2023-10-15 23:45:36,931 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,932 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=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-15 23:45:36,932 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,932 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator 2023-10-15 23:45:36,932 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,932 Train: 20847 sentences 2023-10-15 23:45:36,932 (train_with_dev=False, train_with_test=False) 2023-10-15 23:45:36,932 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,932 Training Params: 2023-10-15 23:45:36,932 - learning_rate: "5e-05" 2023-10-15 23:45:36,933 - mini_batch_size: "4" 2023-10-15 23:45:36,933 - max_epochs: "10" 2023-10-15 23:45:36,933 - shuffle: "True" 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,933 Plugins: 2023-10-15 23:45:36,933 - LinearScheduler | warmup_fraction: '0.1' 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,933 Final evaluation on model from best epoch (best-model.pt) 2023-10-15 23:45:36,933 - metric: "('micro avg', 'f1-score')" 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,933 Computation: 2023-10-15 23:45:36,933 - compute on device: cuda:0 2023-10-15 23:45:36,933 - embedding storage: none 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,933 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:45:36,933 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:46:01,690 epoch 1 - iter 521/5212 - loss 1.37202838 - time (sec): 24.76 - samples/sec: 1438.81 - lr: 0.000005 - momentum: 0.000000 2023-10-15 23:46:26,941 epoch 1 - iter 1042/5212 - loss 0.87856005 - time (sec): 50.01 - samples/sec: 1460.52 - lr: 0.000010 - momentum: 0.000000 2023-10-15 23:46:51,993 epoch 1 - iter 1563/5212 - loss 0.68736436 - time (sec): 75.06 - samples/sec: 1446.56 - lr: 0.000015 - momentum: 0.000000 2023-10-15 23:47:17,119 epoch 1 - iter 2084/5212 - loss 0.58462865 - time (sec): 100.18 - samples/sec: 1440.83 - lr: 0.000020 - momentum: 0.000000 2023-10-15 23:47:42,651 epoch 1 - iter 2605/5212 - loss 0.51553132 - time (sec): 125.72 - samples/sec: 1437.34 - lr: 0.000025 - momentum: 0.000000 2023-10-15 23:48:08,713 epoch 1 - iter 3126/5212 - loss 0.46300876 - time (sec): 151.78 - samples/sec: 1438.92 - lr: 0.000030 - momentum: 0.000000 2023-10-15 23:48:33,975 epoch 1 - iter 3647/5212 - loss 0.43081269 - time (sec): 177.04 - samples/sec: 1443.11 - lr: 0.000035 - momentum: 0.000000 2023-10-15 23:48:59,438 epoch 1 - iter 4168/5212 - loss 0.40412772 - time (sec): 202.50 - samples/sec: 1440.23 - lr: 0.000040 - momentum: 0.000000 2023-10-15 23:49:24,710 epoch 1 - iter 4689/5212 - loss 0.38575539 - time (sec): 227.78 - samples/sec: 1435.07 - lr: 0.000045 - momentum: 0.000000 2023-10-15 23:49:50,594 epoch 1 - iter 5210/5212 - loss 0.36876867 - time (sec): 253.66 - samples/sec: 1448.37 - lr: 0.000050 - momentum: 0.000000 2023-10-15 23:49:50,688 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:49:50,688 EPOCH 1 done: loss 0.3687 - lr: 0.000050 2023-10-15 23:49:56,687 DEV : loss 0.12752242386341095 - f1-score (micro avg) 0.3017 2023-10-15 23:49:56,714 saving best model 2023-10-15 23:49:57,179 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:50:22,567 epoch 2 - iter 521/5212 - loss 0.20030192 - time (sec): 25.39 - samples/sec: 1528.79 - lr: 0.000049 - momentum: 0.000000 2023-10-15 23:50:47,800 epoch 2 - iter 1042/5212 - loss 0.19679440 - time (sec): 50.62 - samples/sec: 1482.50 - lr: 0.000049 - momentum: 0.000000 2023-10-15 23:51:13,032 epoch 2 - iter 1563/5212 - loss 0.18526110 - time (sec): 75.85 - samples/sec: 1470.72 - lr: 0.000048 - momentum: 0.000000 2023-10-15 23:51:38,382 epoch 2 - iter 2084/5212 - loss 0.18140515 - time (sec): 101.20 - samples/sec: 1471.11 - lr: 0.000048 - momentum: 0.000000 2023-10-15 23:52:03,691 epoch 2 - iter 2605/5212 - loss 0.18582562 - time (sec): 126.51 - samples/sec: 1464.80 - lr: 0.000047 - momentum: 0.000000 2023-10-15 23:52:28,906 epoch 2 - iter 3126/5212 - loss 0.18577517 - time (sec): 151.72 - samples/sec: 1469.25 - lr: 0.000047 - momentum: 0.000000 2023-10-15 23:52:53,821 epoch 2 - iter 3647/5212 - loss 0.18828427 - time (sec): 176.64 - samples/sec: 1465.00 - lr: 0.000046 - momentum: 0.000000 2023-10-15 23:53:19,622 epoch 2 - iter 4168/5212 - loss 0.18599284 - time (sec): 202.44 - samples/sec: 1470.56 - lr: 0.000046 - momentum: 0.000000 2023-10-15 23:53:44,440 epoch 2 - iter 4689/5212 - loss 0.18772856 - time (sec): 227.26 - samples/sec: 1455.41 - lr: 0.000045 - momentum: 0.000000 2023-10-15 23:54:09,398 epoch 2 - iter 5210/5212 - loss 0.18732109 - time (sec): 252.22 - samples/sec: 1455.77 - lr: 0.000044 - momentum: 0.000000 2023-10-15 23:54:09,513 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:54:09,513 EPOCH 2 done: loss 0.1873 - lr: 0.000044 2023-10-15 23:54:18,685 DEV : loss 0.14554405212402344 - f1-score (micro avg) 0.3546 2023-10-15 23:54:18,713 saving best model 2023-10-15 23:54:19,332 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:54:44,600 epoch 3 - iter 521/5212 - loss 0.16892477 - time (sec): 25.26 - samples/sec: 1431.22 - lr: 0.000044 - momentum: 0.000000 2023-10-15 23:55:09,173 epoch 3 - iter 1042/5212 - loss 0.15480170 - time (sec): 49.84 - samples/sec: 1382.24 - lr: 0.000043 - momentum: 0.000000 2023-10-15 23:55:34,192 epoch 3 - iter 1563/5212 - loss 0.15253235 - time (sec): 74.86 - samples/sec: 1379.25 - lr: 0.000043 - momentum: 0.000000 2023-10-15 23:55:59,000 epoch 3 - iter 2084/5212 - loss 0.14966988 - time (sec): 99.66 - samples/sec: 1394.82 - lr: 0.000042 - momentum: 0.000000 2023-10-15 23:56:23,271 epoch 3 - iter 2605/5212 - loss 0.14248758 - time (sec): 123.94 - samples/sec: 1422.36 - lr: 0.000042 - momentum: 0.000000 2023-10-15 23:56:48,693 epoch 3 - iter 3126/5212 - loss 0.14250745 - time (sec): 149.36 - samples/sec: 1432.87 - lr: 0.000041 - momentum: 0.000000 2023-10-15 23:57:14,127 epoch 3 - iter 3647/5212 - loss 0.14348930 - time (sec): 174.79 - samples/sec: 1440.23 - lr: 0.000041 - momentum: 0.000000 2023-10-15 23:57:39,517 epoch 3 - iter 4168/5212 - loss 0.14496238 - time (sec): 200.18 - samples/sec: 1439.25 - lr: 0.000040 - momentum: 0.000000 2023-10-15 23:58:05,279 epoch 3 - iter 4689/5212 - loss 0.14349134 - time (sec): 225.94 - samples/sec: 1447.61 - lr: 0.000039 - momentum: 0.000000 2023-10-15 23:58:31,311 epoch 3 - iter 5210/5212 - loss 0.14146075 - time (sec): 251.98 - samples/sec: 1458.09 - lr: 0.000039 - momentum: 0.000000 2023-10-15 23:58:31,401 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:58:31,401 EPOCH 3 done: loss 0.1415 - lr: 0.000039 2023-10-15 