2023-10-17 15:22:40,778 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,779 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): ElectraModel( (embeddings): ElectraEmbeddings( (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): ElectraEncoder( (layer): ModuleList( (0-11): 12 x ElectraLayer( (attention): ElectraAttention( (self): ElectraSelfAttention( (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): ElectraSelfOutput( (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): ElectraIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): ElectraOutput( (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) ) ) ) ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-17 15:22:40,779 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,779 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 2023-10-17 15:22:40,779 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,779 Train: 7142 sentences 2023-10-17 15:22:40,779 (train_with_dev=False, train_with_test=False) 2023-10-17 15:22:40,779 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Training Params: 2023-10-17 15:22:40,780 - learning_rate: "5e-05" 2023-10-17 15:22:40,780 - mini_batch_size: "8" 2023-10-17 15:22:40,780 - max_epochs: "10" 2023-10-17 15:22:40,780 - shuffle: "True" 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Plugins: 2023-10-17 15:22:40,780 - TensorboardLogger 2023-10-17 15:22:40,780 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 15:22:40,780 - metric: "('micro avg', 'f1-score')" 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Computation: 2023-10-17 15:22:40,780 - compute on device: cuda:0 2023-10-17 15:22:40,780 - embedding storage: none 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:22:40,780 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 15:22:48,008 epoch 1 - iter 89/893 - loss 2.84068290 - time (sec): 7.23 - samples/sec: 3501.73 - lr: 0.000005 - momentum: 0.000000 2023-10-17 15:22:55,293 epoch 1 - iter 178/893 - loss 1.76508006 - time (sec): 14.51 - samples/sec: 3476.96 - lr: 0.000010 - momentum: 0.000000 2023-10-17 15:23:02,323 epoch 1 - iter 267/893 - loss 1.32887323 - time (sec): 21.54 - samples/sec: 3444.56 - lr: 0.000015 - momentum: 0.000000 2023-10-17 15:23:09,344 epoch 1 - iter 356/893 - loss 1.07961683 - time (sec): 28.56 - samples/sec: 3429.95 - lr: 0.000020 - momentum: 0.000000 2023-10-17 15:23:15,876 epoch 1 - iter 445/893 - loss 0.91537277 - time (sec): 35.09 - samples/sec: 3450.08 - lr: 0.000025 - momentum: 0.000000 2023-10-17 15:23:22,550 epoch 1 - iter 534/893 - loss 0.79441400 - time (sec): 41.77 - samples/sec: 3488.61 - lr: 0.000030 - momentum: 0.000000 2023-10-17 15:23:29,582 epoch 1 - iter 623/893 - loss 0.70440578 - time (sec): 48.80 - samples/sec: 3492.34 - lr: 0.000035 - momentum: 0.000000 2023-10-17 15:23:37,058 epoch 1 - iter 712/893 - loss 0.62842482 - time (sec): 56.28 - samples/sec: 3498.94 - lr: 0.000040 - momentum: 0.000000 2023-10-17 15:23:44,694 epoch 1 - iter 801/893 - loss 0.57176466 - time (sec): 63.91 - samples/sec: 3491.49 - lr: 0.000045 - momentum: 0.000000 2023-10-17 15:23:52,068 epoch 1 - iter 890/893 - loss 0.52978902 - time (sec): 71.29 - samples/sec: 3481.64 - lr: 0.000050 - momentum: 0.000000 2023-10-17 15:23:52,243 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:23:52,243 EPOCH 