2023-10-14 10:43:55,873 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,874 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 10:43:55,874 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,874 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 10:43:55,874 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,874 Train: 5777 sentences 2023-10-14 10:43:55,874 (train_with_dev=False, train_with_test=False) 2023-10-14 10:43:55,874 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 Training Params: 2023-10-14 10:43:55,875 - learning_rate: "5e-05" 2023-10-14 10:43:55,875 - mini_batch_size: "4" 2023-10-14 10:43:55,875 - max_epochs: "10" 2023-10-14 10:43:55,875 - shuffle: "True" 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 Plugins: 2023-10-14 10:43:55,875 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 10:43:55,875 - metric: "('micro avg', 'f1-score')" 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 Computation: 2023-10-14 10:43:55,875 - compute on device: cuda:0 2023-10-14 10:43:55,875 - embedding storage: none 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:43:55,875 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:44:03,371 epoch 1 - iter 144/1445 - loss 1.53614842 - time (sec): 7.49 - samples/sec: 2488.14 - lr: 0.000005 - momentum: 0.000000 2023-10-14 10:44:10,666 epoch 1 - iter 288/1445 - loss 0.92543100 - time (sec): 14.79 - samples/sec: 2442.39 - lr: 0.000010 - momentum: 0.000000 2023-10-14 10:44:18,047 epoch 1 - iter 432/1445 - loss 0.68772860 - time (sec): 22.17 - samples/sec: 2403.60 - lr: 0.000015 - momentum: 0.000000 2023-10-14 10:44:25,279 epoch 1 - iter 576/1445 - loss 0.56173958 - time (sec): 29.40 - samples/sec: 2387.31 - lr: 0.000020 - momentum: 0.000000 2023-10-14 10:44:32,595 epoch 1 - iter 720/1445 - loss 0.47974609 - time (sec): 36.72 - samples/sec: 2404.37 - lr: 0.000025 - momentum: 0.000000 2023-10-14 10:44:39,880 epoch 1 - iter 864/1445 - loss 0.42590462 - time (sec): 44.00 - samples/sec: 2411.14 - lr: 0.000030 - momentum: 0.000000 2023-10-14 10:44:47,180 epoch 1 - iter 1008/1445 - loss 0.38604397 - time (sec): 51.30 - samples/sec: 2409.43 - lr: 0.000035 - momentum: 0.000000 2023-10-14 10:44:54,827 epoch 1 - iter 1152/1445 - loss 0.35511393 - time (sec): 58.95 - samples/sec: 2419.50 - lr: 0.000040 - momentum: 0.000000 2023-10-14 10:45:01,879 epoch 1 - iter 1296/1445 - loss 0.33007466 - time (sec): 66.00 - samples/sec: 2423.12 - lr: 0.000045 - momentum: 0.000000 2023-10-14 10:45:08,740 epoch 1 - iter 1440/1445 - loss 0.31318374 - time (sec): 72.86 - samples/sec: 2410.77 - lr: 0.000050 - momentum: 0.000000 2023-10-14 10:45:08,985 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:45:08,986 EPOCH 1 done: loss 0.3125 - lr: 0.000050 2023-10-14 10:45:12,844 DEV : loss 0.1323496401309967 - f1-score (micro avg) 0.6425 2023-10-14 10:45:12,863 saving best model 2023-10-14 10:45:13,235 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:45:20,499 epoch 2 - iter 144/1445 - loss 0.12655175 - time (sec): 7.26 - samples/sec: 2233.65 - lr: 0.000049 - momentum: 0.000000 2023-10-14 10:45:28,359 epoch 2 - iter 288/1445 - loss 0.11664640 - time (sec): 15.12 - samples/sec: 2234.54 - lr: 0.000049 - momentum: 