| 2023-10-16 13:43:46,897 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,898 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() | |
| )" | |
| 2023-10-16 13:43:46,898 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,898 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 | |
| 2023-10-16 13:43:46,898 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,898 Train: 7142 sentences | |
| 2023-10-16 13:43:46,898 (train_with_dev=False, train_with_test=False) | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 Training Params: | |
| 2023-10-16 13:43:46,899 - learning_rate: "3e-05" | |
| 2023-10-16 13:43:46,899 - mini_batch_size: "8" | |
| 2023-10-16 13:43:46,899 - max_epochs: "10" | |
| 2023-10-16 13:43:46,899 - shuffle: "True" | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 Plugins: | |
| 2023-10-16 13:43:46,899 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-16 13:43:46,899 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 Computation: | |
| 2023-10-16 13:43:46,899 - compute on device: cuda:0 | |
| 2023-10-16 13:43:46,899 - embedding storage: none | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 Model training base path: "hmbench-newseye/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:46,899 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:43:53,652 epoch 1 - iter 89/893 - loss 2.49154230 - time (sec): 6.75 - samples/sec: 3658.17 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 13:44:00,646 epoch 1 - iter 178/893 - loss 1.62126567 - time (sec): 13.75 - samples/sec: 3614.22 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 13:44:07,488 epoch 1 - iter 267/893 - loss 1.22582402 - time (sec): 20.59 - samples/sec: 3653.03 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 13:44:14,523 epoch 1 - iter 356/893 - loss 0.99528059 - time (sec): 27.62 - samples/sec: 3661.62 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 13:44:21,361 epoch 1 - iter 445/893 - loss 0.85563832 - time (sec): 34.46 - samples/sec: 3639.09 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 13:44:27,857 epoch 1 - iter 534/893 - loss 0.75454568 - time (sec): 40.96 - samples/sec: 3635.00 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 13:44:34,891 epoch 1 - iter 623/893 - loss 0.67109967 - time (sec): 47.99 - samples/sec: 3647.33 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 13:44:41,532 epoch 1 - iter 712/893 - loss 0.61218176 - time (sec): 54.63 - samples/sec: 3661.46 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 13:44:48,148 epoch 1 - iter 801/893 - loss 0.56314889 - time (sec): 61.25 - samples/sec: 3652.60 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 13:44:55,012 epoch 1 - iter 890/893 - loss 0.52200864 - time (sec): 68.11 - samples/sec: 3640.49 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 13:44:55,226 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:44:55,226 EPOCH 1 done: loss 0.5208 - lr: 0.000030 | |
| 2023-10-16 13:44:58,272 DEV : loss 0.12075067311525345 - f1-score (micro avg) 0.7066 | |
| 2023-10-16 13:44:58,288 saving best model | |
| 2023-10-16 13:44:58,731 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:45:05,320 epoch 2 - iter 89/893 - loss 0.13241194 - time (sec): 6.59 - samples/sec: 3790.57 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 13:45:12,294 epoch 2 - iter 178/893 - loss 0.12021519 - time (sec): 13.56 - samples/sec: 3795.99 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 13:45:19,078 epoch 2 - iter 267/893 - loss 0.11750837 - time (sec): 20.35 - samples/sec: 3792.31 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 13:45:25,904 epoch 2 - iter 356/893 - loss 0.11841414 - time (sec): 27.17 - samples/sec: 3728.66 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 13:45:32,684 epoch 2 - iter 445/893 - loss 0.11544678 - time (sec): 33.95 - samples/sec: 3718.33 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 13:45:39,485 epoch 2 - iter 534/893 - loss 0.11398255 - time (sec): 40.75 - samples/sec: 3711.64 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 13:45:46,276 epoch 2 - iter 623/893 - loss 0.11078897 - time (sec): 47.54 - samples/sec: 3690.56 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 13:45:52,795 epoch 2 - iter 712/893 - loss 0.10867892 - time (sec): 54.06 - samples/sec: 3704.94 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 13:45:59,583 epoch 2 - iter 801/893 - loss 0.10557874 - time (sec): 60.85 - samples/sec: 3706.50 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 13:46:05,978 epoch 2 - iter 890/893 - loss 0.10564723 - time (sec): 67.25 - samples/sec: 3683.34 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 13:46:06,201 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:46:06,201 EPOCH 2 done: loss 0.1055 - lr: 0.000027 | |
| 2023-10-16 13:46:10,199 DEV : loss 0.11806550621986389 - f1-score (micro avg) 0.7094 | |
| 2023-10-16 13:46:10,215 saving best model | |
| 2023-10-16 13:46:10,792 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:46:17,508 epoch 3 - iter 89/893 - loss 0.06756403 - time (sec): 6.71 - samples/sec: 3546.43 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 13:46:24,311 epoch 3 - iter 178/893 - loss 0.06905887 - time (sec): 13.52 - samples/sec: 3559.14 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 13:46:31,239 epoch 3 - iter 267/893 - loss 0.06890371 - time (sec): 20.44 - samples/sec: 3556.52 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 13:46:38,289 epoch 3 - iter 356/893 - loss 0.06587791 - time (sec): 27.49 - samples/sec: 3546.44 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 13:46:45,243 epoch 3 - iter 445/893 - loss 0.06381783 - time (sec): 34.45 - samples/sec: 3560.17 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 13:46:51,806 epoch 3 - iter 534/893 - loss 0.06384950 - time (sec): 41.01 - samples/sec: 3583.39 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 13:46:59,175 epoch 3 - iter 623/893 - loss 0.06292702 - time (sec): 48.38 - samples/sec: 3566.95 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 13:47:06,251 epoch 3 - iter 712/893 - loss 0.06408425 - time (sec): 55.45 - samples/sec: 3577.80 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 13:47:13,444 epoch 3 - iter 801/893 - loss 0.06538790 - time (sec): 62.65 - samples/sec: 3554.54 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 13:47:20,279 epoch 3 - iter 890/893 - loss 0.06456476 - time (sec): 69.48 - samples/sec: 3566.45 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 13:47:20,513 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:47:20,514 EPOCH 3 done: loss 0.0644 - lr: 0.000023 | |
| 2023-10-16 13:47:24,535 DEV : loss 0.11452696472406387 - f1-score (micro avg) 0.7827 | |
| 2023-10-16 13:47:24,551 saving best model | |
| 2023-10-16 13:47:25,042 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:47:32,102 epoch 4 - iter 89/893 - loss 0.04287257 - time (sec): 7.06 - samples/sec: 3684.25 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 13:47:39,186 epoch 4 - iter 178/893 - loss 0.04063102 - time (sec): 14.14 - samples/sec: 3584.36 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 13:47:45,973 epoch 4 - iter 267/893 - loss 0.04147794 - time (sec): 20.93 - samples/sec: 3584.08 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 13:47:53,237 epoch 4 - iter 356/893 - loss 0.04278598 - time (sec): 28.19 - samples/sec: 3559.01 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 13:47:59,557 epoch 4 - iter 445/893 - loss 0.04283638 - time (sec): 34.51 - samples/sec: 3604.20 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 13:48:06,337 epoch 4 - iter 534/893 - loss 0.04347932 - time (sec): 41.29 - samples/sec: 3619.66 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 13:48:12,846 epoch 4 - iter 623/893 - loss 0.04461082 - time (sec): 47.80 - samples/sec: 3609.56 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 13:48:19,452 epoch 4 - iter 712/893 - loss 0.04492176 - time (sec): 54.41 - samples/sec: 3624.66 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 13:48:26,362 epoch 4 - iter 801/893 - loss 0.04597372 - time (sec): 61.32 - samples/sec: 3619.82 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 13:48:33,542 epoch 4 - iter 890/893 - loss 0.04676504 - time (sec): 68.50 - samples/sec: 3619.95 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 13:48:33,772 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:48:33,772 EPOCH 4 done: loss 0.0467 - lr: 0.000020 | |
