2023-10-14 19:34:13,664 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,665 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=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-14 19:34:13,665 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,665 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator 2023-10-14 19:34:13,665 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,665 Train: 14465 sentences 2023-10-14 19:34:13,665 (train_with_dev=False, train_with_test=False) 2023-10-14 19:34:13,665 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,665 Training Params: 2023-10-14 19:34:13,665 - learning_rate: "3e-05" 2023-10-14 19:34:13,665 - mini_batch_size: "8" 2023-10-14 19:34:13,665 - max_epochs: "10" 2023-10-14 19:34:13,665 - shuffle: "True" 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,666 Plugins: 2023-10-14 19:34:13,666 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,666 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 19:34:13,666 - metric: "('micro avg', 'f1-score')" 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,666 Computation: 2023-10-14 19:34:13,666 - compute on device: cuda:0 2023-10-14 19:34:13,666 - embedding storage: none 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,666 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:13,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:34:24,866 epoch 1 - iter 180/1809 - loss 1.87339049 - time (sec): 11.20 - samples/sec: 3416.59 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:34:36,164 epoch 1 - iter 360/1809 - loss 1.06251129 - time (sec): 22.50 - samples/sec: 3376.96 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:34:47,458 epoch 1 - iter 540/1809 - loss 0.77046509 - time (sec): 33.79 - samples/sec: 3359.59 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:34:58,728 epoch 1 - iter 720/1809 - loss 0.60884730 - time (sec): 45.06 - samples/sec: 3383.44 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:35:09,821 epoch 1 - iter 900/1809 - loss 0.51478582 - time (sec): 56.15 - samples/sec: 3381.75 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:35:20,824 epoch 1 - iter 1080/1809 - loss 0.45038851 - time (sec): 67.16 - samples/sec: 3386.76 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:35:31,910 epoch 1 - iter 1260/1809 - loss 0.40226510 - time (sec): 78.24 - samples/sec: 3377.71 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:35:43,094 epoch 1 - iter 1440/1809 - loss 0.36377365 - time (sec): 89.43 - samples/sec: 3397.83 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:35:54,127 epoch 1 - iter 1620/1809 - loss 0.33618725 - time (sec): 100.46 - samples/sec: 3394.46 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:36:05,245 epoch 1 - iter 1800/1809 - loss 0.31254600 - time (sec): 111.58 - samples/sec: 3390.41 - lr: 0.000030 - momentum: 0.000000 2023-10-14 19:36:05,747 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:36:05,748 EPOCH 1 done: loss 0.3115 - lr: 0.000030 2023-10-14 19:36:11,319 DEV : loss 0.1074068620800972 - f1-score (micro avg) 0.6227 2023-10-14 19:36:11,359 saving best model 2023-10-14 19:36:11,761 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:36:23,545 epoch 2 - iter 180/1809 - loss 0.08399045 - time (sec): 11.78 - samples/sec: 3227.45 - lr: 0.000030 - momentum: 0.000000 2023-10-14 19:36:35,128 epoch 2 - iter 360/1809 - loss 0.08189791 - time (sec): 23.37 - samples/sec: 3255.60 - lr: 0.000029 - momentum: 0.000000 2023-10-14 19:36:46,819 epoch 2 - iter 540/1809 - loss 0.08429012 - time (sec): 35.06 - samples/sec: 3269.66 - lr: 0.000029 - momentum: 0.000000 2023-10-14 19:36:57,832 epoch 2 - iter 720/1809 - loss 0.08515928 - time (sec): 46.07 - samples/sec: 3290.94 - lr: 0.000029 - momentum: 0.000000 2023-10-14 19:37:08,723 epoch 2 - iter 900/1809 - loss 0.08559861 - time (sec): 56.96 - samples/sec: 3309.87 - lr: 0.000028 - momentum: 0.000000 2023-10-14 19:37:20,230 epoch 2 - iter 1080/1809 - loss 0.08401278 - time (sec): 68.47 - samples/sec: 3319.10 - lr: 0.000028 - momentum: 0.000000 2023-10-14 19:37:31,469 epoch 2 - iter 1260/1809 - loss 0.08491285 - time (sec): 79.71 - samples/sec: 3330.62 - lr: 0.000028 - momentum: 0.000000 2023-10-14 19:37:42,280 epoch 2 - iter 1440/1809 - loss 0.08263217 - time (sec): 90.52 - samples/sec: 3343.72 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:37:53,300 epoch 2 - iter 1620/1809 - loss 0.08200069 - time (sec): 101.54 - samples/sec: 3356.23 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:38:04,107 epoch 2 - iter 1800/1809 - loss 0.08208246 - time (sec): 112.34 - samples/sec: 3365.44 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:38:04,597 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:38:04,597 EPOCH 2 done: loss 0.0822 - lr: 0.000027 2023-10-14 19:38:10,890 DEV : loss 0.10114093124866486 - f1-score (micro avg) 0.6484 2023-10-14 19:38:10,920 saving best model 2023-10-14 19:38:11,500 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:38:22,010 epoch 3 - iter 180/1809 - loss 0.05838759 - time (sec): 10.51 - samples/sec: 3366.01 - lr: 0.000026 - momentum: 0.000000 2023-10-14 19:38:33,197 epoch 3 - iter 360/1809 - loss 0.05874179 - time (sec): 21.70 - samples/sec: 3385.52 - lr: 0.000026 - momentum: 0.000000 2023-10-14 19:38:44,444 epoch 3 - iter 540/1809 - loss 0.05854957 - time (sec): 32.94 - samples/sec: 3374.75 - lr: 0.000026 - momentum: 0.000000 2023-10-14 19:38:55,688 epoch 3 - iter 720/1809 - loss 0.05766306 - time (sec): 44.19 - samples/sec: 3401.61 - lr: 0.000025 - momentum: 0.000000 2023-10-14 19:39:06,774 epoch 3 - iter 900/1809 - loss 0.05644867 - time (sec): 55.27 - samples/sec: 3397.78 - lr: 0.000025 - momentum: 0.000000 2023-10-14 19:39:18,191 epoch 3 - iter 1080/1809 - loss 0.05882154 - time (sec): 66.69 - samples/sec: 3399.07 - lr: 0.000025 - momentum: 0.000000 2023-10-14 19:39:29,277 epoch 3 - iter 1260/1809 - loss 0.05771495 - time (sec): 77.78 - samples/sec: 3409.11 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:39:40,304 epoch 3 - iter 1440/1809 - loss 0.05784555 - time (sec): 88.80 - samples/sec: 3408.50 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:39:51,247 epoch 3 - iter 1620/1809 - loss 0.05830648 - time (sec): 99.75 - samples/sec: 3407.59 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:40:02,524 epoch 3 - iter 1800/1809 - loss 0.05809712 - time (sec): 111.02 - samples/sec: 3408.39 - lr: 0.000023 - momentum: 0.000000 2023-10-14 19:40:03,015 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:40:03,016 EPOCH 3 done: loss 0.0580 - lr: 0.000023 