0.000000 2023-10-14 10:45:35,769 epoch 2 - iter 432/1445 - loss 0.11764083 - time (sec): 22.53 - samples/sec: 2297.07 - lr: 0.000048 - momentum: 0.000000 2023-10-14 10:45:43,380 epoch 2 - iter 576/1445 - loss 0.11288782 - time (sec): 30.14 - samples/sec: 2351.55 - lr: 0.000048 - momentum: 0.000000 2023-10-14 10:45:50,679 epoch 2 - iter 720/1445 - loss 0.11005752 - time (sec): 37.44 - samples/sec: 2372.40 - lr: 0.000047 - momentum: 0.000000 2023-10-14 10:45:57,858 epoch 2 - iter 864/1445 - loss 0.10861039 - time (sec): 44.62 - samples/sec: 2366.36 - lr: 0.000047 - momentum: 0.000000 2023-10-14 10:46:04,905 epoch 2 - iter 1008/1445 - loss 0.11009884 - time (sec): 51.67 - samples/sec: 2363.61 - lr: 0.000046 - momentum: 0.000000 2023-10-14 10:46:12,036 epoch 2 - iter 1152/1445 - loss 0.10756740 - time (sec): 58.80 - samples/sec: 2371.45 - lr: 0.000046 - momentum: 0.000000 2023-10-14 10:46:19,797 epoch 2 - iter 1296/1445 - loss 0.10553537 - time (sec): 66.56 - samples/sec: 2362.48 - lr: 0.000045 - momentum: 0.000000 2023-10-14 10:46:27,342 epoch 2 - iter 1440/1445 - loss 0.10547907 - time (sec): 74.10 - samples/sec: 2370.93 - lr: 0.000044 - momentum: 0.000000 2023-10-14 10:46:27,595 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:46:27,595 EPOCH 2 done: loss 0.1053 - lr: 0.000044 2023-10-14 10:46:31,969 DEV : loss 0.10467828810214996 - f1-score (micro avg) 0.7101 2023-10-14 10:46:31,987 saving best model 2023-10-14 10:46:32,513 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:46:40,845 epoch 3 - iter 144/1445 - loss 0.06046434 - time (sec): 8.33 - samples/sec: 2176.51 - lr: 0.000044 - momentum: 0.000000 2023-10-14 10:46:49,225 epoch 3 - iter 288/1445 - loss 0.06004263 - time (sec): 16.71 - samples/sec: 2131.19 - lr: 0.000043 - momentum: 0.000000 2023-10-14 10:46:56,492 epoch 3 - iter 432/1445 - loss 0.06632407 - time (sec): 23.98 - samples/sec: 2165.44 - lr: 0.000043 - momentum: 0.000000 2023-10-14 10:47:03,797 epoch 3 - iter 576/1445 - loss 0.06736150 - time (sec): 31.28 - samples/sec: 2213.57 - lr: 0.000042 - momentum: 0.000000 2023-10-14 10:47:11,200 epoch 3 - iter 720/1445 - loss 0.06752059 - time (sec): 38.68 - samples/sec: 2266.94 - lr: 0.000042 - momentum: 0.000000 2023-10-14 10:47:18,528 epoch 3 - iter 864/1445 - loss 0.07237618 - time (sec): 46.01 - samples/sec: 2285.16 - lr: 0.000041 - momentum: 0.000000 2023-10-14 10:47:26,048 epoch 3 - iter 1008/1445 - loss 0.07408386 - time (sec): 53.53 - samples/sec: 2314.03 - lr: 0.000041 - momentum: 0.000000 2023-10-14 10:47:33,156 epoch 3 - iter 1152/1445 - loss 0.07290332 - time (sec): 60.64 - samples/sec: 2314.08 - lr: 0.000040 - momentum: 0.000000 2023-10-14 10:47:40,398 epoch 3 - iter 1296/1445 - loss 0.07248165 - time (sec): 67.88 - samples/sec: 2319.56 - lr: 0.000039 - momentum: 0.000000 2023-10-14 10:47:47,740 epoch 3 - iter 1440/1445 - loss 0.07325238 - time (sec): 75.22 - samples/sec: 2331.61 - lr: 0.000039 - momentum: 0.000000 2023-10-14 10:47:48,047 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:47:48,048 EPOCH 3 done: loss 0.0730 - lr: 0.000039 2023-10-14 10:47:51,583 DEV : loss 0.09553560614585876 - f1-score (micro avg) 0.8021 2023-10-14 10:47:51,599 saving best model 2023-10-14 