| 2023-10-16 13:48:38,330 DEV : loss 0.1235646903514862 - f1-score (micro avg) 0.7821 | |
| 2023-10-16 13:48:38,345 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:48:45,005 epoch 5 - iter 89/893 - loss 0.02387243 - time (sec): 6.66 - samples/sec: 3482.39 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 13:48:52,056 epoch 5 - iter 178/893 - loss 0.03032860 - time (sec): 13.71 - samples/sec: 3679.13 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 13:48:58,977 epoch 5 - iter 267/893 - loss 0.03259763 - time (sec): 20.63 - samples/sec: 3668.08 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 13:49:05,752 epoch 5 - iter 356/893 - loss 0.03570155 - time (sec): 27.41 - samples/sec: 3680.40 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 13:49:12,035 epoch 5 - iter 445/893 - loss 0.03481697 - time (sec): 33.69 - samples/sec: 3660.13 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 13:49:19,355 epoch 5 - iter 534/893 - loss 0.03425923 - time (sec): 41.01 - samples/sec: 3658.78 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 13:49:26,083 epoch 5 - iter 623/893 - loss 0.03439068 - time (sec): 47.74 - samples/sec: 3652.14 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 13:49:32,693 epoch 5 - iter 712/893 - loss 0.03544740 - time (sec): 54.35 - samples/sec: 3649.21 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 13:49:39,657 epoch 5 - iter 801/893 - loss 0.03607589 - time (sec): 61.31 - samples/sec: 3633.76 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 13:49:46,685 epoch 5 - iter 890/893 - loss 0.03580004 - time (sec): 68.34 - samples/sec: 3632.42 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 13:49:46,906 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:49:46,906 EPOCH 5 done: loss 0.0358 - lr: 0.000017 | |
| 2023-10-16 13:49:50,900 DEV : loss 0.16850945353507996 - f1-score (micro avg) 0.8003 | |
| 2023-10-16 13:49:50,916 saving best model | |
| 2023-10-16 13:49:52,046 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:49:58,748 epoch 6 - iter 89/893 - loss 0.02777395 - time (sec): 6.70 - samples/sec: 3551.20 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 13:50:05,732 epoch 6 - iter 178/893 - loss 0.02629674 - time (sec): 13.68 - samples/sec: 3643.60 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 13:50:12,637 epoch 6 - iter 267/893 - loss 0.02593427 - time (sec): 20.59 - samples/sec: 3624.05 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 13:50:19,772 epoch 6 - iter 356/893 - loss 0.02773488 - time (sec): 27.72 - samples/sec: 3605.78 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 13:50:26,258 epoch 6 - iter 445/893 - loss 0.02870583 - time (sec): 34.21 - samples/sec: 3627.65 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 13:50:33,263 epoch 6 - iter 534/893 - loss 0.02775510 - time (sec): 41.21 - samples/sec: 3667.80 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 13:50:39,881 epoch 6 - iter 623/893 - loss 0.02870404 - time (sec): 47.83 - samples/sec: 3678.92 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 13:50:46,806 epoch 6 - iter 712/893 - loss 0.02927233 - time (sec): 54.76 - samples/sec: 3670.61 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 13:50:53,440 epoch 6 - iter 801/893 - loss 0.02905059 - time (sec): 61.39 - samples/sec: 3651.61 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 13:51:00,351 epoch 6 - iter 890/893 - loss 0.02892722 - time (sec): 68.30 - samples/sec: 3634.00 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 13:51:00,543 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:51:00,543 EPOCH 6 done: loss 0.0290 - lr: 0.000013 | |
| 2023-10-16 13:51:04,562 DEV : loss 0.17858995497226715 - f1-score (micro avg) 0.8013 | |
| 2023-10-16 13:51:04,578 saving best model | |
