2023-10-14 10:57:26,424 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,425 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 10:57:26,425 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,425 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 10:57:26,425 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,425 Train: 5777 sentences 2023-10-14 10:57:26,425 (train_with_dev=False, train_with_test=False) 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,425 Training Params: 2023-10-14 10:57:26,425 - learning_rate: "3e-05" 2023-10-14 10:57:26,425 - mini_batch_size: "8" 2023-10-14 10:57:26,425 - max_epochs: "10" 2023-10-14 10:57:26,425 - shuffle: "True" 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,426 Plugins: 2023-10-14 10:57:26,426 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,426 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 10:57:26,426 - metric: "('micro avg', 'f1-score')" 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,426 Computation: 2023-10-14 10:57:26,426 - compute on device: cuda:0 2023-10-14 10:57:26,426 - embedding storage: none 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,426 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:57:32,692 epoch 1 - iter 72/723 - loss 2.31506993 - time (sec): 6.26 - samples/sec: 2976.58 - lr: 0.000003 - momentum: 0.000000 2023-10-14 10:57:38,328 epoch 1 - iter 144/723 - loss 1.40098098 - time (sec): 11.90 - samples/sec: 3035.24 - lr: 0.000006 - momentum: 0.000000 2023-10-14 10:57:44,275 epoch 1 - iter 216/723 - loss 1.02739094 - time (sec): 17.85 - samples/sec: 2985.78 - lr: 0.000009 - momentum: 0.000000 2023-10-14 10:57:49,976 epoch 1 - iter 288/723 - loss 0.82673104 - time (sec): 23.55 - samples/sec: 2980.75 - lr: 0.000012 - momentum: 0.000000 2023-10-14 10:57:55,969 epoch 1 - iter 360/723 - loss 0.69531849 - time (sec): 29.54 - samples/sec: 2988.43 - lr: 0.000015 - momentum: 0.000000 2023-10-14 10:58:01,677 epoch 1 - iter 432/723 - loss 0.60767546 - time (sec): 35.25 - samples/sec: 3009.88 - lr: 0.000018 - momentum: 0.000000 2023-10-14 10:58:07,438 epoch 1 - iter 504/723 - loss 0.54162968 - time (sec): 41.01 - samples/sec: 3014.13 - lr: 0.000021 - momentum: 0.000000 2023-10-14 10:58:14,071 epoch 1 - iter 576/723 - loss 0.48974416 - time (sec): 47.64 - samples/sec: 2993.71 - lr: 0.000024 - momentum: 0.000000 2023-10-14 10:58:20,287 epoch 1 - iter 648/723 - loss 0.45020073 - time (sec): 53.86 - samples/sec: 2969.41 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:58:25,550 epoch 1 - iter 720/723 - loss 0.42217115 - time (sec): 59.12 - samples/sec: 2971.06 - lr: 0.000030 - momentum: 0.000000 2023-10-14 10:58:25,760 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:58:25,760 EPOCH 1 done: loss 0.4213 - lr: 0.000030 2023-10-14 10:58:28,750 DEV : loss 0.12855297327041626 - f1-score (micro avg) 0.7041 2023-10-14 10:58:28,768 saving best model 2023-10-14 10:58:29,152 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:58:34,794 epoch 2 - iter 72/723 - loss 0.12156124 - time (sec): 5.64 - samples/sec: 2875.61 - lr: 0.000030 - momentum: 0.000000 2023-10-14 10:58:40,804 epoch 2 - iter 144/723 - loss 0.11146254 - time (sec): 11.65 - samples/sec: 2900.46 - lr: 0.000029 - momentum: 0.000000 2023-10-14 10:58:46,896 epoch 2 - iter 216/723 - loss 0.11686624 - time (sec): 17.74 - samples/sec: 2917.23 - lr: 0.000029 - momentum: 0.000000 2023-10-14 10:58:53,571 epoch 2 - iter 288/723 - loss 0.11075586 - time (sec): 24.42 - samples/sec: 2902.96 - lr: 0.000029 - momentum: 0.000000 2023-10-14 10:58:59,700 epoch 2 - iter 360/723 - loss 0.10651151 - time (sec): 30.55 - samples/sec: 2907.89 - lr: 0.000028 - momentum: 0.000000 2023-10-14 10:59:05,443 epoch 2 - iter 432/723 - loss 0.10541663 - time (sec): 36.29 - samples/sec: 2909.61 - lr: 0.000028 - momentum: 0.000000 2023-10-14 10:59:10,993 epoch 2 - iter 504/723 - loss 