| 2023-10-25 21:24:56,312 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,313 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (word_embeddings): Embedding(64001, 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() | |
| )" | |
| 2023-10-25 21:24:56,313 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 MultiCorpus: 1085 train + 148 dev + 364 test sentences | |
| - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Train: 1085 sentences | |
| 2023-10-25 21:24:56,314 (train_with_dev=False, train_with_test=False) | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Training Params: | |
| 2023-10-25 21:24:56,314 - learning_rate: "3e-05" | |
| 2023-10-25 21:24:56,314 - mini_batch_size: "4" | |
| 2023-10-25 21:24:56,314 - max_epochs: "10" | |
| 2023-10-25 21:24:56,314 - shuffle: "True" | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Plugins: | |
| 2023-10-25 21:24:56,314 - TensorboardLogger | |
| 2023-10-25 21:24:56,314 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-25 21:24:56,314 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Computation: | |
| 2023-10-25 21:24:56,314 - compute on device: cuda:0 | |
| 2023-10-25 21:24:56,314 - embedding storage: none | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Model training base path: "hmbench-newseye/sv-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:24:56,314 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-25 21:24:57,790 epoch 1 - iter 27/272 - loss 2.95478253 - time (sec): 1.47 - samples/sec: 3722.53 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:24:59,293 epoch 1 - iter 54/272 - loss 2.36462168 - time (sec): 2.98 - samples/sec: 3644.52 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:25:00,680 epoch 1 - iter 81/272 - loss 1.87243562 - time (sec): 4.36 - samples/sec: 3545.80 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:25:02,180 epoch 1 - iter 108/272 - loss 1.49436340 - time (sec): 5.86 - samples/sec: 3560.63 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:25:03,621 epoch 1 - iter 135/272 - loss 1.28098397 - time (sec): 7.31 - samples/sec: 3486.44 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:25:05,100 epoch 1 - iter 162/272 - loss 1.10859938 - time (sec): 8.78 - samples/sec: 3539.33 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:25:06,640 epoch 1 - iter 189/272 - loss 0.99184857 - time (sec): 10.33 - samples/sec: 3494.32 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:25:08,116 epoch 1 - iter 216/272 - loss 0.89139646 - time (sec): 11.80 - samples/sec: 3510.77 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:25:09,621 epoch 1 - iter 243/272 - loss 0.82167559 - time (sec): 13.31 - samples/sec: 3471.31 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:25:11,185 epoch 1 - iter 270/272 - loss 0.76133078 - time (sec): 14.87 - samples/sec: 3480.77 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 21:25:11,284 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:11,285 EPOCH 1 done: loss 0.7584 - lr: 0.000030 | |
| 2023-10-25 21:25:12,457 DEV : loss 0.14451949298381805 - f1-score (micro avg) 0.6804 | |
| 2023-10-25 21:25:12,463 saving best model | |
| 2023-10-25 21:25:12,965 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:14,476 epoch 2 - iter 27/272 - loss 0.10550064 - time (sec): 1.51 - samples/sec: 3122.04 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 21:25:16,011 epoch 2 - iter 54/272 - loss 0.11014367 - time (sec): 3.04 - samples/sec: 3476.83 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:25:17,480 epoch 2 - iter 81/272 - loss 0.13176071 - time (sec): 4.51 - samples/sec: 3324.86 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:25:18,967 epoch 2 - iter 108/272 - loss 0.13399368 - time (sec): 6.00 - samples/sec: 3535.90 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:25:20,463 epoch 2 - iter 135/272 - loss 0.12917079 - time (sec): 7.50 - samples/sec: 3535.18 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:25:21,972 epoch 2 - iter 162/272 - loss 0.13057126 - time (sec): 9.01 - samples/sec: 3483.46 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:25:23,459 epoch 2 - iter 189/272 - loss 0.13256525 - time (sec): 10.49 - samples/sec: 3536.97 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:25:24,953 epoch 2 - iter 216/272 - loss 0.12890332 - time (sec): 11.99 - samples/sec: 3606.43 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:25:26,348 epoch 2 - iter 243/272 - loss 0.12876898 - time (sec): 13.38 - samples/sec: 3557.32 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:25:27,704 epoch 2 - iter 270/272 - loss 0.12992069 - time (sec): 14.74 - samples/sec: 3518.57 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:25:27,801 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:27,801 EPOCH 2 done: loss 0.1300 - lr: 0.000027 | |
| 2023-10-25 21:25:29,026 DEV : loss 0.10889776796102524 - f1-score (micro avg) 0.7868 | |
| 2023-10-25 21:25:29,033 saving best model | |
| 2023-10-25 21:25:29,757 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:31,157 epoch 3 - iter 27/272 - loss 0.09712327 - time (sec): 1.40 - samples/sec: 3564.88 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:25:32,529 epoch 3 - iter 54/272 - loss 0.08242375 - time (sec): 2.77 - samples/sec: 3892.24 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:25:33,921 epoch 3 - iter 81/272 - loss 0.07445624 - time (sec): 4.16 - samples/sec: 3744.19 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:25:35,305 epoch 3 - iter 108/272 - loss 0.07196821 - time (sec): 5.55 - samples/sec: 3795.36 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:25:36,802 epoch 3 - iter 135/272 - loss 0.07055271 - time (sec): 7.04 - samples/sec: 3797.39 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:25:38,252 epoch 3 - iter 162/272 - loss 0.06865725 - time (sec): 8.49 - samples/sec: 3704.75 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:25:39,756 epoch 3 - iter 189/272 - loss 0.07384271 - time (sec): 10.00 - samples/sec: 3626.70 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:25:41,233 epoch 3 - iter 216/272 - loss 0.07124307 - time (sec): 11.47 - samples/sec: 3613.06 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:25:42,692 epoch 3 - iter 243/272 - loss 0.07419483 - time (sec): 12.93 - samples/sec: 3580.42 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:25:44,265 epoch 3 - iter 270/272 - loss 0.07300144 - time (sec): 14.51 - samples/sec: 3570.19 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:25:44,377 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:44,377 EPOCH 3 done: loss 0.0727 - lr: 0.000023 | |
| 2023-10-25 21:25:45,552 DEV : loss 0.12385641783475876 - f1-score (micro avg) 0.789 | |
| 2023-10-25 21:25:45,558 saving best model | |
| 2023-10-25 21:25:46,276 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:25:47,852 epoch 4 - iter 27/272 - loss 0.03286519 - time (sec): 1.57 - samples/sec: 3279.89 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:25:49,219 epoch 4 - iter 54/272 - loss 0.04511852 - time (sec): 2.94 - samples/sec: 3410.45 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:25:50,715 epoch 4 - iter 81/272 - loss 0.04150325 - time (sec): 4.44 - samples/sec: 3644.86 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:25:52,114 epoch 4 - iter 108/272 - loss 0.04650275 - time (sec): 5.84 - samples/sec: 3609.30 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:25:53,527 epoch 4 - iter 135/272 - loss 0.04301268 - time (sec): 7.25 - samples/sec: 3632.60 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:25:54,928 epoch 4 - iter 162/272 - loss 0.04424032 - time (sec): 8.65 - samples/sec: 3684.89 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:25:56,365 epoch 4 - iter 189/272 - loss 0.04374274 - time (sec): 10.09 - samples/sec: 3666.06 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:25:57,803 epoch 4 - iter 216/272 - loss 0.04162559 - time (sec): 11.52 - samples/sec: 3659.98 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:25:59,204 epoch 4 - iter 243/272 - loss 0.04400279 - time (sec): 12.93 - samples/sec: 3652.24 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:26:00,599 epoch 4 - iter 270/272 - loss 0.04402940 - time (sec): 14.32 - samples/sec: 3618.64 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:26:00,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:00,698 EPOCH 4 done: loss 0.0439 - lr: 0.000020 | |