10:47:52,265 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:47:59,539 epoch 4 - iter 144/1445 - loss 0.04698177 - time (sec): 7.27 - samples/sec: 2415.48 - lr: 0.000038 - momentum: 0.000000 2023-10-14 10:48:07,133 epoch 4 - iter 288/1445 - loss 0.06031754 - time (sec): 14.87 - samples/sec: 2411.66 - lr: 0.000038 - momentum: 0.000000 2023-10-14 10:48:14,165 epoch 4 - iter 432/1445 - loss 0.06263096 - time (sec): 21.90 - samples/sec: 2410.81 - lr: 0.000037 - momentum: 0.000000 2023-10-14 10:48:21,491 epoch 4 - iter 576/1445 - loss 0.05725339 - time (sec): 29.22 - samples/sec: 2417.83 - lr: 0.000037 - momentum: 0.000000 2023-10-14 10:48:28,487 epoch 4 - iter 720/1445 - loss 0.05516418 - time (sec): 36.22 - samples/sec: 2406.40 - lr: 0.000036 - momentum: 0.000000 2023-10-14 10:48:36,217 epoch 4 - iter 864/1445 - loss 0.05343786 - time (sec): 43.95 - samples/sec: 2404.89 - lr: 0.000036 - momentum: 0.000000 2023-10-14 10:48:43,475 epoch 4 - iter 1008/1445 - loss 0.05296204 - time (sec): 51.21 - samples/sec: 2397.09 - lr: 0.000035 - momentum: 0.000000 2023-10-14 10:48:50,864 epoch 4 - iter 1152/1445 - loss 0.05396480 - time (sec): 58.60 - samples/sec: 2397.71 - lr: 0.000034 - momentum: 0.000000 2023-10-14 10:48:58,163 epoch 4 - iter 1296/1445 - loss 0.05465199 - time (sec): 65.90 - samples/sec: 2406.38 - lr: 0.000034 - momentum: 0.000000 2023-10-14 10:49:05,509 epoch 4 - iter 1440/1445 - loss 0.05379926 - time (sec): 73.24 - samples/sec: 2397.56 - lr: 0.000033 - momentum: 0.000000 2023-10-14 10:49:05,752 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:49:05,752 EPOCH 4 done: loss 0.0541 - lr: 0.000033 2023-10-14 10:49:09,309 DEV : loss 0.12118156254291534 - f1-score (micro avg) 0.7946 2023-10-14 10:49:09,326 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:49:16,954 epoch 5 - iter 144/1445 - loss 0.04673101 - time (sec): 7.63 - samples/sec: 2414.26 - lr: 0.000033 - momentum: 0.000000 2023-10-14 10:49:24,004 epoch 5 - iter 288/1445 - loss 0.04488133 - time (sec): 14.68 - samples/sec: 2414.51 - lr: 0.000032 - momentum: 0.000000 2023-10-14 10:49:31,520 epoch 5 - iter 432/1445 - loss 0.04016346 - time (sec): 22.19 - samples/sec: 2439.19 - lr: 0.000032 - momentum: 0.000000 2023-10-14 10:49:38,723 epoch 5 - iter 576/1445 - loss 0.04147102 - time (sec): 29.40 - samples/sec: 2409.52 - lr: 0.000031 - momentum: 0.000000 2023-10-14 10:49:46,016 epoch 5 - iter 720/1445 - loss 0.04117617 - time (sec): 36.69 - samples/sec: 2388.99 - lr: 0.000031 - momentum: 0.000000 2023-10-14 10:49:53,185 epoch 5 - iter 864/1445 - loss 0.04145618 - time (sec): 43.86 - samples/sec: 2366.07 - lr: 0.000030 - momentum: 0.000000 2023-10-14 10:50:00,848 epoch 5 - iter 1008/1445 - loss 0.03979828 - time (sec): 51.52 - samples/sec: 2367.21 - lr: 0.000029 - momentum: 0.000000 2023-10-14 10:50:08,189 epoch 5 - iter 1152/1445 - loss 0.03982370 - time (sec): 58.86 - samples/sec: 2374.01 - lr: 0.000029 - momentum: 0.000000 2023-10-14 10:50:15,584 epoch 5 - iter 1296/1445 - loss 0.04077806 - time (sec): 66.26 - samples/sec: 2388.64 - lr: 0.000028 - momentum: 0.000000 2023-10-14 10:50:23,148 epoch 5 - iter 1440/1445 - loss 0.03927038 - time (sec): 73.82 - samples/sec: 2380.09 - lr: 0.000028 - momentum: 