| 2023-10-11 06:39:23,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,994 Model: "SequenceTagger( | |
| (embeddings): ByT5Embeddings( | |
| (model): T5EncoderModel( | |
| (shared): Embedding(384, 1472) | |
| (encoder): T5Stack( | |
| (embed_tokens): Embedding(384, 1472) | |
| (block): ModuleList( | |
| (0): T5Block( | |
| (layer): ModuleList( | |
| (0): T5LayerSelfAttention( | |
| (SelfAttention): T5Attention( | |
| (q): Linear(in_features=1472, out_features=384, bias=False) | |
| (k): Linear(in_features=1472, out_features=384, bias=False) | |
| (v): Linear(in_features=1472, out_features=384, bias=False) | |
| (o): Linear(in_features=384, out_features=1472, bias=False) | |
| (relative_attention_bias): Embedding(32, 6) | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (1): T5LayerFF( | |
| (DenseReluDense): T5DenseGatedActDense( | |
| (wi_0): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wi_1): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wo): Linear(in_features=3584, out_features=1472, bias=False) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| (act): NewGELUActivation() | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| (1-11): 11 x T5Block( | |
| (layer): ModuleList( | |
| (0): T5LayerSelfAttention( | |
| (SelfAttention): T5Attention( | |
| (q): Linear(in_features=1472, out_features=384, bias=False) | |
| (k): Linear(in_features=1472, out_features=384, bias=False) | |
| (v): Linear(in_features=1472, out_features=384, bias=False) | |
| (o): Linear(in_features=384, out_features=1472, bias=False) | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (1): T5LayerFF( | |
| (DenseReluDense): T5DenseGatedActDense( | |
| (wi_0): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wi_1): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wo): Linear(in_features=3584, out_features=1472, bias=False) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| (act): NewGELUActivation() | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=1472, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-11 06:39:23,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,995 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 | |
| 2023-10-11 06:39:23,995 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,995 Train: 7142 sentences | |
| 2023-10-11 06:39:23,995 (train_with_dev=False, train_with_test=False) | |
| 2023-10-11 06:39:23,995 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,995 Training Params: | |
| 2023-10-11 06:39:23,995 - learning_rate: "0.00016" | |
| 2023-10-11 06:39:23,995 - mini_batch_size: "4" | |
| 2023-10-11 06:39:23,995 - max_epochs: "10" | |
| 2023-10-11 06:39:23,995 - shuffle: "True" | |
| 2023-10-11 06:39:23,995 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,995 Plugins: | |
| 2023-10-11 06:39:23,995 - TensorboardLogger | |
| 2023-10-11 06:39:23,995 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-11 06:39:23,995 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,996 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-11 06:39:23,996 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-11 06:39:23,996 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,996 Computation: | |
| 2023-10-11 06:39:23,996 - compute on device: cuda:0 | |
| 2023-10-11 06:39:23,996 - embedding storage: none | |
| 2023-10-11 06:39:23,996 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,996 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-11 06:39:23,996 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,996 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:39:23,996 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-11 06:40:19,912 epoch 1 - iter 178/1786 - loss 2.82900261 - time (sec): 55.91 - samples/sec: 474.93 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-11 06:41:14,029 epoch 1 - iter 356/1786 - loss 2.67404787 - time (sec): 110.03 - samples/sec: 470.57 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-11 06:42:09,632 epoch 1 - iter 534/1786 - loss 2.38436083 - time (sec): 165.63 - samples/sec: 468.96 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-11 06:43:05,404 epoch 1 - iter 712/1786 - loss 2.06699231 - time (sec): 221.41 - samples/sec: 464.97 - lr: 0.000064 - momentum: 0.000000 | |
