter 712/893 - loss 0.06408425 - time (sec): 55.45 - samples/sec: 3577.80 - lr: 0.000024 - momentum: 0.000000 2023-10-16 13:47:13,444 epoch 3 - iter 801/893 - loss 0.06538790 - time (sec): 62.65 - samples/sec: 3554.54 - lr: 0.000024 - momentum: 0.000000 2023-10-16 13:47:20,279 epoch 3 - iter 890/893 - loss 0.06456476 - time (sec): 69.48 - samples/sec: 3566.45 - lr: 0.000023 - momentum: 0.000000 2023-10-16 13:47:20,513 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:47:20,514 EPOCH 3 done: loss 0.0644 - lr: 0.000023 2023-10-16 13:47:24,535 DEV : loss 0.11452696472406387 - f1-score (micro avg) 0.7827 2023-10-16 13:47:24,551 saving best model 2023-10-16 13:47:25,042 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:47:32,102 epoch 4 - iter 89/893 - loss 0.04287257 - time (sec): 7.06 - samples/sec: 3684.25 - lr: 0.000023 - momentum: 0.000000 2023-10-16 13:47:39,186 epoch 4 - iter 178/893 - loss 0.04063102 - time (sec): 14.14 - samples/sec: 3584.36 - lr: 0.000023 - momentum: 0.000000 2023-10-16 13:47:45,973 epoch 4 - iter 267/893 - loss 0.04147794 - time (sec): 20.93 - samples/sec: 3584.08 - lr: 0.000022 - momentum: 0.000000 2023-10-16 13:47:53,237 epoch 4 - iter 356/893 - loss 0.04278598 - time (sec): 28.19 - samples/sec: 3559.01 - lr: 0.000022 - momentum: 0.000000 2023-10-16 13:47:59,557 epoch 4 - iter 445/893 - loss 0.04283638 - time (sec): 34.51 - samples/sec: 3604.20 - lr: 0.000022 - momentum: 0.000000 2023-10-16 13:48:06,337 epoch 4 - iter 534/893 - loss 0.04347932 - time (sec): 41.29 - samples/sec: 3619.66 - lr: 0.000021 - momentum: 0.000000 2023-10-16 13:48:12,846 epoch 4 - iter 623/893 - loss 0.04461082 - time (sec): 47.80 - samples/sec: 3609.56 - lr: 0.000021 - momentum: 0.000000 2023-10-16 13:48:19,452 epoch 4 - iter 712/893 - loss 0.04492176 - time (sec): 54.41 - samples/sec: 3624.66 - lr: 0.000021 - momentum: 0.000000 2023-10-16 13:48:26,362 epoch 4 - iter 801/893 - loss 0.04597372 - time (sec): 61.32 - samples/sec: 3619.82 - lr: 0.000020 - momentum: 0.000000 2023-10-16 13:48:33,542 epoch 4 - iter 890/893 - loss 0.04676504 - time (sec): 68.50 - samples/sec: 3619.95 - lr: 0.000020 - momentum: 0.000000 2023-10-16 13:48:33,772 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:48:33,772 EPOCH 4 done: loss 0.0467 - lr: 0.000020 2023-10-16 13:48:38,330 DEV : loss 0.1235646903514862 - f1-score (micro avg) 0.7821 2023-10-16 13:48:38,345 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:48:45,005 epoch 5 - iter 89/893 - loss 0.02387243 - time (sec): 6.66 - samples/sec: 3482.39 - lr: 0.000020 - momentum: 0.000000 2023-10-16 13:48:52,056 epoch 5 - iter 178/893 - loss 0.03032860 - time (sec): 13.71 - samples/sec: 3679.13 - lr: 0.000019 - momentum: 0.000000 2023-10-16 13:48:58,977 epoch 5 - iter 267/893 - loss 0.03259763 - time (sec): 20.63 - samples/sec: 3668.08 - lr: 0.000019 - momentum: 0.000000 2023-10-16 13:49:05,752 epoch 5 - iter 356/893 - loss 0.03570155 - time (sec): 27.41 - samples/sec: 3680.40 - lr: 0.000019 - momentum: 0.000000 2023-10-16 13:49:12,035 epoch 5 - iter 445/893 - loss 0.03481697 - time (sec): 33.69 - samples/sec: 3660.13 - lr: 0.000018 - momentum: 0.000000 2023-10-16 13:49:19,355 epoch 5 - iter 534/893 - loss 0.03425923 - time (sec): 41.01 - samples/sec: 3658.78 - lr: 0.000018 - momentum: 0.000000 2023-10-16 13:49:26,083 epoch 5 - iter 623/893 - loss 0.03439068 - time (sec): 47.74 - samples/sec: 3652.14 - lr: 