0.10566055 - time (sec): 41.84 - samples/sec: 2918.83 - lr: 0.000028 - momentum: 0.000000 2023-10-14 10:59:16,688 epoch 2 - iter 576/723 - loss 0.10315725 - time (sec): 47.53 - samples/sec: 2933.43 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:59:22,656 epoch 2 - iter 648/723 - loss 0.10168054 - time (sec): 53.50 - samples/sec: 2939.05 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:59:28,642 epoch 2 - iter 720/723 - loss 0.10158311 - time (sec): 59.49 - samples/sec: 2953.47 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:59:28,880 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:59:28,880 EPOCH 2 done: loss 0.1014 - lr: 0.000027 2023-10-14 10:59:32,771 DEV : loss 0.0938434973359108 - f1-score (micro avg) 0.7002 2023-10-14 10:59:32,788 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:59:38,964 epoch 3 - iter 72/723 - loss 0.06077009 - time (sec): 6.17 - samples/sec: 2935.87 - lr: 0.000026 - momentum: 0.000000 2023-10-14 10:59:45,122 epoch 3 - iter 144/723 - loss 0.06201343 - time (sec): 12.33 - samples/sec: 2887.51 - lr: 0.000026 - momentum: 0.000000 2023-10-14 10:59:50,971 epoch 3 - iter 216/723 - loss 0.06525859 - time (sec): 18.18 - samples/sec: 2855.52 - lr: 0.000026 - momentum: 0.000000 2023-10-14 10:59:56,686 epoch 3 - iter 288/723 - loss 0.06305458 - time (sec): 23.90 - samples/sec: 2897.57 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:00:02,887 epoch 3 - iter 360/723 - loss 0.06172832 - time (sec): 30.10 - samples/sec: 2913.59 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:00:08,780 epoch 3 - iter 432/723 - loss 0.06330693 - time (sec): 35.99 - samples/sec: 2921.37 - lr: 0.000025 - momentum: 0.000000 2023-10-14 11:00:15,222 epoch 3 - iter 504/723 - loss 0.06448096 - time (sec): 42.43 - samples/sec: 2919.30 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:00:20,867 epoch 3 - iter 576/723 - loss 0.06382838 - time (sec): 48.08 - samples/sec: 2918.70 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:00:26,929 epoch 3 - iter 648/723 - loss 0.06273635 - time (sec): 54.14 - samples/sec: 2908.32 - lr: 0.000024 - momentum: 0.000000 2023-10-14 11:00:33,161 epoch 3 - iter 720/723 - loss 0.06321271 - time (sec): 60.37 - samples/sec: 2905.22 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:00:33,487 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:00:33,488 EPOCH 3 done: loss 0.0631 - lr: 0.000023 2023-10-14 11:00:36,981 DEV : loss 0.08630853146314621 - f1-score (micro avg) 0.8069 2023-10-14 11:00:36,999 saving best model 2023-10-14 11:00:37,532 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:00:43,552 epoch 4 - iter 72/723 - loss 0.03595234 - time (sec): 6.02 - samples/sec: 2919.12 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:00:49,964 epoch 4 - iter 144/723 - loss 0.04721867 - time (sec): 12.43 - samples/sec: 2884.50 - lr: 0.000023 - momentum: 0.000000 2023-10-14 11:00:56,286 epoch 4 - iter 216/723 - loss 0.04688850 - time (sec): 18.75 - samples/sec: 2815.40 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:01:02,694 epoch 4 - iter 288/723 - loss 0.04288223 - time (sec): 25.16 - samples/sec: 2808.48 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:01:08,221 epoch 4 - iter 360/723 - loss 0.04159157 - time (sec): 30.69 - samples/sec: 2840.38 - lr: 0.000022 - momentum: 0.000000 2023-10-14 11:01:14,265 epoch 4 - iter 432/723 - loss 0.04113805 - time (sec): 36.73 - samples/sec: 2877.65 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:01:20,221 epoch 4 - iter 504/723 - loss 0.04112768 - time (sec): 42.69 - samples/sec: 2875.66 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:01:26,212 epoch 4 - iter 576/723 - loss 0.04139046 - time (sec): 48.68 - samples/sec: 2886.33 - lr: 0.000021 - momentum: 0.000000 2023-10-14 11:01:32,305 epoch 4 - iter 648/723 - loss 0.04073847 - time (sec): 54.77 - samples/sec: 2895.25 