| 2023-10-11 06:44:01,010 epoch 1 - iter 890/1786 - loss 1.80287645 - time (sec): 277.01 - samples/sec: 459.40 - lr: 0.000080 - momentum: 0.000000 | |
| 2023-10-11 06:44:56,410 epoch 1 - iter 1068/1786 - loss 1.59842649 - time (sec): 332.41 - samples/sec: 460.39 - lr: 0.000096 - momentum: 0.000000 | |
| 2023-10-11 06:45:50,950 epoch 1 - iter 1246/1786 - loss 1.43900727 - time (sec): 386.95 - samples/sec: 459.78 - lr: 0.000112 - momentum: 0.000000 | |
| 2023-10-11 06:46:43,346 epoch 1 - iter 1424/1786 - loss 1.31683155 - time (sec): 439.35 - samples/sec: 457.72 - lr: 0.000127 - momentum: 0.000000 | |
| 2023-10-11 06:47:34,731 epoch 1 - iter 1602/1786 - loss 1.21390367 - time (sec): 490.73 - samples/sec: 457.50 - lr: 0.000143 - momentum: 0.000000 | |
| 2023-10-11 06:48:27,379 epoch 1 - iter 1780/1786 - loss 1.12581674 - time (sec): 543.38 - samples/sec: 456.87 - lr: 0.000159 - momentum: 0.000000 | |
| 2023-10-11 06:48:28,795 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:48:28,795 EPOCH 1 done: loss 1.1242 - lr: 0.000159 | |
| 2023-10-11 06:48:48,043 DEV : loss 0.21339952945709229 - f1-score (micro avg) 0.4704 | |
| 2023-10-11 06:48:48,073 saving best model | |
| 2023-10-11 06:48:48,971 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:49:40,643 epoch 2 - iter 178/1786 - loss 0.20730434 - time (sec): 51.67 - samples/sec: 486.40 - lr: 0.000158 - momentum: 0.000000 | |
| 2023-10-11 06:50:33,708 epoch 2 - iter 356/1786 - loss 0.19312469 - time (sec): 104.73 - samples/sec: 491.93 - lr: 0.000156 - momentum: 0.000000 | |
| 2023-10-11 06:51:25,317 epoch 2 - iter 534/1786 - loss 0.18172318 - time (sec): 156.34 - samples/sec: 488.33 - lr: 0.000155 - momentum: 0.000000 | |
| 2023-10-11 06:52:16,339 epoch 2 - iter 712/1786 - loss 0.17387846 - time (sec): 207.37 - samples/sec: 484.71 - lr: 0.000153 - momentum: 0.000000 | |
| 2023-10-11 06:53:07,904 epoch 2 - iter 890/1786 - loss 0.16698512 - time (sec): 258.93 - samples/sec: 481.09 - lr: 0.000151 - momentum: 0.000000 | |
| 2023-10-11 06:53:58,781 epoch 2 - iter 1068/1786 - loss 0.16051997 - time (sec): 309.81 - samples/sec: 479.18 - lr: 0.000149 - momentum: 0.000000 | |
| 2023-10-11 06:54:49,112 epoch 2 - iter 1246/1786 - loss 0.15405773 - time (sec): 360.14 - samples/sec: 477.91 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-11 06:55:41,208 epoch 2 - iter 1424/1786 - loss 0.14923648 - time (sec): 412.23 - samples/sec: 478.67 - lr: 0.000146 - momentum: 0.000000 | |
| 2023-10-11 06:56:35,914 epoch 2 - iter 1602/1786 - loss 0.14482467 - time (sec): 466.94 - samples/sec: 478.44 - lr: 0.000144 - momentum: 0.000000 | |
| 2023-10-11 06:57:29,822 epoch 2 - iter 1780/1786 - loss 0.13951975 - time (sec): 520.85 - samples/sec: 476.15 - lr: 0.000142 - momentum: 0.000000 | |
| 2023-10-11 06:57:31,487 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:57:31,487 EPOCH 2 done: loss 0.1394 - lr: 0.000142 | |
| 2023-10-11 06:57:53,454 DEV : loss 0.12035670131444931 - f1-score (micro avg) 0.7478 | |
| 2023-10-11 06:57:53,485 saving best model | |
| 2023-10-11 06:57:56,090 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 06:58:49,895 epoch 3 - iter 178/1786 - loss 0.07648250 - time (sec): 53.80 - samples/sec: 480.38 - lr: 0.000140 - momentum: 0.000000 | |
| 2023-10-11 06:59:42,427 epoch 3 - iter 356/1786 - loss 0.07475891 - time (sec): 106.33 - samples/sec: 457.73 - lr: 0.000139 - momentum: 0.000000 | |