2023-10-14 19:40:09,518 DEV : loss 0.1736098974943161 - f1-score (micro avg) 0.6375 2023-10-14 19:40:09,550 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:40:20,934 epoch 4 - iter 180/1809 - loss 0.03691108 - time (sec): 11.38 - samples/sec: 3440.28 - lr: 0.000023 - momentum: 0.000000 2023-10-14 19:40:31,862 epoch 4 - iter 360/1809 - loss 0.04056818 - time (sec): 22.31 - samples/sec: 3422.32 - lr: 0.000023 - momentum: 0.000000 2023-10-14 19:40:42,991 epoch 4 - iter 540/1809 - loss 0.03990955 - time (sec): 33.44 - samples/sec: 3403.48 - lr: 0.000022 - momentum: 0.000000 2023-10-14 19:40:53,744 epoch 4 - iter 720/1809 - loss 0.03930742 - time (sec): 44.19 - samples/sec: 3401.04 - lr: 0.000022 - momentum: 0.000000 2023-10-14 19:41:04,742 epoch 4 - iter 900/1809 - loss 0.03878415 - time (sec): 55.19 - samples/sec: 3419.03 - lr: 0.000022 - momentum: 0.000000 2023-10-14 19:41:15,576 epoch 4 - iter 1080/1809 - loss 0.03901667 - time (sec): 66.02 - samples/sec: 3423.98 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:41:26,903 epoch 4 - iter 1260/1809 - loss 0.03923070 - time (sec): 77.35 - samples/sec: 3417.32 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:41:37,923 epoch 4 - iter 1440/1809 - loss 0.03883106 - time (sec): 88.37 - samples/sec: 3425.27 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:41:48,896 epoch 4 - iter 1620/1809 - loss 0.03987697 - time (sec): 99.34 - samples/sec: 3426.90 - lr: 0.000020 - momentum: 0.000000 2023-10-14 19:42:00,001 epoch 4 - iter 1800/1809 - loss 0.03999749 - time (sec): 110.45 - samples/sec: 3422.64 - lr: 0.000020 - momentum: 0.000000 2023-10-14 19:42:00,575 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:42:00,575 EPOCH 4 done: loss 0.0399 - lr: 0.000020 2023-10-14 19:42:06,152 DEV : loss 0.23925307393074036 - f1-score (micro avg) 0.6519 2023-10-14 19:42:06,183 saving best model 2023-10-14 19:42:06,655 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:42:18,792 epoch 5 - iter 180/1809 - loss 0.02627690 - time (sec): 12.13 - samples/sec: 3239.69 - lr: 0.000020 - momentum: 0.000000 2023-10-14 19:42:29,688 epoch 5 - iter 360/1809 - loss 0.02735443 - time (sec): 23.03 - samples/sec: 3336.57 - lr: 0.000019 - momentum: 0.000000 2023-10-14 19:42:40,686 epoch 5 - iter 540/1809 - loss 0.02869184 - time (sec): 34.03 - samples/sec: 3376.77 - lr: 0.000019 - momentum: 0.000000 2023-10-14 19:42:51,517 epoch 5 - iter 720/1809 - loss 0.02921413 - time (sec): 44.86 - samples/sec: 3377.93 - lr: 0.000019 - momentum: 0.000000 2023-10-14 19:43:02,583 epoch 5 - iter 900/1809 - loss 0.02806718 - time (sec): 55.92 - samples/sec: 3405.91 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:43:13,617 epoch 5 - iter 1080/1809 - loss 0.02805195 - time (sec): 66.96 - samples/sec: 3418.51 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:43:24,754 epoch 5 - iter 1260/1809 - loss 0.02890258 - time (sec): 78.09 - samples/sec: 3420.80 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:43:35,724 epoch 5 - iter 1440/1809 - loss 0.02875453 - time (sec): 89.06 - samples/sec: 3414.02 - lr: 0.000017 - momentum: 0.000000 2023-10-14 19:43:46,503 epoch 5 - iter 1620/1809 - loss 0.02827331 - time (sec): 99.84 - samples/sec: 3424.55 - lr: 0.000017 - momentum: 0.000000 2023-10-14 19:43:57,328 