| 2023-10-11 10:50:03,874 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,877 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 10:50:03,877 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,877 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 | |
| 2023-10-11 10:50:03,877 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,877 Train: 1085 sentences | |
| 2023-10-11 10:50:03,877 (train_with_dev=False, train_with_test=False) | |
| 2023-10-11 10:50:03,877 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,877 Training Params: | |
| 2023-10-11 10:50:03,877 - learning_rate: "0.00015" | |
| 2023-10-11 10:50:03,878 - mini_batch_size: "4" | |
| 2023-10-11 10:50:03,878 - max_epochs: "10" | |
| 2023-10-11 10:50:03,878 - shuffle: "True" | |
| 2023-10-11 10:50:03,878 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,878 Plugins: | |
| 2023-10-11 10:50:03,878 - TensorboardLogger | |
| 2023-10-11 10:50:03,878 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-11 10:50:03,878 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,878 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-11 10:50:03,878 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-11 10:50:03,878 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,878 Computation: | |
| 2023-10-11 10:50:03,878 - compute on device: cuda:0 | |
| 2023-10-11 10:50:03,878 - embedding storage: none | |
| 2023-10-11 10:50:03,878 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,878 Model training base path: "hmbench-newseye/sv-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3" | |
| 2023-10-11 10:50:03,879 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,879 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:50:03,879 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-11 10:50:13,487 epoch 1 - iter 27/272 - loss 2.82659249 - time (sec): 9.61 - samples/sec: 598.94 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-11 10:50:22,796 epoch 1 - iter 54/272 - loss 2.81733222 - time (sec): 18.92 - samples/sec: 592.05 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-11 10:50:32,122 epoch 1 - iter 81/272 - loss 2.79616710 - time (sec): 28.24 - samples/sec: 580.96 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-11 10:50:41,422 epoch 1 - iter 108/272 - loss 2.75078140 - time (sec): 37.54 - samples/sec: 572.55 - lr: 0.000059 - momentum: 0.000000 | |
| 2023-10-11 10:50:50,767 epoch 1 - iter 135/272 - loss 2.66645771 - time (sec): 46.89 - samples/sec: 571.66 - lr: 0.000074 - momentum: 0.000000 | |
| 2023-10-11 10:50:59,522 epoch 1 - iter 162/272 - loss 2.58487111 - time (sec): 55.64 - samples/sec: 564.72 - lr: 0.000089 - momentum: 0.000000 | |
| 2023-10-11 10:51:08,232 epoch 1 - iter 189/272 - loss 2.48987222 - time (sec): 64.35 - samples/sec: 557.49 - lr: 0.000104 - momentum: 0.000000 | |
| 2023-10-11 10:51:17,966 epoch 1 - iter 216/272 - loss 2.36263029 - time (sec): 74.09 - samples/sec: 561.83 - lr: 0.000119 - momentum: 0.000000 | |
| 2023-10-11 10:51:26,940 epoch 1 - iter 243/272 - loss 2.24603417 - time (sec): 83.06 - samples/sec: 560.39 - lr: 0.000133 - momentum: 0.000000 | |
| 2023-10-11 10:51:36,410 epoch 1 - iter 270/272 - loss 2.12867006 - time (sec): 92.53 - samples/sec: 558.34 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-11 10:51:36,944 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:51:36,945 EPOCH 1 done: loss 2.1205 - lr: 0.000148 | |
| 2023-10-11 10:51:41,788 DEV : loss 0.8181562423706055 - f1-score (micro avg) 0.0 | |
