2023-09-03 20:19:20,809 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,810 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=21, bias=True) (loss_function): CrossEntropyLoss() )" 2023-09-03 20:19:20,810 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,810 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator 2023-09-03 20:19:20,810 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,810 Train: 3575 sentences 2023-09-03 20:19:20,810 (train_with_dev=False, train_with_test=False) 2023-09-03 20:19:20,810 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,810 Training Params: 2023-09-03 20:19:20,810 - learning_rate: "5e-05" 2023-09-03 20:19:20,811 - mini_batch_size: "4" 2023-09-03 20:19:20,811 - max_epochs: "10" 2023-09-03 20:19:20,811 - shuffle: "True" 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,811 Plugins: 2023-09-03 20:19:20,811 - LinearScheduler | warmup_fraction: '0.1' 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,811 Final evaluation on model from best epoch (best-model.pt) 2023-09-03 20:19:20,811 - metric: "('micro avg', 'f1-score')" 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,811 Computation: 2023-09-03 20:19:20,811 - compute on device: cuda:0 2023-09-03 20:19:20,811 - embedding storage: none 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,811 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2" 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:20,811 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:19:29,527 epoch 1 - iter 89/894 - loss 2.89989312 - time (sec): 8.71 - samples/sec: 919.60 - lr: 0.000005 - momentum: 0.000000 2023-09-03 20:19:38,330 epoch 1 - iter 178/894 - loss 1.76162593 - time (sec): 17.52 - samples/sec: 916.67 - lr: 0.000010 - momentum: 0.000000 2023-09-03 20:19:47,454 epoch 1 - iter 267/894 - loss 1.27223185 - time (sec): 26.64 - samples/sec: 935.95 - lr: 0.000015 - momentum: 0.000000 2023-09-03 20:19:56,329 epoch 1 - iter 356/894 - loss 1.05292482 - time (sec): 35.52 - samples/sec: 930.12 - lr: 0.000020 - momentum: 0.000000 2023-09-03 20:20:05,560 epoch 1 - iter 445/894 - loss 0.89522602 - time (sec): 44.75 - samples/sec: 936.09 - lr: 0.000025 - momentum: 0.000000 2023-09-03 20:20:16,121 epoch 1 - iter 534/894 - loss 0.78258018 - time (sec): 55.31 - samples/sec: 943.67 - lr: 0.000030 - momentum: 0.000000 2023-09-03 20:20:25,496 epoch 1 - iter 623/894 - loss 0.71307594 - time (sec): 64.68 - samples/sec: 934.93 - lr: 0.000035 - momentum: 0.000000 2023-09-03 20:20:34,781 epoch 1 - iter 712/894 - loss 0.65376441 - time (sec): 73.97 - samples/sec: 936.05 - lr: 0.000040 - momentum: 0.000000 2023-09-03 20:20:43,688 epoch 1 - iter 801/894 - loss 0.61105348 - time (sec): 82.88 - samples/sec: 931.77 - lr: 0.000045 - momentum: 0.000000 2023-09-03 20:20:52,993 epoch 1 - iter 890/894 - loss 0.57058985 - time (sec): 92.18 - samples/sec: 933.39 - lr: 0.000050 - momentum: 0.000000 2023-09-03 20:20:53,415 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:20:53,416 EPOCH 1 done: loss 0.5683 - lr: 0.000050 2023-09-03 20:21:04,508 DEV : loss 0.17097648978233337 - f1-score (micro avg) 0.6162 2023-09-03 20:21:04,534 saving best model 2023-09-03 20:21:04,992 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:21:14,165 epoch 2 - iter 