23:58:40,504 DEV : loss 0.27121227979660034 - f1-score (micro avg) 0.3217 2023-10-15 23:58:40,532 ---------------------------------------------------------------------------------------------------- 2023-10-15 23:59:05,417 epoch 4 - iter 521/5212 - loss 0.09043585 - time (sec): 24.88 - samples/sec: 1421.95 - lr: 0.000038 - momentum: 0.000000 2023-10-15 23:59:30,195 epoch 4 - iter 1042/5212 - loss 0.10494334 - time (sec): 49.66 - samples/sec: 1447.79 - lr: 0.000038 - momentum: 0.000000 2023-10-15 23:59:55,656 epoch 4 - iter 1563/5212 - loss 0.09724528 - time (sec): 75.12 - samples/sec: 1479.73 - lr: 0.000037 - momentum: 0.000000 2023-10-16 00:00:21,419 epoch 4 - iter 2084/5212 - loss 0.09872646 - time (sec): 100.89 - samples/sec: 1485.87 - lr: 0.000037 - momentum: 0.000000 2023-10-16 00:00:46,935 epoch 4 - iter 2605/5212 - loss 0.10353381 - time (sec): 126.40 - samples/sec: 1480.74 - lr: 0.000036 - momentum: 0.000000 2023-10-16 00:01:12,909 epoch 4 - iter 3126/5212 - loss 0.10532981 - time (sec): 152.38 - samples/sec: 1464.22 - lr: 0.000036 - momentum: 0.000000 2023-10-16 00:01:38,193 epoch 4 - iter 3647/5212 - loss 0.10412690 - time (sec): 177.66 - samples/sec: 1458.40 - lr: 0.000035 - momentum: 0.000000 2023-10-16 00:02:03,668 epoch 4 - iter 4168/5212 - loss 0.10176002 - time (sec): 203.14 - samples/sec: 1462.23 - lr: 0.000034 - momentum: 0.000000 2023-10-16 00:02:28,489 epoch 4 - iter 4689/5212 - loss 0.10219116 - time (sec): 227.96 - samples/sec: 1455.00 - lr: 0.000034 - momentum: 0.000000 2023-10-16 00:02:53,672 epoch 4 - iter 5210/5212 - loss 0.10091424 - time (sec): 253.14 - samples/sec: 1450.58 - lr: 0.000033 - momentum: 0.000000 2023-10-16 00:02:53,769 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:02:53,769 EPOCH 4 done: loss 0.1010 - lr: 0.000033 2023-10-16 00:03:02,079 DEV : loss 0.24610090255737305 - f1-score (micro avg) 0.3722 2023-10-16 00:03:02,108 saving best model 2023-10-16 00:03:02,652 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:03:28,355 epoch 5 - iter 521/5212 - loss 0.08946468 - time (sec): 25.70 - samples/sec: 1499.17 - lr: 0.000033 - momentum: 0.000000 2023-10-16 00:03:53,305 epoch 5 - iter 1042/5212 - loss 0.08272511 - time (sec): 50.65 - samples/sec: 1445.65 - lr: 0.000032 - momentum: 0.000000 2023-10-16 00:04:19,363 epoch 5 - iter 1563/5212 - loss 0.07858447 - time (sec): 76.71 - samples/sec: 1419.47 - lr: 0.000032 - momentum: 0.000000 2023-10-16 00:04:45,160 epoch 5 - iter 2084/5212 - loss 0.07638850 - time (sec): 102.51 - samples/sec: 1440.40 - lr: 0.000031 - momentum: 0.000000 2023-10-16 00:05:10,817 epoch 5 - iter 2605/5212 - loss 0.07945967 - time (sec): 128.16 - samples/sec: 1449.44 - lr: 0.000031 - momentum: 0.000000 2023-10-16 00:05:35,603 epoch 5 - iter 3126/5212 - loss 0.07924286 - time (sec): 152.95 - samples/sec: 1448.28 - lr: 0.000030 - momentum: 0.000000 2023-10-16 00:06:00,818 epoch 5 - iter 3647/5212 - loss 0.07893342 - time (sec): 178.16 - samples/sec: 1442.27 - lr: 0.000029 - momentum: 0.000000 2023-10-16 00:06:26,438 epoch 5 - iter 4168/5212 - loss 0.07696755 - time (sec): 203.78 - samples/sec: 1448.05 - lr: 0.000029 - momentum: 0.000000 2023-10-16 00:06:51,588 epoch 5 - iter 4689/5212 - loss 0.07559505 - time (sec): 228.93 - samples/sec: 1447.40 - lr: 0.000028 - momentum: 