| 2023-10-11 10:51:41,796 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:51:51,485 epoch 2 - iter 27/272 - loss 0.77588845 - time (sec): 9.69 - samples/sec: 602.16 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-11 10:52:00,658 epoch 2 - iter 54/272 - loss 0.73677202 - time (sec): 18.86 - samples/sec: 597.29 - lr: 0.000147 - momentum: 0.000000 | |
| 2023-10-11 10:52:09,718 epoch 2 - iter 81/272 - loss 0.70960269 - time (sec): 27.92 - samples/sec: 587.29 - lr: 0.000145 - momentum: 0.000000 | |
| 2023-10-11 10:52:18,476 epoch 2 - iter 108/272 - loss 0.67231305 - time (sec): 36.68 - samples/sec: 574.11 - lr: 0.000143 - momentum: 0.000000 | |
| 2023-10-11 10:52:27,575 epoch 2 - iter 135/272 - loss 0.63398133 - time (sec): 45.78 - samples/sec: 571.45 - lr: 0.000142 - momentum: 0.000000 | |
| 2023-10-11 10:52:36,162 epoch 2 - iter 162/272 - loss 0.60685794 - time (sec): 54.36 - samples/sec: 561.27 - lr: 0.000140 - momentum: 0.000000 | |
| 2023-10-11 10:52:45,245 epoch 2 - iter 189/272 - loss 0.57321778 - time (sec): 63.45 - samples/sec: 556.34 - lr: 0.000138 - momentum: 0.000000 | |
| 2023-10-11 10:52:54,524 epoch 2 - iter 216/272 - loss 0.55390885 - time (sec): 72.73 - samples/sec: 554.04 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-11 10:53:04,155 epoch 2 - iter 243/272 - loss 0.53466124 - time (sec): 82.36 - samples/sec: 553.70 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-11 10:53:14,344 epoch 2 - iter 270/272 - loss 0.51513046 - time (sec): 92.55 - samples/sec: 558.92 - lr: 0.000134 - momentum: 0.000000 | |
| 2023-10-11 10:53:14,819 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:53:14,819 EPOCH 2 done: loss 0.5129 - lr: 0.000134 | |
| 2023-10-11 10:53:20,319 DEV : loss 0.30094385147094727 - f1-score (micro avg) 0.318 | |
| 2023-10-11 10:53:20,328 saving best model | |
| 2023-10-11 10:53:21,176 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:53:30,341 epoch 3 - iter 27/272 - loss 0.36013238 - time (sec): 9.16 - samples/sec: 557.38 - lr: 0.000132 - momentum: 0.000000 | |
| 2023-10-11 10:53:39,870 epoch 3 - iter 54/272 - loss 0.33093762 - time (sec): 18.69 - samples/sec: 566.19 - lr: 0.000130 - momentum: 0.000000 | |
| 2023-10-11 10:53:49,723 epoch 3 - iter 81/272 - loss 0.32749068 - time (sec): 28.54 - samples/sec: 583.92 - lr: 0.000128 - momentum: 0.000000 | |
| 2023-10-11 10:53:58,383 epoch 3 - iter 108/272 - loss 0.33782747 - time (sec): 37.20 - samples/sec: 565.12 - lr: 0.000127 - momentum: 0.000000 | |
| 2023-10-11 10:54:07,944 epoch 3 - iter 135/272 - loss 0.33686699 - time (sec): 46.77 - samples/sec: 551.43 - lr: 0.000125 - momentum: 0.000000 | |
| 2023-10-11 10:54:18,169 epoch 3 - iter 162/272 - loss 0.32243636 - time (sec): 56.99 - samples/sec: 557.39 - lr: 0.000123 - momentum: 0.000000 | |
| 2023-10-11 10:54:27,286 epoch 3 - iter 189/272 - loss 0.31031933 - time (sec): 66.11 - samples/sec: 558.27 - lr: 0.000122 - momentum: 0.000000 | |
| 2023-10-11 10:54:36,651 epoch 3 - iter 216/272 - loss 0.29566962 - time (sec): 75.47 - samples/sec: 555.53 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-11 10:54:45,706 epoch 3 - iter 243/272 - loss 0.28469825 - time (sec): 84.53 - samples/sec: 552.48 - lr: 0.000119 - momentum: 0.000000 | |
| 2023-10-11 10:54:54,928 epoch 3 - iter 270/272 - loss 0.28376485 - time (sec): 93.75 - samples/sec: 552.23 - lr: 0.000117 - momentum: 0.000000 | |
