| 2023-10-25 21:09:57,475 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,476 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (word_embeddings): Embedding(64001, 768) | |
| (position_embeddings): Embedding(512, 768) | |
| (token_type_embeddings): Embedding(2, 768) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): BertEncoder( | |
| (layer): ModuleList( | |
| (0-11): 12 x BertLayer( | |
| (attention): BertAttention( | |
| (self): BertSelfAttention( | |
| (query): Linear(in_features=768, out_features=768, bias=True) | |
| (key): Linear(in_features=768, out_features=768, bias=True) | |
| (value): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (output): BertSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): BertIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): BertOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): BertPooler( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (activation): Tanh() | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=768, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-25 21:09:57,476 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,476 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 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Train: 1166 sentences | |
| 2023-10-25 21:09:57,477 (train_with_dev=False, train_with_test=False) | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Training Params: | |
| 2023-10-25 21:09:57,477 - learning_rate: "3e-05" | |
| 2023-10-25 21:09:57,477 - mini_batch_size: "4" | |
| 2023-10-25 21:09:57,477 - max_epochs: "10" | |
| 2023-10-25 21:09:57,477 - shuffle: "True" | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Plugins: | |
| 2023-10-25 21:09:57,477 - TensorboardLogger | |
| 2023-10-25 21:09:57,477 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-25 21:09:57,477 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Computation: | |
| 2023-10-25 21:09:57,477 - compute on device: cuda:0 | |
| 2023-10-25 21:09:57,477 - embedding storage: none | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3" | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:09:57,477 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-25 21:09:58,743 epoch 1 - iter 29/292 - loss 2.73833700 - time (sec): 1.26 - samples/sec: 2733.50 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:10:00,016 epoch 1 - iter 58/292 - loss 2.06223157 - time (sec): 2.54 - samples/sec: 2941.83 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:10:01,360 epoch 1 - iter 87/292 - loss 1.51849599 - time (sec): 3.88 - samples/sec: 3112.72 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:10:02,634 epoch 1 - iter 116/292 - loss 1.26889759 - time (sec): 5.16 - samples/sec: 3123.06 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:10:03,888 epoch 1 - iter 145/292 - loss 1.09220417 - time (sec): 6.41 - samples/sec: 3199.90 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:10:05,205 epoch 1 - iter 174/292 - loss 0.98328127 - time (sec): 7.73 - samples/sec: 3233.86 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:10:06,492 epoch 1 - iter 203/292 - loss 0.88448692 - time (sec): 9.01 - samples/sec: 3301.34 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:10:07,957 epoch 1 - iter 232/292 - loss 0.81219091 - time (sec): 10.48 - samples/sec: 3328.88 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:10:09,285 epoch 1 - iter 261/292 - loss 0.73736454 - time (sec): 11.81 - samples/sec: 3386.30 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:10:10,589 epoch 1 - iter 290/292 - loss 0.68903885 - time (sec): 13.11 - samples/sec: 3376.55 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 21:10:10,669 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:10,669 EPOCH 1 done: loss 0.6887 - lr: 0.000030 | |
| 2023-10-25 21:10:11,337 DEV : loss 0.14741386473178864 - f1-score (micro avg) 0.5684 | |
| 2023-10-25 21:10:11,341 saving best model | |
