2023-10-14 11:47:09,968 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,969 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 11:47:09,969 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,969 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 2023-10-14 11:47:09,969 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,969 Train: 5777 sentences 2023-10-14 11:47:09,969 (train_with_dev=False, train_with_test=False) 2023-10-14 11:47:09,969 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,969 Training Params: 2023-10-14 11:47:09,969 - learning_rate: "3e-05" 2023-10-14 11:47:09,970 - mini_batch_size: "8" 2023-10-14 11:47:09,970 - max_epochs: "10" 2023-10-14 11:47:09,970 - shuffle: "True" 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,970 Plugins: 2023-10-14 11:47:09,970 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,970 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 11:47:09,970 - metric: "('micro avg', 'f1-score')" 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,970 Computation: 2023-10-14 11:47:09,970 - compute on device: cuda:0 2023-10-14 11:47:09,970 - embedding storage: none 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,970 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:09,970 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:47:16,044 epoch 1 - iter 72/723 - loss 2.17936848 - time (sec): 6.07 - samples/sec: 3053.65 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:47:22,048 epoch 1 - iter 144/723 - loss 1.30488853 - time (sec): 12.08 - samples/sec: 2974.16 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:47:27,819 epoch 1 - iter 216/723 - loss 0.96288539 - time (sec): 17.85 - samples/sec: 2988.26 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:47:34,101 epoch 1 - iter 288/723 - loss 0.77877705 - time (sec): 24.13 - samples/sec: 2957.82 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:47:40,166 epoch 1 - iter 360/723 - loss 0.66184851 - time (sec): 30.19 - samples/sec: 2937.05 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:47:46,237 epoch 1 - iter 432/723 - loss 0.58058110 - time (sec): 36.27 - samples/sec: 2929.70 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:47:52,424 epoch 1 - iter 504/723 - loss 0.52285800 - time (sec): 42.45 - samples/sec: 2920.34 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:47:58,303 epoch 1 - iter 576/723 - loss 0.47873115 - time (sec): 48.33 - samples/sec: 2901.73 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:48:04,095 epoch 1 - iter 648/723 - loss 0.43866282 - time (sec): 54.12 - samples/sec: 2916.97 - lr: 0.000027 - momentum: 0.000000 2023-10-14 11:48:10,154 epoch 1 - iter 720/723 - loss 0.40791889 - time (sec): 60.18 - samples/sec: 2918.46 - lr: 0.000030 - momentum: 0.000000 2023-10-14 11:48:10,356 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:48:10,356 EPOCH 1 done: loss 0.4072 - lr: 0.000030 2023-10-14 11:48:13,522 DEV : loss 0.13588625192642212 - f1-score (micro avg) 0.6361 2023-10-14 11:48:13,538 saving best model 2023-10-14 11:48:13,953 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:48:19,937 epoch 2 - iter 72/723 - loss 0.11712269 - time (sec): 5.98 - samples/sec: 2859.68 - lr: 0.000030 - momentum: 0.000000 2023-10-14 11:48:26,043 epoch 2 - iter 144/723 - loss 0.11668248 - time (sec): 12.09 - samples/sec: 2824.30 - lr: 0.000029 - momentum: 0.000000 2023-10-14 11:48:32,261 epoch 2 - iter 216/723 - loss 0.11217901 - time (sec): 18.31 - samples/sec: 2863.21 - lr: 0.000029 - momentum: 0.000000 2023-10-14 11:48:38,786 epoch 2 - iter 288/723 - loss 0.10925944 - time (sec): 24.83 - samples/sec: 2855.63 - lr: 0.000029 - momentum: 0.000000 2023-10-14 11:48:44,379 epoch 2 - iter 360/723 - loss 0.10959979 - time (sec): 30.43 - samples/sec: 2891.40 - lr: 0.000028 - momentum: 0.000000 2023-10-14 11:48:50,066 epoch 2 - iter 432/723 - loss 0.10627704 - time (sec): 36.11 - samples/sec: 2899.92 - lr: 0.000028 - momentum: 0.000000 2023-10-14 11:48:56,429 epoch 2 - iter 504/723 - loss 0.10609633 - time (sec): 42.48 - samples/sec: 2880.18 - lr: 0.000028 - momentum: 0.000000 2023-10-14 11:49:02,960 epoch 2 - iter 576/723 - loss 0.10378393 - time (sec): 49.01 - samples/sec: 2860.76 - lr: 0.000027 - momentum: 0.000000 2023-10-14 11:49:09,150 epoch 2 - iter 648/723 - loss 0.10374395 - time (sec): 55.20 - samples/sec: 2866.12 - lr: 0.000027 - momentum: 0.000000 2023-10-14 11:49:14,970 epoch 2 - iter 720/723 - loss 0.10227966 - time (sec): 61.02 - samples/sec: 2880.91 - lr: 0.000027 - momentum: 0.000000 2023-10-14 11:49:15,136 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:49:15,136 EPOCH 2 done: loss 0.1023 - lr: 0.000027 2023-10-14 11:49:18,826 DEV : loss 0.09264427423477173 - f1-score (micro avg) 0.7781 2023-10-14 11:49:18,843 saving best model 2023-10-14 11:49:19,322 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:49:25,171 epoch 3 - iter 72/723 - loss 0.07984702 - time (sec): 5.85 - samples/sec: 2901.93 - lr: 0.000026 - momentum: 0.000000 2023-10-14 11:49:31,011 epoch 3 - iter 144/723 - loss 0.07300924 - time (sec): 11.69 - samples/sec: 2916.01 - lr: 0.000026 - momentum: 0.000000 2023-10-14 11:49:37,186 epoch 3 - iter 216/723 - loss 0.06832100 - time (sec): 17.86 - samples/sec: 2865.29 - lr: 0.000026 - momentum: 0.000000 2023-10-14 11:49:42,899 epoch 3 - iter 288/723 - loss 0.06940238 - time (sec): 23.57 - samples/sec: 2881.77 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:49:48,505 epoch 3 - iter 360/723 - loss 0.06705455 - time (sec): 29.18 - samples/sec: 2889.13 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:49:54,710 epoch 3 - iter 432/723 - loss 0.06525111 - time (sec): 35.39 - samples/sec: 2917.74 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:50:00,599 epoch 3 - iter 504/723 - loss 0.06489497 - time (sec): 41.28 - samples/sec: 2917.45 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:50:06,623 epoch 3 - iter 576/723 - loss 0.06635288 - time (sec): 47.30 - samples/sec: 2941.51 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:50:12,929 epoch 3 - iter 648/723 - loss 0.06505342 - time (sec): 53.60 - samples/sec: 2926.74 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:50:19,279 epoch 3 - iter 720/723 - loss 0.06394731 - time (sec): 59.95 - samples/sec: 2932.02 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:50:19,444 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:50:19,444 EPOCH 3 done: loss 0.0641 - lr: 0.000023 2023-10-14 11:50:24,040 DEV : loss 0.08042255789041519 - f1-score (micro avg) 0.7954 2023-10-14 11:50:24,072 saving best model 2023-10-14 11:50:24,642 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:50:30,665 epoch 4 - iter 72/723 - loss 0.03888401 - time (sec): 6.02 - samples/sec: 2815.75 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:50:36,602 epoch 4 - iter 144/723 - loss 0.04190461 - time (sec): 11.96 - samples/sec: 2998.16 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:50:42,498 epoch 4 - iter 216/723 - loss 0.04041996 - time (sec): 17.85 - samples/sec: 2969.21 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:50:48,871 epoch 4 - iter 288/723 - loss 0.04268068 - time (sec): 24.23 - samples/sec: 2917.23 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:50:54,653 epoch 4 - iter 360/723 - loss 0.04323841 - time (sec): 30.01 - samples/sec: 2913.19 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:51:00,881 epoch 4 - iter 432/723 - loss 0.04422879 - time (sec): 36.24 - samples/sec: 2899.02 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:51:07,029 epoch 4 - iter 504/723 - loss 0.04346939 - time (sec): 42.38 - samples/sec: 2903.77 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:51:12,738 epoch 4 - iter 576/723 - loss 0.04268000 - time (sec): 48.09 - samples/sec: 2905.38 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:51:18,686 epoch 4 - iter 648/723 - loss 0.04192132 - time (sec): 54.04 - samples/sec: 2920.00 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:51:24,862 epoch 4 - iter 720/723 - loss 0.04244714 - time (sec): 60.22 - samples/sec: 2919.39 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:51:25,034 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:51:25,034 EPOCH 4 done: loss 0.0424 - lr: 0.000020 2023-10-14 11:51:28,568 DEV : loss 0.09498978406190872 - f1-score (micro avg) 0.7943 2023-10-14 11:51:28,585 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:51:34,533 epoch 5 - iter 72/723 - loss 0.02557454 - time (sec): 5.95 - samples/sec: 2796.46 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:51:40,625 epoch 5 - iter 144/723 - loss 0.02813156 - time (sec): 12.04 - samples/sec: 2775.57 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:51:47,161 epoch 5 - iter 216/723 - loss 0.03143363 - time (sec): 18.57 - samples/sec: 2722.72 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:51:53,361 epoch 5 - iter 288/723 - loss 0.03092080 - time (sec): 24.77 - samples/sec: 2791.88 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:51:59,575 epoch 5 - iter 360/723 - loss 0.03267232 - time (sec): 30.99 - samples/sec: 2813.37 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:52:05,683 epoch 5 - iter 432/723 - loss 0.03342599 - time (sec): 37.10 - samples/sec: 2839.84 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:52:12,172 epoch 5 - iter 504/723 - loss 0.03230870 - time (sec): 43.58 - samples/sec: 2848.04 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:52:18,077 epoch 5 - iter 576/723 - loss 0.03186647 - time (sec): 49.49 - samples/sec: 2846.56 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:52:23,851 epoch 5 - iter 648/723 - loss 0.03038680 - time (sec): 55.26 - samples/sec: 2860.04 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:52:30,050 epoch 5 - iter 720/723 - loss 0.03171654 - time (sec): 61.46 - samples/sec: 2853.61 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:52:30,317 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:52:30,317 EPOCH 5 done: loss 0.0319 - lr: 0.000017 2023-10-14 11:52:33,961 DEV : loss 0.11846506595611572 - f1-score (micro avg) 0.8055 2023-10-14 11:52:33,977 saving best model 2023-10-14 11:52:34,393 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:52:40,195 epoch 6 - iter 72/723 - loss 0.02635934 - time (sec): 5.80 - samples/sec: 2915.02 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:52:45,847 epoch 6 - iter 144/723 - loss 0.02567053 - time (sec): 11.45 - samples/sec: 2989.60 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:52:52,001 epoch 6 - iter 216/723 - loss 0.02594002 - time (sec): 17.61 - samples/sec: 2958.72 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:52:58,150 epoch 6 - iter 288/723 - loss 0.02839596 - time (sec): 23.76 - samples/sec: 2961.05 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:53:04,782 epoch 6 - iter 360/723 - loss 0.02953778 - time (sec): 30.39 - samples/sec: 2945.44 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:53:10,905 epoch 6 - iter 432/723 - loss 0.02804276 - time (sec): 36.51 - samples/sec: 2926.53 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:53:16,320 epoch 6 - iter 504/723 - loss 0.02683702 - time (sec): 41.92 - samples/sec: 2948.93 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:53:22,244 epoch 6 - iter 576/723 - loss 0.02562191 - time (sec): 47.85 - samples/sec: 2942.84 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:53:27,883 epoch 6 - iter 648/723 - loss 0.02530607 - time (sec): 53.49 - samples/sec: 2958.77 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:53:33,702 epoch 6 - iter 720/723 - loss 0.02457646 - time (sec): 59.31 - samples/sec: 2962.58 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:53:33,918 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:53:33,919 EPOCH 6 done: loss 0.0246 - lr: 0.000013 