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:01:38,302 epoch 4 - iter 720/723 - loss 0.04096798 - time (sec): 60.77 - samples/sec: 2889.77 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:01:38,500 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:01:38,501 EPOCH 4 done: loss 0.0412 - lr: 0.000020 2023-10-14 11:01:42,023 DEV : loss 0.08693055063486099 - f1-score (micro avg) 0.8288 2023-10-14 11:01:42,042 saving best model 2023-10-14 11:01:42,516 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:01:48,799 epoch 5 - iter 72/723 - loss 0.03262287 - time (sec): 6.28 - samples/sec: 2932.38 - lr: 0.000020 - momentum: 0.000000 2023-10-14 11:01:54,248 epoch 5 - iter 144/723 - loss 0.03076967 - time (sec): 11.73 - samples/sec: 3021.49 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:02:00,487 epoch 5 - iter 216/723 - loss 0.02828212 - time (sec): 17.97 - samples/sec: 3012.82 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:02:06,303 epoch 5 - iter 288/723 - loss 0.03125818 - time (sec): 23.78 - samples/sec: 2978.07 - lr: 0.000019 - momentum: 0.000000 2023-10-14 11:02:11,988 epoch 5 - iter 360/723 - loss 0.02911501 - time (sec): 29.47 - samples/sec: 2974.33 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:02:17,343 epoch 5 - iter 432/723 - loss 0.02975581 - time (sec): 34.82 - samples/sec: 2979.90 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:02:23,371 epoch 5 - iter 504/723 - loss 0.02903271 - time (sec): 40.85 - samples/sec: 2985.46 - lr: 0.000018 - momentum: 0.000000 2023-10-14 11:02:29,523 epoch 5 - iter 576/723 - loss 0.03013901 - time (sec): 47.00 - samples/sec: 2972.90 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:02:35,695 epoch 5 - iter 648/723 - loss 0.03126828 - time (sec): 53.18 - samples/sec: 2976.24 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:02:41,551 epoch 5 - iter 720/723 - loss 0.03072279 - time (sec): 59.03 - samples/sec: 2976.35 - lr: 0.000017 - momentum: 0.000000 2023-10-14 11:02:41,720 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:02:41,721 EPOCH 5 done: loss 0.0308 - lr: 0.000017 2023-10-14 11:02:45,637 DEV : loss 0.13127633929252625 - f1-score (micro avg) 0.8044 2023-10-14 11:02:45,653 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:02:51,633 epoch 6 - iter 72/723 - loss 0.01842860 - time (sec): 5.98 - samples/sec: 2902.58 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:02:57,718 epoch 6 - iter 144/723 - loss 0.02104149 - time (sec): 12.06 - samples/sec: 2907.63 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:03:03,381 epoch 6 - iter 216/723 - loss 0.02082948 - time (sec): 17.73 - samples/sec: 2961.34 - lr: 0.000016 - momentum: 0.000000 2023-10-14 11:03:09,572 epoch 6 - iter 288/723 - loss 0.02184263 - time (sec): 23.92 - samples/sec: 2949.13 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:03:15,404 epoch 6 - iter 360/723 - loss 0.02072094 - time (sec): 29.75 - samples/sec: 2938.63 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:03:21,504 epoch 6 - iter 432/723 - loss 0.02069835 - time (sec): 35.85 - samples/sec: 2930.06 - lr: 0.000015 - momentum: 0.000000 2023-10-14 11:03:27,963 epoch 6 - iter 504/723 - loss 0.02129765 - time (sec): 42.31 - samples/sec: 2904.18 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:03:34,651 epoch 6 - iter 576/723 - loss 0.02050509 - time (sec): 49.00 - samples/sec: 2903.26 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:03:40,533 epoch 6 - iter 648/723 - loss 0.02175670 - time (sec): 54.88 - samples/sec: 2899.49 - lr: 0.000014 - momentum: 0.000000 2023-10-14 11:03:46,229 epoch 6 - iter 720/723 - loss 0.02231132 - time (sec): 60.57 - samples/sec: 2901.48 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:03:46,397 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:03:46,397 EPOCH 6 done: loss 0.0223 - lr: 0.000013 2023-10-14 11:03:49,947 DEV : loss 0.12690779566764832 - f1-score (micro avg) 0.8249 2023-10-14 11:03:49,964 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:03:56,165 epoch 7 - iter 72/723 - loss 0.00937309 - time (sec): 6.20 - samples/sec: 2830.04 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:04:02,678 epoch 7 - iter 144/723 - loss 0.01350239 - time (sec): 12.71 - samples/sec: 2882.76 - lr: 0.000013 - momentum: 0.000000 2023-10-14 11:04:08,323 epoch 7 - iter 216/723 - loss 0.01358360 - time (sec): 18.36 - samples/sec: 2915.57 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:04:15,072 epoch 7 - iter 288/723 - loss 0.01452212 - time (sec): 25.11 - samples/sec: 2864.73 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:04:20,800 epoch 7 - iter 360/723 - loss 0.01439804 - time (sec): 30.84 - samples/sec: 2869.66 - lr: 0.000012 - momentum: 0.000000 2023-10-14 11:04:26,288 epoch 7 - iter 432/723 - loss 0.01516061 - time (sec): 36.32 - samples/sec: 2893.06 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:04:32,788 epoch 7 - iter 504/723 - loss 0.01712164 - time (sec): 42.82 - samples/sec: 2892.01 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:04:38,780 epoch 7 - iter 576/723 - loss 0.01729432 - time (sec): 48.81 - samples/sec: 2906.42 - lr: 0.000011 - momentum: 0.000000 2023-10-14 11:04:44,374 epoch 7 - iter 648/723 - loss 0.01749135 - time (sec): 54.41 - samples/sec: 2924.73 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:04:50,330 epoch 7 - iter 720/723 - loss 0.01711051 - time (sec): 60.37 - samples/sec: 2912.37 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:04:50,524 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:04:50,525 EPOCH 7 done: loss 0.0174 - lr: 0.000010 2023-10-14 11:04:54,070 DEV : loss 0.15461641550064087 - f1-score (micro avg) 0.8145 2023-10-14 11:04:54,088 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:04:59,959 epoch 8 - iter 72/723 - loss 0.01665349 - time (sec): 5.87 - samples/sec: 2848.03 - lr: 0.000010 - momentum: 0.000000 2023-10-14 11:05:06,094 epoch 8 - iter 144/723 - loss 0.01446601 - time (sec): 12.00 - samples/sec: 2881.67 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:05:12,405 epoch 8 - iter 216/723 - loss 0.01410012 - time (sec): 18.32 - samples/sec: 2857.36 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:05:18,576 epoch 8 - iter 288/723 - loss 0.01366919 - time (sec): 24.49 - samples/sec: 2845.71 - lr: 0.000009 - momentum: 0.000000 2023-10-14 11:05:24,652 epoch 8 - iter 360/723 - loss 0.01239443 - time (sec): 30.56 - samples/sec: 2877.55 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:05:30,144 epoch 8 - iter 432/723 - loss 0.01265536 - time (sec): 36.05 - samples/sec: 2900.06 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:05:36,539 epoch 8 - iter 504/723 - loss 0.01358128 - time (sec): 42.45 - samples/sec: 2891.45 - lr: 0.000008 - momentum: 0.000000 2023-10-14 11:05:42,619 epoch 8 - iter 576/723 - loss 0.01348813 - time (sec): 48.53 - samples/sec: 2893.15 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:05:48,324 epoch 8 - iter 648/723 - loss 0.01308338 - time (sec): 54.23 - samples/sec: 2905.38 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:05:54,660 epoch 8 - iter 720/723 - loss 0.01321059 - time (sec): 60.57 - samples/sec: 2896.19 - lr: 0.000007 - momentum: 0.000000 2023-10-14 11:05:54,878 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:05:54,878 EPOCH 8 done: loss 0.0132 - lr: 0.000007 2023-10-14 11:05:58,864 DEV : loss 0.150771364569664 - f1-score (micro avg) 0.8431 2023-10-14 11:05:58,880 saving best model 2023-10-14 11:05:59,413 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:06:05,658 epoch 9 - iter 72/723 - loss 0.01017083 - time (sec): 6.24 - samples/sec: 2911.90 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:06:11,476 epoch 9 - iter 144/723 - loss 0.00911976 - time (sec): 12.06 - samples/sec: 2930.73 