| 2023-10-25 21:26:01,832 DEV : loss 0.13214778900146484 - f1-score (micro avg) 0.8022 | |
| 2023-10-25 21:26:01,838 saving best model | |
| 2023-10-25 21:26:02,546 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:04,042 epoch 5 - iter 27/272 - loss 0.01630458 - time (sec): 1.49 - samples/sec: 3393.60 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:26:05,505 epoch 5 - iter 54/272 - loss 0.03437239 - time (sec): 2.96 - samples/sec: 3323.21 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:26:07,020 epoch 5 - iter 81/272 - loss 0.03191448 - time (sec): 4.47 - samples/sec: 3306.77 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:26:08,585 epoch 5 - iter 108/272 - loss 0.02979947 - time (sec): 6.04 - samples/sec: 3311.14 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:26:10,374 epoch 5 - iter 135/272 - loss 0.02958984 - time (sec): 7.82 - samples/sec: 3099.23 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:26:11,974 epoch 5 - iter 162/272 - loss 0.03062299 - time (sec): 9.42 - samples/sec: 3172.34 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:26:13,507 epoch 5 - iter 189/272 - loss 0.03100487 - time (sec): 10.96 - samples/sec: 3208.04 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:26:14,971 epoch 5 - iter 216/272 - loss 0.02954207 - time (sec): 12.42 - samples/sec: 3235.39 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:26:16,462 epoch 5 - iter 243/272 - loss 0.03056086 - time (sec): 13.91 - samples/sec: 3321.39 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:26:17,920 epoch 5 - iter 270/272 - loss 0.03180794 - time (sec): 15.37 - samples/sec: 3371.03 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:26:18,023 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:18,023 EPOCH 5 done: loss 0.0320 - lr: 0.000017 | |
| 2023-10-25 21:26:19,214 DEV : loss 0.1537138819694519 - f1-score (micro avg) 0.7964 | |
| 2023-10-25 21:26:19,220 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:20,792 epoch 6 - iter 27/272 - loss 0.02666730 - time (sec): 1.57 - samples/sec: 3471.94 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:26:22,333 epoch 6 - iter 54/272 - loss 0.01962865 - time (sec): 3.11 - samples/sec: 3446.68 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:26:23,857 epoch 6 - iter 81/272 - loss 0.01821303 - time (sec): 4.64 - samples/sec: 3337.98 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:26:25,433 epoch 6 - iter 108/272 - loss 0.02318386 - time (sec): 6.21 - samples/sec: 3341.21 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:26:26,946 epoch 6 - iter 135/272 - loss 0.02155185 - time (sec): 7.72 - samples/sec: 3320.13 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:26:28,462 epoch 6 - iter 162/272 - loss 0.02185104 - time (sec): 9.24 - samples/sec: 3366.02 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:26:29,970 epoch 6 - iter 189/272 - loss 0.02306003 - time (sec): 10.75 - samples/sec: 3410.78 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:26:31,474 epoch 6 - iter 216/272 - loss 0.02366588 - time (sec): 12.25 - samples/sec: 3337.30 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:26:32,976 epoch 6 - iter 243/272 - loss 0.02325498 - time (sec): 13.75 - samples/sec: 3382.56 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:26:34,454 epoch 6 - iter 270/272 - loss 0.02340385 - time (sec): 15.23 - samples/sec: 3385.88 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:26:34,566 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:34,566 EPOCH 6 done: loss 0.0232 - lr: 0.000013 | |
| 2023-10-25 21:26:35,844 DEV : loss 0.14856071770191193 - f1-score (micro avg) 0.8118 | |
