2023-10-25 20:59:54,156 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,157 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(64001, 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() )" 2023-10-25 20:59:54,157 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,157 MultiCorpus: 1166 train + 165 dev + 415 test sentences - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator 2023-10-25 20:59:54,157 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,157 Train: 1166 sentences 2023-10-25 20:59:54,157 (train_with_dev=False, train_with_test=False) 2023-10-25 20:59:54,157 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,157 Training Params: 2023-10-25 20:59:54,157 - learning_rate: "3e-05" 2023-10-25 20:59:54,157 - mini_batch_size: "4" 2023-10-25 20:59:54,157 - max_epochs: "10" 2023-10-25 20:59:54,157 - shuffle: "True" 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 Plugins: 2023-10-25 20:59:54,158 - TensorboardLogger 2023-10-25 20:59:54,158 - LinearScheduler | warmup_fraction: '0.1' 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 Final evaluation on model from best epoch (best-model.pt) 2023-10-25 20:59:54,158 - metric: "('micro avg', 'f1-score')" 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 Computation: 2023-10-25 20:59:54,158 - compute on device: cuda:0 2023-10-25 20:59:54,158 - embedding storage: none 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 ---------------------------------------------------------------------------------------------------- 2023-10-25 20:59:54,158 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-25 20:59:55,465 epoch 1 - iter 29/292 - loss 2.86541598 - time (sec): 1.31 - samples/sec: 2860.19 - lr: 0.000003 - momentum: 0.000000 2023-10-25 20:59:56,853 epoch 1 - iter 58/292 - loss 2.05975293 - time (sec): 2.69 - samples/sec: 3359.48 - lr: 0.000006 - momentum: 0.000000 2023-10-25 20:59:58,259 epoch 1 - iter 87/292 - loss 1.51451020 - time (sec): 4.10 - samples/sec: 3531.96 - lr: 0.000009 - momentum: 0.000000 2023-10-25 20:59:59,549 epoch 1 - iter 116/292 - loss 1.27384924 - time (sec): 5.39 - samples/sec: 3495.41 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:00:00,811 epoch 1 - iter 145/292 - loss 1.11376262 - time (sec): 6.65 - samples/sec: 3466.71 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:00:02,161 epoch 1 - iter 174/292 - loss 0.97422500 - time (sec): 8.00 - samples/sec: 3503.92 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:00:03,427 epoch 1 - iter 203/292 - loss 0.89929848 - time (sec): 9.27 - samples/sec: 3393.73 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:00:04,663 epoch 1 - iter 232/292 - loss 0.82959634 - time (sec): 10.50 - samples/sec: 3372.16 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:00:05,904 epoch 1 - iter 261/292 - loss 0.78238070 - time (sec): 11.74 - samples/sec: 3319.59 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:00:07,217 epoch 1 - iter 290/292 - loss 0.72116046 - time (sec): 13.06 - samples/sec: 3383.27 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:00:07,293 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:07,293 EPOCH 1 done: loss 0.7173 - lr: 0.000030 2023-10-25 21:00:07,947 DEV : loss 0.15696528553962708 - f1-score (micro avg) 0.5511 2023-10-25 21:00:07,951 saving best model 2023-10-25 21:00:08,347 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:09,548 epoch 2 - iter 29/292 - loss 0.17890778 - time (sec): 1.20 - samples/sec: 3436.36 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:00:10,790 epoch 2 - iter 58/292 - loss 0.19515003 - time (sec): 2.44 - samples/sec: 3323.93 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:00:12,088 epoch 2 - iter 87/292 - loss 0.17002125 - time (sec): 3.74 - samples/sec: 3331.71 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:00:13,413 epoch 2 - iter 116/292 - loss 0.16239763 - time (sec): 5.06 - samples/sec: 3330.73 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:00:14,715 epoch 2 - iter 145/292 - loss 0.15872220 - time (sec): 6.37 - samples/sec: 3353.82 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:00:16,024 epoch 2 - iter 174/292 - loss 0.17064690 - time (sec): 7.68 - samples/sec: 3268.81 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:00:17,377 epoch 2 - iter 203/292 - loss 0.17532894 - time (sec): 9.03 - samples/sec: 3244.48 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:00:18,717 epoch 2 - iter 232/292 - loss 0.17700183 - time (sec): 10.37 - samples/sec: 3216.85 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:00:20,133 epoch 2 - iter 261/292 - loss 0.16918114 - time (sec): 11.78 - samples/sec: 3325.36 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:00:21,438 epoch 2 - iter 290/292 - loss 0.15999812 - time (sec): 13.09 - samples/sec: 3384.76 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:00:21,523 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:21,523 EPOCH 2 done: loss 0.1600 - lr: 0.000027 