| 2023-10-11 10:54:55,379 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:54:55,380 EPOCH 3 done: loss 0.2831 - lr: 0.000117 | |
| 2023-10-11 10:55:00,783 DEV : loss 0.20688828825950623 - f1-score (micro avg) 0.5714 | |
| 2023-10-11 10:55:00,792 saving best model | |
| 2023-10-11 10:55:03,337 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:55:12,297 epoch 4 - iter 27/272 - loss 0.18883592 - time (sec): 8.96 - samples/sec: 538.43 - lr: 0.000115 - momentum: 0.000000 | |
| 2023-10-11 10:55:20,299 epoch 4 - iter 54/272 - loss 0.21454971 - time (sec): 16.96 - samples/sec: 510.04 - lr: 0.000113 - momentum: 0.000000 | |
| 2023-10-11 10:55:29,545 epoch 4 - iter 81/272 - loss 0.22342544 - time (sec): 26.20 - samples/sec: 537.13 - lr: 0.000112 - momentum: 0.000000 | |
| 2023-10-11 10:55:38,397 epoch 4 - iter 108/272 - loss 0.20820804 - time (sec): 35.06 - samples/sec: 541.85 - lr: 0.000110 - momentum: 0.000000 | |
| 2023-10-11 10:55:47,376 epoch 4 - iter 135/272 - loss 0.20439682 - time (sec): 44.03 - samples/sec: 543.39 - lr: 0.000108 - momentum: 0.000000 | |
| 2023-10-11 10:55:55,921 epoch 4 - iter 162/272 - loss 0.19740033 - time (sec): 52.58 - samples/sec: 537.66 - lr: 0.000107 - momentum: 0.000000 | |
| 2023-10-11 10:56:06,557 epoch 4 - iter 189/272 - loss 0.18830402 - time (sec): 63.22 - samples/sec: 555.38 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-11 10:56:15,674 epoch 4 - iter 216/272 - loss 0.18412200 - time (sec): 72.33 - samples/sec: 557.92 - lr: 0.000103 - momentum: 0.000000 | |
| 2023-10-11 10:56:25,318 epoch 4 - iter 243/272 - loss 0.17948316 - time (sec): 81.98 - samples/sec: 559.34 - lr: 0.000102 - momentum: 0.000000 | |
| 2023-10-11 10:56:35,122 epoch 4 - iter 270/272 - loss 0.17495580 - time (sec): 91.78 - samples/sec: 562.76 - lr: 0.000100 - momentum: 0.000000 | |
| 2023-10-11 10:56:35,663 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:56:35,663 EPOCH 4 done: loss 0.1745 - lr: 0.000100 | |
| 2023-10-11 10:56:41,543 DEV : loss 0.16108796000480652 - f1-score (micro avg) 0.636 | |
| 2023-10-11 10:56:41,552 saving best model | |
| 2023-10-11 10:56:44,080 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:56:54,081 epoch 5 - iter 27/272 - loss 0.11279072 - time (sec): 10.00 - samples/sec: 575.65 - lr: 0.000098 - momentum: 0.000000 | |
| 2023-10-11 10:57:04,141 epoch 5 - iter 54/272 - loss 0.12313934 - time (sec): 20.06 - samples/sec: 554.27 - lr: 0.000097 - momentum: 0.000000 | |
| 2023-10-11 10:57:13,844 epoch 5 - iter 81/272 - loss 0.11655880 - time (sec): 29.76 - samples/sec: 552.92 - lr: 0.000095 - momentum: 0.000000 | |
| 2023-10-11 10:57:22,628 epoch 5 - iter 108/272 - loss 0.11981029 - time (sec): 38.54 - samples/sec: 540.70 - lr: 0.000093 - momentum: 0.000000 | |
| 2023-10-11 10:57:31,316 epoch 5 - iter 135/272 - loss 0.12033932 - time (sec): 47.23 - samples/sec: 534.06 - lr: 0.000092 - momentum: 0.000000 | |
| 2023-10-11 10:57:40,567 epoch 5 - iter 162/272 - loss 0.12185608 - time (sec): 56.48 - samples/sec: 536.12 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-11 10:57:49,789 epoch 5 - iter 189/272 - loss 0.11473005 - time (sec): 65.71 - samples/sec: 536.88 - lr: 0.000088 - momentum: 0.000000 | |
| 2023-10-11 10:57:59,333 epoch 5 - iter 216/272 - loss 0.11661259 - time (sec): 75.25 - samples/sec: 538.49 - lr: 0.000087 - momentum: 0.000000 | |
| 2023-10-11 10:58:09,636 epoch 5 - iter 243/272 - loss 0.11767849 - time (sec): 85.55 - samples/sec: 547.14 - lr: 0.000085 - momentum: 0.000000 | |