epoch 5 - iter 1800/1809 - loss 0.02888219 - time (sec): 110.67 - samples/sec: 3418.00 - lr: 0.000017 - momentum: 0.000000 2023-10-14 19:43:57,818 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:43:57,818 EPOCH 5 done: loss 0.0288 - lr: 0.000017 2023-10-14 19:44:03,397 DEV : loss 0.3241709768772125 - f1-score (micro avg) 0.6413 2023-10-14 19:44:03,430 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:44:14,543 epoch 6 - iter 180/1809 - loss 0.01726600 - time (sec): 11.11 - samples/sec: 3401.18 - lr: 0.000016 - momentum: 0.000000 2023-10-14 19:44:25,391 epoch 6 - iter 360/1809 - loss 0.02130409 - time (sec): 21.96 - samples/sec: 3418.16 - lr: 0.000016 - momentum: 0.000000 2023-10-14 19:44:36,249 epoch 6 - iter 540/1809 - loss 0.01945479 - time (sec): 32.82 - samples/sec: 3401.46 - lr: 0.000016 - momentum: 0.000000 2023-10-14 19:44:47,162 epoch 6 - iter 720/1809 - loss 0.01798882 - time (sec): 43.73 - samples/sec: 3412.71 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:44:58,270 epoch 6 - iter 900/1809 - loss 0.01805154 - time (sec): 54.84 - samples/sec: 3425.09 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:45:10,066 epoch 6 - iter 1080/1809 - loss 0.01928162 - time (sec): 66.64 - samples/sec: 3389.98 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:45:21,094 epoch 6 - iter 1260/1809 - loss 0.01948046 - time (sec): 77.66 - samples/sec: 3392.34 - lr: 0.000014 - momentum: 0.000000 2023-10-14 19:45:32,425 epoch 6 - iter 1440/1809 - loss 0.01954946 - time (sec): 88.99 - samples/sec: 3404.88 - lr: 0.000014 - momentum: 0.000000 2023-10-14 19:45:43,426 epoch 6 - iter 1620/1809 - loss 0.01979056 - time (sec): 100.00 - samples/sec: 3398.79 - lr: 0.000014 - momentum: 0.000000 2023-10-14 19:45:54,379 epoch 6 - iter 1800/1809 - loss 0.01952738 - time (sec): 110.95 - samples/sec: 3408.26 - lr: 0.000013 - momentum: 0.000000 2023-10-14 19:45:54,891 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:45:54,891 EPOCH 6 done: loss 0.0195 - lr: 0.000013 2023-10-14 19:46:00,459 DEV : loss 0.32730063796043396 - f1-score (micro avg) 0.6469 2023-10-14 19:46:00,489 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:46:11,510 epoch 7 - iter 180/1809 - loss 0.01375259 - time (sec): 11.02 - samples/sec: 3507.41 - lr: 0.000013 - momentum: 0.000000 2023-10-14 19:46:22,844 epoch 7 - iter 360/1809 - loss 0.01337537 - time (sec): 22.35 - samples/sec: 3435.97 - lr: 0.000013 - momentum: 0.000000 2023-10-14 19:46:33,788 epoch 7 - iter 540/1809 - loss 0.01292578 - time (sec): 33.30 - samples/sec: 3437.99 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:46:44,656 epoch 7 - iter 720/1809 - loss 0.01256781 - time (sec): 44.17 - samples/sec: 3454.85 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:46:55,743 epoch 7 - iter 900/1809 - loss 0.01292955 - time (sec): 55.25 - samples/sec: 3441.61 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:47:06,590 epoch 7 - iter 1080/1809 - loss 0.01366189 - time (sec): 66.10 - samples/sec: 3444.85 - lr: 0.000011 - momentum: 0.000000 2023-10-14 19:47:17,485 epoch 7 - iter 1260/1809 - loss 0.01294217 - time (sec): 76.99 - samples/sec: 3446.87 - lr: 0.000011 - momentum: 0.000000 2023-10-14 19:47:28,684 epoch 7 - iter 1440/1809 - loss 0.01355941 - time (sec): 88.19 - samples/sec: 