| 2023-10-25 20:47:46,272 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,273 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 20:47:46,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,273 MultiCorpus: 1166 train + 165 dev + 415 test sentences | |
| - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator | |
| 2023-10-25 20:47:46,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,273 Train: 1166 sentences | |
| 2023-10-25 20:47:46,273 (train_with_dev=False, train_with_test=False) | |
| 2023-10-25 20:47:46,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,273 Training Params: | |
| 2023-10-25 20:47:46,273 - learning_rate: "5e-05" | |
| 2023-10-25 20:47:46,273 - mini_batch_size: "8" | |
| 2023-10-25 20:47:46,273 - max_epochs: "10" | |
| 2023-10-25 20:47:46,273 - shuffle: "True" | |
| 2023-10-25 20:47:46,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,273 Plugins: | |
| 2023-10-25 20:47:46,273 - TensorboardLogger | |
| 2023-10-25 20:47:46,273 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-25 20:47:46,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,274 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-25 20:47:46,274 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-25 20:47:46,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,274 Computation: | |
| 2023-10-25 20:47:46,274 - compute on device: cuda:0 | |
| 2023-10-25 20:47:46,274 - embedding storage: none | |
| 2023-10-25 20:47:46,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,274 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" | |
| 2023-10-25 20:47:46,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:46,274 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-25 20:47:47,228 epoch 1 - iter 14/146 - loss 3.32619155 - time (sec): 0.95 - samples/sec: 4905.02 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 20:47:48,090 epoch 1 - iter 28/146 - loss 2.77463951 - time (sec): 1.81 - samples/sec: 4662.84 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 20:47:49,025 epoch 1 - iter 42/146 - loss 2.06209058 - time (sec): 2.75 - samples/sec: 4645.68 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 20:47:49,928 epoch 1 - iter 56/146 - loss 1.66590766 - time (sec): 3.65 - samples/sec: 4744.10 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 20:47:50,870 epoch 1 - iter 70/146 - loss 1.41744737 - time (sec): 4.60 - samples/sec: 4763.62 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 20:47:51,793 epoch 1 - iter 84/146 - loss 1.23294191 - time (sec): 5.52 - samples/sec: 4814.17 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 20:47:52,708 epoch 1 - iter 98/146 - loss 1.10453887 - time (sec): 6.43 - samples/sec: 4784.49 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-25 20:47:53,444 epoch 1 - iter 112/146 - loss 1.01746390 - time (sec): 7.17 - samples/sec: 4779.66 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-25 20:47:54,435 epoch 1 - iter 126/146 - loss 0.91742517 - time (sec): 8.16 - samples/sec: 4783.24 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-25 20:47:55,261 epoch 1 - iter 140/146 - loss 0.85738814 - time (sec): 8.99 - samples/sec: 4759.96 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-25 20:47:55,630 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:55,630 EPOCH 1 done: loss 0.8327 - lr: 0.000048 | |
| 2023-10-25 20:47:56,280 DEV : loss 0.17366763949394226 - f1-score (micro avg) 0.4951 | |
| 2023-10-25 20:47:56,285 saving best model | |
| 2023-10-25 20:47:56,703 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:47:57,513 epoch 2 - iter 14/146 - loss 0.18263706 - time (sec): 0.81 - samples/sec: 4908.96 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-25 20:47:58,350 epoch 2 - iter 28/146 - loss 0.18295105 - time (sec): 1.65 - samples/sec: 5022.09 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-25 20:47:59,282 epoch 2 - iter 42/146 - loss 0.19154440 - time (sec): 2.58 - samples/sec: 5039.20 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-25 20:48:00,078 epoch 2 - iter 56/146 - loss 0.18243784 - time (sec): 3.37 - samples/sec: 