2023-10-14 11:53:38,487 DEV : loss 0.13894404470920563 - f1-score (micro avg) 0.8147 2023-10-14 11:53:38,512 saving best model 2023-10-14 11:53:39,053 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:53:45,100 epoch 7 - iter 72/723 - loss 0.01514089 - time (sec): 6.04 - samples/sec: 2816.37 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:53:51,034 epoch 7 - iter 144/723 - loss 0.01504761 - time (sec): 11.98 - samples/sec: 2857.79 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:53:57,277 epoch 7 - iter 216/723 - loss 0.01712868 - time (sec): 18.22 - samples/sec: 2888.95 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:54:03,341 epoch 7 - iter 288/723 - loss 0.01831387 - time (sec): 24.29 - samples/sec: 2883.50 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:54:09,339 epoch 7 - iter 360/723 - loss 0.01652720 - time (sec): 30.28 - samples/sec: 2908.98 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:54:15,105 epoch 7 - iter 432/723 - loss 0.01612436 - time (sec): 36.05 - samples/sec: 2923.73 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:54:21,245 epoch 7 - iter 504/723 - loss 0.01609187 - time (sec): 42.19 - samples/sec: 2914.96 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:54:27,377 epoch 7 - iter 576/723 - loss 0.01654164 - time (sec): 48.32 - samples/sec: 2910.10 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:54:33,068 epoch 7 - iter 648/723 - loss 0.01719631 - time (sec): 54.01 - samples/sec: 2909.29 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:54:39,697 epoch 7 - iter 720/723 - loss 0.01721697 - time (sec): 60.64 - samples/sec: 2897.14 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:54:39,934 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:54:39,934 EPOCH 7 done: loss 0.0172 - lr: 0.000010 2023-10-14 11:54:43,657 DEV : loss 0.16778483986854553 - f1-score (micro avg) 0.8077 2023-10-14 11:54:43,680 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:54:50,765 epoch 8 - iter 72/723 - loss 0.01257707 - time (sec): 7.08 - samples/sec: 2514.43 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:54:56,831 epoch 8 - iter 144/723 - loss 0.01317721 - time (sec): 13.15 - samples/sec: 2673.46 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:55:03,315 epoch 8 - iter 216/723 - loss 0.01362361 - time (sec): 19.63 - samples/sec: 2712.80 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:55:09,075 epoch 8 - iter 288/723 - loss 0.01440453 - time (sec): 25.39 - samples/sec: 2780.28 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:55:15,429 epoch 8 - iter 360/723 - loss 0.01453039 - time (sec): 31.75 - samples/sec: 2789.63 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:55:21,249 epoch 8 - iter 432/723 - loss 0.01405394 - time (sec): 37.57 - samples/sec: 2820.91 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:55:27,058 epoch 8 - iter 504/723 - loss 0.01345119 - time (sec): 43.38 - samples/sec: 2820.92 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:55:33,497 epoch 8 - iter 576/723 - loss 0.01275603 - time (sec): 49.82 - samples/sec: 2817.53 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:55:39,761 epoch 8 - iter 648/723 - loss 0.01366196 - time (sec): 56.08 - samples/sec: 2826.80 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:55:45,544 epoch 8 - iter 720/723 - loss 0.01335513 - time (sec): 61.86 - samples/sec: 2836.20 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:55:45,852 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:55:45,852 EPOCH 8 done: loss 0.0133 - lr: 0.000007 2023-10-14 11:55:49,362 DEV : loss 0.18129222095012665 - f1-score (micro avg) 0.8109 2023-10-14 11:55:49,380 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:55:55,373 epoch 9 - iter 72/723 - loss 0.00469641 - time (sec): 5.99 - samples/sec: 2909.93 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:56:00,990 epoch 9 - iter 144/723 - loss 0.00558362 - time (sec): 