0.000000 2023-10-14 10:50:23,369 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:50:23,369 EPOCH 5 done: loss 0.0393 - lr: 0.000028 2023-10-14 10:50:27,269 DEV : loss 0.15388108789920807 - f1-score (micro avg) 0.7917 2023-10-14 10:50:27,287 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:50:34,633 epoch 6 - iter 144/1445 - loss 0.03233428 - time (sec): 7.34 - samples/sec: 2362.50 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:50:41,959 epoch 6 - iter 288/1445 - loss 0.03501451 - time (sec): 14.67 - samples/sec: 2390.85 - lr: 0.000027 - momentum: 0.000000 2023-10-14 10:50:49,055 epoch 6 - iter 432/1445 - loss 0.03123834 - time (sec): 21.77 - samples/sec: 2411.58 - lr: 0.000026 - momentum: 0.000000 2023-10-14 10:50:56,443 epoch 6 - iter 576/1445 - loss 0.03274913 - time (sec): 29.16 - samples/sec: 2419.30 - lr: 0.000026 - momentum: 0.000000 2023-10-14 10:51:03,609 epoch 6 - iter 720/1445 - loss 0.03314690 - time (sec): 36.32 - samples/sec: 2406.97 - lr: 0.000025 - momentum: 0.000000 2023-10-14 10:51:10,863 epoch 6 - iter 864/1445 - loss 0.03210885 - time (sec): 43.58 - samples/sec: 2410.60 - lr: 0.000024 - momentum: 0.000000 2023-10-14 10:51:18,264 epoch 6 - iter 1008/1445 - loss 0.03115240 - time (sec): 50.98 - samples/sec: 2410.40 - lr: 0.000024 - momentum: 0.000000 2023-10-14 10:51:25,820 epoch 6 - iter 1152/1445 - loss 0.03008407 - time (sec): 58.53 - samples/sec: 2430.27 - lr: 0.000023 - momentum: 0.000000 2023-10-14 10:51:32,892 epoch 6 - iter 1296/1445 - loss 0.03095943 - time (sec): 65.60 - samples/sec: 2425.45 - lr: 0.000023 - momentum: 0.000000 2023-10-14 10:51:39,991 epoch 6 - iter 1440/1445 - loss 0.03162133 - time (sec): 72.70 - samples/sec: 2417.44 - lr: 0.000022 - momentum: 0.000000 2023-10-14 10:51:40,215 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:51:40,215 EPOCH 6 done: loss 0.0317 - lr: 0.000022 2023-10-14 10:51:43,805 DEV : loss 0.19756034016609192 - f1-score (micro avg) 0.8016 2023-10-14 10:51:43,821 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:51:51,088 epoch 7 - iter 144/1445 - loss 0.01816229 - time (sec): 7.27 - samples/sec: 2414.93 - lr: 0.000022 - momentum: 0.000000 2023-10-14 10:51:58,649 epoch 7 - iter 288/1445 - loss 0.01954064 - time (sec): 14.83 - samples/sec: 2471.92 - lr: 0.000021 - momentum: 0.000000 2023-10-14 10:52:05,759 epoch 7 - iter 432/1445 - loss 0.01867134 - time (sec): 21.94 - samples/sec: 2439.90 - lr: 0.000021 - momentum: 0.000000 2023-10-14 10:52:13,556 epoch 7 - iter 576/1445 - loss 0.01895690 - time (sec): 29.73 - samples/sec: 2418.92 - lr: 0.000020 - momentum: 0.000000 2023-10-14 10:52:20,679 epoch 7 - iter 720/1445 - loss 0.01836345 - time (sec): 36.86 - samples/sec: 2400.83 - lr: 0.000019 - momentum: 0.000000 2023-10-14 10:52:27,667 epoch 7 - iter 864/1445 - loss 0.01877869 - time (sec): 43.84 - samples/sec: 2396.78 - lr: 0.000019 - momentum: 0.000000 2023-10-14 10:52:35,009 epoch 7 - iter 1008/1445 - loss 0.02080686 - time (sec): 51.19 - samples/sec: 2419.50 - lr: 0.000018 - momentum: 0.000000 2023-10-14 10:52:42,373 epoch 7 - iter 1152/1445 - loss 0.02134693 - time (sec): 58.55 - samples/sec: 2423.15 - lr: 0.000018 - momentum: 0.000000 2023-10-14 10:52:49,576 epoch 