5084.29 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-25 20:48:00,853 epoch 2 - iter 70/146 - loss 0.18313209 - time (sec): 4.15 - samples/sec: 5020.62 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-25 20:48:01,828 epoch 2 - iter 84/146 - loss 0.17440435 - time (sec): 5.12 - samples/sec: 4918.17 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-25 20:48:02,875 epoch 2 - iter 98/146 - loss 0.16903145 - time (sec): 6.17 - samples/sec: 4880.88 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-25 20:48:03,747 epoch 2 - iter 112/146 - loss 0.16353225 - time (sec): 7.04 - samples/sec: 4891.08 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-25 20:48:04,623 epoch 2 - iter 126/146 - loss 0.16247055 - time (sec): 7.92 - samples/sec: 4827.66 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-25 20:48:05,426 epoch 2 - iter 140/146 - loss 0.16260366 - time (sec): 8.72 - samples/sec: 4806.35 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-25 20:48:06,000 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:06,000 EPOCH 2 done: loss 0.1626 - lr: 0.000045 | |
| 2023-10-25 20:48:06,908 DEV : loss 0.10925206542015076 - f1-score (micro avg) 0.683 | |
| 2023-10-25 20:48:06,913 saving best model | |
| 2023-10-25 20:48:07,451 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:08,430 epoch 3 - iter 14/146 - loss 0.08329505 - time (sec): 0.98 - samples/sec: 5121.07 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-25 20:48:09,436 epoch 3 - iter 28/146 - loss 0.07646522 - time (sec): 1.98 - samples/sec: 5046.33 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-25 20:48:10,199 epoch 3 - iter 42/146 - loss 0.07857219 - time (sec): 2.75 - samples/sec: 4897.73 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-25 20:48:11,036 epoch 3 - iter 56/146 - loss 0.08146631 - time (sec): 3.58 - samples/sec: 4909.24 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-25 20:48:11,890 epoch 3 - iter 70/146 - loss 0.08586188 - time (sec): 4.44 - samples/sec: 4941.40 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-25 20:48:12,633 epoch 3 - iter 84/146 - loss 0.08659125 - time (sec): 5.18 - samples/sec: 4866.47 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-25 20:48:13,560 epoch 3 - iter 98/146 - loss 0.08365767 - time (sec): 6.11 - samples/sec: 4921.59 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-25 20:48:14,480 epoch 3 - iter 112/146 - loss 0.08564022 - time (sec): 7.03 - samples/sec: 4903.22 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-25 20:48:15,417 epoch 3 - iter 126/146 - loss 0.08934483 - time (sec): 7.96 - samples/sec: 4888.54 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-25 20:48:16,286 epoch 3 - iter 140/146 - loss 0.08642318 - time (sec): 8.83 - samples/sec: 4847.70 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-25 20:48:16,655 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:16,655 EPOCH 3 done: loss 0.0862 - lr: 0.000039 | |
| 2023-10-25 20:48:17,571 DEV : loss 0.09392837435007095 - f1-score (micro avg) 0.7484 | |
| 2023-10-25 20:48:17,576 saving best model | |
| 2023-10-25 20:48:18,398 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:19,170 epoch 4 - iter 14/146 - loss 0.03589195 - time (sec): 0.77 - samples/sec: 4505.55 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-25 20:48:20,000 epoch 4 - iter 28/146 - loss 0.04570279 - time (sec): 1.60 - samples/sec: 4779.15 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-25 20:48:21,050 epoch 4 - iter 42/146 - loss 0.04554306 - time (sec): 2.65 - samples/sec: 4530.48 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-25 20:48:22,028 epoch 4 - iter 56/146 - loss 0.04561147 - time (sec): 3.62 - samples/sec: 4602.70 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-25 20:48:23,042 epoch 4 - iter 70/146 - loss 0.04012849 - time (sec): 4.64 - samples/sec: 4799.02 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-25 20:48:23,878 epoch 4 - iter 84/146 - loss 0.04312134 - time (sec): 5.47 - samples/sec: 4736.42 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-25 20:48:24,937 