89/894 - loss 0.20605836 - time (sec): 9.17 - samples/sec: 938.05 - lr: 0.000049 - momentum: 0.000000 2023-09-03 20:21:23,535 epoch 2 - iter 178/894 - loss 0.19007741 - time (sec): 18.54 - samples/sec: 918.64 - lr: 0.000049 - momentum: 0.000000 2023-09-03 20:21:32,430 epoch 2 - iter 267/894 - loss 0.18247790 - time (sec): 27.44 - samples/sec: 919.45 - lr: 0.000048 - momentum: 0.000000 2023-09-03 20:21:41,692 epoch 2 - iter 356/894 - loss 0.18202885 - time (sec): 36.70 - samples/sec: 928.16 - lr: 0.000048 - momentum: 0.000000 2023-09-03 20:21:50,582 epoch 2 - iter 445/894 - loss 0.17738266 - time (sec): 45.59 - samples/sec: 925.06 - lr: 0.000047 - momentum: 0.000000 2023-09-03 20:22:00,394 epoch 2 - iter 534/894 - loss 0.17474082 - time (sec): 55.40 - samples/sec: 929.14 - lr: 0.000047 - momentum: 0.000000 2023-09-03 20:22:09,359 epoch 2 - iter 623/894 - loss 0.16799398 - time (sec): 64.37 - samples/sec: 929.21 - lr: 0.000046 - momentum: 0.000000 2023-09-03 20:22:19,256 epoch 2 - iter 712/894 - loss 0.16240455 - time (sec): 74.26 - samples/sec: 930.96 - lr: 0.000046 - momentum: 0.000000 2023-09-03 20:22:28,965 epoch 2 - iter 801/894 - loss 0.16209682 - time (sec): 83.97 - samples/sec: 927.51 - lr: 0.000045 - momentum: 0.000000 2023-09-03 20:22:37,984 epoch 2 - iter 890/894 - loss 0.16158978 - time (sec): 92.99 - samples/sec: 926.44 - lr: 0.000044 - momentum: 0.000000 2023-09-03 20:22:38,376 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:22:38,376 EPOCH 2 done: loss 0.1613 - lr: 0.000044 2023-09-03 20:22:51,909 DEV : loss 0.16291926801204681 - f1-score (micro avg) 0.6627 2023-09-03 20:22:51,935 saving best model 2023-09-03 20:22:53,255 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:23:02,702 epoch 3 - iter 89/894 - loss 0.08768279 - time (sec): 9.45 - samples/sec: 913.47 - lr: 0.000044 - momentum: 0.000000 2023-09-03 20:23:12,631 epoch 3 - iter 178/894 - loss 0.08725946 - time (sec): 19.37 - samples/sec: 942.88 - lr: 0.000043 - momentum: 0.000000 2023-09-03 20:23:22,249 epoch 3 - iter 267/894 - loss 0.09362897 - time (sec): 28.99 - samples/sec: 948.89 - lr: 0.000043 - momentum: 0.000000 2023-09-03 20:23:31,711 epoch 3 - iter 356/894 - loss 0.08860942 - time (sec): 38.45 - samples/sec: 948.63 - lr: 0.000042 - momentum: 0.000000 2023-09-03 20:23:41,240 epoch 3 - iter 445/894 - loss 0.09576555 - time (sec): 47.98 - samples/sec: 947.72 - lr: 0.000042 - momentum: 0.000000 2023-09-03 20:23:50,052 epoch 3 - iter 534/894 - loss 0.10063010 - time (sec): 56.80 - samples/sec: 935.90 - lr: 0.000041 - momentum: 0.000000 2023-09-03 20:23:58,894 epoch 3 - iter 623/894 - loss 0.09832043 - time (sec): 65.64 - samples/sec: 937.51 - lr: 0.000041 - momentum: 0.000000 2023-09-03 20:24:07,624 epoch 3 - iter 712/894 - loss 0.09817305 - time (sec): 74.37 - samples/sec: 934.63 - lr: 0.000040 - momentum: 0.000000 2023-09-03 20:24:16,848 epoch 3 - iter 801/894 - loss 0.10180100 - time (sec): 83.59 - samples/sec: 931.72 - lr: 0.000039 - momentum: 0.000000 2023-09-03 20:24:25,737 epoch 3 - iter 890/894 - loss 0.10148223 - time (sec): 92.48 - samples/sec: 931.35 - lr: 0.000039 - momentum: 0.000000 2023-09-03 20:24:26,139 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:24:26,139 EPOCH 3 done: loss 0.1014 - lr: 0.000039 