0.000000 2023-10-16 00:07:16,799 epoch 5 - iter 5210/5212 - loss 0.07550940 - time (sec): 254.14 - samples/sec: 1445.61 - lr: 0.000028 - momentum: 0.000000 2023-10-16 00:07:16,884 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:07:16,885 EPOCH 5 done: loss 0.0756 - lr: 0.000028 2023-10-16 00:07:25,246 DEV : loss 0.27587252855300903 - f1-score (micro avg) 0.3933 2023-10-16 00:07:25,276 saving best model 2023-10-16 00:07:25,899 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:07:51,242 epoch 6 - iter 521/5212 - loss 0.04500721 - time (sec): 25.34 - samples/sec: 1438.74 - lr: 0.000027 - momentum: 0.000000 2023-10-16 00:08:16,452 epoch 6 - iter 1042/5212 - loss 0.05219254 - time (sec): 50.55 - samples/sec: 1458.09 - lr: 0.000027 - momentum: 0.000000 2023-10-16 00:08:41,812 epoch 6 - iter 1563/5212 - loss 0.05073335 - time (sec): 75.91 - samples/sec: 1454.84 - lr: 0.000026 - momentum: 0.000000 2023-10-16 00:09:07,218 epoch 6 - iter 2084/5212 - loss 0.05244273 - time (sec): 101.32 - samples/sec: 1458.61 - lr: 0.000026 - momentum: 0.000000 2023-10-16 00:09:32,274 epoch 6 - iter 2605/5212 - loss 0.05597317 - time (sec): 126.37 - samples/sec: 1450.57 - lr: 0.000025 - momentum: 0.000000 2023-10-16 00:09:58,365 epoch 6 - iter 3126/5212 - loss 0.05870564 - time (sec): 152.46 - samples/sec: 1447.89 - lr: 0.000024 - momentum: 0.000000 2023-10-16 00:10:23,762 epoch 6 - iter 3647/5212 - loss 0.05885555 - time (sec): 177.86 - samples/sec: 1458.55 - lr: 0.000024 - momentum: 0.000000 2023-10-16 00:10:49,124 epoch 6 - iter 4168/5212 - loss 0.05780173 - time (sec): 203.22 - samples/sec: 1461.81 - lr: 0.000023 - momentum: 0.000000 2023-10-16 00:11:14,399 epoch 6 - iter 4689/5212 - loss 0.05720709 - time (sec): 228.50 - samples/sec: 1459.04 - lr: 0.000023 - momentum: 0.000000 2023-10-16 00:11:39,181 epoch 6 - iter 5210/5212 - loss 0.05635932 - time (sec): 253.28 - samples/sec: 1448.97 - lr: 0.000022 - momentum: 0.000000 2023-10-16 00:11:39,333 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:11:39,333 EPOCH 6 done: loss 0.0563 - lr: 0.000022 2023-10-16 00:11:47,640 DEV : loss 0.3152172863483429 - f1-score (micro avg) 0.3546 2023-10-16 00:11:47,669 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:12:12,705 epoch 7 - iter 521/5212 - loss 0.05081149 - time (sec): 25.03 - samples/sec: 1394.27 - lr: 0.000022 - momentum: 0.000000 2023-10-16 00:12:37,682 epoch 7 - iter 1042/5212 - loss 0.04120887 - time (sec): 50.01 - samples/sec: 1432.98 - lr: 0.000021 - momentum: 0.000000 2023-10-16 00:13:03,240 epoch 7 - iter 1563/5212 - loss 0.04350707 - time (sec): 75.57 - samples/sec: 1433.54 - lr: 0.000021 - momentum: 0.000000 2023-10-16 00:13:29,103 epoch 7 - iter 2084/5212 - loss 0.04429995 - time (sec): 101.43 - samples/sec: 1456.00 - lr: 0.000020 - momentum: 0.000000 2023-10-16 00:13:54,490 epoch 7 - iter 2605/5212 - loss 0.04318346 - time (sec): 126.82 - samples/sec: 1447.52 - lr: 0.000019 - momentum: 0.000000 2023-10-16 00:14:19,664 epoch 7 - iter 3126/5212 - loss 0.04497001 - time (sec): 151.99 - samples/sec: 1451.89 - lr: 0.000019 - momentum: 0.000000 2023-10-16 00:14:44,996 epoch 7 - iter 3647/5212 - loss 0.04321396 - time (sec): 177.33 - samples/sec: 1459.04 - lr: 0.000018 - momentum: 0.000000 2023-10-16 00:15:10,347 epoch 7 - iter 4168/5212 - loss 