2023-10-19 19:51:06,768 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,768 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(32001, 128) (position_embeddings): Embedding(512, 128) (token_type_embeddings): Embedding(2, 128) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-1): 2 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=128, out_features=128, bias=True) (key): Linear(in_features=128, out_features=128, bias=True) (value): Linear(in_features=128, out_features=128, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=128, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=128, out_features=512, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=512, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=128, out_features=128, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=128, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-19 19:51:06,768 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,768 MultiCorpus: 7142 train + 698 dev + 2570 test sentences - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator 2023-10-19 19:51:06,768 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,768 Train: 7142 sentences 2023-10-19 19:51:06,768 (train_with_dev=False, train_with_test=False) 2023-10-19 19:51:06,768 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,768 Training Params: 2023-10-19 19:51:06,768 - learning_rate: "3e-05" 2023-10-19 19:51:06,768 - mini_batch_size: "4" 2023-10-19 19:51:06,769 - max_epochs: "10" 2023-10-19 19:51:06,769 - shuffle: "True" 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 Plugins: 2023-10-19 19:51:06,769 - TensorboardLogger 2023-10-19 19:51:06,769 - LinearScheduler | warmup_fraction: '0.1' 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 Final evaluation on model from best epoch (best-model.pt) 2023-10-19 19:51:06,769 - metric: "('micro avg', 'f1-score')" 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 Computation: 2023-10-19 19:51:06,769 - compute on device: cuda:0 2023-10-19 19:51:06,769 - embedding storage: none 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 Model training base path: "hmbench-newseye/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:06,769 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-19 19:51:09,948 epoch 1 - iter 178/1786 - loss 2.79687367 - time (sec): 3.18 - samples/sec: 8405.70 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:51:13,196 epoch 1 - iter 356/1786 - loss 2.50644631 - time (sec): 6.43 - samples/sec: 7917.10 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:51:16,242 epoch 1 - iter 534/1786 - loss 2.11669177 - time (sec): 9.47 - samples/sec: 7847.71 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:51:19,447 epoch 1 - iter 712/1786 - loss 1.78468271 - time (sec): 12.68 - samples/sec: 7832.18 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:51:22,749 epoch 1 - iter 890/1786 - loss 1.58099334 - time (sec): 15.98 - samples/sec: 7696.48 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:51:25,906 epoch 1 - iter 1068/1786 - loss 1.44626933 - time (sec): 19.14 - samples/sec: 7688.35 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:51:29,188 epoch 1 - iter 1246/1786 - loss 1.32135288 - time (sec): 22.42 - samples/sec: 7711.92 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:51:32,297 epoch 1 - iter 1424/1786 - loss 1.22571550 - time (sec): 25.53 - samples/sec: 7701.62 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:51:35,449 epoch 