2023-10-17 17:28:02,197 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,198 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=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-17 17:28:02,198 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 MultiCorpus: 5777 train + 722 dev + 723 test sentences - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Train: 5777 sentences 2023-10-17 17:28:02,199 (train_with_dev=False, train_with_test=False) 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Training Params: 2023-10-17 17:28:02,199 - learning_rate: "5e-05" 2023-10-17 17:28:02,199 - mini_batch_size: "8" 2023-10-17 17:28:02,199 - max_epochs: "10" 2023-10-17 17:28:02,199 - shuffle: "True" 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Plugins: 2023-10-17 17:28:02,199 - TensorboardLogger 2023-10-17 17:28:02,199 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 17:28:02,199 - metric: "('micro avg', 'f1-score')" 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Computation: 2023-10-17 17:28:02,199 - compute on device: cuda:0 2023-10-17 17:28:02,199 - embedding storage: none 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Model training base path: "hmbench-icdar/nl-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3" 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:02,199 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 17:28:07,437 epoch 1 - iter 72/723 - loss 2.62229683 - time (sec): 5.24 - samples/sec: 3280.94 - lr: 0.000005 - momentum: 0.000000 2023-10-17 17:28:12,712 epoch 1 - iter 144/723 - loss 1.53564932 - time (sec): 10.51 - samples/sec: 3238.36 - lr: 0.000010 - momentum: 0.000000 2023-10-17 17:28:17,693 epoch 1 - iter 216/723 - loss 1.07363928 - time (sec): 15.49 - samples/sec: 3338.32 - lr: 0.000015 - momentum: 0.000000 2023-10-17 17:28:23,105 epoch 1 - iter 288/723 - loss 0.84654223 - time (sec): 20.90 - samples/sec: 3324.64 - lr: 0.000020 - momentum: 0.000000 2023-10-17 17:28:28,670 epoch 1 - iter 360/723 - loss 0.69584770 - time (sec): 26.47 - samples/sec: 3332.68 - lr: 0.000025 - momentum: 0.000000 2023-10-17 17:28:33,963 epoch 1 - iter 432/723 - loss 0.59690308 - time (sec): 31.76 - samples/sec: 3353.05 - lr: 0.000030 - momentum: 0.000000 2023-10-17 17:28:39,179 epoch 1 - iter 504/723 - loss 0.52741417 - time (sec): 36.98 - samples/sec: 3347.54 - lr: 0.000035 - momentum: 0.000000 2023-10-17 17:28:44,472 epoch 1 - iter 576/723 - loss 0.47603842 - time (sec): 42.27 - samples/sec: 3345.25 - lr: 0.000040 - momentum: 0.000000 2023-10-17 17:28:49,270 epoch 1 - iter 648/723 - loss 0.44069521 - time (sec): 47.07 - samples/sec: 3335.95 - lr: 0.000045 - momentum: 0.000000 2023-10-17 17:28:54,524 epoch 1 - iter 720/723 - loss 0.40554441 - time (sec): 52.32 - samples/sec: 3352.88 - lr: 0.000050 - momentum: 0.000000 2023-10-17 17:28:54,815 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:28:54,815 EPOCH 1 done: loss 0.4040 - lr: 0.000050 2023-10-17 17:28:58,191 DEV : loss 0.08499432355165482 - f1-score (micro avg) 0.8175 2023-10-17 17:28:58,207 saving best model 2023-10-17 17:28:58,558 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:29:03,714 epoch 2 - iter 72/723 - loss 0.08166951 - time (sec): 5.16 - samples/sec: 