1 done: loss 0.5288 - lr: 0.000050 2023-10-17 15:23:55,932 DEV : loss 0.11837812513113022 - f1-score (micro avg) 0.6897 2023-10-17 15:23:55,949 saving best model 2023-10-17 15:23:56,359 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:24:03,910 epoch 2 - iter 89/893 - loss 0.11416371 - time (sec): 7.55 - samples/sec: 3668.50 - lr: 0.000049 - momentum: 0.000000 2023-10-17 15:24:10,734 epoch 2 - iter 178/893 - loss 0.11054462 - time (sec): 14.37 - samples/sec: 3596.28 - lr: 0.000049 - momentum: 0.000000 2023-10-17 15:24:17,679 epoch 2 - iter 267/893 - loss 0.10944617 - time (sec): 21.32 - samples/sec: 3587.36 - lr: 0.000048 - momentum: 0.000000 2023-10-17 15:24:24,969 epoch 2 - iter 356/893 - loss 0.10974003 - time (sec): 28.61 - samples/sec: 3510.45 - lr: 0.000048 - momentum: 0.000000 2023-10-17 15:24:31,656 epoch 2 - iter 445/893 - loss 0.12542965 - time (sec): 35.29 - samples/sec: 3506.52 - lr: 0.000047 - momentum: 0.000000 2023-10-17 15:24:38,799 epoch 2 - iter 534/893 - loss 0.12518621 - time (sec): 42.44 - samples/sec: 3480.14 - lr: 0.000047 - momentum: 0.000000 2023-10-17 15:24:46,264 epoch 2 - iter 623/893 - loss 0.12341348 - time (sec): 49.90 - samples/sec: 3444.96 - lr: 0.000046 - momentum: 0.000000 2023-10-17 15:24:54,011 epoch 2 - iter 712/893 - loss 0.12016815 - time (sec): 57.65 - samples/sec: 3435.92 - lr: 0.000046 - momentum: 0.000000 2023-10-17 15:25:01,036 epoch 2 - iter 801/893 - loss 0.11705570 - time (sec): 64.67 - samples/sec: 3438.85 - lr: 0.000045 - momentum: 0.000000 2023-10-17 15:25:08,460 epoch 2 - iter 890/893 - loss 0.11562128 - time (sec): 72.10 - samples/sec: 3443.42 - lr: 0.000044 - momentum: 0.000000 2023-10-17 15:25:08,631 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:25:08,632 EPOCH 2 done: loss 0.1156 - lr: 0.000044 2023-10-17 15:25:14,003 DEV : loss 0.09630837291479111 - f1-score (micro avg) 0.7789 2023-10-17 15:25:14,021 saving best model 2023-10-17 15:25:14,500 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:25:21,435 epoch 3 - iter 89/893 - loss 0.07621002 - time (sec): 6.93 - samples/sec: 3510.78 - lr: 0.000044 - momentum: 0.000000 2023-10-17 15:25:28,317 epoch 3 - iter 178/893 - loss 0.07028836 - time (sec): 13.81 - samples/sec: 3606.54 - lr: 0.000043 - momentum: 0.000000 2023-10-17 15:25:35,746 epoch 3 - iter 267/893 - loss 0.06895379 - time (sec): 21.24 - samples/sec: 3639.95 - lr: 0.000043 - momentum: 0.000000 2023-10-17 15:25:42,631 epoch 3 - iter 356/893 - loss 0.06871513 - time (sec): 28.12 - samples/sec: 3624.74 - lr: 0.000042 - momentum: 0.000000 2023-10-17 15:25:50,043 epoch 3 - iter 445/893 - loss 0.07020606 - time (sec): 35.54 - samples/sec: 3608.17 - lr: 0.000042 - momentum: 0.000000 2023-10-17 15:25:56,658 epoch 3 - iter 534/893 - loss 0.07084888 - time (sec): 42.15 - samples/sec: 3570.61 - lr: 0.000041 - momentum: 0.000000 2023-10-17 15:26:03,774 epoch 3 - iter 623/893 - loss 0.07076245 - time (sec): 49.27 - samples/sec: 3525.50 - lr: 0.000041 - momentum: 0.000000 2023-10-17 15:26:11,199 epoch 3 - iter 712/893 - loss 0.07034170 - time (sec): 56.69 - samples/sec: 3514.66 - lr: 0.000040 - momentum: 0.000000 2023-10-17 15:26:19,031 epoch 3 - iter 801/893 - loss 0.06997454 - time (sec): 64.52 - samples/sec: 3487.38 - lr: 0.000039 - momentum: 0.000000 2023-10-17 15:26:25,774 epoch 3 - iter 