0.04275074 - time (sec): 202.68 - samples/sec: 1460.57 - lr: 0.000018 - momentum: 0.000000 2023-10-16 00:15:36,189 epoch 7 - iter 4689/5212 - loss 0.04205276 - time (sec): 228.52 - samples/sec: 1443.40 - lr: 0.000017 - momentum: 0.000000 2023-10-16 00:16:01,971 epoch 7 - iter 5210/5212 - loss 0.04183362 - time (sec): 254.30 - samples/sec: 1444.10 - lr: 0.000017 - momentum: 0.000000 2023-10-16 00:16:02,089 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:16:02,090 EPOCH 7 done: loss 0.0418 - lr: 0.000017 2023-10-16 00:16:10,408 DEV : loss 0.3469476103782654 - f1-score (micro avg) 0.3672 2023-10-16 00:16:10,453 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:16:37,044 epoch 8 - iter 521/5212 - loss 0.02217721 - time (sec): 26.59 - samples/sec: 1468.68 - lr: 0.000016 - momentum: 0.000000 2023-10-16 00:17:02,282 epoch 8 - iter 1042/5212 - loss 0.02729592 - time (sec): 51.83 - samples/sec: 1486.36 - lr: 0.000016 - momentum: 0.000000 2023-10-16 00:17:27,560 epoch 8 - iter 1563/5212 - loss 0.02832787 - time (sec): 77.10 - samples/sec: 1473.74 - lr: 0.000015 - momentum: 0.000000 2023-10-16 00:17:53,339 epoch 8 - iter 2084/5212 - loss 0.02989946 - time (sec): 102.88 - samples/sec: 1460.61 - lr: 0.000014 - momentum: 0.000000 2023-10-16 00:18:18,756 epoch 8 - iter 2605/5212 - loss 0.02875976 - time (sec): 128.30 - samples/sec: 1453.04 - lr: 0.000014 - momentum: 0.000000 2023-10-16 00:18:44,249 epoch 8 - iter 3126/5212 - loss 0.02972707 - time (sec): 153.79 - samples/sec: 1457.00 - lr: 0.000013 - momentum: 0.000000 2023-10-16 00:19:08,428 epoch 8 - iter 3647/5212 - loss 0.02955815 - time (sec): 177.97 - samples/sec: 1459.29 - lr: 0.000013 - momentum: 0.000000 2023-10-16 00:19:32,685 epoch 8 - iter 4168/5212 - loss 0.02992807 - time (sec): 202.23 - samples/sec: 1463.77 - lr: 0.000012 - momentum: 0.000000 2023-10-16 00:19:56,993 epoch 8 - iter 4689/5212 - loss 0.02991417 - time (sec): 226.54 - samples/sec: 1458.61 - lr: 0.000012 - momentum: 0.000000 2023-10-16 00:20:22,203 epoch 8 - iter 5210/5212 - loss 0.02960201 - time (sec): 251.75 - samples/sec: 1459.33 - lr: 0.000011 - momentum: 0.000000 2023-10-16 00:20:22,290 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:20:22,290 EPOCH 8 done: loss 0.0296 - lr: 0.000011 2023-10-16 00:20:31,432 DEV : loss 0.4151654839515686 - f1-score (micro avg) 0.3626 2023-10-16 00:20:31,464 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:20:56,517 epoch 9 - iter 521/5212 - loss 0.01705744 - time (sec): 25.05 - samples/sec: 1507.99 - lr: 0.000011 - momentum: 0.000000 2023-10-16 00:21:21,288 epoch 9 - iter 1042/5212 - loss 0.02156692 - time (sec): 49.82 - samples/sec: 1460.72 - lr: 0.000010 - momentum: 0.000000 2023-10-16 00:21:46,082 epoch 9 - iter 1563/5212 - loss 0.02449670 - time (sec): 74.62 - samples/sec: 1440.44 - lr: 0.000009 - momentum: 0.000000 2023-10-16 00:22:10,930 epoch 9 - iter 2084/5212 - loss 0.02337409 - time (sec): 99.46 - samples/sec: 1440.07 - lr: 0.000009 - momentum: 0.000000 2023-10-16 00:22:36,386 epoch 9 - iter 2605/5212 - loss 0.02237419 - time (sec): 124.92 - samples/sec: 1453.59 - lr: 0.000008 - momentum: 0.000000 2023-10-16 00:23:01,612 epoch 9 - iter 3126/5212 - loss 0.02194368 - time (sec): 150.15 - samples/sec: 1459.66 - lr: 0.000008 - momentum: 0.000000 2023-10-16 