3366.35 - lr: 0.000049 - momentum: 0.000000 2023-10-17 17:29:09,042 epoch 2 - iter 144/723 - loss 0.09238710 - time (sec): 10.48 - samples/sec: 3318.53 - lr: 0.000049 - momentum: 0.000000 2023-10-17 17:29:14,060 epoch 2 - iter 216/723 - loss 0.09656663 - time (sec): 15.50 - samples/sec: 3340.45 - lr: 0.000048 - momentum: 0.000000 2023-10-17 17:29:19,239 epoch 2 - iter 288/723 - loss 0.09515758 - time (sec): 20.68 - samples/sec: 3340.74 - lr: 0.000048 - momentum: 0.000000 2023-10-17 17:29:24,949 epoch 2 - iter 360/723 - loss 0.09351682 - time (sec): 26.39 - samples/sec: 3330.41 - lr: 0.000047 - momentum: 0.000000 2023-10-17 17:29:31,143 epoch 2 - iter 432/723 - loss 0.09171543 - time (sec): 32.58 - samples/sec: 3302.14 - lr: 0.000047 - momentum: 0.000000 2023-10-17 17:29:36,313 epoch 2 - iter 504/723 - loss 0.08991783 - time (sec): 37.75 - samples/sec: 3317.34 - lr: 0.000046 - momentum: 0.000000 2023-10-17 17:29:41,332 epoch 2 - iter 576/723 - loss 0.08743025 - time (sec): 42.77 - samples/sec: 3322.67 - lr: 0.000046 - momentum: 0.000000 2023-10-17 17:29:46,509 epoch 2 - iter 648/723 - loss 0.08684948 - time (sec): 47.95 - samples/sec: 3308.97 - lr: 0.000045 - momentum: 0.000000 2023-10-17 17:29:52,156 epoch 2 - iter 720/723 - loss 0.08531715 - time (sec): 53.60 - samples/sec: 3275.07 - lr: 0.000044 - momentum: 0.000000 2023-10-17 17:29:52,352 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:29:52,352 EPOCH 2 done: loss 0.0854 - lr: 0.000044 2023-10-17 17:29:56,717 DEV : loss 0.12996011972427368 - f1-score (micro avg) 0.6985 2023-10-17 17:29:56,733 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:30:02,368 epoch 3 - iter 72/723 - loss 0.07263265 - time (sec): 5.63 - samples/sec: 3086.04 - lr: 0.000044 - momentum: 0.000000 2023-10-17 17:30:08,080 epoch 3 - iter 144/723 - loss 0.06576995 - time (sec): 11.34 - samples/sec: 3162.51 - lr: 0.000043 - momentum: 0.000000 2023-10-17 17:30:13,506 epoch 3 - iter 216/723 - loss 0.06486849 - time (sec): 16.77 - samples/sec: 3233.55 - lr: 0.000043 - momentum: 0.000000 2023-10-17 17:30:18,957 epoch 3 - iter 288/723 - loss 0.05999593 - time (sec): 22.22 - samples/sec: 3239.41 - lr: 0.000042 - momentum: 0.000000 2023-10-17 17:30:24,068 epoch 3 - iter 360/723 - loss 0.05991573 - time (sec): 27.33 - samples/sec: 3235.14 - lr: 0.000042 - momentum: 0.000000 2023-10-17 17:30:29,685 epoch 3 - iter 432/723 - loss 0.06066152 - time (sec): 32.95 - samples/sec: 3234.13 - lr: 0.000041 - momentum: 0.000000 2023-10-17 17:30:35,555 epoch 3 - iter 504/723 - loss 0.06270855 - time (sec): 38.82 - samples/sec: 3207.12 - lr: 0.000041 - momentum: 0.000000 2023-10-17 17:30:40,841 epoch 3 - iter 576/723 - loss 0.06209895 - time (sec): 44.11 - samples/sec: 3200.17 - lr: 0.000040 - momentum: 0.000000 2023-10-17 17:30:46,401 epoch 3 - iter 648/723 - loss 0.06130730 - time (sec): 49.67 - samples/sec: 3186.94 - lr: 0.000039 - momentum: 0.000000 2023-10-17 17:30:52,184 epoch 3 - iter 720/723 - loss 0.06221705 - time (sec): 55.45 - samples/sec: 3172.81 - lr: 0.000039 - momentum: 0.000000 2023-10-17 17:30:52,371 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:30:52,371 EPOCH 3 done: loss 0.0622 - lr: 0.000039 2023-10-17 17:30:56,033 DEV : loss 