epoch 4 - iter 98/146 - loss 0.04547308 - time (sec): 6.53 - samples/sec: 4683.90 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-25 20:48:25,734 epoch 4 - iter 112/146 - loss 0.04785940 - time (sec): 7.33 - samples/sec: 4702.37 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-25 20:48:26,619 epoch 4 - iter 126/146 - loss 0.04800961 - time (sec): 8.22 - samples/sec: 4673.38 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-25 20:48:27,443 epoch 4 - iter 140/146 - loss 0.04853540 - time (sec): 9.04 - samples/sec: 4727.50 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-25 20:48:27,795 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:27,795 EPOCH 4 done: loss 0.0492 - lr: 0.000034 | |
| 2023-10-25 20:48:28,710 DEV : loss 0.14798803627490997 - f1-score (micro avg) 0.7111 | |
| 2023-10-25 20:48:28,715 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:29,538 epoch 5 - iter 14/146 - loss 0.02945627 - time (sec): 0.82 - samples/sec: 4595.35 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-25 20:48:30,394 epoch 5 - iter 28/146 - loss 0.03208317 - time (sec): 1.68 - samples/sec: 4783.75 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-25 20:48:31,343 epoch 5 - iter 42/146 - loss 0.03529632 - time (sec): 2.63 - samples/sec: 4876.90 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-25 20:48:32,177 epoch 5 - iter 56/146 - loss 0.03385465 - time (sec): 3.46 - samples/sec: 4834.24 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-25 20:48:33,089 epoch 5 - iter 70/146 - loss 0.03310305 - time (sec): 4.37 - samples/sec: 4832.85 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-25 20:48:33,897 epoch 5 - iter 84/146 - loss 0.03279721 - time (sec): 5.18 - samples/sec: 4806.60 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 20:48:34,879 epoch 5 - iter 98/146 - loss 0.03501715 - time (sec): 6.16 - samples/sec: 4786.64 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 20:48:35,798 epoch 5 - iter 112/146 - loss 0.03321133 - time (sec): 7.08 - samples/sec: 4785.39 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 20:48:36,745 epoch 5 - iter 126/146 - loss 0.03182421 - time (sec): 8.03 - samples/sec: 4743.23 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 20:48:37,639 epoch 5 - iter 140/146 - loss 0.02979870 - time (sec): 8.92 - samples/sec: 4755.87 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 20:48:38,075 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:38,076 EPOCH 5 done: loss 0.0316 - lr: 0.000028 | |
| 2023-10-25 20:48:38,990 DEV : loss 0.14484462141990662 - f1-score (micro avg) 0.7101 | |
| 2023-10-25 20:48:38,995 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:39,886 epoch 6 - iter 14/146 - loss 0.03930537 - time (sec): 0.89 - samples/sec: 4602.61 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 20:48:40,777 epoch 6 - iter 28/146 - loss 0.02555020 - time (sec): 1.78 - samples/sec: 4689.85 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 20:48:41,846 epoch 6 - iter 42/146 - loss 0.02053346 - time (sec): 2.85 - samples/sec: 4560.50 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 20:48:42,830 epoch 6 - iter 56/146 - loss 0.02171275 - time (sec): 3.83 - samples/sec: 4695.94 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 20:48:43,736 epoch 6 - iter 70/146 - loss 0.02182934 - time (sec): 4.74 - samples/sec: 4617.08 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 20:48:44,587 epoch 6 - iter 84/146 - loss 0.02098662 - time (sec): 5.59 - samples/sec: 4613.56 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 20:48:45,499 epoch 6 - iter 98/146 - loss 0.02256168 - time (sec): 6.50 - samples/sec: 4659.28 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 20:48:46,401 epoch 6 - iter 112/146 - loss 0.02105759 - time (sec): 7.41 - samples/sec: 4694.00 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 20:48:47,177 epoch 6 - iter 126/146 - loss 0.02169497 - time (sec): 8.18 - samples/sec: 4703.57 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 20:48:47,967 epoch 6 - iter 140/146 - loss 0.02216839 - time (sec): 8.97 - samples/sec: 4782.72 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 20:48:48,373 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:48,373 EPOCH 6 done: loss 0.0229 - lr: 0.000023 | |
