2023-10-18 23:11:51,022 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 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=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-18 23:11:51,023 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 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 2023-10-18 23:11:51,023 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 Train: 5777 sentences 2023-10-18 23:11:51,023 (train_with_dev=False, train_with_test=False) 2023-10-18 23:11:51,023 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 Training Params: 2023-10-18 23:11:51,023 - learning_rate: "3e-05" 2023-10-18 23:11:51,023 - mini_batch_size: "8" 2023-10-18 23:11:51,023 - max_epochs: "10" 2023-10-18 23:11:51,023 - shuffle: "True" 2023-10-18 23:11:51,023 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 Plugins: 2023-10-18 23:11:51,023 - TensorboardLogger 2023-10-18 23:11:51,023 - LinearScheduler | warmup_fraction: '0.1' 2023-10-18 23:11:51,023 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,023 Final evaluation on model from best epoch (best-model.pt) 2023-10-18 23:11:51,024 - metric: "('micro avg', 'f1-score')" 2023-10-18 23:11:51,024 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,024 Computation: 2023-10-18 23:11:51,024 - compute on device: cuda:0 2023-10-18 23:11:51,024 - embedding storage: none 2023-10-18 23:11:51,024 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,024 Model training base path: "hmbench-icdar/nl-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-18 23:11:51,024 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,024 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:11:51,024 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-18 23:11:52,810 epoch 1 - iter 72/723 - loss 2.44216644 - time (sec): 1.79 - samples/sec: 9891.33 - lr: 0.000003 - momentum: 0.000000 2023-10-18 23:11:54,513 epoch 1 - iter 144/723 - loss 2.30997642 - time (sec): 3.49 - samples/sec: 9666.55 - lr: 0.000006 - momentum: 0.000000 2023-10-18 23:11:56,360 epoch 1 - iter 216/723 - loss 2.05393390 - time (sec): 5.34 - samples/sec: 9829.87 - lr: 0.000009 - momentum: 0.000000 2023-10-18 23:11:58,201 epoch 1 - iter 288/723 - loss 1.77045723 - time (sec): 7.18 - samples/sec: 9840.23 - lr: 0.000012 - momentum: 0.000000 2023-10-18 23:12:00,137 epoch 1 - iter 360/723 - loss 1.52008804 - time (sec): 9.11 - samples/sec: 9790.22 - lr: 0.000015 - momentum: 0.000000 2023-10-18 23:12:01,901 epoch 1 - iter 432/723 - loss 1.35013626 - time (sec): 10.88 - samples/sec: 9666.70 - lr: 0.000018 - momentum: 0.000000 2023-10-18 23:12:03,716 epoch 1 - iter 504/723 - loss 1.20226231 - time (sec): 12.69 - samples/sec: 9703.98 - lr: 0.000021 - momentum: 0.000000 2023-10-18 23:12:05,525 epoch 1 - iter 576/723 - loss 1.08283856 - time (sec): 14.50 - samples/sec: 9753.81 - lr: 0.000024 - momentum: 0.000000 2023-10-18 23:12:07,307 epoch 1 - iter 648/723 - loss 0.99626271 - time (sec): 16.28 - samples/sec: 9763.13 - lr: 0.000027 - momentum: 0.000000 2023-10-18 23:12:09,067 epoch 1 - iter 720/723 - loss 0.93060332 - time (sec): 18.04 - samples/sec: 9729.50 - lr: 0.000030 - momentum: 0.000000 2023-10-18 23:12:09,137 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:09,137 EPOCH 1 done: loss 0.9281 - lr: 0.000030 2023-10-18 