0.06001274287700653 - f1-score (micro avg) 0.8684 2023-10-17 17:30:56,053 saving best model 2023-10-17 17:30:56,585 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:31:01,784 epoch 4 - iter 72/723 - loss 0.03813632 - time (sec): 5.20 - samples/sec: 3213.96 - lr: 0.000038 - momentum: 0.000000 2023-10-17 17:31:07,908 epoch 4 - iter 144/723 - loss 0.03747693 - time (sec): 11.32 - samples/sec: 3060.36 - lr: 0.000038 - momentum: 0.000000 2023-10-17 17:31:13,233 epoch 4 - iter 216/723 - loss 0.03936375 - time (sec): 16.64 - samples/sec: 3109.39 - lr: 0.000037 - momentum: 0.000000 2023-10-17 17:31:18,623 epoch 4 - iter 288/723 - loss 0.04307423 - time (sec): 22.03 - samples/sec: 3142.16 - lr: 0.000037 - momentum: 0.000000 2023-10-17 17:31:23,678 epoch 4 - iter 360/723 - loss 0.04236315 - time (sec): 27.09 - samples/sec: 3185.58 - lr: 0.000036 - momentum: 0.000000 2023-10-17 17:31:29,478 epoch 4 - iter 432/723 - loss 0.04209825 - time (sec): 32.89 - samples/sec: 3158.73 - lr: 0.000036 - momentum: 0.000000 2023-10-17 17:31:34,915 epoch 4 - iter 504/723 - loss 0.04200339 - time (sec): 38.33 - samples/sec: 3176.76 - lr: 0.000035 - momentum: 0.000000 2023-10-17 17:31:40,837 epoch 4 - iter 576/723 - loss 0.04275515 - time (sec): 44.25 - samples/sec: 3185.87 - lr: 0.000034 - momentum: 0.000000 2023-10-17 17:31:46,159 epoch 4 - iter 648/723 - loss 0.04212727 - time (sec): 49.57 - samples/sec: 3186.36 - lr: 0.000034 - momentum: 0.000000 2023-10-17 17:31:51,490 epoch 4 - iter 720/723 - loss 0.04197729 - time (sec): 54.90 - samples/sec: 3201.14 - lr: 0.000033 - momentum: 0.000000 2023-10-17 17:31:51,670 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:31:51,671 EPOCH 4 done: loss 0.0420 - lr: 0.000033 2023-10-17 17:31:55,071 DEV : loss 0.0824296697974205 - f1-score (micro avg) 0.8726 2023-10-17 17:31:55,090 saving best model 2023-10-17 17:31:55,766 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:32:01,345 epoch 5 - iter 72/723 - loss 0.02976008 - time (sec): 5.58 - samples/sec: 3219.59 - lr: 0.000033 - momentum: 0.000000 2023-10-17 17:32:06,577 epoch 5 - iter 144/723 - loss 0.02756584 - time (sec): 10.81 - samples/sec: 3239.64 - lr: 0.000032 - momentum: 0.000000 2023-10-17 17:32:12,023 epoch 5 - iter 216/723 - loss 0.03103086 - time (sec): 16.26 - samples/sec: 3261.33 - lr: 0.000032 - momentum: 0.000000 2023-10-17 17:32:17,738 epoch 5 - iter 288/723 - loss 0.02922852 - time (sec): 21.97 - samples/sec: 3237.78 - lr: 0.000031 - momentum: 0.000000 2023-10-17 17:32:23,061 epoch 5 - iter 360/723 - loss 0.02855856 - time (sec): 27.29 - samples/sec: 3222.87 - lr: 0.000031 - momentum: 0.000000 2023-10-17 17:32:28,710 epoch 5 - iter 432/723 - loss 0.03052075 - time (sec): 32.94 - samples/sec: 3218.79 - lr: 0.000030 - momentum: 0.000000 2023-10-17 17:32:34,177 epoch 5 - iter 504/723 - loss 0.03129817 - time (sec): 38.41 - samples/sec: 3228.96 - lr: 0.000029 - momentum: 0.000000 2023-10-17 17:32:39,137 epoch 5 - iter 576/723 - loss 0.03242960 - time (sec): 43.37 - samples/sec: 3246.68 - lr: 0.000029 - momentum: 0.000000 2023-10-17 17:32:44,212 epoch 5 - iter 648/723 - loss 0.03218350 - time (sec): 48.44 - samples/sec: 3259.18 - lr: 0.000028 - momentum: 0.000000 2023-10-17 17:32:49,585 epoch 