| 2023-10-25 20:48:49,285 DEV : loss 0.1573459953069687 - f1-score (micro avg) 0.7339 | |
| 2023-10-25 20:48:49,290 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:50,319 epoch 7 - iter 14/146 - loss 0.02023463 - time (sec): 1.03 - samples/sec: 3920.05 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 20:48:51,207 epoch 7 - iter 28/146 - loss 0.01645952 - time (sec): 1.92 - samples/sec: 4177.15 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 20:48:52,071 epoch 7 - iter 42/146 - loss 0.01621473 - time (sec): 2.78 - samples/sec: 4372.48 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 20:48:52,893 epoch 7 - iter 56/146 - loss 0.01498802 - time (sec): 3.60 - samples/sec: 4437.54 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 20:48:54,004 epoch 7 - iter 70/146 - loss 0.01718484 - time (sec): 4.71 - samples/sec: 4498.40 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 20:48:55,044 epoch 7 - iter 84/146 - loss 0.01586359 - time (sec): 5.75 - samples/sec: 4509.60 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 20:48:55,972 epoch 7 - iter 98/146 - loss 0.01684853 - time (sec): 6.68 - samples/sec: 4510.54 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 20:48:56,896 epoch 7 - iter 112/146 - loss 0.01618138 - time (sec): 7.61 - samples/sec: 4548.68 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 20:48:57,791 epoch 7 - iter 126/146 - loss 0.01618698 - time (sec): 8.50 - samples/sec: 4505.04 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 20:48:58,621 epoch 7 - iter 140/146 - loss 0.01703696 - time (sec): 9.33 - samples/sec: 4586.23 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 20:48:58,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:48:58,968 EPOCH 7 done: loss 0.0171 - lr: 0.000017 | |
| 2023-10-25 20:48:59,889 DEV : loss 0.16221855580806732 - f1-score (micro avg) 0.7226 | |
| 2023-10-25 20:48:59,893 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:00,821 epoch 8 - iter 14/146 - loss 0.00624075 - time (sec): 0.93 - samples/sec: 4750.92 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 20:49:01,689 epoch 8 - iter 28/146 - loss 0.00947817 - time (sec): 1.79 - samples/sec: 5225.63 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 20:49:02,544 epoch 8 - iter 42/146 - loss 0.00932840 - time (sec): 2.65 - samples/sec: 5019.16 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 20:49:03,436 epoch 8 - iter 56/146 - loss 0.00899173 - time (sec): 3.54 - samples/sec: 4972.49 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 20:49:04,307 epoch 8 - iter 70/146 - loss 0.00929944 - time (sec): 4.41 - samples/sec: 4876.07 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 20:49:05,239 epoch 8 - iter 84/146 - loss 0.01037046 - time (sec): 5.34 - samples/sec: 4857.64 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 20:49:06,385 epoch 8 - iter 98/146 - loss 0.01144120 - time (sec): 6.49 - samples/sec: 4718.27 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 20:49:07,293 epoch 8 - iter 112/146 - loss 0.01108653 - time (sec): 7.40 - samples/sec: 4721.18 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 20:49:08,200 epoch 8 - iter 126/146 - loss 0.01315505 - time (sec): 8.31 - samples/sec: 4639.70 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 20:49:09,092 epoch 8 - iter 140/146 - loss 0.01296013 - time (sec): 9.20 - samples/sec: 4659.37 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 20:49:09,419 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:09,419 EPOCH 8 done: loss 0.0131 - lr: 0.000012 | |
| 2023-10-25 20:49:10,337 DEV : loss 0.17299844324588776 - f1-score (micro avg) 0.7257 | |