23:12:10,425 DEV : loss 0.3564930558204651 - f1-score (micro avg) 0.0 2023-10-18 23:12:10,439 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:12,188 epoch 2 - iter 72/723 - loss 0.30663308 - time (sec): 1.75 - samples/sec: 9893.05 - lr: 0.000030 - momentum: 0.000000 2023-10-18 23:12:13,958 epoch 2 - iter 144/723 - loss 0.28240631 - time (sec): 3.52 - samples/sec: 10127.51 - lr: 0.000029 - momentum: 0.000000 2023-10-18 23:12:15,633 epoch 2 - iter 216/723 - loss 0.27645540 - time (sec): 5.19 - samples/sec: 9965.04 - lr: 0.000029 - momentum: 0.000000 2023-10-18 23:12:17,358 epoch 2 - iter 288/723 - loss 0.25965837 - time (sec): 6.92 - samples/sec: 10137.11 - lr: 0.000029 - momentum: 0.000000 2023-10-18 23:12:19,033 epoch 2 - iter 360/723 - loss 0.25438816 - time (sec): 8.59 - samples/sec: 10112.60 - lr: 0.000028 - momentum: 0.000000 2023-10-18 23:12:20,694 epoch 2 - iter 432/723 - loss 0.24919098 - time (sec): 10.26 - samples/sec: 10093.78 - lr: 0.000028 - momentum: 0.000000 2023-10-18 23:12:22,452 epoch 2 - iter 504/723 - loss 0.24538984 - time (sec): 12.01 - samples/sec: 10092.18 - lr: 0.000028 - momentum: 0.000000 2023-10-18 23:12:24,250 epoch 2 - iter 576/723 - loss 0.24291302 - time (sec): 13.81 - samples/sec: 10182.61 - lr: 0.000027 - momentum: 0.000000 2023-10-18 23:12:26,001 epoch 2 - iter 648/723 - loss 0.23897319 - time (sec): 15.56 - samples/sec: 10175.96 - lr: 0.000027 - momentum: 0.000000 2023-10-18 23:12:27,797 epoch 2 - iter 720/723 - loss 0.23500077 - time (sec): 17.36 - samples/sec: 10117.24 - lr: 0.000027 - momentum: 0.000000 2023-10-18 23:12:27,861 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:27,861 EPOCH 2 done: loss 0.2347 - lr: 0.000027 2023-10-18 23:12:29,600 DEV : loss 0.25880512595176697 - f1-score (micro avg) 0.0802 2023-10-18 23:12:29,616 saving best model 2023-10-18 23:12:29,648 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:31,747 epoch 3 - iter 72/723 - loss 0.19921052 - time (sec): 2.10 - samples/sec: 8841.74 - lr: 0.000026 - momentum: 0.000000 2023-10-18 23:12:33,512 epoch 3 - iter 144/723 - loss 0.20066322 - time (sec): 3.86 - samples/sec: 9294.18 - lr: 0.000026 - momentum: 0.000000 2023-10-18 23:12:35,351 epoch 3 - iter 216/723 - loss 0.19999346 - time (sec): 5.70 - samples/sec: 9508.30 - lr: 0.000026 - momentum: 0.000000 2023-10-18 23:12:37,134 epoch 3 - iter 288/723 - loss 0.19507221 - time (sec): 7.49 - samples/sec: 9594.94 - lr: 0.000025 - momentum: 0.000000 2023-10-18 23:12:38,901 epoch 3 - iter 360/723 - loss 0.19575824 - time (sec): 9.25 - samples/sec: 9552.28 - lr: 0.000025 - momentum: 0.000000 2023-10-18 23:12:40,666 epoch 3 - iter 432/723 - loss 0.19871723 - time (sec): 11.02 - samples/sec: 9595.97 - lr: 0.000025 - momentum: 0.000000 2023-10-18 23:12:42,391 epoch 3 - iter 504/723 - loss 0.19742757 - time (sec): 12.74 - samples/sec: 9601.55 - lr: 0.000024 - momentum: 0.000000 2023-10-18 23:12:44,209 epoch 3 - iter 576/723 - loss 0.19808476 - time (sec): 14.56 - samples/sec: 9670.87 - lr: 0.000024 - momentum: 0.000000 2023-10-18 23:12:45,925 epoch 3 - iter 648/723 - loss 0.19493970 - time (sec): 16.28 - samples/sec: 9717.32 - lr: 0.000024 - momentum: 0.000000 2023-10-18 23:12:47,666 epoch 3 - iter 720/723 - loss 0.19510831 - time (sec): 18.02 - samples/sec: 9751.67 - lr: 