11.61 - samples/sec: 2883.12 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:56:07,785 epoch 9 - iter 216/723 - loss 0.00934811 - time (sec): 18.40 - samples/sec: 2914.61 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:56:13,316 epoch 9 - iter 288/723 - loss 0.00967709 - time (sec): 23.93 - samples/sec: 2938.46 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:56:19,287 epoch 9 - iter 360/723 - loss 0.00972270 - time (sec): 29.91 - samples/sec: 2955.84 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:56:25,175 epoch 9 - iter 432/723 - loss 0.00996302 - time (sec): 35.79 - samples/sec: 2957.64 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:56:31,060 epoch 9 - iter 504/723 - loss 0.00950339 - time (sec): 41.68 - samples/sec: 2959.25 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:56:37,078 epoch 9 - iter 576/723 - loss 0.01010530 - time (sec): 47.70 - samples/sec: 2943.69 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:56:42,906 epoch 9 - iter 648/723 - loss 0.01002914 - time (sec): 53.52 - samples/sec: 2946.96 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:56:48,906 epoch 9 - iter 720/723 - loss 0.01036201 - time (sec): 59.52 - samples/sec: 2948.08 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:56:49,182 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:56:49,182 EPOCH 9 done: loss 0.0103 - lr: 0.000003 2023-10-14 11:56:53,096 DEV : loss 0.1801426112651825 - f1-score (micro avg) 0.8155 2023-10-14 11:56:53,111 saving best model 2023-10-14 11:56:53,771 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:57:00,146 epoch 10 - iter 72/723 - loss 0.00771571 - time (sec): 6.37 - samples/sec: 2841.33 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:57:05,943 epoch 10 - iter 144/723 - loss 0.00799529 - time (sec): 12.17 - samples/sec: 2929.37 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:57:12,088 epoch 10 - iter 216/723 - loss 0.00964900 - time (sec): 18.31 - samples/sec: 2909.61 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:57:18,469 epoch 10 - iter 288/723 - loss 0.00910715 - time (sec): 24.69 - samples/sec: 2908.74 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:57:24,261 epoch 10 - iter 360/723 - loss 0.00785801 - time (sec): 30.49 - samples/sec: 2927.64 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:57:29,982 epoch 10 - iter 432/723 - loss 0.00782112 - time (sec): 36.21 - samples/sec: 2949.53 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:57:36,136 epoch 10 - iter 504/723 - loss 0.00784393 - time (sec): 42.36 - samples/sec: 2934.43 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:57:41,890 epoch 10 - iter 576/723 - loss 0.00793032 - time (sec): 48.12 - samples/sec: 2940.39 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:57:47,590 epoch 10 - iter 648/723 - loss 0.00792955 - time (sec): 53.82 - samples/sec: 2936.65 - lr: 0.000000 - momentum: 0.000000 2023-10-14 11:57:53,365 epoch 10 - iter 720/723 - loss 0.00802050 - time (sec): 59.59 - samples/sec: 2946.14 - lr: 0.000000 - momentum: 0.000000 2023-10-14 11:57:53,666 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:57:53,667 EPOCH 10 done: loss 0.0080 - lr: 0.000000 2023-10-14 11:57:57,150 DEV : loss 0.1832604557275772 - f1-score (micro avg) 0.8127 2023-10-14 11:57:57,618 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:57:57,619 Loading model from best epoch ... 2023-10-14 11:57:59,232 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 2023-10-14 11:58:02,393 Results: - F-score (micro) 0.8004 - F-score (macro) 0.6962 - Accuracy 0.6799 By class: precision recall f1-score support PER 0.8323 0.8237 0.8279 482 LOC 0.8741 0.7882 0.8289 458 ORG 0.4286 0.4348 0.4317 69 micro avg 0.8208 0.7810 0.8004 1009 macro avg 0.7116 0.6822 0.6962 1009 weighted avg 0.8237 0.7810 0.8013 1009 2023-10-14 11:58:02,393 ----------------------------------------------------------------------------------------------------