2023-10-17 15:33:07,867 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,868 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 15:33:07,868 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,868 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 15:33:07,868 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,868 Train: 5777 sentences 2023-10-17 15:33:07,868 (train_with_dev=False, train_with_test=False) 2023-10-17 15:33:07,868 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,868 Training Params: 2023-10-17 15:33:07,869 - learning_rate: "5e-05" 2023-10-17 15:33:07,869 - mini_batch_size: "4" 2023-10-17 15:33:07,869 - max_epochs: "10" 2023-10-17 15:33:07,869 - shuffle: "True" 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 Plugins: 2023-10-17 15:33:07,869 - TensorboardLogger 2023-10-17 15:33:07,869 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 15:33:07,869 - metric: "('micro avg', 'f1-score')" 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 Computation: 2023-10-17 15:33:07,869 - compute on device: cuda:0 2023-10-17 15:33:07,869 - embedding storage: none 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 Model training base path: "hmbench-icdar/nl-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:33:07,869 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 15:33:14,818 epoch 1 - iter 144/1445 - loss 1.92593897 - time (sec): 6.95 - samples/sec: 2670.87 - lr: 0.000005 - momentum: 0.000000 2023-10-17 15:33:21,656 epoch 1 - iter 288/1445 - loss 1.14848659 - time (sec): 13.79 - samples/sec: 2498.28 - lr: 0.000010 - momentum: 0.000000 2023-10-17 15:33:28,548 epoch 1 - iter 432/1445 - loss 0.81417525 - time (sec): 20.68 - samples/sec: 2519.75 - lr: 0.000015 - momentum: 0.000000 2023-10-17 15:33:35,383 epoch 1 - iter 576/1445 - loss 0.65607071 - time (sec): 27.51 - samples/sec: 2481.77 - lr: 0.000020 - momentum: 0.000000 2023-10-17 15:33:42,441 epoch 1 - iter 720/1445 - loss 0.55106262 - time (sec): 34.57 - samples/sec: 2502.18 - lr: 0.000025 - momentum: 0.000000 2023-10-17 15:33:49,471 epoch 1 - iter 864/1445 - loss 0.47665927 - time (sec): 41.60 - samples/sec: 2536.30 - lr: 0.000030 - momentum: 0.000000 2023-10-17 15:33:56,060 epoch 1 - iter 1008/1445 - loss 0.42564629 - time (sec): 48.19 - samples/sec: 2555.27 - lr: 0.000035 - momentum: 0.000000 2023-10-17 15:34:02,950 epoch 1 - iter 1152/1445 - loss 0.38691122 - time (sec): 55.08 - samples/sec: 2562.18 - lr: 0.000040 - momentum: 0.000000 2023-10-17 15:34:09,702 epoch 1 - iter 1296/1445 - loss 0.35796946 - time (sec): 61.83 - samples/sec: 2563.72 - lr: 0.000045 - momentum: 0.000000 2023-10-17 15:34:16,350 epoch 1 - iter 1440/1445 - loss 0.33514967 - time (sec): 68.48 - samples/sec: 2565.26 - lr: 0.000050 - momentum: 0.000000 2023-10-17 15:34:16,569 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:34:16,569 EPOCH 1 done: loss 0.3344 - lr: 0.000050 2023-10-17 15:34:19,829 DEV : loss 0.10861274600028992 - f1-score (micro avg) 0.6573 2023-10-17 15:34:19,861 saving best model 2023-10-17 15:34:20,273 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:34:27,269 epoch 2 - iter 144/1445 - loss 0.11625048 - time (sec): 6.99 - samples/sec: 2379.25 - lr: 0.000049 - momentum: 0.000000 2023-10-17 