| 2023-10-11 07:00:36,639 epoch 3 - iter 534/1786 - loss 0.07760944 - time (sec): 160.54 - samples/sec: 461.01 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-11 07:01:30,450 epoch 3 - iter 712/1786 - loss 0.07885752 - time (sec): 214.36 - samples/sec: 458.24 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-11 07:02:24,329 epoch 3 - iter 890/1786 - loss 0.07553472 - time (sec): 268.23 - samples/sec: 458.26 - lr: 0.000133 - momentum: 0.000000 | |
| 2023-10-11 07:03:19,934 epoch 3 - iter 1068/1786 - loss 0.07803250 - time (sec): 323.84 - samples/sec: 457.48 - lr: 0.000132 - momentum: 0.000000 | |
| 2023-10-11 07:04:18,636 epoch 3 - iter 1246/1786 - loss 0.07541694 - time (sec): 382.54 - samples/sec: 456.93 - lr: 0.000130 - momentum: 0.000000 | |
| 2023-10-11 07:05:16,926 epoch 3 - iter 1424/1786 - loss 0.07566700 - time (sec): 440.83 - samples/sec: 452.91 - lr: 0.000128 - momentum: 0.000000 | |
| 2023-10-11 07:06:14,488 epoch 3 - iter 1602/1786 - loss 0.07521801 - time (sec): 498.39 - samples/sec: 449.09 - lr: 0.000126 - momentum: 0.000000 | |
| 2023-10-11 07:07:11,671 epoch 3 - iter 1780/1786 - loss 0.07599665 - time (sec): 555.58 - samples/sec: 446.70 - lr: 0.000125 - momentum: 0.000000 | |
| 2023-10-11 07:07:13,371 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:07:13,371 EPOCH 3 done: loss 0.0763 - lr: 0.000125 | |
| 2023-10-11 07:07:36,874 DEV : loss 0.12293554842472076 - f1-score (micro avg) 0.7694 | |
| 2023-10-11 07:07:36,908 saving best model | |
| 2023-10-11 07:07:39,619 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:08:32,498 epoch 4 - iter 178/1786 - loss 0.06175889 - time (sec): 52.87 - samples/sec: 468.15 - lr: 0.000123 - momentum: 0.000000 | |
| 2023-10-11 07:09:25,152 epoch 4 - iter 356/1786 - loss 0.05291025 - time (sec): 105.53 - samples/sec: 453.32 - lr: 0.000121 - momentum: 0.000000 | |
| 2023-10-11 07:10:25,644 epoch 4 - iter 534/1786 - loss 0.05155940 - time (sec): 166.02 - samples/sec: 445.22 - lr: 0.000119 - momentum: 0.000000 | |
| 2023-10-11 07:11:24,877 epoch 4 - iter 712/1786 - loss 0.05289251 - time (sec): 225.25 - samples/sec: 446.72 - lr: 0.000117 - momentum: 0.000000 | |
| 2023-10-11 07:12:16,721 epoch 4 - iter 890/1786 - loss 0.05247741 - time (sec): 277.10 - samples/sec: 446.69 - lr: 0.000116 - momentum: 0.000000 | |
| 2023-10-11 07:13:08,568 epoch 4 - iter 1068/1786 - loss 0.05140588 - time (sec): 328.94 - samples/sec: 451.81 - lr: 0.000114 - momentum: 0.000000 | |
| 2023-10-11 07:14:02,792 epoch 4 - iter 1246/1786 - loss 0.05313146 - time (sec): 383.17 - samples/sec: 455.46 - lr: 0.000112 - momentum: 0.000000 | |
| 2023-10-11 07:14:58,620 epoch 4 - iter 1424/1786 - loss 0.05344596 - time (sec): 439.00 - samples/sec: 452.79 - lr: 0.000110 - momentum: 0.000000 | |
| 2023-10-11 07:15:55,417 epoch 4 - iter 1602/1786 - loss 0.05307124 - time (sec): 495.79 - samples/sec: 450.00 - lr: 0.000109 - momentum: 0.000000 | |
| 2023-10-11 07:16:48,635 epoch 4 - iter 1780/1786 - loss 0.05340878 - time (sec): 549.01 - samples/sec: 452.21 - lr: 0.000107 - momentum: 0.000000 | |
| 2023-10-11 07:16:50,081 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:16:50,081 EPOCH 4 done: loss 0.0533 - lr: 0.000107 | |
| 2023-10-11 07:17:11,531 DEV : loss 0.15573516488075256 - f1-score (micro avg) 0.7904 | |
| 2023-10-11 07:17:11,562 saving best model | |
| 2023-10-11 07:17:14,160 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:18:09,045 epoch 5 - iter 178/1786 - loss 0.04583648 - time (sec): 54.87 - samples/sec: 453.51 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-11 07:19:01,699 epoch 5 - iter 356/1786 - loss 0.04299453 - time (sec): 107.53 - samples/sec: 442.06 - lr: 0.000103 - momentum: 0.000000 | |