1 - iter 1602/1786 - loss 1.14718597 - time (sec): 28.68 - samples/sec: 7738.29 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:51:38,565 epoch 1 - iter 1780/1786 - loss 1.08157944 - time (sec): 31.80 - samples/sec: 7802.23 - lr: 0.000030 - momentum: 0.000000 2023-10-19 19:51:38,668 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:38,668 EPOCH 1 done: loss 1.0801 - lr: 0.000030 2023-10-19 19:51:40,134 DEV : loss 0.3227035701274872 - f1-score (micro avg) 0.1395 2023-10-19 19:51:40,149 saving best model 2023-10-19 19:51:40,183 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:51:43,289 epoch 2 - iter 178/1786 - loss 0.50600833 - time (sec): 3.11 - samples/sec: 7616.92 - lr: 0.000030 - momentum: 0.000000 2023-10-19 19:51:46,333 epoch 2 - iter 356/1786 - loss 0.46557022 - time (sec): 6.15 - samples/sec: 7898.55 - lr: 0.000029 - momentum: 0.000000 2023-10-19 19:51:49,371 epoch 2 - iter 534/1786 - loss 0.46513364 - time (sec): 9.19 - samples/sec: 7766.67 - lr: 0.000029 - momentum: 0.000000 2023-10-19 19:51:52,594 epoch 2 - iter 712/1786 - loss 0.45220596 - time (sec): 12.41 - samples/sec: 7737.80 - lr: 0.000029 - momentum: 0.000000 2023-10-19 19:51:56,019 epoch 2 - iter 890/1786 - loss 0.45404700 - time (sec): 15.84 - samples/sec: 7667.36 - lr: 0.000028 - momentum: 0.000000 2023-10-19 19:51:59,181 epoch 2 - iter 1068/1786 - loss 0.44855842 - time (sec): 19.00 - samples/sec: 7742.35 - lr: 0.000028 - momentum: 0.000000 2023-10-19 19:52:02,417 epoch 2 - iter 1246/1786 - loss 0.44009549 - time (sec): 22.23 - samples/sec: 7836.30 - lr: 0.000028 - momentum: 0.000000 2023-10-19 19:52:05,463 epoch 2 - iter 1424/1786 - loss 0.43715972 - time (sec): 25.28 - samples/sec: 7889.50 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:52:08,578 epoch 2 - iter 1602/1786 - loss 0.43518582 - time (sec): 28.39 - samples/sec: 7891.60 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:52:11,635 epoch 2 - iter 1780/1786 - loss 0.43341001 - time (sec): 31.45 - samples/sec: 7891.97 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:52:11,727 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:52:11,727 EPOCH 2 done: loss 0.4333 - lr: 0.000027 2023-10-19 19:52:14,547 DEV : loss 0.2583433985710144 - f1-score (micro avg) 0.3512 2023-10-19 19:52:14,562 saving best model 2023-10-19 19:52:14,594 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:52:17,843 epoch 3 - iter 178/1786 - loss 0.36689807 - time (sec): 3.25 - samples/sec: 7327.59 - lr: 0.000026 - momentum: 0.000000 2023-10-19 19:52:21,112 epoch 3 - iter 356/1786 - loss 0.34773508 - time (sec): 6.52 - samples/sec: 7774.91 - lr: 0.000026 - momentum: 0.000000 2023-10-19 19:52:24,217 epoch 3 - iter 534/1786 - loss 0.34418896 - time (sec): 9.62 - samples/sec: 7853.08 - lr: 0.000026 - momentum: 0.000000 2023-10-19 19:52:27,388 epoch 3 - iter 712/1786 - loss 0.35168554 - time (sec): 12.79 - samples/sec: 7808.77 - lr: 0.000025 - momentum: 0.000000 2023-10-19 19:52:30,291 epoch 3 - iter 890/1786 - loss 0.35595638 - time (sec): 15.70 - samples/sec: 8022.78 - lr: 0.000025 - momentum: 0.000000 2023-10-19 19:52:33,085 epoch 3 - iter 1068/1786 - loss 0.35812865 - time (sec): 18.49 - samples/sec: 8195.12 - lr: 0.000025 - momentum: 0.000000 2023-10-19 19:52:36,081 epoch 3 - iter 1246/1786 - loss 0.35668010 - time (sec): 21.49 - samples/sec: 8128.47 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:52:39,100 epoch 3 - iter 1424/1786 - loss 0.35997953 - time (sec): 24.50 - samples/sec: 8132.30 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:52:42,129 epoch 3 - iter 1602/1786 - loss 0.36107656 - time (sec): 27.53 - samples/sec: 8152.51 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:52:45,104 epoch 3 - iter 1780/1786 - loss 0.35849403 - time (sec): 30.51 - samples/sec: 8127.99 - lr: 0.000023 - momentum: 0.000000 2023-10-19 19:52:45,209 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:52:45,209 EPOCH 3 done: loss 0.3587 - lr: 0.000023 2023-10-19 19:52:47,579 DEV : loss 0.2307889312505722 - f1-score (micro avg) 0.4343 2023-10-19 19:52:47,594 saving best model 2023-10-19 19:52:47,627 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:52:50,603 epoch 4 - iter 178/1786 - loss 0.32503973 - time (sec): 2.98 - samples/sec: 8709.72 - lr: 0.000023 - momentum: 0.000000 2023-10-19 19:52:53,661 epoch 4 - iter 356/1786 - loss 0.33876825 - time (sec): 6.03 - samples/sec: 8239.92 - lr: 0.000023 - momentum: 0.000000 2023-10-19 19:52:56,706 epoch 4 - iter 534/1786 - loss 0.34909168 - time (sec): 9.08 - samples/sec: 8139.72 - lr: 0.000022 - momentum: 0.000000 2023-10-19 19:52:59,800 epoch 4 - iter 712/1786 - loss 0.33361868 - time (sec): 12.17 - samples/sec: 8180.84 - lr: 0.000022 - momentum: 0.000000 2023-10-19 19:53:02,796 epoch 4 - iter 890/1786 - loss 0.32921053 - time (sec): 15.17 - samples/sec: 8202.07 - lr: 0.000022 - momentum: 0.000000 2023-10-19 19:53:05,850 epoch 4 - iter 1068/1786 - loss 0.32434636 - time (sec): 18.22 - samples/sec: 8142.54 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:53:08,954 epoch 4 - iter 1246/1786 - loss 0.32469591 - time (sec): 21.33 - samples/sec: 8072.21 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:53:11,970 epoch 4 - iter 1424/1786 - loss 0.32393392 - time (sec): 24.34 - samples/sec: 8076.36 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:53:15,084 epoch 4 - iter 1602/1786 - loss 0.32421030 - time (sec): 27.46 - samples/sec: 8099.84 - lr: 0.000020 - momentum: 0.000000 2023-10-19 19:53:18,196 epoch 4 - iter 1780/1786 - loss 0.32088289 - time (sec): 30.57 - samples/sec: 8119.54 - lr: 0.000020 - momentum: 0.000000 2023-10-19 19:53:18,287 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:53:18,287 EPOCH 4 done: loss 0.3211 - lr: 0.000020 2023-10-19 19:53:21,110 DEV : loss 0.2131872922182083 - f1-score (micro avg) 0.4691 2023-10-19 19:53:21,125 saving best model 2023-10-19 19:53:21,160 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:53:24,078 epoch 5 - iter 178/1786 - loss 0.31966126 - time (sec): 2.92 - samples/sec: 8571.95 - lr: 0.000020 - momentum: 0.000000 2023-10-19 19:53:27,158 epoch 5 - iter 356/1786 - loss 0.30979983 - time (sec): 6.00 - samples/sec: 8542.72 - lr: 0.000019 - momentum: 0.000000 2023-10-19 19:53:30,224 epoch 5 - iter 534/1786 - loss 0.30109050 - time (sec): 9.06 - samples/sec: 8366.97 - lr: 0.000019 - momentum: 0.000000 2023-10-19 19:53:33,352 epoch 5 - iter 712/1786 - loss 0.30298422 - time (sec): 12.19 - samples/sec: 8163.31 - lr: 0.000019 - momentum: 0.000000 2023-10-19 19:53:36,499 epoch 5 - iter 890/1786 - loss 0.30175508 - time (sec): 15.34 - samples/sec: 7974.28 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:53:39,560 epoch 5 - iter 1068/1786 - loss 0.29354489 - time (sec): 18.40 - samples/sec: 8027.67 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:53:42,547 epoch 5 - iter 1246/1786 - loss 0.29628320 - time (sec): 21.39 - samples/sec: 