| 2023-10-11 10:58:18,905 epoch 5 - iter 270/272 - loss 0.11723516 - time (sec): 94.82 - samples/sec: 546.86 - lr: 0.000084 - momentum: 0.000000 | |
| 2023-10-11 10:58:19,303 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:58:19,303 EPOCH 5 done: loss 0.1171 - lr: 0.000084 | |
| 2023-10-11 10:58:24,880 DEV : loss 0.13878858089447021 - f1-score (micro avg) 0.7559 | |
| 2023-10-11 10:58:24,889 saving best model | |
| 2023-10-11 10:58:27,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 10:58:36,265 epoch 6 - iter 27/272 - loss 0.07309716 - time (sec): 8.83 - samples/sec: 529.07 - lr: 0.000082 - momentum: 0.000000 | |
| 2023-10-11 10:58:45,128 epoch 6 - iter 54/272 - loss 0.08693004 - time (sec): 17.70 - samples/sec: 521.43 - lr: 0.000080 - momentum: 0.000000 | |
| 2023-10-11 10:58:54,405 epoch 6 - iter 81/272 - loss 0.09456387 - time (sec): 26.97 - samples/sec: 523.42 - lr: 0.000078 - momentum: 0.000000 | |
| 2023-10-11 10:59:04,184 epoch 6 - iter 108/272 - loss 0.08984032 - time (sec): 36.75 - samples/sec: 537.09 - lr: 0.000077 - momentum: 0.000000 | |
| 2023-10-11 10:59:14,018 epoch 6 - iter 135/272 - loss 0.08508482 - time (sec): 46.59 - samples/sec: 552.64 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-11 10:59:23,202 epoch 6 - iter 162/272 - loss 0.08590144 - time (sec): 55.77 - samples/sec: 541.76 - lr: 0.000073 - momentum: 0.000000 | |
| 2023-10-11 10:59:32,673 epoch 6 - iter 189/272 - loss 0.08188597 - time (sec): 65.24 - samples/sec: 546.70 - lr: 0.000072 - momentum: 0.000000 | |
| 2023-10-11 10:59:41,961 epoch 6 - iter 216/272 - loss 0.08590837 - time (sec): 74.53 - samples/sec: 544.08 - lr: 0.000070 - momentum: 0.000000 | |
| 2023-10-11 10:59:51,615 epoch 6 - iter 243/272 - loss 0.08500069 - time (sec): 84.18 - samples/sec: 546.89 - lr: 0.000069 - momentum: 0.000000 | |
| 2023-10-11 11:00:01,257 epoch 6 - iter 270/272 - loss 0.08427618 - time (sec): 93.83 - samples/sec: 548.96 - lr: 0.000067 - momentum: 0.000000 | |
| 2023-10-11 11:00:01,944 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:00:01,944 EPOCH 6 done: loss 0.0842 - lr: 0.000067 | |
| 2023-10-11 11:00:07,586 DEV : loss 0.13508526980876923 - f1-score (micro avg) 0.7784 | |
| 2023-10-11 11:00:07,594 saving best model | |
| 2023-10-11 11:00:10,077 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:00:19,490 epoch 7 - iter 27/272 - loss 0.06349629 - time (sec): 9.41 - samples/sec: 572.82 - lr: 0.000065 - momentum: 0.000000 | |
| 2023-10-11 11:00:28,354 epoch 7 - iter 54/272 - loss 0.07168990 - time (sec): 18.27 - samples/sec: 558.79 - lr: 0.000063 - momentum: 0.000000 | |
| 2023-10-11 11:00:38,273 epoch 7 - iter 81/272 - loss 0.06744160 - time (sec): 28.19 - samples/sec: 569.92 - lr: 0.000062 - momentum: 0.000000 | |
| 2023-10-11 11:00:47,594 epoch 7 - iter 108/272 - loss 0.06533570 - time (sec): 37.51 - samples/sec: 569.37 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-11 11:00:56,990 epoch 7 - iter 135/272 - loss 0.07004227 - time (sec): 46.91 - samples/sec: 569.69 - lr: 0.000058 - momentum: 0.000000 | |
| 2023-10-11 11:01:06,041 epoch 7 - iter 162/272 - loss 0.06680624 - time (sec): 55.96 - samples/sec: 564.74 - lr: 0.000057 - momentum: 0.000000 | |
| 2023-10-11 11:01:15,636 epoch 7 - iter 189/272 - loss 0.07011620 - time (sec): 65.56 - samples/sec: 563.17 - lr: 0.000055 - momentum: 0.000000 | |
| 2023-10-11 11:01:24,444 epoch 7 - iter 216/272 - loss 0.06882717 - time (sec): 74.36 - samples/sec: 557.55 - lr: 0.000053 - momentum: 0.000000 | |