890/893 - loss 0.06927984 - time (sec): 71.27 - samples/sec: 3479.19 - lr: 0.000039 - momentum: 0.000000 2023-10-17 15:26:26,014 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:26:26,015 EPOCH 3 done: loss 0.0693 - lr: 0.000039 2023-10-17 15:26:30,286 DEV : loss 0.12525002658367157 - f1-score (micro avg) 0.7809 2023-10-17 15:26:30,306 saving best model 2023-10-17 15:26:30,824 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:26:38,462 epoch 4 - iter 89/893 - loss 0.05510630 - time (sec): 7.63 - samples/sec: 3358.06 - lr: 0.000038 - momentum: 0.000000 2023-10-17 15:26:45,580 epoch 4 - iter 178/893 - loss 0.04948991 - time (sec): 14.75 - samples/sec: 3444.13 - lr: 0.000038 - momentum: 0.000000 2023-10-17 15:26:52,737 epoch 4 - iter 267/893 - loss 0.04962954 - time (sec): 21.91 - samples/sec: 3459.17 - lr: 0.000037 - momentum: 0.000000 2023-10-17 15:26:59,532 epoch 4 - iter 356/893 - loss 0.04790656 - time (sec): 28.70 - samples/sec: 3465.08 - lr: 0.000037 - momentum: 0.000000 2023-10-17 15:27:06,747 epoch 4 - iter 445/893 - loss 0.04939163 - time (sec): 35.92 - samples/sec: 3439.54 - lr: 0.000036 - momentum: 0.000000 2023-10-17 15:27:13,550 epoch 4 - iter 534/893 - loss 0.04991515 - time (sec): 42.72 - samples/sec: 3455.63 - lr: 0.000036 - momentum: 0.000000 2023-10-17 15:27:20,682 epoch 4 - iter 623/893 - loss 0.04845630 - time (sec): 49.85 - samples/sec: 3476.52 - lr: 0.000035 - momentum: 0.000000 2023-10-17 15:27:27,805 epoch 4 - iter 712/893 - loss 0.04875852 - time (sec): 56.98 - samples/sec: 3480.22 - lr: 0.000034 - momentum: 0.000000 2023-10-17 15:27:35,059 epoch 4 - iter 801/893 - loss 0.04852640 - time (sec): 64.23 - samples/sec: 3481.36 - lr: 0.000034 - momentum: 0.000000 2023-10-17 15:27:42,111 epoch 4 - iter 890/893 - loss 0.04900835 - time (sec): 71.28 - samples/sec: 3476.67 - lr: 0.000033 - momentum: 0.000000 2023-10-17 15:27:42,383 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:27:42,383 EPOCH 4 done: loss 0.0490 - lr: 0.000033 2023-10-17 15:27:47,452 DEV : loss 0.14032277464866638 - f1-score (micro avg) 0.7902 2023-10-17 15:27:47,469 saving best model 2023-10-17 15:27:48,074 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:27:55,354 epoch 5 - iter 89/893 - loss 0.03298580 - time (sec): 7.28 - samples/sec: 3427.76 - lr: 0.000033 - momentum: 0.000000 2023-10-17 15:28:02,257 epoch 5 - iter 178/893 - loss 0.03246417 - time (sec): 14.18 - samples/sec: 3456.52 - lr: 0.000032 - momentum: 0.000000 2023-10-17 15:28:09,321 epoch 5 - iter 267/893 - loss 0.03531081 - time (sec): 21.25 - samples/sec: 3460.53 - lr: 0.000032 - momentum: 0.000000 2023-10-17 15:28:15,814 epoch 5 - iter 356/893 - loss 0.03662109 - time (sec): 27.74 - samples/sec: 3493.64 - lr: 0.000031 - momentum: 0.000000 2023-10-17 15:28:22,989 epoch 5 - iter 445/893 - loss 0.03546838 - time (sec): 34.91 - samples/sec: 3475.11 - lr: 0.000031 - momentum: 0.000000 2023-10-17 15:28:30,063 epoch 5 - iter 534/893 - loss 0.03683038 - time (sec): 41.99 - samples/sec: 3489.91 - lr: 0.000030 - momentum: 0.000000 2023-10-17 15:28:37,101 epoch 5 - iter 623/893 - loss 0.03593909 - time (sec): 49.03 - samples/sec: 3508.58 - lr: 0.000029 - momentum: 0.000000 2023-10-17 15:28:44,180 epoch 5 - iter 712/893 - loss 0.03641271 - time (sec): 56.10 - samples/sec: 3514.28 - lr: 0.000029 - momentum: 0.000000 2023-10-17 15:28:51,506 epoch 5 - iter 801/893 - loss 0.03619958 - time (sec): 63.43 - samples/sec: 3519.41 - lr: 0.000028 - momentum: 0.000000 2023-10-17 15:28:58,451 epoch 5 - iter 890/893 - loss 0.03553096 - time (sec): 70.38 - samples/sec: 3526.38 - lr: 0.000028 - momentum: 0.000000 2023-10-17 15:28:58,615 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:28:58,615 EPOCH 5 done: loss 0.0356 - lr: 0.000028 2023-10-17 15:29:03,077 DEV : loss 0.15836651623249054 - f1-score (micro avg) 0.796 2023-10-17 15:29:03,100 saving best model 2023-10-17 15:29:04,345 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:29:11,305 epoch 6 - iter 89/893 - loss 0.01800969 - time (sec): 6.96 - samples/sec: 3567.96 - lr: 0.000027 - momentum: 0.000000 2023-10-17 15:29:17,844 epoch 6 - iter 178/893 - loss 0.02091387 - time (sec): 13.50 - samples/sec: 3542.18 - lr: 0.000027 - momentum: 0.000000 2023-10-17 15:29:25,110 epoch 6 - iter 267/893 - loss 0.02236790 - time (sec): 20.76 - samples/sec: 3520.46 - lr: 0.000026 - momentum: 0.000000 2023-10-17 15:29:32,572 epoch 6 - iter 356/893 - loss 0.02351990 - time (sec): 28.23 - samples/sec: 3490.62 - lr: 0.000026 - momentum: 0.000000 2023-10-17 15:29:39,486 epoch 6 - iter 445/893 - loss 0.02468375 - time (sec): 35.14 - samples/sec: 3507.96 - lr: 0.000025 - momentum: 0.000000 2023-10-17 15:29:46,768 epoch 6 - iter 534/893 - loss 0.02606959 - time (sec): 42.42 - samples/sec: 3524.54 - lr: 0.000024 - momentum: 0.000000 2023-10-17 15:29:54,075 epoch 6 - iter 623/893 - loss 0.02639857 - time (sec): 49.73 - samples/sec: 3513.88 - lr: 0.000024 - momentum: 0.000000 2023-10-17 15:30:00,956 epoch 6 - iter 712/893 - loss 0.02599939 - time (sec): 56.61 - samples/sec: 3524.55 - lr: 0.000023 - momentum: 0.000000 2023-10-17 15:30:07,994 epoch 6 - iter 801/893 - loss 0.02730520 - time (sec): 63.65 - samples/sec: 3523.50 - lr: 0.000023 - momentum: 0.000000 2023-10-17 15:30:14,951 epoch 6 - iter 890/893 - loss 0.02699016 - time (sec): 70.60 - samples/sec: 3512.72 - lr: 0.000022 - momentum: 0.000000 2023-10-17 15:30:15,147 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:30:15,147 EPOCH 6 done: loss 0.0269 - lr: 0.000022 2023-10-17 15:30:19,463 DEV : loss 0.18827223777770996 - f1-score (micro avg) 0.8073 2023-10-17 15:30:19,483 saving best model 2023-10-17 15:30:20,021 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:30:27,186 epoch 7 - iter 89/893 - loss 0.01580982 - time (sec): 7.16 - samples/sec: 3649.38 - lr: 0.000022 - momentum: 0.000000 2023-10-17 15:30:34,189 epoch 7 - iter 178/893 - loss 0.01835420 - time (sec): 14.17 - samples/sec: 3574.06 - lr: 0.000021 - momentum: 0.000000 2023-10-17 15:30:41,197 epoch 7 - iter 267/893 - loss 0.01852637 - time (sec): 21.17 - samples/sec: 3502.01 - lr: 0.000021 - momentum: 0.000000 2023-10-17 15:30:48,198 epoch 7 - iter 356/893 - loss 0.01824809 - time (sec): 28.17 - samples/sec: 3531.46 - lr: 0.000020 - momentum: 0.000000 2023-10-17 15:30:55,196 epoch 7 - iter 445/893 - loss 0.01887441 - time (sec): 35.17 - samples/sec: 3536.41 - lr: 0.000019 - momentum: 0.000000 2023-10-17 15:31:01,894 epoch 7 - iter 534/893 - loss 0.01977477 - time (sec): 41.87 - samples/sec: 3560.21 - lr: 0.000019 - momentum: 0.000000 2023-10-17 15:31:08,667 epoch 7 - iter 623/893 - loss 0.01999407 - time (sec): 48.64 - samples/sec: 3563.02 - lr: 0.000018 - momentum: 0.000000 2023-10-17 15:31:15,718 epoch 7 - iter 712/893 - loss 0.02000396 - time (sec): 55.69 - samples/sec: 3540.87 - lr: 0.000018 - momentum: 0.000000 2023-10-17 15:31:23,232 epoch 7 - iter 801/893 - loss 0.02042434 - time (sec): 63.21 - samples/sec: 3529.38 - lr: 0.000017 - momentum: 0.000000 2023-10-17 15:31:30,175 epoch 7 - iter 890/893 - loss 0.01995623 - time (sec): 70.15 - samples/sec: 3538.79 - lr: 0.000017 - momentum: 0.000000 2023-10-17 15:31:30,393 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:31:30,393 EPOCH 7 done: loss 0.0200 - lr: 0.000017 2023-10-17 15:31:35,286 DEV : loss 0.20268814265727997 - f1-score (micro avg) 0.8297 2023-10-17 15:31:35,303 saving best model 2023-10-17 15:31:35,839 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:31:42,909 epoch 8 - iter 89/893 - loss 0.01596133 - time (sec): 7.07 - samples/sec: 3402.93 - lr: 0.000016 - momentum: 0.000000 2023-10-17 15:31:49,701 epoch 8 - iter 178/893 - loss 0.01480964 - time (sec): 13.86 - samples/sec: 3462.00 - lr: 0.000016 - momentum: 0.000000 2023-10-17 15:31:57,025 epoch 8 - iter 267/893 - loss 0.01675137 - time (sec): 21.18 - samples/sec: 3458.33 - lr: 0.000015 - momentum: 0.000000 2023-10-17 15:32:04,151 epoch 8 - iter 356/893 - loss 0.01522643 - time (sec): 28.31 - samples/sec: 3507.66 - lr: 0.000014 - momentum: 0.000000 2023-10-17 15:32:11,825 epoch 8 - iter 445/893 - loss 0.01508987 - time (sec): 35.98 - samples/sec: 3519.28 - lr: 0.000014 - momentum: 0.000000 2023-10-17 15:32:19,048 epoch 8 - iter 534/893 - loss 0.01436513 - time (sec): 43.21 - samples/sec: 3523.03 - lr: 0.000013 - momentum: 0.000000 2023-10-17 15:32:26,166 epoch 8 - iter 623/893 - loss 0.01425423 - time (sec): 50.32 - samples/sec: 3515.51 - lr: 0.000013 - momentum: 0.000000 2023-10-17 15:32:33,130 epoch 8 - iter 712/893 - loss 0.01422439 - time (sec): 57.29 - samples/sec: 3501.42 - lr: 0.000012 - momentum: 0.000000 2023-10-17 15:32:40,051 epoch 8 - iter 801/893 - loss 0.01401622 - time (sec): 64.21 - samples/sec: 3498.11 - lr: 0.000012 - momentum: 0.000000 2023-10-17 15:32:46,708 epoch 8 - iter 890/893 - loss 0.01447625 - time (sec): 70.87 - samples/sec: 3496.21 - lr: 0.000011 - momentum: 0.000000 2023-10-17 15:32:46,993 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:32:46,993 EPOCH 8 done: loss 0.0145 - lr: 0.000011 2023-10-17 15:32:51,297 DEV : loss 0.18800464272499084 - f1-score (micro avg) 0.8212 2023-10-17 15:32:51,315 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:32:58,164 epoch 9 - iter 89/893 - loss 0.01188764 - time (sec): 6.85 - samples/sec: 3518.48 - lr: 0.000011 - momentum: 0.000000 2023-10-17 15:33:04,766 epoch 9 - iter 178/893 - loss 0.01149941 - time (sec): 13.45 - samples/sec: 3575.04 - lr: 0.000010 - momentum: 0.000000 2023-10-17 15:33:11,658 epoch 9 - iter 267/893 - loss 0.01223126 - time (sec): 20.34 - samples/sec: 3572.33 - lr: 0.000009 - momentum: 0.000000 2023-10-17 15:33:18,377 epoch 9 - iter 356/893 - loss 0.01060371 - time (sec): 27.06 - samples/sec: 3578.76 - lr: 0.000009 - momentum: 0.000000 2023-10-17 15:33:26,375 epoch 9 - iter 445/893 - loss 0.01014695 - time (sec): 35.06 - samples/sec: 3508.51 - lr: 0.000008 - momentum: 0.000000 2023-10-17 15:33:33,290 epoch 9 - iter 534/893 - loss 0.01151378 - time (sec): 41.97 - samples/sec: 3544.73 - lr: 0.000008 - momentum: 0.000000 2023-10-17 15:33:40,363 epoch 9 - iter 623/893 - loss 0.01164043 - time (sec): 49.05 - samples/sec: 3520.41 - lr: 0.000007 - momentum: 0.000000 2023-10-17 15:33:47,206 epoch 9 - iter 712/893 - loss 0.01090414 - time (sec): 55.89 - samples/sec: 3536.84 - lr: 0.000007 - momentum: 0.000000 2023-10-17 15:33:54,230 epoch 9 - iter 801/893 - loss 0.01064507 - time (sec): 62.91 - samples/sec: 3534.84 - lr: 0.000006 - momentum: 0.000000 2023-10-17 15:34:01,797 epoch 9 - iter 890/893 - loss 0.01012024 - time (sec): 70.48 - samples/sec: 3516.33 - lr: 0.000006 - momentum: 0.000000 2023-10-17 15:34:02,023 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:34:02,023 EPOCH 9 done: loss 0.0101 - lr: 0.000006 2023-10-17 15:34:06,256 DEV : loss 0.20825740694999695 - f1-score (micro avg) 0.8223 2023-10-17 15:34:06,274 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:34:13,338 epoch 10 - iter 89/893 - loss 0.01106391 - time (sec): 7.06 - samples/sec: 3428.06 - lr: 0.000005 - momentum: 0.000000 2023-10-17 15:34:19,986 epoch 10 - iter 178/893 - loss 0.00869302 - time (sec): 13.71 - samples/sec: 3528.09 - lr: 0.000004 - momentum: 0.000000 2023-10-17 15:34:27,221 epoch 10 - iter 267/893 - loss 0.00786190 - time (sec): 20.95 - samples/sec: 3535.88 - lr: 0.000004 - momentum: 0.000000 2023-10-17 15:34:33,970 epoch 10 - iter 356/893 - loss 0.00840030 - time (sec): 27.69 - samples/sec: 3499.03 - lr: 0.000003 - momentum: 0.000000 2023-10-17 15:34:40,742 epoch 10 - iter 445/893 - loss 0.00768042 - time (sec): 34.47 - samples/sec: 3487.42 - lr: 0.000003 - momentum: 0.000000 2023-10-17 15:34:48,133 epoch 10 - iter 534/893 - loss 0.00760238 - time (sec): 41.86 - samples/sec: 3472.82 - lr: 0.000002 - momentum: 0.000000 2023-10-17 15:34:55,488 epoch 10 - iter 623/893 - loss 0.00744283 - time (sec): 49.21 - samples/sec: 3468.31 - lr: 0.000002 - momentum: 0.000000 2023-10-17 15:35:02,728 epoch 10 - iter 712/893 - loss 0.00710376 - time (sec): 56.45 - samples/sec: 3457.79 - lr: 0.000001 - momentum: 0.000000 2023-10-17 15:35:09,784 epoch 10 - iter 801/893 - loss 0.00664551 - time (sec): 63.51 - samples/sec: 3472.68 - lr: 0.000001 - momentum: 0.000000 2023-10-17 15:35:17,319 epoch 10 - iter 890/893 - loss 0.00642147 - time (sec): 71.04 - samples/sec: 3490.94 - lr: 0.000000 - momentum: 0.000000 2023-10-17 15:35:17,555 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:35:17,555 EPOCH 10 done: loss 0.0064 - lr: 0.000000 2023-10-17 15:35:22,442 DEV : loss 0.20835766196250916 - f1-score (micro avg) 0.8245 2023-10-17 15:35:22,857 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:35:22,859 Loading model from best epoch ... 2023-10-17 15:35:24,412 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 2023-10-17 15:35:34,665 Results: - F-score (micro) 0.721 - F-score (macro) 0.6426 - Accuracy 0.5799 By class: precision recall f1-score support LOC 0.7364 0.7397 0.7380 1095 PER 0.7921 0.7717 0.7818 1012 ORG 0.5012 0.5826 0.5389 357 HumanProd 0.4151 0.6667 0.5116 33 micro avg 0.7130 0.7293 0.7210 2497 macro avg 0.6112 0.6902 0.6426 2497 weighted avg 0.7211 0.7293 0.7243 2497 2023-10-17 15:35:34,665 ----------------------------------------------------------------------------------------------------