| 2023-10-25 21:26:35,850 saving best model | |
| 2023-10-25 21:26:36,568 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:38,086 epoch 7 - iter 27/272 - loss 0.01152928 - time (sec): 1.51 - samples/sec: 3665.41 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:26:39,572 epoch 7 - iter 54/272 - loss 0.01071992 - time (sec): 3.00 - samples/sec: 3522.80 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:26:41,063 epoch 7 - iter 81/272 - loss 0.01059353 - time (sec): 4.49 - samples/sec: 3519.69 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:26:42,575 epoch 7 - iter 108/272 - loss 0.01206115 - time (sec): 6.00 - samples/sec: 3579.45 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:26:44,092 epoch 7 - iter 135/272 - loss 0.01324880 - time (sec): 7.52 - samples/sec: 3495.96 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:26:45,553 epoch 7 - iter 162/272 - loss 0.01362028 - time (sec): 8.98 - samples/sec: 3493.65 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:26:47,112 epoch 7 - iter 189/272 - loss 0.01636107 - time (sec): 10.54 - samples/sec: 3516.51 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:26:48,600 epoch 7 - iter 216/272 - loss 0.01678638 - time (sec): 12.03 - samples/sec: 3514.09 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:26:50,082 epoch 7 - iter 243/272 - loss 0.01800984 - time (sec): 13.51 - samples/sec: 3469.39 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:26:51,588 epoch 7 - iter 270/272 - loss 0.01706655 - time (sec): 15.02 - samples/sec: 3438.63 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:26:51,705 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:51,705 EPOCH 7 done: loss 0.0170 - lr: 0.000010 | |
| 2023-10-25 21:26:52,958 DEV : loss 0.15049101412296295 - f1-score (micro avg) 0.817 | |
| 2023-10-25 21:26:52,964 saving best model | |
| 2023-10-25 21:26:53,672 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:26:55,233 epoch 8 - iter 27/272 - loss 0.01955441 - time (sec): 1.56 - samples/sec: 3900.71 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:26:56,670 epoch 8 - iter 54/272 - loss 0.02251273 - time (sec): 3.00 - samples/sec: 3684.76 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:26:58,230 epoch 8 - iter 81/272 - loss 0.01870268 - time (sec): 4.56 - samples/sec: 3653.96 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:26:59,730 epoch 8 - iter 108/272 - loss 0.01802886 - time (sec): 6.06 - samples/sec: 3597.69 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:27:01,270 epoch 8 - iter 135/272 - loss 0.01675014 - time (sec): 7.60 - samples/sec: 3588.73 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:27:02,817 epoch 8 - iter 162/272 - loss 0.01636457 - time (sec): 9.14 - samples/sec: 3512.11 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:27:04,347 epoch 8 - iter 189/272 - loss 0.01653653 - time (sec): 10.67 - samples/sec: 3493.60 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:27:05,891 epoch 8 - iter 216/272 - loss 0.01494514 - time (sec): 12.22 - samples/sec: 3482.53 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:27:07,413 epoch 8 - iter 243/272 - loss 0.01358305 - time (sec): 13.74 - samples/sec: 3451.57 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:27:08,867 epoch 8 - iter 270/272 - loss 0.01344559 - time (sec): 15.19 - samples/sec: 3413.90 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:27:08,973 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:08,973 EPOCH 8 done: loss 0.0134 - lr: 0.000007 | |
| 2023-10-25 21:27:10,570 DEV : loss 0.16718466579914093 - f1-score (micro avg) 0.8244 | |
| 2023-10-25 21:27:10,576 saving best model | |