| 2023-10-11 07:19:53,208 epoch 5 - iter 534/1786 - loss 0.04267254 - time (sec): 159.04 - samples/sec: 455.11 - lr: 0.000101 - momentum: 0.000000 | |
| 2023-10-11 07:20:50,338 epoch 5 - iter 712/1786 - loss 0.04135181 - time (sec): 216.17 - samples/sec: 450.26 - lr: 0.000100 - momentum: 0.000000 | |
| 2023-10-11 07:21:51,130 epoch 5 - iter 890/1786 - loss 0.04091209 - time (sec): 276.96 - samples/sec: 437.86 - lr: 0.000098 - momentum: 0.000000 | |
| 2023-10-11 07:22:51,933 epoch 5 - iter 1068/1786 - loss 0.03936224 - time (sec): 337.76 - samples/sec: 433.05 - lr: 0.000096 - momentum: 0.000000 | |
| 2023-10-11 07:23:51,852 epoch 5 - iter 1246/1786 - loss 0.03988643 - time (sec): 397.68 - samples/sec: 434.38 - lr: 0.000094 - momentum: 0.000000 | |
| 2023-10-11 07:24:47,391 epoch 5 - iter 1424/1786 - loss 0.04034676 - time (sec): 453.22 - samples/sec: 436.01 - lr: 0.000093 - momentum: 0.000000 | |
| 2023-10-11 07:25:43,025 epoch 5 - iter 1602/1786 - loss 0.03940605 - time (sec): 508.85 - samples/sec: 436.78 - lr: 0.000091 - momentum: 0.000000 | |
| 2023-10-11 07:26:37,195 epoch 5 - iter 1780/1786 - loss 0.03943045 - time (sec): 563.02 - samples/sec: 440.61 - lr: 0.000089 - momentum: 0.000000 | |
| 2023-10-11 07:26:38,788 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:26:38,788 EPOCH 5 done: loss 0.0393 - lr: 0.000089 | |
| 2023-10-11 07:26:59,794 DEV : loss 0.16520686447620392 - f1-score (micro avg) 0.7995 | |
| 2023-10-11 07:26:59,824 saving best model | |
| 2023-10-11 07:27:02,424 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:27:57,899 epoch 6 - iter 178/1786 - loss 0.03094883 - time (sec): 55.47 - samples/sec: 446.32 - lr: 0.000087 - momentum: 0.000000 | |
| 2023-10-11 07:28:53,808 epoch 6 - iter 356/1786 - loss 0.03037107 - time (sec): 111.38 - samples/sec: 447.38 - lr: 0.000085 - momentum: 0.000000 | |
| 2023-10-11 07:29:48,736 epoch 6 - iter 534/1786 - loss 0.02899602 - time (sec): 166.31 - samples/sec: 447.72 - lr: 0.000084 - momentum: 0.000000 | |
| 2023-10-11 07:30:44,570 epoch 6 - iter 712/1786 - loss 0.02900632 - time (sec): 222.14 - samples/sec: 446.71 - lr: 0.000082 - momentum: 0.000000 | |
| 2023-10-11 07:31:40,780 epoch 6 - iter 890/1786 - loss 0.02906074 - time (sec): 278.35 - samples/sec: 446.06 - lr: 0.000080 - momentum: 0.000000 | |
| 2023-10-11 07:32:38,978 epoch 6 - iter 1068/1786 - loss 0.02883507 - time (sec): 336.55 - samples/sec: 446.30 - lr: 0.000078 - momentum: 0.000000 | |
| 2023-10-11 07:33:33,658 epoch 6 - iter 1246/1786 - loss 0.02715296 - time (sec): 391.23 - samples/sec: 445.77 - lr: 0.000077 - momentum: 0.000000 | |
| 2023-10-11 07:34:29,044 epoch 6 - iter 1424/1786 - loss 0.02742899 - time (sec): 446.62 - samples/sec: 446.73 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-11 07:35:22,481 epoch 6 - iter 1602/1786 - loss 0.02809173 - time (sec): 500.05 - samples/sec: 450.53 - lr: 0.000073 - momentum: 0.000000 | |
| 2023-10-11 07:36:15,937 epoch 6 - iter 1780/1786 - loss 0.02903797 - time (sec): 553.51 - samples/sec: 448.58 - lr: 0.000071 - momentum: 0.000000 | |
| 2023-10-11 07:36:17,437 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:36:17,438 EPOCH 6 done: loss 0.0290 - lr: 0.000071 | |
| 2023-10-11 07:36:42,730 DEV : loss 0.16866964101791382 - f1-score (micro avg) 0.7947 | |
| 2023-10-11 07:36:42,764 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:37:41,091 epoch 7 - iter 178/1786 - loss 0.02176058 - time (sec): 58.33 - samples/sec: 469.08 - lr: 0.000069 - momentum: 0.000000 | |
| 2023-10-11 07:38:39,578 epoch 7 - iter 356/1786 - loss 0.02292926 - time (sec): 116.81 - samples/sec: 445.15 - lr: 0.000068 - momentum: 0.000000 | |
| 2023-10-11 07:39:34,898 epoch 7 - iter 534/1786 - loss 0.02247289 - time (sec): 172.13 - samples/sec: 439.81 - lr: 0.000066 - momentum: 0.000000 | |