| 2023-10-25 21:10:11,804 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:13,116 epoch 2 - iter 29/292 - loss 0.19802987 - time (sec): 1.31 - samples/sec: 3532.37 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 21:10:14,467 epoch 2 - iter 58/292 - loss 0.16963628 - time (sec): 2.66 - samples/sec: 3655.77 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:10:15,747 epoch 2 - iter 87/292 - loss 0.17025510 - time (sec): 3.94 - samples/sec: 3550.44 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:10:17,076 epoch 2 - iter 116/292 - loss 0.16977186 - time (sec): 5.27 - samples/sec: 3463.69 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 21:10:18,323 epoch 2 - iter 145/292 - loss 0.16943346 - time (sec): 6.52 - samples/sec: 3405.57 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:10:19,568 epoch 2 - iter 174/292 - loss 0.17536090 - time (sec): 7.76 - samples/sec: 3354.69 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:10:20,803 epoch 2 - iter 203/292 - loss 0.17323557 - time (sec): 9.00 - samples/sec: 3361.80 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 21:10:22,166 epoch 2 - iter 232/292 - loss 0.16456716 - time (sec): 10.36 - samples/sec: 3373.62 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:10:23,436 epoch 2 - iter 261/292 - loss 0.16135994 - time (sec): 11.63 - samples/sec: 3407.22 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:10:24,699 epoch 2 - iter 290/292 - loss 0.16019950 - time (sec): 12.89 - samples/sec: 3421.71 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 21:10:24,782 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:24,783 EPOCH 2 done: loss 0.1601 - lr: 0.000027 | |
| 2023-10-25 21:10:25,689 DEV : loss 0.1006804034113884 - f1-score (micro avg) 0.7293 | |
| 2023-10-25 21:10:25,694 saving best model | |
| 2023-10-25 21:10:26,488 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:27,806 epoch 3 - iter 29/292 - loss 0.07594065 - time (sec): 1.31 - samples/sec: 3394.59 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:10:29,004 epoch 3 - iter 58/292 - loss 0.07959637 - time (sec): 2.51 - samples/sec: 3064.08 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:10:30,354 epoch 3 - iter 87/292 - loss 0.08406265 - time (sec): 3.86 - samples/sec: 3187.84 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 21:10:31,639 epoch 3 - iter 116/292 - loss 0.08390541 - time (sec): 5.15 - samples/sec: 3101.09 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:10:33,068 epoch 3 - iter 145/292 - loss 0.08728900 - time (sec): 6.58 - samples/sec: 3334.11 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:10:34,367 epoch 3 - iter 174/292 - loss 0.08901608 - time (sec): 7.88 - samples/sec: 3386.20 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 21:10:35,652 epoch 3 - iter 203/292 - loss 0.09014633 - time (sec): 9.16 - samples/sec: 3410.32 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:10:36,942 epoch 3 - iter 232/292 - loss 0.08866790 - time (sec): 10.45 - samples/sec: 3369.56 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:10:38,213 epoch 3 - iter 261/292 - loss 0.08888259 - time (sec): 11.72 - samples/sec: 3339.15 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 21:10:39,580 epoch 3 - iter 290/292 - loss 0.08998420 - time (sec): 13.09 - samples/sec: 3344.03 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:10:39,684 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:39,685 EPOCH 3 done: loss 0.0907 - lr: 0.000023 | |
| 2023-10-25 21:10:40,596 DEV : loss 0.10108631104230881 - f1-score (micro avg) 0.7149 | |
| 2023-10-25 21:10:40,601 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:41,978 epoch 4 - iter 29/292 - loss 0.06981579 - time (sec): 1.38 - samples/sec: 3683.46 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:10:43,335 epoch 4 - iter 58/292 - loss 0.06503789 - time (sec): 2.73 - samples/sec: 3312.15 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 21:10:44,674 epoch 4 - iter 87/292 - loss 0.06470976 - time (sec): 4.07 - samples/sec: 3261.96 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:10:45,984 epoch 4 - iter 116/292 - loss 0.06055619 - time (sec): 5.38 - samples/sec: 3217.83 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:10:47,230 epoch 4 - iter 145/292 - loss 0.05764284 - time (sec): 6.63 - samples/sec: 3167.14 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 