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:06:17,232 epoch 9 - iter 216/723 - loss 0.00838583 - time (sec): 17.81 - samples/sec: 2926.00 - lr: 0.000006 - momentum: 0.000000 2023-10-14 11:06:23,911 epoch 9 - iter 288/723 - loss 0.00904573 - time (sec): 24.49 - samples/sec: 2913.97 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:06:29,832 epoch 9 - iter 360/723 - loss 0.00913035 - time (sec): 30.41 - samples/sec: 2918.84 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:06:36,140 epoch 9 - iter 432/723 - loss 0.00938579 - time (sec): 36.72 - samples/sec: 2905.02 - lr: 0.000005 - momentum: 0.000000 2023-10-14 11:06:41,831 epoch 9 - iter 504/723 - loss 0.00897802 - time (sec): 42.41 - samples/sec: 2912.32 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:06:48,221 epoch 9 - iter 576/723 - loss 0.00894380 - time (sec): 48.80 - samples/sec: 2899.02 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:06:54,001 epoch 9 - iter 648/723 - loss 0.00958515 - time (sec): 54.58 - samples/sec: 2903.49 - lr: 0.000004 - momentum: 0.000000 2023-10-14 11:06:59,830 epoch 9 - iter 720/723 - loss 0.00943720 - time (sec): 60.41 - samples/sec: 2908.78 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:07:00,059 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:07:00,060 EPOCH 9 done: loss 0.0095 - lr: 0.000003 2023-10-14 11:07:03,577 DEV : loss 0.15790636837482452 - f1-score (micro avg) 0.833 2023-10-14 11:07:03,592 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:07:09,324 epoch 10 - iter 72/723 - loss 0.00876310 - time (sec): 5.73 - samples/sec: 2913.63 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:07:15,547 epoch 10 - iter 144/723 - loss 0.00697092 - time (sec): 11.95 - samples/sec: 2905.71 - lr: 0.000003 - momentum: 0.000000 2023-10-14 11:07:22,240 epoch 10 - iter 216/723 - loss 0.00740430 - time (sec): 18.65 - samples/sec: 2809.93 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:07:28,447 epoch 10 - iter 288/723 - loss 0.00869243 - time (sec): 24.85 - samples/sec: 2862.60 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:07:34,727 epoch 10 - iter 360/723 - loss 0.00813367 - time (sec): 31.13 - samples/sec: 2878.44 - lr: 0.000002 - momentum: 0.000000 2023-10-14 11:07:40,624 epoch 10 - iter 432/723 - loss 0.00870267 - time (sec): 37.03 - samples/sec: 2886.14 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:07:46,030 epoch 10 - iter 504/723 - loss 0.00810991 - time (sec): 42.44 - samples/sec: 2888.69 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:07:51,966 epoch 10 - iter 576/723 - loss 0.00778453 - time (sec): 48.37 - samples/sec: 2879.90 - lr: 0.000001 - momentum: 0.000000 2023-10-14 11:07:58,270 epoch 10 - iter 648/723 - loss 0.00790965 - time (sec): 54.68 - samples/sec: 2885.46 - lr: 0.000000 - momentum: 0.000000 2023-10-14 11:08:04,217 epoch 10 - iter 720/723 - loss 0.00773716 - time (sec): 60.62 - samples/sec: 2894.81 - lr: 0.000000 - momentum: 0.000000 2023-10-14 11:08:04,452 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:08:04,452 EPOCH 10 done: loss 0.0077 - lr: 0.000000 2023-10-14 11:08:07,999 DEV : loss 0.16016288101673126 - f1-score (micro avg) 0.8324 2023-10-14 11:08:08,469 ---------------------------------------------------------------------------------------------------- 2023-10-14 11:08:08,470 Loading model from best epoch ... 2023-10-14 11:08:10,080 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 11:08:13,254 Results: - F-score (micro) 0.8224 - F-score (macro) 0.7445 - Accuracy 0.7093 By class: precision recall f1-score support PER 0.8407 0.8320 0.8363 482 LOC 0.8710 0.8253 0.8475 458 ORG 0.5806 0.5217 0.5496 69 micro avg 0.8376 0.8077 0.8224 1009 macro avg 0.7641 0.7263 0.7445 1009 weighted avg 0.8366 0.8077 0.8218 1009 2023-10-14 11:08:13,254 ----------------------------------------------------------------------------------------------------