0.000023 - momentum: 0.000000 2023-10-18 23:12:47,734 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:47,734 EPOCH 3 done: loss 0.1950 - lr: 0.000023 2023-10-18 23:12:49,485 DEV : loss 0.21803173422813416 - f1-score (micro avg) 0.3538 2023-10-18 23:12:49,499 saving best model 2023-10-18 23:12:49,535 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:12:51,301 epoch 4 - iter 72/723 - loss 0.22975421 - time (sec): 1.77 - samples/sec: 9675.63 - lr: 0.000023 - momentum: 0.000000 2023-10-18 23:12:53,064 epoch 4 - iter 144/723 - loss 0.18735987 - time (sec): 3.53 - samples/sec: 9833.21 - lr: 0.000023 - momentum: 0.000000 2023-10-18 23:12:54,777 epoch 4 - iter 216/723 - loss 0.19159436 - time (sec): 5.24 - samples/sec: 9779.61 - lr: 0.000022 - momentum: 0.000000 2023-10-18 23:12:56,585 epoch 4 - iter 288/723 - loss 0.18563505 - time (sec): 7.05 - samples/sec: 9911.36 - lr: 0.000022 - momentum: 0.000000 2023-10-18 23:12:58,399 epoch 4 - iter 360/723 - loss 0.18791900 - time (sec): 8.86 - samples/sec: 10005.62 - lr: 0.000022 - momentum: 0.000000 2023-10-18 23:13:00,168 epoch 4 - iter 432/723 - loss 0.18324138 - time (sec): 10.63 - samples/sec: 10088.63 - lr: 0.000021 - momentum: 0.000000 2023-10-18 23:13:01,926 epoch 4 - iter 504/723 - loss 0.18133811 - time (sec): 12.39 - samples/sec: 9995.66 - lr: 0.000021 - momentum: 0.000000 2023-10-18 23:13:03,688 epoch 4 - iter 576/723 - loss 0.18072363 - time (sec): 14.15 - samples/sec: 9985.13 - lr: 0.000021 - momentum: 0.000000 2023-10-18 23:13:05,512 epoch 4 - iter 648/723 - loss 0.18091767 - time (sec): 15.98 - samples/sec: 9975.68 - lr: 0.000020 - momentum: 0.000000 2023-10-18 23:13:07,229 epoch 4 - iter 720/723 - loss 0.17928566 - time (sec): 17.69 - samples/sec: 9935.66 - lr: 0.000020 - momentum: 0.000000 2023-10-18 23:13:07,292 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:07,292 EPOCH 4 done: loss 0.1792 - lr: 0.000020 2023-10-18 23:13:09,386 DEV : loss 0.20422407984733582 - f1-score (micro avg) 0.4014 2023-10-18 23:13:09,401 saving best model 2023-10-18 23:13:09,437 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:11,075 epoch 5 - iter 72/723 - loss 0.19248413 - time (sec): 1.64 - samples/sec: 10269.81 - lr: 0.000020 - momentum: 0.000000 2023-10-18 23:13:12,842 epoch 5 - iter 144/723 - loss 0.18350762 - time (sec): 3.41 - samples/sec: 9895.90 - lr: 0.000019 - momentum: 0.000000 2023-10-18 23:13:14,578 epoch 5 - iter 216/723 - loss 0.18053100 - time (sec): 5.14 - samples/sec: 9758.42 - lr: 0.000019 - momentum: 0.000000 2023-10-18 23:13:16,356 epoch 5 - iter 288/723 - loss 0.17488340 - time (sec): 6.92 - samples/sec: 9775.57 - lr: 0.000019 - momentum: 0.000000 2023-10-18 23:13:18,203 epoch 5 - iter 360/723 - loss 0.17643351 - time (sec): 8.77 - samples/sec: 9912.19 - lr: 0.000018 - momentum: 0.000000 2023-10-18 23:13:19,986 epoch 5 - iter 432/723 - loss 0.17288927 - time (sec): 10.55 - samples/sec: 9961.54 - lr: 0.000018 - momentum: 0.000000 2023-10-18 23:13:21,746 epoch 5 - iter 504/723 - loss 0.16875420 - time (sec): 12.31 - samples/sec: 9934.45 - lr: 0.000018 - momentum: 0.000000 2023-10-18 23:13:23,522 epoch 5 - iter 576/723 - loss 0.16965003 - time (sec): 14.08 - samples/sec: 9897.96 - lr: 0.000017 - momentum: 0.000000 2023-10-18 23:13:25,296 epoch 5 - iter 648/723 - loss 0.17161084 - time (sec): 15.86 - samples/sec: 9880.14 - lr: 0.000017 - momentum: 0.000000 2023-10-18 23:13:27,162 epoch 5 - iter 720/723 - loss 0.16754034 - time (sec): 17.73 - samples/sec: 9912.87 - lr: 0.000017 - momentum: 0.000000 2023-10-18 23:13:27,227 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:27,227 EPOCH 5 done: loss 0.1679 - lr: 0.000017 2023-10-18 23:13:29,008 DEV : loss 0.20114754140377045 - f1-score (micro avg) 0.4134 2023-10-18 23:13:29,022 saving best model 2023-10-18 23:13:29,058 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:30,756 epoch 6 - iter 72/723 - loss 0.14807821 - time (sec): 1.70 - samples/sec: 10519.59 - lr: 0.000016 - momentum: 0.000000 2023-10-18 23:13:32,245 epoch 6 - iter 144/723 - loss 0.15415731 - time (sec): 3.19 - samples/sec: 11334.87 - lr: 0.000016 - momentum: 0.000000 2023-10-18 23:13:33,795 epoch 6 - iter 216/723 - loss 0.15522735 - time (sec): 4.74 - samples/sec: 11531.86 - lr: 0.000016 - momentum: 0.000000 2023-10-18 23:13:35,377 epoch 6 - iter 288/723 - loss 0.15473174 - time (sec): 6.32 - samples/sec: 11383.57 - lr: 0.000015 - momentum: 0.000000 2023-10-18 23:13:37,089 epoch 6 - iter 360/723 - loss 0.15550787 - time (sec): 8.03 - samples/sec: 11119.06 - lr: 0.000015 - momentum: 0.000000 2023-10-18 23:13:38,921 epoch 6 - iter 432/723 - loss 0.15664718 - time (sec): 9.86 - samples/sec: 10919.44 - lr: 0.000015 - momentum: 0.000000 2023-10-18 23:13:40,663 epoch 6 - iter 504/723 - loss 0.16128146 - time (sec): 11.60 - samples/sec: 10784.98 - lr: 0.000014 - momentum: 0.000000 2023-10-18 23:13:42,110 epoch 6 - iter 576/723 - loss 0.16186203 - time (sec): 13.05 - samples/sec: 10836.50 - lr: 0.000014 - momentum: 0.000000 2023-10-18 23:13:44,253 epoch 6 - iter 648/723 - loss 0.15979228 - time (sec): 15.19 - samples/sec: 10419.81 - lr: 0.000014 - momentum: 0.000000 2023-10-18 23:13:45,929 epoch 6 - iter 720/723 - loss 0.15793335 - time (sec): 16.87 - samples/sec: 10410.89 - lr: 0.000013 - momentum: 0.000000 2023-10-18 23:13:45,995 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:45,995 EPOCH 6 done: loss 0.1579 - lr: 0.000013 2023-10-18 23:13:47,779 DEV : loss 0.2004448026418686 - f1-score (micro avg) 0.416 2023-10-18 23:13:47,795 saving best model 2023-10-18 23:13:47,834 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:13:49,636 epoch 7 - iter 72/723 - loss 0.13990666 - time (sec): 1.80 - samples/sec: 9771.45 - lr: 0.000013 - momentum: 0.000000 2023-10-18 23:13:51,369 epoch 7 - iter 144/723 - loss 0.14735534 - time (sec): 3.53 - samples/sec: 9523.67 - lr: 0.000013 - momentum: 0.000000 2023-10-18 23:13:53,172 epoch 7 - iter 216/723 - loss 0.14661026 - time (sec): 5.34 - samples/sec: 9781.19 - lr: 0.000012 - momentum: 0.000000 2023-10-18 23:13:54,922 epoch 7 - iter 288/723 - loss 0.14674013 - time (sec): 7.09 - samples/sec: 9725.99 - lr: 0.000012 - momentum: 0.000000 2023-10-18 23:13:56,722 epoch 7 - iter 360/723 - loss 0.14930807 - time (sec): 8.89 - samples/sec: 9778.01 - lr: 0.000012 - momentum: 0.000000 2023-10-18 23:13:58,314 epoch 7 - iter 432/723 - loss 0.15173579 - time (sec): 10.48 - samples/sec: 10017.22 - lr: 0.000011 - momentum: 0.000000 2023-10-18 23:13:59,917 epoch 7 - iter 504/723 - loss 0.15401105 - time (sec): 12.08 - samples/sec: 10112.71 - lr: 