7996.02 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:53:45,679 epoch 5 - iter 1424/1786 - loss 0.29373074 - time (sec): 24.52 - samples/sec: 8009.08 - lr: 0.000017 - momentum: 0.000000 2023-10-19 19:53:48,821 epoch 5 - iter 1602/1786 - loss 0.29243459 - time (sec): 27.66 - samples/sec: 8049.63 - lr: 0.000017 - momentum: 0.000000 2023-10-19 19:53:51,974 epoch 5 - iter 1780/1786 - loss 0.29269761 - time (sec): 30.81 - samples/sec: 8046.10 - lr: 0.000017 - momentum: 0.000000 2023-10-19 19:53:52,092 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:53:52,092 EPOCH 5 done: loss 0.2925 - lr: 0.000017 2023-10-19 19:53:54,463 DEV : loss 0.20653365552425385 - f1-score (micro avg) 0.4815 2023-10-19 19:53:54,477 saving best model 2023-10-19 19:53:54,510 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:53:57,812 epoch 6 - iter 178/1786 - loss 0.26481799 - time (sec): 3.30 - samples/sec: 7606.85 - lr: 0.000016 - momentum: 0.000000 2023-10-19 19:54:00,948 epoch 6 - iter 356/1786 - loss 0.26935936 - time (sec): 6.44 - samples/sec: 7536.84 - lr: 0.000016 - momentum: 0.000000 2023-10-19 19:54:04,139 epoch 6 - iter 534/1786 - loss 0.27348573 - time (sec): 9.63 - samples/sec: 7506.83 - lr: 0.000016 - momentum: 0.000000 2023-10-19 19:54:07,207 epoch 6 - iter 712/1786 - loss 0.27134878 - time (sec): 12.70 - samples/sec: 7760.38 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:54:10,303 epoch 6 - iter 890/1786 - loss 0.26920475 - time (sec): 15.79 - samples/sec: 7915.32 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:54:13,384 epoch 6 - iter 1068/1786 - loss 0.27009143 - time (sec): 18.87 - samples/sec: 7904.76 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:54:16,439 epoch 6 - iter 1246/1786 - loss 0.27126902 - time (sec): 21.93 - samples/sec: 7886.87 - lr: 0.000014 - momentum: 0.000000 2023-10-19 19:54:19,503 epoch 6 - iter 1424/1786 - loss 0.27292987 - time (sec): 24.99 - samples/sec: 7902.13 - lr: 0.000014 - momentum: 0.000000 2023-10-19 19:54:22,691 epoch 6 - iter 1602/1786 - loss 0.27225979 - time (sec): 28.18 - samples/sec: 7926.75 - lr: 0.000014 - momentum: 0.000000 2023-10-19 19:54:25,821 epoch 6 - iter 1780/1786 - loss 0.27262868 - time (sec): 31.31 - samples/sec: 7923.30 - lr: 0.000013 - momentum: 0.000000 2023-10-19 19:54:25,918 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:54:25,918 EPOCH 6 done: loss 0.2727 - lr: 0.000013 2023-10-19 19:54:28,745 DEV : loss 0.2003042846918106 - f1-score (micro avg) 0.4843 2023-10-19 19:54:28,759 saving best model 2023-10-19 19:54:28,791 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:54:31,964 epoch 7 - iter 178/1786 - loss 0.24380929 - time (sec): 3.17 - samples/sec: 8323.54 - lr: 0.000013 - momentum: 0.000000 2023-10-19 19:54:35,071 epoch 7 - iter 356/1786 - loss 0.25726475 - time (sec): 6.28 - samples/sec: 8238.91 - lr: 0.000013 - momentum: 0.000000 2023-10-19 19:54:38,117 epoch 7 - iter 534/1786 - loss 0.25410518 - time (sec): 9.33 - samples/sec: 8058.86 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:54:41,153 epoch 7 - iter 712/1786 - loss 0.25769061 - time (sec): 12.36 - samples/sec: 7964.33 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:54:44,262 epoch 7 - iter 890/1786 - loss 0.25823402 - time (sec): 15.47 - samples/sec: 7972.49 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:54:47,394 epoch 7 - iter 1068/1786 - loss 0.25747742 - time (sec): 18.60 - samples/sec: 7959.44 - lr: 0.000011 - momentum: 0.000000 2023-10-19 19:54:50,595 epoch 7 - iter 1246/1786 - loss 0.25719904 - time (sec): 21.80 - samples/sec: 7962.56 - lr: 0.000011 - momentum: 0.000000 2023-10-19 19:54:53,694 epoch 7 - iter 1424/1786 - loss 0.25589988 - time (sec): 24.90 - samples/sec: 8050.60 - lr: 0.000011 - momentum: 0.000000 2023-10-19 19:54:56,745 epoch 7 - iter 1602/1786 - loss 0.25845771 - time (sec): 27.95 - samples/sec: 8021.59 - lr: 0.000010 - momentum: 0.000000 2023-10-19 19:54:59,724 epoch 7 - iter 1780/1786 - loss 0.25787745 - time (sec): 30.93 - samples/sec: 8027.94 - lr: 0.000010 - momentum: 0.000000 2023-10-19 19:54:59,817 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:54:59,818 EPOCH 7 done: loss 0.2580 - lr: 0.000010 2023-10-19 19:55:02,193 DEV : loss 0.2004682868719101 - f1-score (micro avg) 0.5019 2023-10-19 19:55:02,209 saving best model 2023-10-19 19:55:02,246 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:55:05,316 epoch 8 - iter 178/1786 - loss 0.24861152 - time (sec): 3.07 - samples/sec: 8175.43 - lr: 0.000010 - momentum: 0.000000 2023-10-19 19:55:08,419 epoch 8 - iter 356/1786 - loss 0.24086117 - time (sec): 6.17 - samples/sec: 8098.43 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:55:11,525 epoch 8 - iter 534/1786 - loss 0.24815113 - time (sec): 9.28 - samples/sec: 7956.56 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:55:14,603 epoch 8 - iter 712/1786 - loss 0.24997628 - time (sec): 12.36 - samples/sec: 7987.55 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:55:17,675 epoch 8 - iter 890/1786 - loss 0.24787170 - time (sec): 15.43 - samples/sec: 8019.54 - lr: 0.000008 - momentum: 0.000000 2023-10-19 19:55:20,753 epoch 8 - iter 1068/1786 - loss 0.24807736 - time (sec): 18.51 - samples/sec: 8065.54 - lr: 0.000008 - momentum: 0.000000 2023-10-19 19:55:23,762 epoch 8 - iter 1246/1786 - loss 0.24768224 - time (sec): 21.52 - samples/sec: 8087.21 - lr: 0.000008 - momentum: 0.000000 2023-10-19 19:55:26,840 epoch 8 - iter 1424/1786 - loss 0.24665818 - time (sec): 24.59 - samples/sec: 8095.57 - lr: 0.000007 - momentum: 0.000000 2023-10-19 19:55:29,829 epoch 8 - iter 1602/1786 - loss 0.24985874 - time (sec): 27.58 - samples/sec: 8098.97 - lr: 0.000007 - momentum: 0.000000 2023-10-19 19:55:32,995 epoch 8 - iter 1780/1786 - loss 0.25000798 - time (sec): 30.75 - samples/sec: 8064.42 - lr: 0.000007 - momentum: 0.000000 2023-10-19 19:55:33,104 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:55:33,104 EPOCH 8 done: loss 0.2499 - lr: 0.000007 2023-10-19 19:55:35,972 DEV : loss 0.19668100774288177 - f1-score (micro avg) 0.5031 2023-10-19 19:55:35,986 saving best model 2023-10-19 19:55:36,020 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:55:39,211 epoch 9 - iter 178/1786 - loss 0.23800143 - time (sec): 3.19 - samples/sec: 8302.38 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:55:42,260 epoch 9 - iter 356/1786 - loss 0.23583672 - time (sec): 6.24 - samples/sec: 8301.37 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:55:45,281 epoch 9 - iter 534/1786 - loss 0.23499003 - time (sec): 9.26 - samples/sec: 8227.23 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:55:48,244 epoch 9 - iter 712/1786 - loss 0.23888394 - time (sec): 12.22 - samples/sec: 8139.72 - lr: 0.000005 - momentum: 0.000000 2023-10-19 19:55:51,288 epoch 9 - iter 890/1786 - loss 0.24062823 - time (sec): 15.27 - samples/sec: 8068.23 - lr: 0.000005 - momentum: 0.000000 2023-10-19 