21:10:48,543 epoch 4 - iter 174/292 - loss 0.06190221 - time (sec): 7.94 - samples/sec: 3228.80 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:10:49,817 epoch 4 - iter 203/292 - loss 0.06220813 - time (sec): 9.22 - samples/sec: 3221.19 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:10:51,171 epoch 4 - iter 232/292 - loss 0.06042297 - time (sec): 10.57 - samples/sec: 3177.20 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 21:10:52,526 epoch 4 - iter 261/292 - loss 0.06188990 - time (sec): 11.92 - samples/sec: 3277.30 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:10:53,812 epoch 4 - iter 290/292 - loss 0.06067152 - time (sec): 13.21 - samples/sec: 3353.33 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:10:53,891 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:53,892 EPOCH 4 done: loss 0.0605 - lr: 0.000020 | |
| 2023-10-25 21:10:54,800 DEV : loss 0.12220078706741333 - f1-score (micro avg) 0.7566 | |
| 2023-10-25 21:10:54,805 saving best model | |
| 2023-10-25 21:10:55,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:10:56,618 epoch 5 - iter 29/292 - loss 0.07226843 - time (sec): 1.31 - samples/sec: 3651.83 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 21:10:57,855 epoch 5 - iter 58/292 - loss 0.05576230 - time (sec): 2.55 - samples/sec: 3360.82 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:10:59,178 epoch 5 - iter 87/292 - loss 0.05222793 - time (sec): 3.87 - samples/sec: 3532.22 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:11:00,461 epoch 5 - iter 116/292 - loss 0.04893205 - time (sec): 5.15 - samples/sec: 3442.37 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 21:11:01,744 epoch 5 - iter 145/292 - loss 0.04364075 - time (sec): 6.44 - samples/sec: 3410.85 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:11:03,003 epoch 5 - iter 174/292 - loss 0.04155609 - time (sec): 7.70 - samples/sec: 3347.83 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:11:04,289 epoch 5 - iter 203/292 - loss 0.04349718 - time (sec): 8.98 - samples/sec: 3332.55 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 21:11:05,576 epoch 5 - iter 232/292 - loss 0.04323634 - time (sec): 10.27 - samples/sec: 3396.22 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:11:06,959 epoch 5 - iter 261/292 - loss 0.04192164 - time (sec): 11.65 - samples/sec: 3438.20 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:11:08,205 epoch 5 - iter 290/292 - loss 0.04216845 - time (sec): 12.90 - samples/sec: 3436.94 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 21:11:08,280 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:08,280 EPOCH 5 done: loss 0.0421 - lr: 0.000017 | |
| 2023-10-25 21:11:09,192 DEV : loss 0.14462324976921082 - f1-score (micro avg) 0.7615 | |
| 2023-10-25 21:11:09,197 saving best model | |
| 2023-10-25 21:11:09,811 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:11,084 epoch 6 - iter 29/292 - loss 0.03071517 - time (sec): 1.27 - samples/sec: 3367.38 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:11:12,392 epoch 6 - iter 58/292 - loss 0.03420363 - time (sec): 2.58 - samples/sec: 3399.63 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:11:13,735 epoch 6 - iter 87/292 - loss 0.02882773 - time (sec): 3.92 - samples/sec: 3494.61 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 21:11:15,013 epoch 6 - iter 116/292 - loss 0.02863387 - time (sec): 5.20 - samples/sec: 3502.54 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:11:16,288 epoch 6 - iter 145/292 - loss 0.02715294 - time (sec): 6.47 - samples/sec: 3430.36 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:11:17,594 epoch 6 - iter 174/292 - loss 0.02561636 - time (sec): 7.78 - samples/sec: 3368.67 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 21:11:18,908 epoch 6 - iter 203/292 - loss 0.02415975 - time (sec): 9.09 - samples/sec: 3379.04 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:11:20,254 epoch 6 - iter 232/292 - loss 0.02654283 - time (sec): 10.44 - samples/sec: 3388.04 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:11:21,502 epoch 6 - iter 261/292 - loss 0.02923450 - time (sec): 11.69 - samples/sec: 3425.09 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 21:11:22,697 epoch 6 - iter 290/292 - loss 0.02964102 - time (sec): 12.88 - samples/sec: 3434.90 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:11:22,773 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:22,774 EPOCH 6 done: loss 0.0298 - lr: 0.000013 | |