0.000018 - momentum: 0.000000 2023-10-16 13:49:32,693 epoch 5 - iter 712/893 - loss 0.03544740 - time (sec): 54.35 - samples/sec: 3649.21 - lr: 0.000017 - momentum: 0.000000 2023-10-16 13:49:39,657 epoch 5 - iter 801/893 - loss 0.03607589 - time (sec): 61.31 - samples/sec: 3633.76 - lr: 0.000017 - momentum: 0.000000 2023-10-16 13:49:46,685 epoch 5 - iter 890/893 - loss 0.03580004 - time (sec): 68.34 - samples/sec: 3632.42 - lr: 0.000017 - momentum: 0.000000 2023-10-16 13:49:46,906 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:49:46,906 EPOCH 5 done: loss 0.0358 - lr: 0.000017 2023-10-16 13:49:50,900 DEV : loss 0.16850945353507996 - f1-score (micro avg) 0.8003 2023-10-16 13:49:50,916 saving best model 2023-10-16 13:49:52,046 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:49:58,748 epoch 6 - iter 89/893 - loss 0.02777395 - time (sec): 6.70 - samples/sec: 3551.20 - lr: 0.000016 - momentum: 0.000000 2023-10-16 13:50:05,732 epoch 6 - iter 178/893 - loss 0.02629674 - time (sec): 13.68 - samples/sec: 3643.60 - lr: 0.000016 - momentum: 0.000000 2023-10-16 13:50:12,637 epoch 6 - iter 267/893 - loss 0.02593427 - time (sec): 20.59 - samples/sec: 3624.05 - lr: 0.000016 - momentum: 0.000000 2023-10-16 13:50:19,772 epoch 6 - iter 356/893 - loss 0.02773488 - time (sec): 27.72 - samples/sec: 3605.78 - lr: 0.000015 - momentum: 0.000000 2023-10-16 13:50:26,258 epoch 6 - iter 445/893 - loss 0.02870583 - time (sec): 34.21 - samples/sec: 3627.65 - lr: 0.000015 - momentum: 0.000000 2023-10-16 13:50:33,263 epoch 6 - iter 534/893 - loss 0.02775510 - time (sec): 41.21 - samples/sec: 3667.80 - lr: 0.000015 - momentum: 0.000000 2023-10-16 13:50:39,881 epoch 6 - iter 623/893 - loss 0.02870404 - time (sec): 47.83 - samples/sec: 3678.92 - lr: 0.000014 - momentum: 0.000000 2023-10-16 13:50:46,806 epoch 6 - iter 712/893 - loss 0.02927233 - time (sec): 54.76 - samples/sec: 3670.61 - lr: 0.000014 - momentum: 0.000000 2023-10-16 13:50:53,440 epoch 6 - iter 801/893 - loss 0.02905059 - time (sec): 61.39 - samples/sec: 3651.61 - lr: 0.000014 - momentum: 0.000000 2023-10-16 13:51:00,351 epoch 6 - iter 890/893 - loss 0.02892722 - time (sec): 68.30 - samples/sec: 3634.00 - lr: 0.000013 - momentum: 0.000000 2023-10-16 13:51:00,543 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:51:00,543 EPOCH 6 done: loss 0.0290 - lr: 0.000013 2023-10-16 13:51:04,562 DEV : loss 0.17858995497226715 - f1-score (micro avg) 0.8013 2023-10-16 13:51:04,578 saving best model 2023-10-16 13:51:05,103 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:51:11,331 epoch 7 - iter 89/893 - loss 0.01538452 - time (sec): 6.22 - samples/sec: 3772.50 - lr: 0.000013 - momentum: 0.000000 2023-10-16 13:51:17,722 epoch 7 - iter 178/893 - loss 0.02131337 - time (sec): 12.62 - samples/sec: 3726.95 - lr: 0.000013 - momentum: 0.000000 2023-10-16 13:51:24,347 epoch 7 - iter 267/893 - loss 0.02097900 - time (sec): 19.24 - samples/sec: 3757.18 - lr: 0.000012 - momentum: 0.000000 2023-10-16 13:51:31,181 epoch 7 - iter 356/893 - loss 0.02190829 - time (sec): 26.07 - samples/sec: 3745.44 - lr: 0.000012 - momentum: 0.000000 2023-10-16 13:51:37,865 epoch 7 - iter 445/893 - loss 0.02094727 - time (sec): 32.76 - samples/sec: 3702.99 - lr: 0.000012 - momentum: 0.000000 2023-10-16 13:51:44,624 epoch 7 - iter 534/893 - loss 0.02163093 - time (sec): 39.52 - samples/sec: 3697.98 - lr: 0.000011 - momentum: 0.000000 2023-10-16 