| 2023-10-25 21:27:11,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:12,843 epoch 9 - iter 27/272 - loss 0.00164870 - time (sec): 1.57 - samples/sec: 3747.02 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:27:14,311 epoch 9 - iter 54/272 - loss 0.00851133 - time (sec): 3.04 - samples/sec: 3601.08 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:27:15,778 epoch 9 - iter 81/272 - loss 0.00761523 - time (sec): 4.51 - samples/sec: 3449.84 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:27:17,240 epoch 9 - iter 108/272 - loss 0.00949545 - time (sec): 5.97 - samples/sec: 3405.25 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:27:18,722 epoch 9 - iter 135/272 - loss 0.00962367 - time (sec): 7.45 - samples/sec: 3446.74 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:27:20,186 epoch 9 - iter 162/272 - loss 0.00970501 - time (sec): 8.91 - samples/sec: 3481.51 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:27:21,676 epoch 9 - iter 189/272 - loss 0.00915997 - time (sec): 10.40 - samples/sec: 3534.96 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:27:23,133 epoch 9 - iter 216/272 - loss 0.00823915 - time (sec): 11.86 - samples/sec: 3493.39 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:27:24,602 epoch 9 - iter 243/272 - loss 0.00817118 - time (sec): 13.33 - samples/sec: 3507.63 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:27:26,104 epoch 9 - iter 270/272 - loss 0.01010203 - time (sec): 14.83 - samples/sec: 3495.59 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:27:26,198 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:26,199 EPOCH 9 done: loss 0.0101 - lr: 0.000003 | |
| 2023-10-25 21:27:27,425 DEV : loss 0.16382966935634613 - f1-score (micro avg) 0.8183 | |
| 2023-10-25 21:27:27,431 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:28,874 epoch 10 - iter 27/272 - loss 0.00996204 - time (sec): 1.44 - samples/sec: 3784.50 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:27:30,250 epoch 10 - iter 54/272 - loss 0.00822023 - time (sec): 2.82 - samples/sec: 3628.66 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:27:31,708 epoch 10 - iter 81/272 - loss 0.00718997 - time (sec): 4.28 - samples/sec: 3668.97 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:27:33,179 epoch 10 - iter 108/272 - loss 0.00636107 - time (sec): 5.75 - samples/sec: 3707.51 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:27:34,708 epoch 10 - iter 135/272 - loss 0.00714120 - time (sec): 7.28 - samples/sec: 3587.09 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:27:36,272 epoch 10 - iter 162/272 - loss 0.00626853 - time (sec): 8.84 - samples/sec: 3523.31 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:27:37,846 epoch 10 - iter 189/272 - loss 0.00615876 - time (sec): 10.41 - samples/sec: 3525.63 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:27:39,413 epoch 10 - iter 216/272 - loss 0.00562173 - time (sec): 11.98 - samples/sec: 3477.07 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:27:40,880 epoch 10 - iter 243/272 - loss 0.00638001 - time (sec): 13.45 - samples/sec: 3458.88 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 21:27:42,267 epoch 10 - iter 270/272 - loss 0.00607942 - time (sec): 14.83 - samples/sec: 3488.34 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 21:27:42,364 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:42,364 EPOCH 10 done: loss 0.0061 - lr: 0.000000 | |
| 2023-10-25 21:27:43,597 DEV : loss 0.16699165105819702 - f1-score (micro avg) 0.8281 | |
| 2023-10-25 21:27:43,604 saving best model | |
| 2023-10-25 21:27:44,815 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:27:44,817 Loading model from best epoch ... | |
| 2023-10-25 21:27:46,707 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG | |
| 2023-10-25 21:27:48,940 | |
| Results: | |
| - F-score (micro) 0.7769 | |
| - F-score (macro) 0.7273 | |
| - Accuracy 0.6538 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.8173 0.8462 0.8315 312 | |
| PER 0.6923 0.8654 0.7692 208 | |
| ORG 0.4643 0.4727 0.4685 55 | |
| HumanProd 0.7500 0.9545 0.8400 22 | |
| micro avg 0.7361 0.8224 0.7769 597 | |
| macro avg 0.6810 0.7847 0.7273 597 | |
| weighted avg 0.7388 0.8224 0.7767 597 | |
| 2023-10-25 21:27:48,940 ---------------------------------------------------------------------------------------------------- | |