00:23:26,731 epoch 9 - iter 3647/5212 - loss 0.02224411 - time (sec): 175.26 - samples/sec: 1458.11 - lr: 0.000007 - momentum: 0.000000 2023-10-16 00:23:51,921 epoch 9 - iter 4168/5212 - loss 0.02139231 - time (sec): 200.46 - samples/sec: 1462.79 - lr: 0.000007 - momentum: 0.000000 2023-10-16 00:24:17,364 epoch 9 - iter 4689/5212 - loss 0.02129007 - time (sec): 225.90 - samples/sec: 1465.66 - lr: 0.000006 - momentum: 0.000000 2023-10-16 00:24:42,558 epoch 9 - iter 5210/5212 - loss 0.02095764 - time (sec): 251.09 - samples/sec: 1463.02 - lr: 0.000006 - momentum: 0.000000 2023-10-16 00:24:42,651 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:24:42,652 EPOCH 9 done: loss 0.0210 - lr: 0.000006 2023-10-16 00:24:52,948 DEV : loss 0.40386104583740234 - f1-score (micro avg) 0.3607 2023-10-16 00:24:52,983 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:25:18,379 epoch 10 - iter 521/5212 - loss 0.01307094 - time (sec): 25.39 - samples/sec: 1403.60 - lr: 0.000005 - momentum: 0.000000 2023-10-16 00:25:43,265 epoch 10 - iter 1042/5212 - loss 0.01629748 - time (sec): 50.28 - samples/sec: 1423.93 - lr: 0.000004 - momentum: 0.000000 2023-10-16 00:26:08,138 epoch 10 - iter 1563/5212 - loss 0.01516469 - time (sec): 75.15 - samples/sec: 1421.72 - lr: 0.000004 - momentum: 0.000000 2023-10-16 00:26:33,158 epoch 10 - iter 2084/5212 - loss 0.01544442 - time (sec): 100.17 - samples/sec: 1426.48 - lr: 0.000003 - momentum: 0.000000 2023-10-16 00:26:58,058 epoch 10 - iter 2605/5212 - loss 0.01529237 - time (sec): 125.07 - samples/sec: 1425.16 - lr: 0.000003 - momentum: 0.000000 2023-10-16 00:27:23,342 epoch 10 - iter 3126/5212 - loss 0.01463251 - time (sec): 150.36 - samples/sec: 1442.88 - lr: 0.000002 - momentum: 0.000000 2023-10-16 00:27:48,516 epoch 10 - iter 3647/5212 - loss 0.01441137 - time (sec): 175.53 - samples/sec: 1454.61 - lr: 0.000002 - momentum: 0.000000 2023-10-16 00:28:14,171 epoch 10 - iter 4168/5212 - loss 0.01390284 - time (sec): 201.19 - samples/sec: 1462.31 - lr: 0.000001 - momentum: 0.000000 2023-10-16 00:28:39,493 epoch 10 - iter 4689/5212 - loss 0.01394799 - time (sec): 226.51 - samples/sec: 1464.10 - lr: 0.000001 - momentum: 0.000000 2023-10-16 00:29:04,370 epoch 10 - iter 5210/5212 - loss 0.01412248 - time (sec): 251.39 - samples/sec: 1461.38 - lr: 0.000000 - momentum: 0.000000 2023-10-16 00:29:04,465 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:29:04,465 EPOCH 10 done: loss 0.0141 - lr: 0.000000 2023-10-16 00:29:13,621 DEV : loss 0.41680729389190674 - f1-score (micro avg) 0.3728 2023-10-16 00:29:14,169 ---------------------------------------------------------------------------------------------------- 2023-10-16 00:29:14,171 Loading model from best epoch ... 2023-10-16 00:29:15,700 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-16 00:29:30,958 Results: - F-score (micro) 0.4193 - F-score (macro) 0.2665 - Accuracy 0.2701 By class: precision recall f1-score support LOC 0.4918 0.5206 0.5058 1214 PER 0.3522 0.4084 0.3782 808 ORG 0.2314 0.1501 0.1821 353 HumanProd 0.0000 0.0000 0.0000 15 micro avg 0.4141 0.4247 0.4193 2390 macro avg 0.2689 0.2698 0.2665 2390 weighted avg 0.4031 0.4247 0.4117 2390 2023-10-16 00:29:30,958 ----------------------------------------------------------------------------------------------------