2023-10-25 21:00:22,424 DEV : loss 0.13564454019069672 - f1-score (micro avg) 0.7072 2023-10-25 21:00:22,428 saving best model 2023-10-25 21:00:22,981 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:24,323 epoch 3 - iter 29/292 - loss 0.14544586 - time (sec): 1.34 - samples/sec: 3595.36 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:00:25,651 epoch 3 - iter 58/292 - loss 0.10941303 - time (sec): 2.67 - samples/sec: 3579.86 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:00:26,955 epoch 3 - iter 87/292 - loss 0.09430565 - time (sec): 3.97 - samples/sec: 3464.62 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:00:28,224 epoch 3 - iter 116/292 - loss 0.09566966 - time (sec): 5.24 - samples/sec: 3428.46 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:00:29,476 epoch 3 - iter 145/292 - loss 0.09347375 - time (sec): 6.49 - samples/sec: 3358.49 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:00:30,736 epoch 3 - iter 174/292 - loss 0.09163215 - time (sec): 7.75 - samples/sec: 3302.95 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:00:32,016 epoch 3 - iter 203/292 - loss 0.08722664 - time (sec): 9.03 - samples/sec: 3361.36 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:00:33,295 epoch 3 - iter 232/292 - loss 0.08761881 - time (sec): 10.31 - samples/sec: 3382.93 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:00:34,563 epoch 3 - iter 261/292 - loss 0.08941822 - time (sec): 11.58 - samples/sec: 3385.11 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:00:35,926 epoch 3 - iter 290/292 - loss 0.08796314 - time (sec): 12.94 - samples/sec: 3404.11 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:00:36,011 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:36,011 EPOCH 3 done: loss 0.0893 - lr: 0.000023 2023-10-25 21:00:36,917 DEV : loss 0.13917604088783264 - f1-score (micro avg) 0.7064 2023-10-25 21:00:36,922 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:38,201 epoch 4 - iter 29/292 - loss 0.06653490 - time (sec): 1.28 - samples/sec: 3265.55 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:00:39,479 epoch 4 - iter 58/292 - loss 0.05686471 - time (sec): 2.56 - samples/sec: 3556.95 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:00:40,763 epoch 4 - iter 87/292 - loss 0.05278725 - time (sec): 3.84 - samples/sec: 3383.00 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:00:42,031 epoch 4 - iter 116/292 - loss 0.05170961 - time (sec): 5.11 - samples/sec: 3384.12 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:00:43,330 epoch 4 - iter 145/292 - loss 0.05608612 - time (sec): 6.41 - samples/sec: 3383.63 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:00:44,645 epoch 4 - iter 174/292 - loss 0.05282409 - time (sec): 7.72 - samples/sec: 3404.02 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:00:45,906 epoch 4 - iter 203/292 - loss 0.05927506 - time (sec): 8.98 - samples/sec: 3389.18 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:00:47,284 epoch 4 - iter 232/292 - loss 0.05914609 - time (sec): 10.36 - samples/sec: 3417.43 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:00:48,539 epoch 4 - iter 261/292 - loss 0.05675851 - time (sec): 11.62 - samples/sec: 3436.37 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:00:49,827 epoch 4 - iter 290/292 - loss 0.05609862 - time (sec): 12.90 - samples/sec: 3427.20 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:00:49,908 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:49,908 EPOCH 4 done: loss 0.0559 - lr: 0.000020 2023-10-25 21:00:50,821 DEV : loss 0.13928386569023132 - f1-score (micro avg) 0.7093 2023-10-25 21:00:50,825 saving best model 2023-10-25 21:00:51,682 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:00:52,908 epoch 5 - iter 29/292 - loss 0.02277200 - time (sec): 1.22 - samples/sec: 3015.46 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:00:54,240 epoch 5 - iter 58/292 - loss 0.03421365 - time (sec): 2.56 - samples/sec: 3261.14 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:00:55,565 epoch 5 - iter 87/292 - loss 0.03319117 - time (sec): 3.88 - samples/sec: 3447.28 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:00:56,821 epoch 5 - iter 116/292 - loss 0.03424275 - time (sec): 5.14 - samples/sec: 3422.46 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:00:58,079 epoch 5 - iter 145/292 - loss 0.03661161 - time (sec): 6.39 - samples/sec: 3365.61 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:00:59,374 epoch 5 - iter 174/292 - loss 0.03815798 - time (sec): 7.69 - samples/sec: 3355.52 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:01:00,716 epoch 5 - iter 203/292 - loss 0.04010743 - time (sec): 9.03 - samples/sec: 3417.96 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:01:02,019 epoch 5 - iter 232/292 - loss 0.03710160 - time (sec): 10.33 - samples/sec: 3430.04 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:01:03,316 epoch 5 - iter 261/292 - loss 0.03759294 - time (sec): 11.63 - samples/sec: 3417.16 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:01:04,653 epoch 5 - iter 290/292 - loss 0.03667198 - time (sec): 12.97 - samples/sec: 3414.67 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:01:04,732 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:04,733 EPOCH 5 done: loss 0.0367 - lr: 0.000017 2023-10-25 21:01:05,633 DEV : loss 0.14212724566459656 - f1-score (micro avg) 0.714 2023-10-25 21:01:05,637 saving best model 2023-10-25 21:01:06,315 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:07,672 epoch 6 - iter 29/292 - loss 0.02796383 - time (sec): 1.35 - samples/sec: 3938.43 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:01:08,973 epoch 6 - iter 58/292 - loss 0.02811284 - time (sec): 2.66 - samples/sec: 3543.28 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:01:10,276 epoch 6 - iter 87/292 - loss 0.03036771 - time (sec): 3.96 - samples/sec: 3475.85 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:01:11,696 epoch 6 - iter 116/292 - loss 0.02940868 - time (sec): 5.38 - samples/sec: 3517.10 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:01:13,051 epoch 6 - iter 145/292 - loss 0.02904224 - time (sec): 6.73 - samples/sec: 3393.12 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:01:14,484 epoch 6 - iter 174/292 - loss 0.02614340 - time (sec): 8.17 - samples/sec: 3365.99 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:01:15,834 epoch 6 - iter 203/292 - loss 0.02631470 - time (sec): 9.52 - samples/sec: 3344.04 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:01:17,211 epoch 6 - iter 232/292 - loss 0.02881547 - time (sec): 10.89 - samples/sec: 3326.96 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:01:18,573 epoch 6 - iter 261/292 - loss 0.02920586 - time (sec): 12.25 - samples/sec: 3328.26 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:01:19,809 epoch 6 - iter 290/292 - loss 0.02831814 - time (sec): 13.49 - samples/sec: 3271.32 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:01:19,899 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:19,900 EPOCH 6 done: loss 0.0283 - lr: 0.000013 2023-10-25 21:01:20,807 DEV : loss 0.1747400164604187 - f1-score (micro avg) 0.7122 2023-10-25 21:01:20,811 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:22,111 epoch 7 - iter 29/292 - loss 0.04120099 - time (sec): 1.30 - samples/sec: 3294.77 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:01:23,385 epoch 7 - iter 58/292 - loss 0.02762419 - time (sec): 2.57 - samples/sec: 3376.34 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:01:24,813 epoch 7 - iter 87/292 - loss 0.02358102 - time (sec): 4.00 - samples/sec: 3458.12 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:01:26,154 epoch 7 - iter 116/292 - loss 0.02313168 - time (sec): 5.34 - samples/sec: 3412.19 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:01:27,465 epoch 7 - iter 145/292 - loss 0.02003259 - time (sec): 6.65 - samples/sec: 3395.06 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:01:28,781 epoch 7 - iter 174/292 - loss 0.01901409 - time (sec): 7.97 - samples/sec: 3406.98 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:01:30,060 epoch 7 - iter 203/292 - loss 0.01913836 - time (sec): 9.25 - samples/sec: 3379.93 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:01:31,363 epoch 7 - iter 232/292 - loss 0.02025448 - time (sec): 10.55 - samples/sec: 3344.38 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:01:32,656 epoch 7 - iter 261/292 - loss 0.02111959 - time (sec): 11.84 - samples/sec: 3351.11 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:01:33,948 epoch 7 - iter 290/292 - loss 0.02062935 - time (sec): 13.14 - samples/sec: 3356.78 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:01:34,042 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:34,042 EPOCH 7 done: loss 0.0205 - lr: 0.000010 2023-10-25 21:01:34,950 DEV : loss 0.17978323996067047 - f1-score (micro avg) 0.7419 2023-10-25 21:01:34,954 saving best model 2023-10-25 21:01:35,635 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:36,928 epoch 8 - iter 29/292 - loss 0.01127436 - time (sec): 1.29 - samples/sec: 3561.67 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:01:38,170 epoch 8 - iter 58/292 - loss 0.01585167 - time (sec): 2.53 - samples/sec: 3458.37 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:01:39,442 epoch 8 - iter 87/292 - loss 0.01729172 - time (sec): 3.80 - samples/sec: 3291.43 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:01:40,697 epoch 8 - iter 116/292 - loss 0.01428530 - time (sec): 5.06 - samples/sec: 3342.05 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:01:41,956 epoch 8 - iter 145/292 - loss 0.01396528 - time (sec): 6.32 - samples/sec: 3384.13 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:01:43,276 epoch 8 - iter 174/292 - loss 0.01386840 - time (sec): 7.64 - samples/sec: 3420.43 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:01:44,553 