2023-09-03 20:24:39,577 DEV : loss 0.1718726009130478 - f1-score (micro avg) 0.7266 2023-09-03 20:24:39,603 saving best model 2023-09-03 20:24:40,951 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:24:49,636 epoch 4 - iter 89/894 - loss 0.07154590 - time (sec): 8.68 - samples/sec: 877.87 - lr: 0.000038 - momentum: 0.000000 2023-09-03 20:24:59,559 epoch 4 - iter 178/894 - loss 0.06212479 - time (sec): 18.61 - samples/sec: 912.49 - lr: 0.000038 - momentum: 0.000000 2023-09-03 20:25:08,695 epoch 4 - iter 267/894 - loss 0.06975820 - time (sec): 27.74 - samples/sec: 912.20 - lr: 0.000037 - momentum: 0.000000 2023-09-03 20:25:17,849 epoch 4 - iter 356/894 - loss 0.06914589 - time (sec): 36.90 - samples/sec: 920.20 - lr: 0.000037 - momentum: 0.000000 2023-09-03 20:25:26,598 epoch 4 - iter 445/894 - loss 0.06933892 - time (sec): 45.65 - samples/sec: 910.82 - lr: 0.000036 - momentum: 0.000000 2023-09-03 20:25:37,152 epoch 4 - iter 534/894 - loss 0.06671475 - time (sec): 56.20 - samples/sec: 924.15 - lr: 0.000036 - momentum: 0.000000 2023-09-03 20:25:46,669 epoch 4 - iter 623/894 - loss 0.06771971 - time (sec): 65.72 - samples/sec: 920.47 - lr: 0.000035 - momentum: 0.000000 2023-09-03 20:25:55,680 epoch 4 - iter 712/894 - loss 0.06810537 - time (sec): 74.73 - samples/sec: 918.03 - lr: 0.000034 - momentum: 0.000000 2023-09-03 20:26:05,114 epoch 4 - iter 801/894 - loss 0.06697660 - time (sec): 84.16 - samples/sec: 924.22 - lr: 0.000034 - momentum: 0.000000 2023-09-03 20:26:14,205 epoch 4 - iter 890/894 - loss 0.06654671 - time (sec): 93.25 - samples/sec: 924.97 - lr: 0.000033 - momentum: 0.000000 2023-09-03 20:26:14,594 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:26:14,594 EPOCH 4 done: loss 0.0663 - lr: 0.000033 2023-09-03 20:26:28,159 DEV : loss 0.21245643496513367 - f1-score (micro avg) 0.7368 2023-09-03 20:26:28,186 saving best model 2023-09-03 20:26:30,057 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:26:39,218 epoch 5 - iter 89/894 - loss 0.06250775 - time (sec): 9.16 - samples/sec: 888.32 - lr: 0.000033 - momentum: 0.000000 2023-09-03 20:26:48,153 epoch 5 - iter 178/894 - loss 0.04987455 - time (sec): 18.09 - samples/sec: 886.77 - lr: 0.000032 - momentum: 0.000000 2023-09-03 20:26:57,584 epoch 5 - iter 267/894 - loss 0.05005559 - time (sec): 27.53 - samples/sec: 899.07 - lr: 0.000032 - momentum: 0.000000 2023-09-03 20:27:07,691 epoch 5 - iter 356/894 - loss 0.05457406 - time (sec): 37.63 - samples/sec: 906.19 - lr: 0.000031 - momentum: 0.000000 2023-09-03 20:27:16,826 epoch 5 - iter 445/894 - loss 0.05184580 - time (sec): 46.77 - samples/sec: 919.43 - lr: 0.000031 - momentum: 0.000000 2023-09-03 20:27:25,722 epoch 5 - iter 534/894 - loss 0.05490556 - time (sec): 55.66 - samples/sec: 923.67 - lr: 0.000030 - momentum: 0.000000 2023-09-03 20:27:35,344 epoch 5 - iter 623/894 - loss 0.05272637 - time (sec): 65.28 - samples/sec: 926.24 - lr: 0.000029 - momentum: 0.000000 2023-09-03 20:27:45,383 epoch 5 - iter 712/894 - loss 0.05144270 - time (sec): 75.32 - samples/sec: 926.86 - lr: 0.000029 - momentum: 0.000000 2023-09-03 20:27:54,395 epoch 5 - iter 801/894 - loss 0.04983405 - time (sec): 84.34 - samples/sec: 930.09 - lr: 0.000028 - momentum: 0.000000 2023-09-03 20:28:03,140 epoch 5 - iter 890/894 - loss 0.04937796 - time (sec): 93.08 - samples/sec: 926.12 - lr: 0.000028 - momentum: 0.000000 2023-09-03 20:28:03,510 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:28:03,510 EPOCH 5 done: loss 0.0498 - lr: 0.000028 2023-09-03 20:28:17,043 DEV : loss 0.22385385632514954 - f1-score (micro avg) 0.7632 2023-09-03 20:28:17,070 saving best model 2023-09-03 20:28:18,382 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:28:27,680 epoch 6 - iter 89/894 - loss 0.03521194 - time (sec): 9.30 - samples/sec: 933.60 - lr: 0.000027 - momentum: 0.000000 2023-09-03 20:28:36,764 epoch 6 - iter 178/894 - loss 0.03279992 - time (sec): 18.38 - samples/sec: 921.40 - lr: 0.000027 - momentum: 0.000000 2023-09-03 20:28:45,721 epoch 6 - iter 267/894 - loss 0.03124356 - time (sec): 27.34 - samples/sec: 917.56 - lr: 0.000026 - momentum: 0.000000 2023-09-03 20:28:54,970 epoch 6 - iter 356/894 - loss 0.02894350 - time (sec): 36.59 - samples/sec: 922.08 - lr: 0.000026 - momentum: 0.000000 2023-09-03 20:29:04,198 epoch 6 - iter 445/894 - loss 0.03070838 - time (sec): 45.81 - samples/sec: 916.42 - lr: 0.000025 - momentum: 0.000000 2023-09-03 20:29:13,064 epoch 6 - iter 534/894 - loss 0.02981611 - time (sec): 54.68 - samples/sec: 922.41 - lr: 0.000024 - momentum: 0.000000 2023-09-03 20:29:21,897 epoch 6 - iter 623/894 - loss 0.02928313 - time (sec): 63.51 - samples/sec: 922.19 - lr: 0.000024 - momentum: 0.000000 2023-09-03 20:29:31,119 epoch 6 - iter 712/894 - loss 0.03070513 - time (sec): 72.74 - samples/sec: 920.91 - lr: 0.000023 - momentum: 0.000000 2023-09-03 20:29:40,917 epoch 6 - iter 801/894 - loss 0.03083902 - time (sec): 82.53 - samples/sec: 920.26 - lr: 0.000023 - momentum: 0.000000 2023-09-03 20:29:51,131 epoch 6 - iter 890/894 - loss 0.02973958 - time (sec): 92.75 - samples/sec: 927.19 - lr: 0.000022 - momentum: 0.000000 2023-09-03 20:29:51,649 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:29:51,649 EPOCH 6 done: loss 0.0304 - lr: 0.000022 2023-09-03 20:30:05,098 DEV : loss 0.21197949349880219 - f1-score (micro avg) 0.764 2023-09-03 20:30:05,132 saving best model 2023-09-03 20:30:06,446 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:30:15,471 epoch 7 - iter 89/894 - loss 0.02658940 - time (sec): 9.02 - samples/sec: 962.78 - lr: 0.000022 - momentum: 0.000000 2023-09-03 20:30:24,494 epoch 7 - iter 178/894 - loss 0.02246646 - time (sec): 18.05 - samples/sec: 956.13 - lr: 0.000021 - momentum: 0.000000 2023-09-03 20:30:33,443 epoch 7 - iter 267/894 - loss 0.02172733 - time (sec): 27.00 - samples/sec: 974.31 - lr: 0.000021 - momentum: 0.000000 2023-09-03 20:30:42,859 epoch 7 - iter 356/894 - loss 0.02104734 - time (sec): 36.41 - samples/sec: 963.89 - lr: 0.000020 - momentum: 0.000000 2023-09-03 20:30:51,845 epoch 7 - iter 445/894 - loss 0.02039607 - time (sec): 45.40 - samples/sec: 951.69 - lr: 0.000019 - momentum: 0.000000 2023-09-03 20:31:01,067 epoch 7 - iter 534/894 - loss 0.02106881 - time (sec): 54.62 - samples/sec: 948.49 - lr: 0.000019 - momentum: 0.000000 2023-09-03 20:31:10,101 epoch 7 - iter 623/894 - loss 0.02042294 - time (sec): 63.65 - samples/sec: 943.65 - lr: 0.000018 - momentum: 0.000000 2023-09-03 20:31:19,311 epoch 7 - iter 712/894 - loss 0.02048542 - time (sec): 72.86 - samples/sec: 939.55 - lr: 0.000018 - momentum: 0.000000 2023-09-03 20:31:28,143 epoch 7 - iter 801/894 - loss 0.02025357 - time (sec): 81.70 - samples/sec: 933.03 - lr: 0.000017 - momentum: 0.000000 2023-09-03 20:31:38,718 epoch 7 - iter 890/894 - loss 0.02006831 - time (sec): 92.27 - samples/sec: 932.68 - lr: 0.000017 - momentum: 0.000000 2023-09-03 20:31:39,169 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:31:39,169 EPOCH 7 done: loss 0.0200 - lr: 0.000017 2023-09-03 20:31:52,686 DEV : loss 0.22539937496185303 - f1-score (micro avg) 0.7688 2023-09-03 20:31:52,713 saving best model 2023-09-03 20:31:54,079 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:32:02,989 epoch 8 - iter 89/894 - loss 0.00996259 - time (sec): 8.91 - samples/sec: 942.28 - lr: 0.000016 - momentum: 0.000000 2023-09-03 20:32:13,683 epoch 8 - iter 178/894 - loss 0.01308135 - time (sec): 19.60 - samples/sec: 922.53 - lr: 0.000016 - momentum: 0.000000 2023-09-03 20:32:22,805 epoch 8 - iter 267/894 - loss 0.01115617 - time (sec): 28.72 - samples/sec: 918.94 - lr: 0.000015 - momentum: 0.000000 2023-09-03 20:32:31,984 epoch 8 - iter 356/894 - loss 0.01062211 - time (sec): 37.90 - samples/sec: 924.07 - lr: 0.000014 - momentum: 0.000000 2023-09-03 20:32:40,824 epoch 8 - iter 445/894 - loss 0.01045602 - time (sec): 46.74 - samples/sec: 915.64 - lr: 0.000014 - momentum: 0.000000 2023-09-03 20:32:50,497 epoch 8 - iter 534/894 - loss 0.01026522 - time (sec): 56.42 - samples/sec: 917.51 - lr: 0.000013 - momentum: 0.000000 2023-09-03 20:32:59,618 epoch 8 - iter 623/894 - loss 0.01133332 - time (sec): 65.54 - samples/sec: 924.87 - lr: 0.000013 - momentum: 0.000000 2023-09-03 20:33:08,738 epoch 8 - iter 712/894 - loss 0.01245965 - time (sec): 74.66 - samples/sec: 922.67 - lr: 0.000012 - momentum: 0.000000 2023-09-03 20:33:17,904 epoch 8 - iter 801/894 - loss 0.01295115 - time (sec): 83.82 - samples/sec: 924.48 - lr: 0.000012 - momentum: 0.000000 2023-09-03 20:33:27,219 epoch 8 - iter 890/894 - loss 0.01278545 - time (sec): 93.14 - samples/sec: 925.48 - lr: 0.000011 - momentum: 0.000000 2023-09-03 20:33:27,604 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:33:27,604 EPOCH 8 done: loss 0.0127 - lr: 0.000011 2023-09-03 20:33:41,115 DEV : loss 0.23452451825141907 - f1-score (micro avg) 0.7825 2023-09-03 20:33:41,142 saving best model 2023-09-03 20:33:42,466 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:33:51,655 epoch 9 - iter 89/894 - loss 0.00216462 - time (sec): 9.19 - samples/sec: 941.78 - lr: 0.000011 - momentum: 0.000000 2023-09-03 20:34:00,559 epoch 9 - iter 178/894 - loss 0.00335217 - time (sec): 18.09 - samples/sec: 942.77 - lr: 0.000010 - momentum: 0.000000 2023-09-03 20:34:09,729 epoch 9 - iter 267/894 - loss 0.00587195 - time (sec): 27.26 - samples/sec: 929.47 - lr: 0.000009 - momentum: 0.000000 2023-09-03 20:34:18,819 epoch 9 - iter 356/894 - loss 0.00603339 - time (sec): 36.35 - samples/sec: 936.75 - lr: 0.000009 - momentum: 0.000000 2023-09-03 20:34:29,238 epoch 9 - iter 445/894 - loss 0.00533673 - time (sec): 46.77 - samples/sec: 936.89 - lr: 0.000008 - momentum: 0.000000 2023-09-03 20:34:38,377 epoch 9 - iter 534/894 - loss 0.00548625 - time (sec): 55.91 - samples/sec: 934.06 - lr: 0.000008 - momentum: 0.000000 2023-09-03 20:34:47,598 