3438.84 - lr: 0.000011 - momentum: 0.000000 2023-10-14 19:47:39,588 epoch 7 - iter 1620/1809 - loss 0.01404239 - time (sec): 99.10 - samples/sec: 3436.68 - lr: 0.000010 - momentum: 0.000000 2023-10-14 19:47:50,401 epoch 7 - iter 1800/1809 - loss 0.01415717 - time (sec): 109.91 - samples/sec: 3440.60 - lr: 0.000010 - momentum: 0.000000 2023-10-14 19:47:50,880 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:47:50,881 EPOCH 7 done: loss 0.0142 - lr: 0.000010 2023-10-14 19:47:57,159 DEV : loss 0.34697893261909485 - f1-score (micro avg) 0.6535 2023-10-14 19:47:57,189 saving best model 2023-10-14 19:47:57,664 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:48:08,704 epoch 8 - iter 180/1809 - loss 0.00926758 - time (sec): 11.04 - samples/sec: 3339.97 - lr: 0.000010 - momentum: 0.000000 2023-10-14 19:48:19,824 epoch 8 - iter 360/1809 - loss 0.01045925 - time (sec): 22.16 - samples/sec: 3378.57 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:48:30,922 epoch 8 - iter 540/1809 - loss 0.01079154 - time (sec): 33.25 - samples/sec: 3412.90 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:48:42,001 epoch 8 - iter 720/1809 - loss 0.01092862 - time (sec): 44.33 - samples/sec: 3412.23 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:48:52,717 epoch 8 - iter 900/1809 - loss 0.01101834 - time (sec): 55.05 - samples/sec: 3423.63 - lr: 0.000008 - momentum: 0.000000 2023-10-14 19:49:03,432 epoch 8 - iter 1080/1809 - loss 0.01138095 - time (sec): 65.76 - samples/sec: 3406.13 - lr: 0.000008 - momentum: 0.000000 2023-10-14 19:49:14,961 epoch 8 - iter 1260/1809 - loss 0.01254336 - time (sec): 77.29 - samples/sec: 3406.95 - lr: 0.000008 - momentum: 0.000000 2023-10-14 19:49:25,998 epoch 8 - iter 1440/1809 - loss 0.01162416 - time (sec): 88.33 - samples/sec: 3404.01 - lr: 0.000007 - momentum: 0.000000 2023-10-14 19:49:37,131 epoch 8 - iter 1620/1809 - loss 0.01113848 - time (sec): 99.46 - samples/sec: 3411.17 - lr: 0.000007 - momentum: 0.000000 2023-10-14 19:49:48,424 epoch 8 - iter 1800/1809 - loss 0.01102466 - time (sec): 110.76 - samples/sec: 3410.53 - lr: 0.000007 - momentum: 0.000000 2023-10-14 19:49:49,050 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:49:49,050 EPOCH 8 done: loss 0.0110 - lr: 0.000007 2023-10-14 19:49:55,515 DEV : loss 0.3827722668647766 - f1-score (micro avg) 0.6585 2023-10-14 19:49:55,558 saving best model 2023-10-14 19:49:56,097 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:50:08,129 epoch 9 - iter 180/1809 - loss 0.00799016 - time (sec): 12.03 - samples/sec: 3140.21 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:50:19,453 epoch 9 - iter 360/1809 - loss 0.00806244 - time (sec): 23.35 - samples/sec: 3293.32 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:50:30,505 epoch 9 - iter 540/1809 - loss 0.00701912 - time (sec): 34.41 - samples/sec: 3346.83 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:50:41,490 epoch 9 - iter 720/1809 - loss 0.00725727 - time (sec): 45.39 - samples/sec: 3343.45 - lr: 0.000005 - momentum: 0.000000 2023-10-14 19:50:52,341 epoch 9 - iter 900/1809 - loss 0.00639063 - time (sec): 56.24 - samples/sec: 3362.52 - lr: 0.000005 - momentum: 0.000000 2023-10-14 19:51:03,204 epoch 9 - iter 1080/1809 - loss 0.00665483 - time (sec): 67.11 - samples/sec: 