7 - iter 1296/1445 - loss 0.02105516 - time (sec): 65.75 - samples/sec: 2420.11 - lr: 0.000017 - momentum: 0.000000 2023-10-14 10:52:56,887 epoch 7 - iter 1440/1445 - loss 0.02073299 - time (sec): 73.06 - samples/sec: 2406.17 - lr: 0.000017 - momentum: 0.000000 2023-10-14 10:52:57,114 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:52:57,115 EPOCH 7 done: loss 0.0209 - lr: 0.000017 2023-10-14 10:53:00,712 DEV : loss 0.18008936941623688 - f1-score (micro avg) 0.8011 2023-10-14 10:53:00,729 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:53:07,996 epoch 8 - iter 144/1445 - loss 0.01866497 - time (sec): 7.27 - samples/sec: 2300.59 - lr: 0.000016 - momentum: 0.000000 2023-10-14 10:53:15,313 epoch 8 - iter 288/1445 - loss 0.01466923 - time (sec): 14.58 - samples/sec: 2372.09 - lr: 0.000016 - momentum: 0.000000 2023-10-14 10:53:22,675 epoch 8 - iter 432/1445 - loss 0.01583407 - time (sec): 21.95 - samples/sec: 2384.78 - lr: 0.000015 - momentum: 0.000000 2023-10-14 10:53:29,992 epoch 8 - iter 576/1445 - loss 0.01493921 - time (sec): 29.26 - samples/sec: 2381.30 - lr: 0.000014 - momentum: 0.000000 2023-10-14 10:53:37,264 epoch 8 - iter 720/1445 - loss 0.01503871 - time (sec): 36.53 - samples/sec: 2407.26 - lr: 0.000014 - momentum: 0.000000 2023-10-14 10:53:44,308 epoch 8 - iter 864/1445 - loss 0.01515293 - time (sec): 43.58 - samples/sec: 2399.36 - lr: 0.000013 - momentum: 0.000000 2023-10-14 10:53:51,766 epoch 8 - iter 1008/1445 - loss 0.01576861 - time (sec): 51.04 - samples/sec: 2404.97 - lr: 0.000013 - momentum: 0.000000 2023-10-14 10:53:59,034 epoch 8 - iter 1152/1445 - loss 0.01561594 - time (sec): 58.30 - samples/sec: 2408.13 - lr: 0.000012 - momentum: 0.000000 2023-10-14 10:54:06,080 epoch 8 - iter 1296/1445 - loss 0.01534350 - time (sec): 65.35 - samples/sec: 2411.17 - lr: 0.000012 - momentum: 0.000000 2023-10-14 10:54:13,533 epoch 8 - iter 1440/1445 - loss 0.01509812 - time (sec): 72.80 - samples/sec: 2409.56 - lr: 0.000011 - momentum: 0.000000 2023-10-14 10:54:13,784 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:54:13,784 EPOCH 8 done: loss 0.0151 - lr: 0.000011 2023-10-14 10:54:17,824 DEV : loss 0.20430612564086914 - f1-score (micro avg) 0.7978 2023-10-14 10:54:17,841 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:54:25,386 epoch 9 - iter 144/1445 - loss 0.01055124 - time (sec): 7.54 - samples/sec: 2408.34 - lr: 0.000011 - momentum: 0.000000 2023-10-14 10:54:32,660 epoch 9 - iter 288/1445 - loss 0.00857411 - time (sec): 14.82 - samples/sec: 2384.81 - lr: 0.000010 - momentum: 0.000000 2023-10-14 10:54:39,915 epoch 9 - iter 432/1445 - loss 0.00889903 - time (sec): 22.07 - samples/sec: 2361.30 - lr: 0.000009 - momentum: 0.000000 2023-10-14 10:54:47,643 epoch 9 - iter 576/1445 - loss 0.01030940 - time (sec): 29.80 - samples/sec: 2394.86 - lr: 0.000009 - momentum: 0.000000 2023-10-14 10:54:54,820 epoch 9 - iter 720/1445 - loss 0.00993058 - time (sec): 36.98 - samples/sec: 2400.63 - lr: 0.000008 - momentum: 0.000000 2023-10-14 10:55:02,175 epoch 9 - iter 864/1445 - loss 0.00936448 - time (sec): 44.33 - samples/sec: 2406.24 - lr: 0.000008 - momentum: 0.000000 2023-10-14 10:55:09,201 epoch 9 - iter 1008/1445 - loss 0.00913328 - time (sec): 