15:34:34,119 epoch 2 - iter 288/1445 - loss 0.11444173 - time (sec): 13.84 - samples/sec: 2451.31 - lr: 0.000049 - momentum: 0.000000 2023-10-17 15:34:41,181 epoch 2 - iter 432/1445 - loss 0.10811992 - time (sec): 20.91 - samples/sec: 2455.20 - lr: 0.000048 - momentum: 0.000000 2023-10-17 15:34:48,042 epoch 2 - iter 576/1445 - loss 0.10325462 - time (sec): 27.77 - samples/sec: 2465.58 - lr: 0.000048 - momentum: 0.000000 2023-10-17 15:34:55,055 epoch 2 - iter 720/1445 - loss 0.09805219 - time (sec): 34.78 - samples/sec: 2495.12 - lr: 0.000047 - momentum: 0.000000 2023-10-17 15:35:02,213 epoch 2 - iter 864/1445 - loss 0.09411125 - time (sec): 41.94 - samples/sec: 2524.85 - lr: 0.000047 - momentum: 0.000000 2023-10-17 15:35:08,997 epoch 2 - iter 1008/1445 - loss 0.09339206 - time (sec): 48.72 - samples/sec: 2518.97 - lr: 0.000046 - momentum: 0.000000 2023-10-17 15:35:16,245 epoch 2 - iter 1152/1445 - loss 0.09392903 - time (sec): 55.97 - samples/sec: 2509.82 - lr: 0.000046 - momentum: 0.000000 2023-10-17 15:35:23,150 epoch 2 - iter 1296/1445 - loss 0.09494132 - time (sec): 62.87 - samples/sec: 2504.18 - lr: 0.000045 - momentum: 0.000000 2023-10-17 15:35:30,230 epoch 2 - iter 1440/1445 - loss 0.09822877 - time (sec): 69.95 - samples/sec: 2512.31 - lr: 0.000044 - momentum: 0.000000 2023-10-17 15:35:30,450 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:35:30,450 EPOCH 2 done: loss 0.0984 - lr: 0.000044 2023-10-17 15:35:34,251 DEV : loss 0.13345900177955627 - f1-score (micro avg) 0.6763 2023-10-17 15:35:34,270 saving best model 2023-10-17 15:35:34,739 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:35:41,733 epoch 3 - iter 144/1445 - loss 0.06944847 - time (sec): 6.99 - samples/sec: 2484.40 - lr: 0.000044 - momentum: 0.000000 2023-10-17 15:35:48,620 epoch 3 - iter 288/1445 - loss 0.06628234 - time (sec): 13.88 - samples/sec: 2494.99 - lr: 0.000043 - momentum: 0.000000 2023-10-17 15:35:55,938 epoch 3 - iter 432/1445 - loss 0.06588793 - time (sec): 21.20 - samples/sec: 2528.44 - lr: 0.000043 - momentum: 0.000000 2023-10-17 15:36:02,862 epoch 3 - iter 576/1445 - loss 0.06519428 - time (sec): 28.12 - samples/sec: 2515.02 - lr: 0.000042 - momentum: 0.000000 2023-10-17 15:36:09,625 epoch 3 - iter 720/1445 - loss 0.06602713 - time (sec): 34.88 - samples/sec: 2507.94 - lr: 0.000042 - momentum: 0.000000 2023-10-17 15:36:16,739 epoch 3 - iter 864/1445 - loss 0.06637663 - time (sec): 42.00 - samples/sec: 2507.44 - lr: 0.000041 - momentum: 0.000000 2023-10-17 15:36:23,756 epoch 3 - iter 1008/1445 - loss 0.06655629 - time (sec): 49.02 - samples/sec: 2489.67 - lr: 0.000041 - momentum: 0.000000 2023-10-17 15:36:30,816 epoch 3 - iter 1152/1445 - loss 0.06748428 - time (sec): 56.08 - samples/sec: 2487.24 - lr: 0.000040 - momentum: 0.000000 2023-10-17 15:36:38,008 epoch 3 - iter 1296/1445 - loss 0.06799725 - time (sec): 63.27 - samples/sec: 2491.50 - lr: 0.000039 - momentum: 0.000000 2023-10-17 15:36:44,928 epoch 3 - iter 1440/1445 - loss 0.06778051 - time (sec): 70.19 - samples/sec: 2504.64 - lr: 0.000039 - momentum: 0.000000 2023-10-17 15:36:45,142 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:36:45,143 EPOCH 3 done: loss 0.0681 - lr: 0.000039 2023-10-17 15:36:48,337 DEV : loss 