3375.42 - lr: 0.000005 - momentum: 0.000000 2023-10-14 19:51:14,164 epoch 9 - iter 1260/1809 - loss 0.00668153 - time (sec): 78.06 - samples/sec: 3373.14 - lr: 0.000004 - momentum: 0.000000 2023-10-14 19:51:25,278 epoch 9 - iter 1440/1809 - loss 0.00657144 - time (sec): 89.18 - samples/sec: 3373.75 - lr: 0.000004 - momentum: 0.000000 2023-10-14 19:51:36,498 epoch 9 - iter 1620/1809 - loss 0.00673205 - time (sec): 100.40 - samples/sec: 3381.01 - lr: 0.000004 - momentum: 0.000000 2023-10-14 19:51:47,624 epoch 9 - iter 1800/1809 - loss 0.00652186 - time (sec): 111.52 - samples/sec: 3393.26 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:51:48,165 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:51:48,166 EPOCH 9 done: loss 0.0065 - lr: 0.000003 2023-10-14 19:51:54,459 DEV : loss 0.40861621499061584 - f1-score (micro avg) 0.6496 2023-10-14 19:51:54,491 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:52:05,476 epoch 10 - iter 180/1809 - loss 0.00347599 - time (sec): 10.98 - samples/sec: 3484.27 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:52:16,545 epoch 10 - iter 360/1809 - loss 0.00318849 - time (sec): 22.05 - samples/sec: 3488.76 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:52:27,552 epoch 10 - iter 540/1809 - loss 0.00415644 - time (sec): 33.06 - samples/sec: 3444.26 - lr: 0.000002 - momentum: 0.000000 2023-10-14 19:52:38,746 epoch 10 - iter 720/1809 - loss 0.00401066 - time (sec): 44.25 - samples/sec: 3429.51 - lr: 0.000002 - momentum: 0.000000 2023-10-14 19:52:50,230 epoch 10 - iter 900/1809 - loss 0.00421623 - time (sec): 55.74 - samples/sec: 3410.38 - lr: 0.000002 - momentum: 0.000000 2023-10-14 19:53:01,536 epoch 10 - iter 1080/1809 - loss 0.00490989 - time (sec): 67.04 - samples/sec: 3402.50 - lr: 0.000001 - momentum: 0.000000 2023-10-14 19:53:12,373 epoch 10 - iter 1260/1809 - loss 0.00503163 - time (sec): 77.88 - samples/sec: 3395.85 - lr: 0.000001 - momentum: 0.000000 2023-10-14 19:53:23,229 epoch 10 - iter 1440/1809 - loss 0.00494148 - time (sec): 88.74 - samples/sec: 3381.82 - lr: 0.000001 - momentum: 0.000000 2023-10-14 19:53:34,347 epoch 10 - iter 1620/1809 - loss 0.00474586 - time (sec): 99.86 - samples/sec: 3392.70 - lr: 0.000000 - momentum: 0.000000 2023-10-14 19:53:45,922 epoch 10 - iter 1800/1809 - loss 0.00460738 - time (sec): 111.43 - samples/sec: 3394.00 - lr: 0.000000 - momentum: 0.000000 2023-10-14 19:53:46,432 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:53:46,432 EPOCH 10 done: loss 0.0046 - lr: 0.000000 2023-10-14 19:53:52,077 DEV : loss 0.40256527066230774 - f1-score (micro avg) 0.6524 2023-10-14 19:53:52,610 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:53:52,611 Loading model from best epoch ... 2023-10-14 19:53:55,979 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org 2023-10-14 19:54:03,479 Results: - F-score (micro) 0.6649 - F-score (macro) 0.5416 - Accuracy 0.5127 By class: precision recall f1-score support loc 0.6498 0.8037 0.7186 591 pers 0.5723 0.7647 0.6547 357 org 0.2639 0.2405 0.2517 79 micro avg 0.5992 0.7468 0.6649 1027 macro avg 0.4953 0.6030 0.5416 1027 weighted avg 0.5932 0.7468 0.6605 1027 2023-10-14 19:54:03,479 ----------------------------------------------------------------------------------------------------