| 2023-10-16 13:51:05,103 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:51:11,331 epoch 7 - iter 89/893 - loss 0.01538452 - time (sec): 6.22 - samples/sec: 3772.50 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 13:51:17,722 epoch 7 - iter 178/893 - loss 0.02131337 - time (sec): 12.62 - samples/sec: 3726.95 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 13:51:24,347 epoch 7 - iter 267/893 - loss 0.02097900 - time (sec): 19.24 - samples/sec: 3757.18 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 13:51:31,181 epoch 7 - iter 356/893 - loss 0.02190829 - time (sec): 26.07 - samples/sec: 3745.44 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 13:51:37,865 epoch 7 - iter 445/893 - loss 0.02094727 - time (sec): 32.76 - samples/sec: 3702.99 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 13:51:44,624 epoch 7 - iter 534/893 - loss 0.02163093 - time (sec): 39.52 - samples/sec: 3697.98 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 13:51:51,853 epoch 7 - iter 623/893 - loss 0.02291209 - time (sec): 46.75 - samples/sec: 3689.77 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 13:51:59,294 epoch 7 - iter 712/893 - loss 0.02293232 - time (sec): 54.19 - samples/sec: 3678.83 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 13:52:06,044 epoch 7 - iter 801/893 - loss 0.02333221 - time (sec): 60.94 - samples/sec: 3662.26 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 13:52:12,889 epoch 7 - iter 890/893 - loss 0.02382874 - time (sec): 67.78 - samples/sec: 3659.34 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 13:52:13,099 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:52:13,099 EPOCH 7 done: loss 0.0239 - lr: 0.000010 | |
| 2023-10-16 13:52:17,661 DEV : loss 0.18977805972099304 - f1-score (micro avg) 0.8051 | |
| 2023-10-16 13:52:17,677 saving best model | |
| 2023-10-16 13:52:18,230 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:52:25,001 epoch 8 - iter 89/893 - loss 0.01760355 - time (sec): 6.77 - samples/sec: 3525.99 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 13:52:31,737 epoch 8 - iter 178/893 - loss 0.01600479 - time (sec): 13.51 - samples/sec: 3609.06 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 13:52:38,483 epoch 8 - iter 267/893 - loss 0.01545751 - time (sec): 20.25 - samples/sec: 3645.30 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 13:52:45,395 epoch 8 - iter 356/893 - loss 0.01612712 - time (sec): 27.16 - samples/sec: 3651.36 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 13:52:51,691 epoch 8 - iter 445/893 - loss 0.01694443 - time (sec): 33.46 - samples/sec: 3648.63 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 13:52:58,579 epoch 8 - iter 534/893 - loss 0.01655607 - time (sec): 40.35 - samples/sec: 3624.80 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 13:53:05,827 epoch 8 - iter 623/893 - loss 0.01701668 - time (sec): 47.60 - samples/sec: 3641.53 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 13:53:13,155 epoch 8 - iter 712/893 - loss 0.01685952 - time (sec): 54.92 - samples/sec: 3628.71 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 13:53:19,500 epoch 8 - iter 801/893 - loss 0.01672613 - time (sec): 61.27 - samples/sec: 3625.67 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 13:53:26,651 epoch 8 - iter 890/893 - loss 0.01754531 - time (sec): 68.42 - samples/sec: 3618.81 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 13:53:26,999 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:53:26,999 EPOCH 8 done: loss 0.0175 - lr: 0.000007 | |
| 2023-10-16 13:53:31,625 DEV : loss 0.19642271101474762 - f1-score (micro avg) 0.7989 | |