13:51:51,853 epoch 7 - iter 623/893 - loss 0.02291209 - time (sec): 46.75 - samples/sec: 3689.77 - lr: 0.000011 - momentum: 0.000000 2023-10-16 13:51:59,294 epoch 7 - iter 712/893 - loss 0.02293232 - time (sec): 54.19 - samples/sec: 3678.83 - lr: 0.000011 - momentum: 0.000000 2023-10-16 13:52:06,044 epoch 7 - iter 801/893 - loss 0.02333221 - time (sec): 60.94 - samples/sec: 3662.26 - lr: 0.000010 - momentum: 0.000000 2023-10-16 13:52:12,889 epoch 7 - iter 890/893 - loss 0.02382874 - time (sec): 67.78 - samples/sec: 3659.34 - lr: 0.000010 - momentum: 0.000000 2023-10-16 13:52:13,099 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:52:13,099 EPOCH 7 done: loss 0.0239 - lr: 0.000010 2023-10-16 13:52:17,661 DEV : loss 0.18977805972099304 - f1-score (micro avg) 0.8051 2023-10-16 13:52:17,677 saving best model 2023-10-16 13:52:18,230 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:52:25,001 epoch 8 - iter 89/893 - loss 0.01760355 - time (sec): 6.77 - samples/sec: 3525.99 - lr: 0.000010 - momentum: 0.000000 2023-10-16 13:52:31,737 epoch 8 - iter 178/893 - loss 0.01600479 - time (sec): 13.51 - samples/sec: 3609.06 - lr: 0.000009 - momentum: 0.000000 2023-10-16 13:52:38,483 epoch 8 - iter 267/893 - loss 0.01545751 - time (sec): 20.25 - samples/sec: 3645.30 - lr: 0.000009 - momentum: 0.000000 2023-10-16 13:52:45,395 epoch 8 - iter 356/893 - loss 0.01612712 - time (sec): 27.16 - samples/sec: 3651.36 - lr: 0.000009 - momentum: 0.000000 2023-10-16 13:52:51,691 epoch 8 - iter 445/893 - loss 0.01694443 - time (sec): 33.46 - samples/sec: 3648.63 - lr: 0.000008 - momentum: 0.000000 2023-10-16 13:52:58,579 epoch 8 - iter 534/893 - loss 0.01655607 - time (sec): 40.35 - samples/sec: 3624.80 - lr: 0.000008 - momentum: 0.000000 2023-10-16 13:53:05,827 epoch 8 - iter 623/893 - loss 0.01701668 - time (sec): 47.60 - samples/sec: 3641.53 - lr: 0.000008 - momentum: 0.000000 2023-10-16 13:53:13,155 epoch 8 - iter 712/893 - loss 0.01685952 - time (sec): 54.92 - samples/sec: 3628.71 - lr: 0.000007 - momentum: 0.000000 2023-10-16 13:53:19,500 epoch 8 - iter 801/893 - loss 0.01672613 - time (sec): 61.27 - samples/sec: 3625.67 - lr: 0.000007 - momentum: 0.000000 2023-10-16 13:53:26,651 epoch 8 - iter 890/893 - loss 0.01754531 - time (sec): 68.42 - samples/sec: 3618.81 - lr: 0.000007 - momentum: 0.000000 2023-10-16 13:53:26,999 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:53:26,999 EPOCH 8 done: loss 0.0175 - lr: 0.000007 2023-10-16 13:53:31,625 DEV : loss 0.19642271101474762 - f1-score (micro avg) 0.7989 2023-10-16 13:53:31,642 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:53:38,444 epoch 9 - iter 89/893 - loss 0.01210769 - time (sec): 6.80 - samples/sec: 3501.99 - lr: 0.000006 - momentum: 0.000000 2023-10-16 13:53:45,076 epoch 9 - iter 178/893 - loss 0.01089980 - time (sec): 13.43 - samples/sec: 3607.10 - lr: 0.000006 - momentum: 0.000000 2023-10-16 13:53:51,952 epoch 9 - iter 267/893 - loss 0.01206946 - time (sec): 20.31 - samples/sec: 3558.60 - lr: 0.000006 - momentum: 0.000000 2023-10-16 13:53:58,996 epoch 9 - iter 356/893 - loss 0.01230578 - time (sec): 27.35 - samples/sec: 3545.34 - lr: 0.000005 - momentum: 0.000000 2023-10-16 13:54:05,513 epoch 9 - iter 445/893 - loss 0.01310413 - time (sec): 33.87 - samples/sec: 3557.03 - lr: 0.000005 - momentum: 0.000000 2023-10-16 13:54:12,606 epoch 9 - iter 534/893 - loss 0.01304358 - time (sec): 40.96 - samples/sec: 3563.63 - lr: 0.000005 - momentum: 0.000000 2023-10-16 13:54:19,791 epoch 9 - iter 623/893 - loss 0.01306085 - time (sec): 48.15 - samples/sec: 3546.44 - lr: 0.000004 - momentum: 0.000000 2023-10-16 13:54:26,979 epoch 9 - iter 712/893 - loss 0.01371970 - time (sec): 55.34 - samples/sec: 3565.24 - lr: 0.000004 - momentum: 0.000000 2023-10-16 13:54:33,781 epoch 9 - iter 801/893 - loss 0.01284582 - time (sec): 62.14 - samples/sec: 3588.43 - lr: 0.000004 - momentum: 0.000000 2023-10-16 13:54:40,711 epoch 9 - iter 890/893 - loss 0.01318723 - time (sec): 69.07 - samples/sec: 3589.78 - lr: 0.000003 - momentum: 0.000000 2023-10-16 13:54:40,933 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:54:40,933 EPOCH 9 done: loss 0.0132 - lr: 0.000003 2023-10-16 13:54:44,996 DEV : loss 0.19601404666900635 - f1-score (micro avg) 0.82 2023-10-16 13:54:45,012 saving best model 2023-10-16 13:54:45,569 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:54:52,458 epoch 10 - iter 89/893 - loss 0.01232102 - time (sec): 6.89 - samples/sec: 3580.68 - lr: 0.000003 - momentum: 0.000000 2023-10-16 13:54:59,061 epoch 10 - iter 178/893 - loss 0.01172762 - time (sec): 13.49 - samples/sec: 3553.37 - lr: 0.000003 - momentum: 0.000000 2023-10-16 13:55:05,897 epoch 10 - iter 267/893 - loss 0.01214811 - time (sec): 20.33 - samples/sec: 3580.72 - lr: 0.000002 - momentum: 0.000000 2023-10-16 13:55:12,611 epoch 10 - iter 356/893 - loss 0.01159662 - time (sec): 27.04 - samples/sec: 3624.28 - lr: 0.000002 - momentum: 0.000000 2023-10-16 13:55:19,351 epoch 10 - iter 445/893 - loss 0.01125182 - time (sec): 33.78 - samples/sec: 3627.33 - lr: 0.000002 - momentum: 0.000000 2023-10-16 13:55:26,203 epoch 10 - iter 534/893 - loss 0.01148185 - time (sec): 40.63 - samples/sec: 3623.21 - lr: 0.000001 - momentum: 0.000000 2023-10-16 13:55:32,925 epoch 10 - iter 623/893 - loss 0.01082743 - time (sec): 47.35 - samples/sec: 3628.25 - lr: 0.000001 - momentum: 0.000000 2023-10-16 13:55:39,767 epoch 10 - iter 712/893 - loss 0.01097241 - time (sec): 54.20 - samples/sec: 3633.23 - lr: 0.000001 - momentum: 0.000000 2023-10-16 13:55:46,432 epoch 10 - iter 801/893 - loss 0.01107939 - time (sec): 60.86 - samples/sec: 3640.46 - lr: 0.000000 - momentum: 0.000000 2023-10-16 13:55:53,393 epoch 10 - iter 890/893 - loss 0.01153753 - time (sec): 67.82 - samples/sec: 3653.55 - lr: 0.000000 - momentum: 0.000000 2023-10-16 13:55:53,634 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:55:53,634 EPOCH 10 done: loss 0.0115 - lr: 0.000000 2023-10-16 13:55:58,213 DEV : loss 0.19514599442481995 - f1-score (micro avg) 0.8204 2023-10-16 13:55:58,230 saving best model 2023-10-16 13:55:59,218 ---------------------------------------------------------------------------------------------------- 2023-10-16 13:55:59,219 Loading model from best epoch ... 2023-10-16 13:56:00,770 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-16 13:56:10,110 Results: - F-score (micro) 0.6969 - F-score (macro) 0.6147 - Accuracy 0.559 By class: precision recall f1-score support LOC 0.7240 0.6995 0.7116 1095 PER 0.7760 0.7806 0.7783 1012 ORG 0.4223 0.5938 0.4936 357 HumanProd 0.3529 0.7273 0.4752 33 micro avg 0.6772 0.7177 0.6969 2497 macro avg 0.5688 0.7003 0.6147 2497 weighted avg 0.6971 0.7177 0.7043 2497 2023-10-16 13:56:10,110 ----------------------------------------------------------------------------------------------------