| 2023-10-11 07:40:31,803 epoch 7 - iter 712/1786 - loss 0.02241562 - time (sec): 229.04 - samples/sec: 436.51 - lr: 0.000064 - momentum: 0.000000 | |
| 2023-10-11 07:41:27,757 epoch 7 - iter 890/1786 - loss 0.02220387 - time (sec): 284.99 - samples/sec: 436.33 - lr: 0.000062 - momentum: 0.000000 | |
| 2023-10-11 07:42:23,342 epoch 7 - iter 1068/1786 - loss 0.02137649 - time (sec): 340.58 - samples/sec: 440.01 - lr: 0.000061 - momentum: 0.000000 | |
| 2023-10-11 07:43:18,915 epoch 7 - iter 1246/1786 - loss 0.02099827 - time (sec): 396.15 - samples/sec: 438.43 - lr: 0.000059 - momentum: 0.000000 | |
| 2023-10-11 07:44:18,289 epoch 7 - iter 1424/1786 - loss 0.02038066 - time (sec): 455.52 - samples/sec: 439.21 - lr: 0.000057 - momentum: 0.000000 | |
| 2023-10-11 07:45:16,495 epoch 7 - iter 1602/1786 - loss 0.01985291 - time (sec): 513.73 - samples/sec: 437.28 - lr: 0.000055 - momentum: 0.000000 | |
| 2023-10-11 07:46:09,692 epoch 7 - iter 1780/1786 - loss 0.02003687 - time (sec): 566.93 - samples/sec: 437.53 - lr: 0.000053 - momentum: 0.000000 | |
| 2023-10-11 07:46:11,314 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:46:11,314 EPOCH 7 done: loss 0.0201 - lr: 0.000053 | |
| 2023-10-11 07:46:32,834 DEV : loss 0.19440826773643494 - f1-score (micro avg) 0.7954 | |
| 2023-10-11 07:46:32,868 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:47:26,349 epoch 8 - iter 178/1786 - loss 0.02118880 - time (sec): 53.48 - samples/sec: 450.21 - lr: 0.000052 - momentum: 0.000000 | |
| 2023-10-11 07:48:21,828 epoch 8 - iter 356/1786 - loss 0.01874784 - time (sec): 108.96 - samples/sec: 445.93 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-11 07:49:16,307 epoch 8 - iter 534/1786 - loss 0.01722056 - time (sec): 163.44 - samples/sec: 450.09 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-11 07:50:13,820 epoch 8 - iter 712/1786 - loss 0.01921550 - time (sec): 220.95 - samples/sec: 449.54 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-11 07:51:10,663 epoch 8 - iter 890/1786 - loss 0.01831347 - time (sec): 277.79 - samples/sec: 444.64 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-11 07:52:09,449 epoch 8 - iter 1068/1786 - loss 0.01900935 - time (sec): 336.58 - samples/sec: 440.68 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-11 07:53:04,351 epoch 8 - iter 1246/1786 - loss 0.01828550 - time (sec): 391.48 - samples/sec: 441.25 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-11 07:54:00,162 epoch 8 - iter 1424/1786 - loss 0.01722443 - time (sec): 447.29 - samples/sec: 443.60 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-11 07:54:56,327 epoch 8 - iter 1602/1786 - loss 0.01696995 - time (sec): 503.46 - samples/sec: 442.39 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-11 07:55:53,224 epoch 8 - iter 1780/1786 - loss 0.01668282 - time (sec): 560.35 - samples/sec: 442.21 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-11 07:55:55,029 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:55:55,030 EPOCH 8 done: loss 0.0167 - lr: 0.000036 | |
| 2023-10-11 07:56:17,077 DEV : loss 0.1955002099275589 - f1-score (micro avg) 0.803 | |
| 2023-10-11 07:56:17,108 saving best model | |
| 2023-10-11 07:56:19,773 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 07:57:19,182 epoch 9 - iter 178/1786 - loss 0.00753763 - time (sec): 59.40 - samples/sec: 434.60 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-11 07:58:14,298 epoch 9 - iter 356/1786 - loss 0.01214607 - time (sec): 114.52 - samples/sec: 438.67 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-11 07:59:07,909 epoch 9 - iter 534/1786 - loss 0.00983533 - time (sec): 168.13 - samples/sec: 445.20 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-11 08:00:01,122 epoch 9 - iter 712/1786 - loss 0.01002213 - time (sec): 221.34 - samples/sec: 448.23 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-11 