| 2023-10-25 20:49:10,342 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:11,325 epoch 9 - iter 14/146 - loss 0.01830680 - time (sec): 0.98 - samples/sec: 4488.91 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 20:49:12,187 epoch 9 - iter 28/146 - loss 0.01109420 - time (sec): 1.84 - samples/sec: 4547.76 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 20:49:12,956 epoch 9 - iter 42/146 - loss 0.01001611 - time (sec): 2.61 - samples/sec: 4550.41 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 20:49:13,857 epoch 9 - iter 56/146 - loss 0.00902443 - time (sec): 3.51 - samples/sec: 4624.04 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 20:49:14,691 epoch 9 - iter 70/146 - loss 0.00932559 - time (sec): 4.35 - samples/sec: 4630.51 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 20:49:15,596 epoch 9 - iter 84/146 - loss 0.00988126 - time (sec): 5.25 - samples/sec: 4582.74 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 20:49:16,492 epoch 9 - iter 98/146 - loss 0.00934970 - time (sec): 6.15 - samples/sec: 4703.10 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 20:49:17,401 epoch 9 - iter 112/146 - loss 0.00834335 - time (sec): 7.06 - samples/sec: 4723.11 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 20:49:18,388 epoch 9 - iter 126/146 - loss 0.00786094 - time (sec): 8.04 - samples/sec: 4757.90 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 20:49:19,410 epoch 9 - iter 140/146 - loss 0.00807518 - time (sec): 9.07 - samples/sec: 4739.93 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 20:49:19,762 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:19,762 EPOCH 9 done: loss 0.0082 - lr: 0.000006 | |
| 2023-10-25 20:49:20,679 DEV : loss 0.19021421670913696 - f1-score (micro avg) 0.7032 | |
| 2023-10-25 20:49:20,684 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:21,526 epoch 10 - iter 14/146 - loss 0.00264529 - time (sec): 0.84 - samples/sec: 5002.29 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 20:49:22,516 epoch 10 - iter 28/146 - loss 0.00715687 - time (sec): 1.83 - samples/sec: 4562.72 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 20:49:23,371 epoch 10 - iter 42/146 - loss 0.00756824 - time (sec): 2.69 - samples/sec: 4594.76 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 20:49:24,269 epoch 10 - iter 56/146 - loss 0.00627773 - time (sec): 3.58 - samples/sec: 4685.83 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 20:49:25,223 epoch 10 - iter 70/146 - loss 0.00727587 - time (sec): 4.54 - samples/sec: 4629.20 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 20:49:26,052 epoch 10 - iter 84/146 - loss 0.00623229 - time (sec): 5.37 - samples/sec: 4579.56 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 20:49:27,230 epoch 10 - iter 98/146 - loss 0.00645332 - time (sec): 6.54 - samples/sec: 4523.53 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 20:49:28,185 epoch 10 - iter 112/146 - loss 0.00637249 - time (sec): 7.50 - samples/sec: 4586.34 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 20:49:29,154 epoch 10 - iter 126/146 - loss 0.00621133 - time (sec): 8.47 - samples/sec: 4552.42 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 20:49:29,992 epoch 10 - iter 140/146 - loss 0.00573965 - time (sec): 9.31 - samples/sec: 4551.44 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 20:49:30,393 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:30,393 EPOCH 10 done: loss 0.0055 - lr: 0.000000 | |
| 2023-10-25 20:49:31,304 DEV : loss 0.19148127734661102 - f1-score (micro avg) 0.6975 | |
| 2023-10-25 20:49:31,833 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 20:49:31,834 Loading model from best epoch ... | |
| 2023-10-25 20:49:33,617 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd | |
| 2023-10-25 20:49:35,165 | |
| Results: | |
| - F-score (micro) 0.754 | |
| - F-score (macro) 0.67 | |
| - Accuracy 0.6246 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.7983 0.8305 0.8141 348 | |
| LOC 0.6616 0.8391 0.7399 261 | |
| ORG 0.4565 0.4038 0.4286 52 | |
| HumanProd 0.7143 0.6818 0.6977 22 | |
| micro avg 0.7158 0.7965 0.7540 683 | |
| macro avg 0.6577 0.6888 0.6700 683 | |
| weighted avg 0.7174 0.7965 0.7526 683 | |
| 2023-10-25 20:49:35,166 ---------------------------------------------------------------------------------------------------- | |