5 - iter 720/723 - loss 0.03177889 - time (sec): 53.82 - samples/sec: 3264.47 - lr: 0.000028 - momentum: 0.000000 2023-10-17 17:32:49,775 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:32:49,775 EPOCH 5 done: loss 0.0318 - lr: 0.000028 2023-10-17 17:32:53,581 DEV : loss 0.10424862802028656 - f1-score (micro avg) 0.8471 2023-10-17 17:32:53,601 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:32:58,963 epoch 6 - iter 72/723 - loss 0.02973653 - time (sec): 5.36 - samples/sec: 3496.63 - lr: 0.000027 - momentum: 0.000000 2023-10-17 17:33:04,263 epoch 6 - iter 144/723 - loss 0.02285847 - time (sec): 10.66 - samples/sec: 3353.33 - lr: 0.000027 - momentum: 0.000000 2023-10-17 17:33:09,929 epoch 6 - iter 216/723 - loss 0.02429447 - time (sec): 16.33 - samples/sec: 3326.30 - lr: 0.000026 - momentum: 0.000000 2023-10-17 17:33:15,711 epoch 6 - iter 288/723 - loss 0.02499962 - time (sec): 22.11 - samples/sec: 3282.73 - lr: 0.000026 - momentum: 0.000000 2023-10-17 17:33:20,942 epoch 6 - iter 360/723 - loss 0.02571431 - time (sec): 27.34 - samples/sec: 3278.43 - lr: 0.000025 - momentum: 0.000000 2023-10-17 17:33:26,294 epoch 6 - iter 432/723 - loss 0.02517103 - time (sec): 32.69 - samples/sec: 3294.69 - lr: 0.000024 - momentum: 0.000000 2023-10-17 17:33:31,447 epoch 6 - iter 504/723 - loss 0.02605168 - time (sec): 37.84 - samples/sec: 3287.99 - lr: 0.000024 - momentum: 0.000000 2023-10-17 17:33:36,580 epoch 6 - iter 576/723 - loss 0.02631389 - time (sec): 42.98 - samples/sec: 3304.30 - lr: 0.000023 - momentum: 0.000000 2023-10-17 17:33:41,733 epoch 6 - iter 648/723 - loss 0.02543096 - time (sec): 48.13 - samples/sec: 3295.75 - lr: 0.000023 - momentum: 0.000000 2023-10-17 17:33:46,823 epoch 6 - iter 720/723 - loss 0.02521898 - time (sec): 53.22 - samples/sec: 3302.40 - lr: 0.000022 - momentum: 0.000000 2023-10-17 17:33:47,018 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:33:47,018 EPOCH 6 done: loss 0.0252 - lr: 0.000022 2023-10-17 17:33:50,373 DEV : loss 0.11773881316184998 - f1-score (micro avg) 0.8452 2023-10-17 17:33:50,391 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:33:55,498 epoch 7 - iter 72/723 - loss 0.01211954 - time (sec): 5.11 - samples/sec: 3363.93 - lr: 0.000022 - momentum: 0.000000 2023-10-17 17:34:00,867 epoch 7 - iter 144/723 - loss 0.01732599 - time (sec): 10.47 - samples/sec: 3294.18 - lr: 0.000021 - momentum: 0.000000 2023-10-17 17:34:06,234 epoch 7 - iter 216/723 - loss 0.01866389 - time (sec): 15.84 - samples/sec: 3299.82 - lr: 0.000021 - momentum: 0.000000 2023-10-17 17:34:11,575 epoch 7 - iter 288/723 - loss 0.02100187 - time (sec): 21.18 - samples/sec: 3323.79 - lr: 0.000020 - momentum: 0.000000 2023-10-17 17:34:17,437 epoch 7 - iter 360/723 - loss 0.01933756 - time (sec): 27.04 - samples/sec: 3241.66 - lr: 0.000019 - momentum: 0.000000 2023-10-17 17:34:22,923 epoch 7 - iter 432/723 - loss 0.01996565 - time (sec): 32.53 - samples/sec: 3262.22 - lr: 0.000019 - momentum: 0.000000 2023-10-17 17:34:28,273 epoch 7 - iter 504/723 - loss 0.01973901 - time (sec): 37.88 - samples/sec: 3284.32 - lr: 0.000018 - momentum: 0.000000 2023-10-17 17:34:33,625 epoch 7 - iter 576/723 - loss 0.01903934 - time (sec): 43.23 - samples/sec: 3274.98 - lr: 