epoch 9 - iter 623/894 - loss 0.00623569 - time (sec): 65.13 - samples/sec: 930.15 - lr: 0.000007 - momentum: 0.000000 2023-09-03 20:34:57,098 epoch 9 - iter 712/894 - loss 0.00622240 - time (sec): 74.63 - samples/sec: 930.85 - lr: 0.000007 - momentum: 0.000000 2023-09-03 20:35:05,905 epoch 9 - iter 801/894 - loss 0.00690534 - time (sec): 83.44 - samples/sec: 929.19 - lr: 0.000006 - momentum: 0.000000 2023-09-03 20:35:15,382 epoch 9 - iter 890/894 - loss 0.00691071 - time (sec): 92.91 - samples/sec: 927.59 - lr: 0.000006 - momentum: 0.000000 2023-09-03 20:35:15,781 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:35:15,782 EPOCH 9 done: loss 0.0071 - lr: 0.000006 2023-09-03 20:35:29,298 DEV : loss 0.2623580992221832 - f1-score (micro avg) 0.763 2023-09-03 20:35:29,325 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:35:38,945 epoch 10 - iter 89/894 - loss 0.00043241 - time (sec): 9.62 - samples/sec: 960.91 - lr: 0.000005 - momentum: 0.000000 2023-09-03 20:35:47,979 epoch 10 - iter 178/894 - loss 0.00207187 - time (sec): 18.65 - samples/sec: 929.48 - lr: 0.000004 - momentum: 0.000000 2023-09-03 20:35:57,573 epoch 10 - iter 267/894 - loss 0.00536179 - time (sec): 28.25 - samples/sec: 911.89 - lr: 0.000004 - momentum: 0.000000 2023-09-03 20:36:07,788 epoch 10 - iter 356/894 - loss 0.00509014 - time (sec): 38.46 - samples/sec: 923.95 - lr: 0.000003 - momentum: 0.000000 2023-09-03 20:36:16,901 epoch 10 - iter 445/894 - loss 0.00516233 - time (sec): 47.57 - samples/sec: 922.77 - lr: 0.000003 - momentum: 0.000000 2023-09-03 20:36:25,959 epoch 10 - iter 534/894 - loss 0.00504847 - time (sec): 56.63 - samples/sec: 925.35 - lr: 0.000002 - momentum: 0.000000 2023-09-03 20:36:34,875 epoch 10 - iter 623/894 - loss 0.00481702 - time (sec): 65.55 - samples/sec: 917.34 - lr: 0.000002 - momentum: 0.000000 2023-09-03 20:36:44,411 epoch 10 - iter 712/894 - loss 0.00432165 - time (sec): 75.08 - samples/sec: 915.89 - lr: 0.000001 - momentum: 0.000000 2023-09-03 20:36:53,441 epoch 10 - iter 801/894 - loss 0.00422815 - time (sec): 84.12 - samples/sec: 914.71 - lr: 0.000001 - momentum: 0.000000 2023-09-03 20:37:03,105 epoch 10 - iter 890/894 - loss 0.00396219 - time (sec): 93.78 - samples/sec: 919.69 - lr: 0.000000 - momentum: 0.000000 2023-09-03 20:37:03,504 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:37:03,504 EPOCH 10 done: loss 0.0040 - lr: 0.000000 2023-09-03 20:37:17,166 DEV : loss 0.2543531358242035 - f1-score (micro avg) 0.7802 2023-09-03 20:37:17,642 ---------------------------------------------------------------------------------------------------- 2023-09-03 20:37:17,643 Loading model from best epoch ... 2023-09-03 20:37:19,438 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time 2023-09-03 20:37:30,123 Results: - F-score (micro) 0.7459 - F-score (macro) 0.6693 - Accuracy 0.6167 By class: precision recall f1-score support loc 0.8527 0.8356 0.8441 596 pers 0.6384 0.7688 0.6975 333 org 0.5455 0.5000 0.5217 132 prod 0.6600 0.5000 0.5690 66 time 0.7143 0.7143 0.7143 49 micro avg 0.7369 0.7551 0.7459 1176 macro avg 0.6822 0.6637 0.6693 1176 weighted avg 0.7410 0.7551 0.7456 1176 2023-09-03 20:37:30,123 ----------------------------------------------------------------------------------------------------