0.09013378620147705 - f1-score (micro avg) 0.8588 2023-10-17 15:36:48,354 saving best model 2023-10-17 15:36:48,816 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:36:55,898 epoch 4 - iter 144/1445 - loss 0.04676885 - time (sec): 7.08 - samples/sec: 2579.33 - lr: 0.000038 - momentum: 0.000000 2023-10-17 15:37:02,866 epoch 4 - iter 288/1445 - loss 0.05260260 - time (sec): 14.04 - samples/sec: 2532.21 - lr: 0.000038 - momentum: 0.000000 2023-10-17 15:37:09,745 epoch 4 - iter 432/1445 - loss 0.04637383 - time (sec): 20.92 - samples/sec: 2529.30 - lr: 0.000037 - momentum: 0.000000 2023-10-17 15:37:16,682 epoch 4 - iter 576/1445 - loss 0.04944995 - time (sec): 27.86 - samples/sec: 2523.60 - lr: 0.000037 - momentum: 0.000000 2023-10-17 15:37:23,487 epoch 4 - iter 720/1445 - loss 0.05010050 - time (sec): 34.66 - samples/sec: 2507.18 - lr: 0.000036 - momentum: 0.000000 2023-10-17 15:37:30,605 epoch 4 - iter 864/1445 - loss 0.05081688 - time (sec): 41.78 - samples/sec: 2501.61 - lr: 0.000036 - momentum: 0.000000 2023-10-17 15:37:37,605 epoch 4 - iter 1008/1445 - loss 0.05019035 - time (sec): 48.78 - samples/sec: 2507.35 - lr: 0.000035 - momentum: 0.000000 2023-10-17 15:37:44,603 epoch 4 - iter 1152/1445 - loss 0.04964440 - time (sec): 55.78 - samples/sec: 2508.06 - lr: 0.000034 - momentum: 0.000000 2023-10-17 15:37:51,644 epoch 4 - iter 1296/1445 - loss 0.04892615 - time (sec): 62.82 - samples/sec: 2509.01 - lr: 0.000034 - momentum: 0.000000 2023-10-17 15:37:58,723 epoch 4 - iter 1440/1445 - loss 0.05060487 - time (sec): 69.90 - samples/sec: 2514.90 - lr: 0.000033 - momentum: 0.000000 2023-10-17 15:37:58,946 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:37:58,946 EPOCH 4 done: loss 0.0505 - lr: 0.000033 2023-10-17 15:38:02,111 DEV : loss 0.09323444962501526 - f1-score (micro avg) 0.8574 2023-10-17 15:38:02,127 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:38:09,095 epoch 5 - iter 144/1445 - loss 0.03072807 - time (sec): 6.97 - samples/sec: 2539.84 - lr: 0.000033 - momentum: 0.000000 2023-10-17 15:38:16,057 epoch 5 - iter 288/1445 - loss 0.03358255 - time (sec): 13.93 - samples/sec: 2580.24 - lr: 0.000032 - momentum: 0.000000 2023-10-17 15:38:23,517 epoch 5 - iter 432/1445 - loss 0.03446332 - time (sec): 21.39 - samples/sec: 2512.59 - lr: 0.000032 - momentum: 0.000000 2023-10-17 15:38:30,301 epoch 5 - iter 576/1445 - loss 0.03497844 - time (sec): 28.17 - samples/sec: 2501.32 - lr: 0.000031 - momentum: 0.000000 2023-10-17 15:38:37,245 epoch 5 - iter 720/1445 - loss 0.03494447 - time (sec): 35.12 - samples/sec: 2500.49 - lr: 0.000031 - momentum: 0.000000 2023-10-17 15:38:44,177 epoch 5 - iter 864/1445 - loss 0.03849124 - time (sec): 42.05 - samples/sec: 2494.59 - lr: 0.000030 - momentum: 0.000000 2023-10-17 15:38:51,092 epoch 5 - iter 1008/1445 - loss 0.03868840 - time (sec): 48.96 - samples/sec: 2492.81 - lr: 0.000029 - momentum: 0.000000 2023-10-17 15:38:58,128 epoch 5 - iter 1152/1445 - loss 0.03863802 - time (sec): 56.00 - samples/sec: 2510.90 - lr: 0.000029 - momentum: 0.000000 2023-10-17 15:39:04,931 epoch 5 - iter 1296/1445 - loss 0.03989823 - time (sec): 62.80 - samples/sec: 2513.11 - lr: 0.000028 - momentum: 0.000000 2023-10-17 15:39:12,028 epoch 5 - iter 1440/1445 - loss 