0.000011 - momentum: 0.000000 2023-10-18 23:14:01,793 epoch 7 - iter 576/723 - loss 0.15380009 - time (sec): 13.96 - samples/sec: 10127.35 - lr: 0.000011 - momentum: 0.000000 2023-10-18 23:14:03,567 epoch 7 - iter 648/723 - loss 0.15502582 - time (sec): 15.73 - samples/sec: 10055.51 - lr: 0.000010 - momentum: 0.000000 2023-10-18 23:14:05,325 epoch 7 - iter 720/723 - loss 0.15355652 - time (sec): 17.49 - samples/sec: 10050.03 - lr: 0.000010 - momentum: 0.000000 2023-10-18 23:14:05,386 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:05,387 EPOCH 7 done: loss 0.1535 - lr: 0.000010 2023-10-18 23:14:07,198 DEV : loss 0.1892377883195877 - f1-score (micro avg) 0.4715 2023-10-18 23:14:07,213 saving best model 2023-10-18 23:14:07,246 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:09,021 epoch 8 - iter 72/723 - loss 0.14918626 - time (sec): 1.78 - samples/sec: 10048.93 - lr: 0.000010 - momentum: 0.000000 2023-10-18 23:14:10,805 epoch 8 - iter 144/723 - loss 0.14341743 - time (sec): 3.56 - samples/sec: 9753.10 - lr: 0.000009 - momentum: 0.000000 2023-10-18 23:14:12,659 epoch 8 - iter 216/723 - loss 0.14638127 - time (sec): 5.41 - samples/sec: 9806.40 - lr: 0.000009 - momentum: 0.000000 2023-10-18 23:14:14,509 epoch 8 - iter 288/723 - loss 0.14570047 - time (sec): 7.26 - samples/sec: 9830.45 - lr: 0.000009 - momentum: 0.000000 2023-10-18 23:14:16,626 epoch 8 - iter 360/723 - loss 0.14588077 - time (sec): 9.38 - samples/sec: 9468.27 - lr: 0.000008 - momentum: 0.000000 2023-10-18 23:14:18,438 epoch 8 - iter 432/723 - loss 0.14940172 - time (sec): 11.19 - samples/sec: 9520.91 - lr: 0.000008 - momentum: 0.000000 2023-10-18 23:14:20,233 epoch 8 - iter 504/723 - loss 0.14995954 - time (sec): 12.99 - samples/sec: 9625.72 - lr: 0.000008 - momentum: 0.000000 2023-10-18 23:14:21,993 epoch 8 - iter 576/723 - loss 0.14830811 - time (sec): 14.75 - samples/sec: 9612.22 - lr: 0.000007 - momentum: 0.000000 2023-10-18 23:14:23,804 epoch 8 - iter 648/723 - loss 0.14882827 - time (sec): 16.56 - samples/sec: 9598.29 - lr: 0.000007 - momentum: 0.000000 2023-10-18 23:14:25,585 epoch 8 - iter 720/723 - loss 0.14797825 - time (sec): 18.34 - samples/sec: 9578.73 - lr: 0.000007 - momentum: 0.000000 2023-10-18 23:14:25,657 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:25,658 EPOCH 8 done: loss 0.1479 - lr: 0.000007 2023-10-18 23:14:27,424 DEV : loss 0.19318030774593353 - f1-score (micro avg) 0.4586 2023-10-18 23:14:27,439 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:29,358 epoch 9 - iter 72/723 - loss 0.13183658 - time (sec): 1.92 - samples/sec: 9729.82 - lr: 0.000006 - momentum: 0.000000 2023-10-18 23:14:31,178 epoch 9 - iter 144/723 - loss 0.14774974 - time (sec): 3.74 - samples/sec: 9894.94 - lr: 0.000006 - momentum: 0.000000 2023-10-18 23:14:32,918 epoch 9 - iter 216/723 - loss 0.14096225 - time (sec): 5.48 - samples/sec: 10029.57 - lr: 0.000006 - momentum: 0.000000 2023-10-18 23:14:34,783 epoch 9 - iter 288/723 - loss 0.14464135 - time (sec): 7.34 - samples/sec: 9979.83 - lr: 0.000005 - momentum: 0.000000 2023-10-18 23:14:36,533 epoch 9 - iter 360/723 - loss 0.14935950 - time (sec): 9.09 - samples/sec: 9842.57 - lr: 0.000005 - momentum: 0.000000 2023-10-18 23:14:38,298 epoch 9 - iter 432/723 - loss 0.14704814 - time (sec): 10.86 - samples/sec: 9811.71 - lr: 0.000005 - momentum: 0.000000 2023-10-18 23:14:40,074 epoch 9 - iter 504/723 - loss 0.14643788 - time (sec): 12.63 - samples/sec: 9786.04 - lr: 0.000004 - momentum: 0.000000 2023-10-18 23:14:41,871 epoch 9 - iter 576/723 - loss 0.14449966 - time (sec): 14.43 - samples/sec: 9853.99 - lr: 0.000004 - momentum: 0.000000 2023-10-18 23:14:43,694 epoch 9 - iter 648/723 - loss 0.14535218 - time (sec): 16.26 - samples/sec: 9804.97 - lr: 0.000004 - momentum: 0.000000 2023-10-18 23:14:45,473 epoch 9 - iter 720/723 - loss 0.14713910 - time (sec): 18.03 - samples/sec: 9748.64 - lr: 0.000003 - momentum: 0.000000 2023-10-18 23:14:45,537 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:45,537 EPOCH 9 done: loss 0.1474 - lr: 0.000003 2023-10-18 23:14:47,299 DEV : loss 0.18770474195480347 - f1-score (micro avg) 0.471 2023-10-18 23:14:47,313 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:14:49,152 epoch 10 - iter 72/723 - loss 0.15104826 - time (sec): 1.84 - samples/sec: 9691.05 - lr: 0.000003 - momentum: 0.000000 2023-10-18 23:14:50,921 epoch 10 - iter 144/723 - loss 0.14308456 - time (sec): 3.61 - samples/sec: 9833.81 - lr: 0.000003 - momentum: 0.000000 2023-10-18 23:14:52,704 epoch 10 - iter 216/723 - loss 0.14243977 - time (sec): 5.39 - samples/sec: 9795.49 - lr: 0.000002 - momentum: 0.000000 2023-10-18 23:14:54,769 epoch 10 - iter 288/723 - loss 0.14355974 - time (sec): 7.45 - samples/sec: 9299.45 - lr: 0.000002 - momentum: 0.000000 2023-10-18 23:14:56,644 epoch 10 - iter 360/723 - loss 0.15340840 - time (sec): 9.33 - samples/sec: 9434.86 - lr: 0.000002 - momentum: 0.000000 2023-10-18 23:14:58,530 epoch 10 - iter 432/723 - loss 0.14997278 - time (sec): 11.22 - samples/sec: 9508.69 - lr: 0.000001 - momentum: 0.000000 2023-10-18 23:15:00,395 epoch 10 - iter 504/723 - loss 0.15043498 - time (sec): 13.08 - samples/sec: 9565.34 - lr: 0.000001 - momentum: 0.000000 2023-10-18 23:15:02,186 epoch 10 - iter 576/723 - loss 0.15019473 - time (sec): 14.87 - samples/sec: 9470.10 - lr: 0.000001 - momentum: 0.000000 2023-10-18 23:15:03,925 epoch 10 - iter 648/723 - loss 0.14807576 - time (sec): 16.61 - samples/sec: 9514.24 - lr: 0.000000 - momentum: 0.000000 2023-10-18 23:15:05,690 epoch 10 - iter 720/723 - loss 0.14659442 - time (sec): 18.38 - samples/sec: 9565.63 - lr: 0.000000 - momentum: 0.000000 2023-10-18 23:15:05,744 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:15:05,744 EPOCH 10 done: loss 0.1466 - lr: 0.000000 2023-10-18 23:15:07,520 DEV : loss 0.1885942816734314 - f1-score (micro avg) 0.4686 2023-10-18 23:15:07,565 ---------------------------------------------------------------------------------------------------- 2023-10-18 23:15:07,565 Loading model from best epoch ... 2023-10-18 23:15:07,651 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 2023-10-18 23:15:08,989 Results: - F-score (micro) 0.4786 - F-score (macro) 0.3307 - Accuracy 0.3244 By class: precision recall f1-score support LOC 0.5887 0.5218 0.5532 458 PER 0.5595 0.3610 0.4388 482 ORG 0.0000 0.0000 0.0000 69 micro avg 0.5760 0.4093 0.4786 1009 macro avg 0.3827 0.2943 0.3307 1009 weighted avg 0.5345 0.4093 0.4608 1009 2023-10-18 23:15:08,989 ----------------------------------------------------------------------------------------------------