0.000018 - momentum: 0.000000 2023-10-17 17:34:38,898 epoch 7 - iter 648/723 - loss 0.01808703 - time (sec): 48.51 - samples/sec: 3262.65 - lr: 0.000017 - momentum: 0.000000 2023-10-17 17:34:44,431 epoch 7 - iter 720/723 - loss 0.01730273 - time (sec): 54.04 - samples/sec: 3245.34 - lr: 0.000017 - momentum: 0.000000 2023-10-17 17:34:44,838 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:34:44,838 EPOCH 7 done: loss 0.0174 - lr: 0.000017 2023-10-17 17:34:48,262 DEV : loss 0.1411609798669815 - f1-score (micro avg) 0.8591 2023-10-17 17:34:48,280 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:34:53,518 epoch 8 - iter 72/723 - loss 0.01428987 - time (sec): 5.24 - samples/sec: 3189.97 - lr: 0.000016 - momentum: 0.000000 2023-10-17 17:34:59,013 epoch 8 - iter 144/723 - loss 0.00959426 - time (sec): 10.73 - samples/sec: 3163.77 - lr: 0.000016 - momentum: 0.000000 2023-10-17 17:35:04,311 epoch 8 - iter 216/723 - loss 0.01074675 - time (sec): 16.03 - samples/sec: 3194.32 - lr: 0.000015 - momentum: 0.000000 2023-10-17 17:35:09,779 epoch 8 - iter 288/723 - loss 0.01082205 - time (sec): 21.50 - samples/sec: 3207.07 - lr: 0.000014 - momentum: 0.000000 2023-10-17 17:35:15,456 epoch 8 - iter 360/723 - loss 0.01174131 - time (sec): 27.17 - samples/sec: 3185.76 - lr: 0.000014 - momentum: 0.000000 2023-10-17 17:35:20,762 epoch 8 - iter 432/723 - loss 0.01179153 - time (sec): 32.48 - samples/sec: 3199.23 - lr: 0.000013 - momentum: 0.000000 2023-10-17 17:35:26,144 epoch 8 - iter 504/723 - loss 0.01159015 - time (sec): 37.86 - samples/sec: 3218.57 - lr: 0.000013 - momentum: 0.000000 2023-10-17 17:35:31,465 epoch 8 - iter 576/723 - loss 0.01238696 - time (sec): 43.18 - samples/sec: 3231.32 - lr: 0.000012 - momentum: 0.000000 2023-10-17 17:35:37,327 epoch 8 - iter 648/723 - loss 0.01238150 - time (sec): 49.05 - samples/sec: 3242.41 - lr: 0.000012 - momentum: 0.000000 2023-10-17 17:35:42,459 epoch 8 - iter 720/723 - loss 0.01243442 - time (sec): 54.18 - samples/sec: 3241.49 - lr: 0.000011 - momentum: 0.000000 2023-10-17 17:35:42,643 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:35:42,643 EPOCH 8 done: loss 0.0125 - lr: 0.000011 2023-10-17 17:35:46,562 DEV : loss 0.13958391547203064 - f1-score (micro avg) 0.865 2023-10-17 17:35:46,583 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:35:51,843 epoch 9 - iter 72/723 - loss 0.00536784 - time (sec): 5.26 - samples/sec: 3259.05 - lr: 0.000011 - momentum: 0.000000 2023-10-17 17:35:57,193 epoch 9 - iter 144/723 - loss 0.00538410 - time (sec): 10.61 - samples/sec: 3301.20 - lr: 0.000010 - momentum: 0.000000 2023-10-17 17:36:02,650 epoch 9 - iter 216/723 - loss 0.00537525 - time (sec): 16.07 - samples/sec: 3268.19 - lr: 0.000009 - momentum: 0.000000 2023-10-17 17:36:08,285 epoch 9 - iter 288/723 - loss 0.00698419 - time (sec): 21.70 - samples/sec: 3267.87 - lr: 0.000009 - momentum: 0.000000 2023-10-17 17:36:14,022 epoch 9 - iter 360/723 - loss 0.00741743 - time (sec): 27.44 - samples/sec: 3253.34 - lr: 0.000008 - momentum: 0.000000 2023-10-17 17:36:19,237 epoch 9 - iter 432/723 - loss 0.00850564 - time (sec): 32.65 - samples/sec: 3272.18 - lr: 0.000008 - momentum: 0.000000 2023-10-17 17:36:24,435 epoch 9 - iter 504/723 - loss 