0.03957638 - time (sec): 69.90 - samples/sec: 2512.94 - lr: 0.000028 - momentum: 0.000000 2023-10-17 15:39:12,247 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:39:12,248 EPOCH 5 done: loss 0.0395 - lr: 0.000028 2023-10-17 15:39:15,509 DEV : loss 0.11628924310207367 - f1-score (micro avg) 0.8495 2023-10-17 15:39:15,527 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:39:22,621 epoch 6 - iter 144/1445 - loss 0.02447880 - time (sec): 7.09 - samples/sec: 2467.72 - lr: 0.000027 - momentum: 0.000000 2023-10-17 15:39:29,423 epoch 6 - iter 288/1445 - loss 0.03137841 - time (sec): 13.89 - samples/sec: 2449.60 - lr: 0.000027 - momentum: 0.000000 2023-10-17 15:39:36,131 epoch 6 - iter 432/1445 - loss 0.03035696 - time (sec): 20.60 - samples/sec: 2504.30 - lr: 0.000026 - momentum: 0.000000 2023-10-17 15:39:43,478 epoch 6 - iter 576/1445 - loss 0.02805603 - time (sec): 27.95 - samples/sec: 2497.07 - lr: 0.000026 - momentum: 0.000000 2023-10-17 15:39:50,654 epoch 6 - iter 720/1445 - loss 0.02854656 - time (sec): 35.13 - samples/sec: 2505.16 - lr: 0.000025 - momentum: 0.000000 2023-10-17 15:39:57,367 epoch 6 - iter 864/1445 - loss 0.02796582 - time (sec): 41.84 - samples/sec: 2496.03 - lr: 0.000024 - momentum: 0.000000 2023-10-17 15:40:04,170 epoch 6 - iter 1008/1445 - loss 0.02775939 - time (sec): 48.64 - samples/sec: 2519.64 - lr: 0.000024 - momentum: 0.000000 2023-10-17 15:40:11,115 epoch 6 - iter 1152/1445 - loss 0.02855916 - time (sec): 55.59 - samples/sec: 2501.88 - lr: 0.000023 - momentum: 0.000000 2023-10-17 15:40:18,248 epoch 6 - iter 1296/1445 - loss 0.02841167 - time (sec): 62.72 - samples/sec: 2499.15 - lr: 0.000023 - momentum: 0.000000 2023-10-17 15:40:25,387 epoch 6 - iter 1440/1445 - loss 0.02790215 - time (sec): 69.86 - samples/sec: 2512.16 - lr: 0.000022 - momentum: 0.000000 2023-10-17 15:40:25,612 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:40:25,612 EPOCH 6 done: loss 0.0278 - lr: 0.000022 2023-10-17 15:40:29,051 DEV : loss 0.13435053825378418 - f1-score (micro avg) 0.8472 2023-10-17 15:40:29,073 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:40:36,627 epoch 7 - iter 144/1445 - loss 0.03621832 - time (sec): 7.55 - samples/sec: 2286.98 - lr: 0.000022 - momentum: 0.000000 2023-10-17 15:40:43,645 epoch 7 - iter 288/1445 - loss 0.02724953 - time (sec): 14.57 - samples/sec: 2335.39 - lr: 0.000021 - momentum: 0.000000 2023-10-17 15:40:50,674 epoch 7 - iter 432/1445 - loss 0.02754718 - time (sec): 21.60 - samples/sec: 2405.25 - lr: 0.000021 - momentum: 0.000000 2023-10-17 15:40:57,976 epoch 7 - iter 576/1445 - loss 0.02528310 - time (sec): 28.90 - samples/sec: 2422.96 - lr: 0.000020 - momentum: 0.000000 2023-10-17 15:41:04,930 epoch 7 - iter 720/1445 - loss 0.02377881 - time (sec): 35.86 - samples/sec: 2435.71 - lr: 0.000019 - momentum: 0.000000 2023-10-17 15:41:11,853 epoch 7 - iter 864/1445 - loss 0.02330516 - time (sec): 42.78 - samples/sec: 2478.15 - lr: 0.000019 - momentum: 0.000000 2023-10-17 15:41:18,745 epoch 7 - iter 1008/1445 - loss 0.02094244 - time (sec): 49.67 - samples/sec: 2479.00 - lr: 0.000018 - momentum: 0.000000 2023-10-17 15:41:25,574 epoch 7 - iter 1152/1445 - loss 0.01946340 - time (sec): 56.50 - samples/sec: 2481.85 - lr: 0.000018 - momentum: 0.000000 2023-10-17 15:41:33,047 epoch 7 - iter 1296/1445 - loss 0.01934243 - time (sec): 63.97 - samples/sec: 2471.24 - lr: 0.000017 - momentum: 0.000000 2023-10-17 15:41:39,929 epoch 7 - iter 1440/1445 - loss 0.01931728 - time (sec): 70.85 - samples/sec: 2480.95 - lr: 0.000017 - momentum: 0.000000 2023-10-17 15:41:40,152 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:41:40,153 EPOCH 7 done: loss 0.0193 - lr: 0.000017 2023-10-17 15:41:43,359 DEV : loss 0.13610993325710297 - f1-score (micro avg) 0.8496 2023-10-17 15:41:43,376 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:41:50,179 epoch 8 - iter 144/1445 - loss 0.01156269 - time (sec): 6.80 - samples/sec: 2373.92 - lr: 0.000016 - momentum: 0.000000 2023-10-17 15:41:57,334 epoch 8 - iter 288/1445 - loss 0.01424879 - time (sec): 13.96 - samples/sec: 2475.60 - lr: 0.000016 - momentum: 0.000000 2023-10-17 15:42:04,154 epoch 8 - iter 432/1445 - loss 0.01152694 - time (sec): 20.78 - samples/sec: 2517.25 - lr: 0.000015 - momentum: 0.000000 2023-10-17 15:42:11,076 epoch 8 - iter 576/1445 - loss 0.01252404 - time (sec): 27.70 - samples/sec: 2492.28 - lr: 0.000014 - momentum: 0.000000 2023-10-17 15:42:18,115 epoch 8 - iter 720/1445 - loss 0.01307924 - time (sec): 34.74 - samples/sec: 2478.52 - lr: 0.000014 - momentum: 0.000000 2023-10-17 15:42:25,240 epoch 8 - iter 864/1445 - loss 0.01191157 - time (sec): 41.86 - samples/sec: 2498.03 - lr: 0.000013 - momentum: 0.000000 2023-10-17 15:42:31,873 epoch 8 - iter 1008/1445 - loss 0.01386504 - time (sec): 48.50 - samples/sec: 2523.71 - lr: 0.000013 - momentum: 0.000000 2023-10-17 15:42:38,759 epoch 8 - iter 1152/1445 - loss 0.01425968 - time (sec): 55.38 - samples/sec: 2514.92 - lr: 0.000012 - momentum: 0.000000 2023-10-17 15:42:45,754 epoch 8 - iter 1296/1445 - loss 0.01357515 - time (sec): 62.38 - samples/sec: 2530.49 - lr: 0.000012 - momentum: 0.000000 2023-10-17 15:42:52,599 epoch 8 - iter 1440/1445 - loss 0.01409541 - time (sec): 69.22 - samples/sec: 2534.97 - lr: 0.000011 - momentum: 0.000000 2023-10-17 15:42:52,844 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:42:52,844 EPOCH 8 done: loss 0.0140 - lr: 0.000011 2023-10-17 15:42:56,085 DEV : loss 0.16182565689086914 - f1-score (micro avg) 0.8473 2023-10-17 15:42:56,101 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:43:03,053 epoch 9 - iter 144/1445 - loss 0.01038750 - time (sec): 6.95 - samples/sec: 2763.07 - lr: 0.000011 - momentum: 0.000000 2023-10-17 15:43:09,940 epoch 9 - iter 288/1445 - loss 0.00853687 - time (sec): 13.84 - samples/sec: 2536.32 - lr: 0.000010 - momentum: 0.000000 2023-10-17 15:43:17,344 epoch 9 - iter 432/1445 - loss 0.00758837 - time (sec): 21.24 - samples/sec: 2549.74 - lr: 0.000009 - momentum: 0.000000 2023-10-17 15:43:24,505 epoch 9 - iter 576/1445 - loss 0.00778959 - time (sec): 28.40 - samples/sec: 2550.27 - lr: 0.000009 - momentum: 0.000000 2023-10-17 15:43:31,649 epoch 9 - iter 720/1445 - loss 0.00866403 - time (sec): 35.55 - samples/sec: 2519.68 - lr: 0.000008 - momentum: 0.000000 2023-10-17 15:43:38,340 epoch 9 - iter 864/1445 - loss 0.00829698 - time (sec): 42.24 - samples/sec: 2498.92 - lr: 0.000008 - momentum: 0.000000 2023-10-17 15:43:45,253 epoch 9 - iter 1008/1445 - loss 