19:55:54,409 epoch 9 - iter 1068/1786 - loss 0.24208841 - time (sec): 18.39 - samples/sec: 8095.06 - lr: 0.000005 - momentum: 0.000000 2023-10-19 19:55:57,156 epoch 9 - iter 1246/1786 - loss 0.24572741 - time (sec): 21.13 - samples/sec: 8235.25 - lr: 0.000004 - momentum: 0.000000 2023-10-19 19:56:00,158 epoch 9 - iter 1424/1786 - loss 0.24461181 - time (sec): 24.14 - samples/sec: 8218.35 - lr: 0.000004 - momentum: 0.000000 2023-10-19 19:56:03,242 epoch 9 - iter 1602/1786 - loss 0.24434313 - time (sec): 27.22 - samples/sec: 8220.44 - lr: 0.000004 - momentum: 0.000000 2023-10-19 19:56:06,397 epoch 9 - iter 1780/1786 - loss 0.24230508 - time (sec): 30.38 - samples/sec: 8165.70 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:56:06,497 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:56:06,497 EPOCH 9 done: loss 0.2419 - lr: 0.000003 2023-10-19 19:56:08,853 DEV : loss 0.1973438411951065 - f1-score (micro avg) 0.508 2023-10-19 19:56:08,867 saving best model 2023-10-19 19:56:08,900 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:56:11,976 epoch 10 - iter 178/1786 - loss 0.24379983 - time (sec): 3.08 - samples/sec: 7549.01 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:56:15,113 epoch 10 - iter 356/1786 - loss 0.25116343 - time (sec): 6.21 - samples/sec: 7700.35 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:56:18,116 epoch 10 - iter 534/1786 - loss 0.25060293 - time (sec): 9.22 - samples/sec: 7865.97 - lr: 0.000002 - momentum: 0.000000 2023-10-19 19:56:21,260 epoch 10 - iter 712/1786 - loss 0.25011516 - time (sec): 12.36 - samples/sec: 7898.81 - lr: 0.000002 - momentum: 0.000000 2023-10-19 19:56:24,503 epoch 10 - iter 890/1786 - loss 0.24555631 - time (sec): 15.60 - samples/sec: 7783.06 - lr: 0.000002 - momentum: 0.000000 2023-10-19 19:56:27,557 epoch 10 - iter 1068/1786 - loss 0.24118692 - time (sec): 18.66 - samples/sec: 7834.94 - lr: 0.000001 - momentum: 0.000000 2023-10-19 19:56:30,299 epoch 10 - iter 1246/1786 - loss 0.23655811 - time (sec): 21.40 - samples/sec: 8048.55 - lr: 0.000001 - momentum: 0.000000 2023-10-19 19:56:33,323 epoch 10 - iter 1424/1786 - loss 0.23172756 - time (sec): 24.42 - samples/sec: 8110.64 - lr: 0.000001 - momentum: 0.000000 2023-10-19 19:56:36,431 epoch 10 - iter 1602/1786 - loss 0.23408678 - time (sec): 27.53 - samples/sec: 8114.79 - lr: 0.000000 - momentum: 0.000000 2023-10-19 19:56:39,501 epoch 10 - iter 1780/1786 - loss 0.23658798 - time (sec): 30.60 - samples/sec: 8109.58 - lr: 0.000000 - momentum: 0.000000 2023-10-19 19:56:39,599 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:56:39,599 EPOCH 10 done: loss 0.2373 - lr: 0.000000 2023-10-19 19:56:42,416 DEV : loss 0.19595196843147278 - f1-score (micro avg) 0.5103 2023-10-19 19:56:42,430 saving best model 2023-10-19 19:56:42,489 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:56:42,489 Loading model from best epoch ... 2023-10-19 19:56:42,562 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-19 19:56:47,193 Results: - F-score (micro) 0.414 - F-score (macro) 0.2562 - Accuracy 0.271 By class: precision recall f1-score support LOC 0.3964 0.5169 0.4487 1095 PER 0.4249 0.4951 0.4573 1012 ORG 0.1581 0.0952 0.1189 357 HumanProd 0.0000 0.0000 0.0000 33 micro avg 0.3901 0.4409 0.4140 2497 macro avg 0.2449 0.2768 0.2562 2497 weighted avg 0.3686 0.4409 0.3991 2497 2023-10-19 19:56:47,193 ----------------------------------------------------------------------------------------------------