0.00834734 - time (sec): 37.85 - samples/sec: 3261.14 - lr: 0.000007 - momentum: 0.000000 2023-10-17 17:36:29,366 epoch 9 - iter 576/723 - loss 0.00779422 - time (sec): 42.78 - samples/sec: 3263.01 - lr: 0.000007 - momentum: 0.000000 2023-10-17 17:36:34,772 epoch 9 - iter 648/723 - loss 0.00761217 - time (sec): 48.19 - samples/sec: 3279.14 - lr: 0.000006 - momentum: 0.000000 2023-10-17 17:36:40,289 epoch 9 - iter 720/723 - loss 0.00782569 - time (sec): 53.70 - samples/sec: 3271.12 - lr: 0.000006 - momentum: 0.000000 2023-10-17 17:36:40,464 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:36:40,464 EPOCH 9 done: loss 0.0078 - lr: 0.000006 2023-10-17 17:36:43,871 DEV : loss 0.1421152502298355 - f1-score (micro avg) 0.8627 2023-10-17 17:36:43,891 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:36:49,280 epoch 10 - iter 72/723 - loss 0.01028871 - time (sec): 5.39 - samples/sec: 3344.29 - lr: 0.000005 - momentum: 0.000000 2023-10-17 17:36:54,336 epoch 10 - iter 144/723 - loss 0.00650322 - time (sec): 10.44 - samples/sec: 3325.81 - lr: 0.000004 - momentum: 0.000000 2023-10-17 17:37:00,082 epoch 10 - iter 216/723 - loss 0.00664087 - time (sec): 16.19 - samples/sec: 3281.14 - lr: 0.000004 - momentum: 0.000000 2023-10-17 17:37:05,727 epoch 10 - iter 288/723 - loss 0.00546743 - time (sec): 21.83 - samples/sec: 3259.82 - lr: 0.000003 - momentum: 0.000000 2023-10-17 17:37:11,027 epoch 10 - iter 360/723 - loss 0.00555269 - time (sec): 27.13 - samples/sec: 3271.40 - lr: 0.000003 - momentum: 0.000000 2023-10-17 17:37:16,744 epoch 10 - iter 432/723 - loss 0.00516482 - time (sec): 32.85 - samples/sec: 3237.38 - lr: 0.000002 - momentum: 0.000000 2023-10-17 17:37:21,902 epoch 10 - iter 504/723 - loss 0.00532677 - time (sec): 38.01 - samples/sec: 3248.19 - lr: 0.000002 - momentum: 0.000000 2023-10-17 17:37:27,390 epoch 10 - iter 576/723 - loss 0.00486427 - time (sec): 43.50 - samples/sec: 3241.67 - lr: 0.000001 - momentum: 0.000000 2023-10-17 17:37:32,975 epoch 10 - iter 648/723 - loss 0.00551894 - time (sec): 49.08 - samples/sec: 3237.10 - lr: 0.000001 - momentum: 0.000000 2023-10-17 17:37:38,248 epoch 10 - iter 720/723 - loss 0.00553918 - time (sec): 54.36 - samples/sec: 3235.06 - lr: 0.000000 - momentum: 0.000000 2023-10-17 17:37:38,400 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:37:38,401 EPOCH 10 done: loss 0.0055 - lr: 0.000000 2023-10-17 17:37:41,831 DEV : loss 0.15293623507022858 - f1-score (micro avg) 0.8606 2023-10-17 17:37:42,249 ---------------------------------------------------------------------------------------------------- 2023-10-17 17:37:42,251 Loading model from best epoch ... 2023-10-17 17:37:44,011 SequenceTagger predicts: Dictionary with 13 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 2023-10-17 17:37:47,497 Results: - F-score (micro) 0.86 - F-score (macro) 0.7669 - Accuracy 0.765 By class: precision recall f1-score support PER 0.8685 0.8361 0.8520 482 LOC 0.9177 0.9258 0.9217 458 ORG 0.4937 0.5652 0.5270 69 micro avg 0.8617 0.8583 0.8600 1009 macro avg 0.7600 0.7757 0.7669 1009 weighted avg 0.8652 0.8583 0.8614 1009 2023-10-17 17:37:47,497 ----------------------------------------------------------------------------------------------------