0.00824515 - time (sec): 49.15 - samples/sec: 2506.38 - lr: 0.000007 - momentum: 0.000000 2023-10-17 15:43:52,687 epoch 9 - iter 1152/1445 - loss 0.00842807 - time (sec): 56.58 - samples/sec: 2497.57 - lr: 0.000007 - momentum: 0.000000 2023-10-17 15:43:59,636 epoch 9 - iter 1296/1445 - loss 0.00853763 - time (sec): 63.53 - samples/sec: 2501.91 - lr: 0.000006 - momentum: 0.000000 2023-10-17 15:44:06,350 epoch 9 - iter 1440/1445 - loss 0.00875377 - time (sec): 70.25 - samples/sec: 2497.52 - lr: 0.000006 - momentum: 0.000000 2023-10-17 15:44:06,618 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:44:06,619 EPOCH 9 done: loss 0.0087 - lr: 0.000006 2023-10-17 15:44:10,219 DEV : loss 0.14013005793094635 - f1-score (micro avg) 0.8622 2023-10-17 15:44:10,236 saving best model 2023-10-17 15:44:10,722 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:44:17,455 epoch 10 - iter 144/1445 - loss 0.00579021 - time (sec): 6.73 - samples/sec: 2592.22 - lr: 0.000005 - momentum: 0.000000 2023-10-17 15:44:24,391 epoch 10 - iter 288/1445 - loss 0.00521433 - time (sec): 13.67 - samples/sec: 2564.98 - lr: 0.000004 - momentum: 0.000000 2023-10-17 15:44:31,282 epoch 10 - iter 432/1445 - loss 0.00599340 - time (sec): 20.56 - samples/sec: 2514.52 - lr: 0.000004 - momentum: 0.000000 2023-10-17 15:44:38,028 epoch 10 - iter 576/1445 - loss 0.00550622 - time (sec): 27.30 - samples/sec: 2488.42 - lr: 0.000003 - momentum: 0.000000 2023-10-17 15:44:45,428 epoch 10 - iter 720/1445 - loss 0.00533641 - time (sec): 34.70 - samples/sec: 2497.69 - lr: 0.000003 - momentum: 0.000000 2023-10-17 15:44:52,515 epoch 10 - iter 864/1445 - loss 0.00570968 - time (sec): 41.79 - samples/sec: 2521.83 - lr: 0.000002 - momentum: 0.000000 2023-10-17 15:44:59,374 epoch 10 - iter 1008/1445 - loss 0.00509566 - time (sec): 48.65 - samples/sec: 2508.54 - lr: 0.000002 - momentum: 0.000000 2023-10-17 15:45:06,472 epoch 10 - iter 1152/1445 - loss 0.00510136 - time (sec): 55.75 - samples/sec: 2513.10 - lr: 0.000001 - momentum: 0.000000 2023-10-17 15:45:13,388 epoch 10 - iter 1296/1445 - loss 0.00492028 - time (sec): 62.66 - samples/sec: 2523.18 - lr: 0.000001 - momentum: 0.000000 2023-10-17 15:45:20,396 epoch 10 - iter 1440/1445 - loss 0.00498631 - time (sec): 69.67 - samples/sec: 2522.46 - lr: 0.000000 - momentum: 0.000000 2023-10-17 15:45:20,612 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:45:20,612 EPOCH 10 done: loss 0.0050 - lr: 0.000000 2023-10-17 15:45:23,866 DEV : loss 0.16260549426078796 - f1-score (micro avg) 0.8548 2023-10-17 15:45:24,223 ---------------------------------------------------------------------------------------------------- 2023-10-17 15:45:24,225 Loading model from best epoch ... 2023-10-17 15:45:25,583 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 15:45:28,550 Results: - F-score (micro) 0.8439 - F-score (macro) 0.7461 - Accuracy 0.7378 By class: precision recall f1-score support PER 0.8556 0.8361 0.8458 482 LOC 0.9392 0.8428 0.8884 458 ORG 0.6000 0.4348 0.5042 69 micro avg 0.8788 0.8117 0.8439 1009 macro avg 0.7983 0.7046 0.7461 1009 weighted avg 0.8761 0.8117 0.8417 1009 2023-10-17 15:45:28,551 ----------------------------------------------------------------------------------------------------