epoch 8 - iter 203/292 - loss 0.01341978 - time (sec): 8.91 - samples/sec: 3435.77 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:01:45,972 epoch 8 - iter 232/292 - loss 0.01198981 - time (sec): 10.33 - samples/sec: 3441.57 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:01:47,238 epoch 8 - iter 261/292 - loss 0.01283600 - time (sec): 11.60 - samples/sec: 3447.91 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:01:48,534 epoch 8 - iter 290/292 - loss 0.01389923 - time (sec): 12.90 - samples/sec: 3440.30 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:01:48,615 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:48,615 EPOCH 8 done: loss 0.0139 - lr: 0.000007 2023-10-25 21:01:49,531 DEV : loss 0.1903691589832306 - f1-score (micro avg) 0.7183 2023-10-25 21:01:49,536 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:01:50,791 epoch 9 - iter 29/292 - loss 0.00703447 - time (sec): 1.25 - samples/sec: 3230.77 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:01:52,270 epoch 9 - iter 58/292 - loss 0.01503000 - time (sec): 2.73 - samples/sec: 3130.91 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:01:53,536 epoch 9 - iter 87/292 - loss 0.01311829 - time (sec): 4.00 - samples/sec: 3223.13 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:01:54,838 epoch 9 - iter 116/292 - loss 0.01085621 - time (sec): 5.30 - samples/sec: 3138.99 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:01:56,138 epoch 9 - iter 145/292 - loss 0.00936798 - time (sec): 6.60 - samples/sec: 3217.65 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:01:57,488 epoch 9 - iter 174/292 - loss 0.00878729 - time (sec): 7.95 - samples/sec: 3312.64 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:01:58,753 epoch 9 - iter 203/292 - loss 0.00963486 - time (sec): 9.22 - samples/sec: 3320.35 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:02:00,043 epoch 9 - iter 232/292 - loss 0.01175699 - time (sec): 10.51 - samples/sec: 3347.16 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:02:01,331 epoch 9 - iter 261/292 - loss 0.01082544 - time (sec): 11.79 - samples/sec: 3336.12 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:02:02,644 epoch 9 - iter 290/292 - loss 0.01194434 - time (sec): 13.11 - samples/sec: 3364.77 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:02:02,725 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:02:02,726 EPOCH 9 done: loss 0.0121 - lr: 0.000003 2023-10-25 21:02:03,635 DEV : loss 0.20435144007205963 - f1-score (micro avg) 0.7343 2023-10-25 21:02:03,639 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:02:04,904 epoch 10 - iter 29/292 - loss 0.00257441 - time (sec): 1.26 - samples/sec: 3070.38 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:02:06,161 epoch 10 - iter 58/292 - loss 0.00640555 - time (sec): 2.52 - samples/sec: 3097.68 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:02:07,517 epoch 10 - iter 87/292 - loss 0.00952813 - time (sec): 3.88 - samples/sec: 3268.70 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:02:08,853 epoch 10 - iter 116/292 - loss 0.00793282 - time (sec): 5.21 - samples/sec: 3338.84 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:02:10,204 epoch 10 - iter 145/292 - loss 0.00718311 - time (sec): 6.56 - samples/sec: 3416.28 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:02:11,520 epoch 10 - iter 174/292 - loss 0.00721069 - time (sec): 7.88 - samples/sec: 3472.31 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:02:12,820 epoch 10 - iter 203/292 - loss 0.00752348 - time (sec): 9.18 - samples/sec: 3499.53 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:02:14,047 epoch 10 - iter 232/292 - loss 0.00774661 - time (sec): 10.41 - samples/sec: 3454.80 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:02:15,293 epoch 10 - iter 261/292 - loss 0.00819500 - time (sec): 11.65 - samples/sec: 3424.87 - lr: 0.000000 - momentum: 0.000000 2023-10-25 21:02:16,548 epoch 10 - iter 290/292 - loss 0.00778659 - time (sec): 12.91 - samples/sec: 3421.32 - lr: 0.000000 - momentum: 0.000000 2023-10-25 21:02:16,633 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:02:16,634 EPOCH 10 done: loss 0.0079 - lr: 0.000000 2023-10-25 21:02:17,546 DEV : loss 0.2082153558731079 - f1-score (micro avg) 0.7284 2023-10-25 21:02:18,071 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:02:18,073 Loading model from best epoch ... 2023-10-25 21:02:19,750 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-25 21:02:21,279 Results: - F-score (micro) 0.7611 - F-score (macro) 0.6901 - Accuracy 0.6413 By class: precision recall f1-score support PER 0.7962 0.8534 0.8239 348 LOC 0.6646 0.8352 0.7402 261 ORG 0.4528 0.4615 0.4571 52 HumanProd 0.7083 0.7727 0.7391 22 micro avg 0.7147 0.8141 0.7611 683 macro avg 0.6555 0.7307 0.6901 683 weighted avg 0.7170 0.8141 0.7613 683 2023-10-25 21:02:21,279 ----------------------------------------------------------------------------------------------------