08:00:56,745 epoch 9 - iter 890/1786 - loss 0.00888437 - time (sec): 276.97 - samples/sec: 447.41 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-11 08:01:52,410 epoch 9 - iter 1068/1786 - loss 0.00895876 - time (sec): 332.63 - samples/sec: 446.60 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-11 08:02:51,663 epoch 9 - iter 1246/1786 - loss 0.00900417 - time (sec): 391.89 - samples/sec: 437.63 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-11 08:03:51,512 epoch 9 - iter 1424/1786 - loss 0.01017587 - time (sec): 451.73 - samples/sec: 436.07 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-11 08:04:46,799 epoch 9 - iter 1602/1786 - loss 0.01074771 - time (sec): 507.02 - samples/sec: 440.42 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-11 08:05:40,266 epoch 9 - iter 1780/1786 - loss 0.01046865 - time (sec): 560.49 - samples/sec: 442.27 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-11 08:05:42,023 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 08:05:42,023 EPOCH 9 done: loss 0.0104 - lr: 0.000018 | |
| 2023-10-11 08:06:04,094 DEV : loss 0.21326804161071777 - f1-score (micro avg) 0.7971 | |
| 2023-10-11 08:06:04,125 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 08:06:58,019 epoch 10 - iter 178/1786 - loss 0.00672035 - time (sec): 53.89 - samples/sec: 436.00 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-11 08:07:53,539 epoch 10 - iter 356/1786 - loss 0.00687932 - time (sec): 109.41 - samples/sec: 441.96 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-11 08:08:52,210 epoch 10 - iter 534/1786 - loss 0.00685882 - time (sec): 168.08 - samples/sec: 439.86 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-11 08:09:49,775 epoch 10 - iter 712/1786 - loss 0.00638074 - time (sec): 225.65 - samples/sec: 433.50 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-11 08:10:46,408 epoch 10 - iter 890/1786 - loss 0.00750964 - time (sec): 282.28 - samples/sec: 439.14 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-11 08:11:42,983 epoch 10 - iter 1068/1786 - loss 0.00846706 - time (sec): 338.86 - samples/sec: 444.69 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-11 08:12:37,467 epoch 10 - iter 1246/1786 - loss 0.00856117 - time (sec): 393.34 - samples/sec: 441.99 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-11 08:13:35,598 epoch 10 - iter 1424/1786 - loss 0.00826761 - time (sec): 451.47 - samples/sec: 441.54 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-11 08:14:36,646 epoch 10 - iter 1602/1786 - loss 0.00793073 - time (sec): 512.52 - samples/sec: 436.65 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-11 08:15:31,095 epoch 10 - iter 1780/1786 - loss 0.00780604 - time (sec): 566.97 - samples/sec: 437.59 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-11 08:15:32,665 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 08:15:32,666 EPOCH 10 done: loss 0.0078 - lr: 0.000000 | |
| 2023-10-11 08:15:53,506 DEV : loss 0.21688954532146454 - f1-score (micro avg) 0.7973 | |
| 2023-10-11 08:15:54,599 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 08:15:54,601 Loading model from best epoch ... | |
| 2023-10-11 08:15:58,969 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 | |
| 2023-10-11 08:17:06,782 | |
| Results: | |
| - F-score (micro) 0.7048 | |
| - F-score (macro) 0.6244 | |
| - Accuracy 0.5644 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7416 0.7078 0.7243 1095 | |
| PER 0.7849 0.7826 0.7838 1012 | |
| ORG 0.4219 0.5826 0.4894 357 | |
| HumanProd 0.4000 0.6667 0.5000 33 | |
| micro avg 0.6906 0.7197 0.7048 2497 | |
| macro avg 0.5871 0.6849 0.6244 2497 | |
| weighted avg 0.7090 0.7197 0.7119 2497 | |
| 2023-10-11 08:17:06,782 ---------------------------------------------------------------------------------------------------- | |