51.36 - samples/sec: 2404.99 - lr: 0.000007 - momentum: 0.000000 2023-10-14 10:55:16,575 epoch 9 - iter 1152/1445 - loss 0.00941487 - time (sec): 58.73 - samples/sec: 2408.84 - lr: 0.000007 - momentum: 0.000000 2023-10-14 10:55:23,825 epoch 9 - iter 1296/1445 - loss 0.01026341 - time (sec): 65.98 - samples/sec: 2401.83 - lr: 0.000006 - momentum: 0.000000 2023-10-14 10:55:31,036 epoch 9 - iter 1440/1445 - loss 0.01047293 - time (sec): 73.19 - samples/sec: 2400.81 - lr: 0.000006 - momentum: 0.000000 2023-10-14 10:55:31,280 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:55:31,280 EPOCH 9 done: loss 0.0105 - lr: 0.000006 2023-10-14 10:55:34,878 DEV : loss 0.1839500218629837 - f1-score (micro avg) 0.7974 2023-10-14 10:55:34,896 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:55:42,196 epoch 10 - iter 144/1445 - loss 0.00490526 - time (sec): 7.30 - samples/sec: 2287.22 - lr: 0.000005 - momentum: 0.000000 2023-10-14 10:55:49,779 epoch 10 - iter 288/1445 - loss 0.00524218 - time (sec): 14.88 - samples/sec: 2333.86 - lr: 0.000004 - momentum: 0.000000 2023-10-14 10:55:57,744 epoch 10 - iter 432/1445 - loss 0.00648842 - time (sec): 22.85 - samples/sec: 2293.26 - lr: 0.000004 - momentum: 0.000000 2023-10-14 10:56:05,487 epoch 10 - iter 576/1445 - loss 0.00656819 - time (sec): 30.59 - samples/sec: 2325.72 - lr: 0.000003 - momentum: 0.000000 2023-10-14 10:56:12,959 epoch 10 - iter 720/1445 - loss 0.00698945 - time (sec): 38.06 - samples/sec: 2354.45 - lr: 0.000003 - momentum: 0.000000 2023-10-14 10:56:20,142 epoch 10 - iter 864/1445 - loss 0.00778598 - time (sec): 45.24 - samples/sec: 2362.14 - lr: 0.000002 - momentum: 0.000000 2023-10-14 10:56:27,101 epoch 10 - iter 1008/1445 - loss 0.00737225 - time (sec): 52.20 - samples/sec: 2348.21 - lr: 0.000002 - momentum: 0.000000 2023-10-14 10:56:34,107 epoch 10 - iter 1152/1445 - loss 0.00697948 - time (sec): 59.21 - samples/sec: 2352.75 - lr: 0.000001 - momentum: 0.000000 2023-10-14 10:56:41,417 epoch 10 - iter 1296/1445 - loss 0.00700128 - time (sec): 66.52 - samples/sec: 2371.69 - lr: 0.000001 - momentum: 0.000000 2023-10-14 10:56:48,753 epoch 10 - iter 1440/1445 - loss 0.00696742 - time (sec): 73.86 - samples/sec: 2376.15 - lr: 0.000000 - momentum: 0.000000 2023-10-14 10:56:49,020 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:56:49,021 EPOCH 10 done: loss 0.0070 - lr: 0.000000 2023-10-14 10:56:52,539 DEV : loss 0.19384992122650146 - f1-score (micro avg) 0.8093 2023-10-14 10:56:52,555 saving best model 2023-10-14 10:56:53,410 ---------------------------------------------------------------------------------------------------- 2023-10-14 10:56:53,411 Loading model from best epoch ... 2023-10-14 10:56:55,129 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 10:56:58,276 Results: - F-score (micro) 0.7959 - F-score (macro) 0.6975 - Accuracy 0.6749 By class: precision recall f1-score support PER 0.8184 0.7759 0.7966 482 LOC 0.8949 0.7991 0.8443 458 ORG 0.5091 0.4058 0.4516 69 micro avg 0.8339 0.7611 0.7959 1009 macro avg 0.7408 0.6603 0.6975 1009 weighted avg 0.8319 0.7611 0.7947 1009 2023-10-14 10:56:58,276 ----------------------------------------------------------------------------------------------------