| 2023-10-11 11:01:34,079 epoch 7 - iter 243/272 - loss 0.06683075 - time (sec): 84.00 - samples/sec: 557.83 - lr: 0.000052 - momentum: 0.000000 | |
| 2023-10-11 11:01:43,455 epoch 7 - iter 270/272 - loss 0.06398691 - time (sec): 93.37 - samples/sec: 554.75 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-11 11:01:43,855 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:01:43,855 EPOCH 7 done: loss 0.0638 - lr: 0.000050 | |
| 2023-10-11 11:01:49,480 DEV : loss 0.13904628157615662 - f1-score (micro avg) 0.7544 | |
| 2023-10-11 11:01:49,488 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:01:58,810 epoch 8 - iter 27/272 - loss 0.05151019 - time (sec): 9.32 - samples/sec: 555.05 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-11 11:02:07,667 epoch 8 - iter 54/272 - loss 0.04875397 - time (sec): 18.18 - samples/sec: 538.60 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-11 11:02:17,611 epoch 8 - iter 81/272 - loss 0.05284993 - time (sec): 28.12 - samples/sec: 547.95 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-11 11:02:26,809 epoch 8 - iter 108/272 - loss 0.05991550 - time (sec): 37.32 - samples/sec: 544.64 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-11 11:02:36,284 epoch 8 - iter 135/272 - loss 0.05652978 - time (sec): 46.79 - samples/sec: 546.34 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-11 11:02:45,701 epoch 8 - iter 162/272 - loss 0.05626381 - time (sec): 56.21 - samples/sec: 547.48 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-11 11:02:55,335 epoch 8 - iter 189/272 - loss 0.05419822 - time (sec): 65.85 - samples/sec: 549.05 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-11 11:03:05,159 epoch 8 - iter 216/272 - loss 0.05356223 - time (sec): 75.67 - samples/sec: 549.84 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-11 11:03:14,930 epoch 8 - iter 243/272 - loss 0.05043893 - time (sec): 85.44 - samples/sec: 551.54 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-11 11:03:23,724 epoch 8 - iter 270/272 - loss 0.05041046 - time (sec): 94.23 - samples/sec: 548.22 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-11 11:03:24,272 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:03:24,272 EPOCH 8 done: loss 0.0502 - lr: 0.000034 | |
| 2023-10-11 11:03:30,009 DEV : loss 0.13503918051719666 - f1-score (micro avg) 0.7964 | |
| 2023-10-11 11:03:30,018 saving best model | |
| 2023-10-11 11:03:32,544 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:03:42,316 epoch 9 - iter 27/272 - loss 0.04416837 - time (sec): 9.77 - samples/sec: 597.14 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-11 11:03:51,802 epoch 9 - iter 54/272 - loss 0.04781217 - time (sec): 19.25 - samples/sec: 577.94 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-11 11:04:00,376 epoch 9 - iter 81/272 - loss 0.04608928 - time (sec): 27.83 - samples/sec: 558.18 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-11 11:04:09,553 epoch 9 - iter 108/272 - loss 0.04616787 - time (sec): 37.01 - samples/sec: 556.51 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-11 11:04:19,183 epoch 9 - iter 135/272 - loss 0.04546524 - time (sec): 46.63 - samples/sec: 558.30 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-11 11:04:28,689 epoch 9 - iter 162/272 - loss 0.04381471 - time (sec): 56.14 - samples/sec: 559.12 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-11 11:04:38,019 epoch 9 - iter 189/272 - loss 0.04496020 - time (sec): 65.47 - samples/sec: 554.38 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-11 11:04:47,008 epoch 9 - iter 216/272 - loss 0.04477837 - time (sec): 74.46 - samples/sec: 551.81 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-11 11:04:57,105 epoch 9 - iter 243/272 - loss 0.04362140 - time (sec): 84.56 - samples/sec: 556.23 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-11 11:05:06,298 epoch 9 - iter 270/272 - loss 0.04226270 - time (sec): 93.75 - samples/sec: 553.27 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-11 11:05:06,661 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:05:06,662 EPOCH 9 done: loss 0.0423 - lr: 0.000017 | |
| 2023-10-11 11:05:12,189 DEV : loss 0.13971544802188873 - f1-score (micro avg) 0.7899 | |
| 2023-10-11 11:05:12,198 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:05:22,115 epoch 10 - iter 27/272 - loss 0.04362523 - time (sec): 9.92 - samples/sec: 549.26 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-11 11:05:30,817 epoch 10 - iter 54/272 - loss 0.04805838 - time (sec): 18.62 - samples/sec: 523.99 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-11 11:05:41,534 epoch 10 - iter 81/272 - loss 0.04944586 - time (sec): 29.33 - samples/sec: 557.68 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-11 11:05:51,898 epoch 10 - iter 108/272 - loss 0.04975532 - time (sec): 39.70 - samples/sec: 565.25 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-11 11:06:01,700 epoch 10 - iter 135/272 - loss 0.04678516 - time (sec): 49.50 - samples/sec: 561.01 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-11 11:06:10,214 epoch 10 - iter 162/272 - loss 0.04446697 - time (sec): 58.01 - samples/sec: 549.99 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-11 11:06:20,152 epoch 10 - iter 189/272 - loss 0.04288849 - time (sec): 67.95 - samples/sec: 550.37 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-11 11:06:29,363 epoch 10 - iter 216/272 - loss 0.04070236 - time (sec): 77.16 - samples/sec: 543.35 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-11 11:06:38,730 epoch 10 - iter 243/272 - loss 0.03991694 - time (sec): 86.53 - samples/sec: 543.33 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-11 11:06:47,930 epoch 10 - iter 270/272 - loss 0.03815397 - time (sec): 95.73 - samples/sec: 540.55 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-11 11:06:48,386 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:06:48,387 EPOCH 10 done: loss 0.0382 - lr: 0.000000 | |
| 2023-10-11 11:06:54,102 DEV : loss 0.1388687789440155 - f1-score (micro avg) 0.7985 | |
| 2023-10-11 11:06:54,111 saving best model | |
| 2023-10-11 11:06:57,461 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 11:06:57,463 Loading model from best epoch ... | |
| 2023-10-11 11:07:01,068 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 | |
| 2023-10-11 11:07:13,976 | |
| Results: | |
| - F-score (micro) 0.7448 | |
| - F-score (macro) 0.6813 | |
| - Accuracy 0.6137 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7652 0.8462 0.8037 312 | |
| PER 0.6357 0.8558 0.7295 208 | |
| ORG 0.4706 0.4364 0.4528 55 | |
| HumanProd 0.7083 0.7727 0.7391 22 | |
| micro avg 0.6900 0.8090 0.7448 597 | |
| macro avg 0.6450 0.7278 0.6813 597 | |
| weighted avg 0.6909 0.8090 0.7431 597 | |
| 2023-10-11 11:07:13,976 ---------------------------------------------------------------------------------------------------- | |