| 2023-10-16 13:53:31,642 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:53:38,444 epoch 9 - iter 89/893 - loss 0.01210769 - time (sec): 6.80 - samples/sec: 3501.99 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 13:53:45,076 epoch 9 - iter 178/893 - loss 0.01089980 - time (sec): 13.43 - samples/sec: 3607.10 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 13:53:51,952 epoch 9 - iter 267/893 - loss 0.01206946 - time (sec): 20.31 - samples/sec: 3558.60 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 13:53:58,996 epoch 9 - iter 356/893 - loss 0.01230578 - time (sec): 27.35 - samples/sec: 3545.34 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 13:54:05,513 epoch 9 - iter 445/893 - loss 0.01310413 - time (sec): 33.87 - samples/sec: 3557.03 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 13:54:12,606 epoch 9 - iter 534/893 - loss 0.01304358 - time (sec): 40.96 - samples/sec: 3563.63 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 13:54:19,791 epoch 9 - iter 623/893 - loss 0.01306085 - time (sec): 48.15 - samples/sec: 3546.44 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 13:54:26,979 epoch 9 - iter 712/893 - loss 0.01371970 - time (sec): 55.34 - samples/sec: 3565.24 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 13:54:33,781 epoch 9 - iter 801/893 - loss 0.01284582 - time (sec): 62.14 - samples/sec: 3588.43 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 13:54:40,711 epoch 9 - iter 890/893 - loss 0.01318723 - time (sec): 69.07 - samples/sec: 3589.78 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 13:54:40,933 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:54:40,933 EPOCH 9 done: loss 0.0132 - lr: 0.000003 | |
| 2023-10-16 13:54:44,996 DEV : loss 0.19601404666900635 - f1-score (micro avg) 0.82 | |
| 2023-10-16 13:54:45,012 saving best model | |
| 2023-10-16 13:54:45,569 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:54:52,458 epoch 10 - iter 89/893 - loss 0.01232102 - time (sec): 6.89 - samples/sec: 3580.68 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 13:54:59,061 epoch 10 - iter 178/893 - loss 0.01172762 - time (sec): 13.49 - samples/sec: 3553.37 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 13:55:05,897 epoch 10 - iter 267/893 - loss 0.01214811 - time (sec): 20.33 - samples/sec: 3580.72 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 13:55:12,611 epoch 10 - iter 356/893 - loss 0.01159662 - time (sec): 27.04 - samples/sec: 3624.28 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 13:55:19,351 epoch 10 - iter 445/893 - loss 0.01125182 - time (sec): 33.78 - samples/sec: 3627.33 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 13:55:26,203 epoch 10 - iter 534/893 - loss 0.01148185 - time (sec): 40.63 - samples/sec: 3623.21 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 13:55:32,925 epoch 10 - iter 623/893 - loss 0.01082743 - time (sec): 47.35 - samples/sec: 3628.25 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 13:55:39,767 epoch 10 - iter 712/893 - loss 0.01097241 - time (sec): 54.20 - samples/sec: 3633.23 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 13:55:46,432 epoch 10 - iter 801/893 - loss 0.01107939 - time (sec): 60.86 - samples/sec: 3640.46 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 13:55:53,393 epoch 10 - iter 890/893 - loss 0.01153753 - time (sec): 67.82 - samples/sec: 3653.55 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 13:55:53,634 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:55:53,634 EPOCH 10 done: loss 0.0115 - lr: 0.000000 | |
| 2023-10-16 13:55:58,213 DEV : loss 0.19514599442481995 - f1-score (micro avg) 0.8204 | |
| 2023-10-16 13:55:58,230 saving best model | |
| 2023-10-16 13:55:59,218 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 13:55:59,219 Loading model from best epoch ... | |
| 2023-10-16 13:56:00,770 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 | |
| 2023-10-16 13:56:10,110 | |
| Results: | |
| - F-score (micro) 0.6969 | |
| - F-score (macro) 0.6147 | |
| - Accuracy 0.559 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7240 0.6995 0.7116 1095 | |
| PER 0.7760 0.7806 0.7783 1012 | |
| ORG 0.4223 0.5938 0.4936 357 | |
| HumanProd 0.3529 0.7273 0.4752 33 | |
| micro avg 0.6772 0.7177 0.6969 2497 | |
| macro avg 0.5688 0.7003 0.6147 2497 | |
| weighted avg 0.6971 0.7177 0.7043 2497 | |
| 2023-10-16 13:56:10,110 ---------------------------------------------------------------------------------------------------- | |