| 2023-10-25 21:11:23,685 DEV : loss 0.14538371562957764 - f1-score (micro avg) 0.7451 | |
| 2023-10-25 21:11:23,689 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:24,920 epoch 7 - iter 29/292 - loss 0.01672833 - time (sec): 1.23 - samples/sec: 3498.10 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:11:26,397 epoch 7 - iter 58/292 - loss 0.02256440 - time (sec): 2.71 - samples/sec: 3800.73 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 21:11:27,816 epoch 7 - iter 87/292 - loss 0.02204005 - time (sec): 4.13 - samples/sec: 3378.64 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:11:29,078 epoch 7 - iter 116/292 - loss 0.02236449 - time (sec): 5.39 - samples/sec: 3287.29 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:11:30,408 epoch 7 - iter 145/292 - loss 0.02209624 - time (sec): 6.72 - samples/sec: 3341.87 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 21:11:31,689 epoch 7 - iter 174/292 - loss 0.02162009 - time (sec): 8.00 - samples/sec: 3385.55 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:11:32,976 epoch 7 - iter 203/292 - loss 0.02205763 - time (sec): 9.29 - samples/sec: 3396.11 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:11:34,194 epoch 7 - iter 232/292 - loss 0.02172002 - time (sec): 10.50 - samples/sec: 3344.86 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 21:11:35,497 epoch 7 - iter 261/292 - loss 0.02105659 - time (sec): 11.81 - samples/sec: 3360.74 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:11:36,791 epoch 7 - iter 290/292 - loss 0.01955965 - time (sec): 13.10 - samples/sec: 3379.17 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:11:36,874 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:36,874 EPOCH 7 done: loss 0.0195 - lr: 0.000010 | |
| 2023-10-25 21:11:37,790 DEV : loss 0.13841482996940613 - f1-score (micro avg) 0.7592 | |
| 2023-10-25 21:11:37,794 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:39,074 epoch 8 - iter 29/292 - loss 0.02671843 - time (sec): 1.28 - samples/sec: 3108.12 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 21:11:40,476 epoch 8 - iter 58/292 - loss 0.02178925 - time (sec): 2.68 - samples/sec: 3397.93 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:11:41,813 epoch 8 - iter 87/292 - loss 0.01899837 - time (sec): 4.02 - samples/sec: 3467.31 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:11:43,060 epoch 8 - iter 116/292 - loss 0.02281207 - time (sec): 5.27 - samples/sec: 3471.94 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 21:11:44,356 epoch 8 - iter 145/292 - loss 0.02038146 - time (sec): 6.56 - samples/sec: 3427.58 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:11:45,606 epoch 8 - iter 174/292 - loss 0.01938016 - time (sec): 7.81 - samples/sec: 3442.67 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:11:46,852 epoch 8 - iter 203/292 - loss 0.01829205 - time (sec): 9.06 - samples/sec: 3394.12 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 21:11:48,119 epoch 8 - iter 232/292 - loss 0.01759759 - time (sec): 10.32 - samples/sec: 3435.64 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:11:49,392 epoch 8 - iter 261/292 - loss 0.01611240 - time (sec): 11.60 - samples/sec: 3441.24 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:11:50,615 epoch 8 - iter 290/292 - loss 0.01512526 - time (sec): 12.82 - samples/sec: 3457.28 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 21:11:50,691 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:50,691 EPOCH 8 done: loss 0.0151 - lr: 0.000007 | |
| 2023-10-25 21:11:51,601 DEV : loss 0.158660426735878 - f1-score (micro avg) 0.7716 | |
| 2023-10-25 21:11:51,605 saving best model | |
| 2023-10-25 21:11:52,218 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:11:53,533 epoch 9 - iter 29/292 - loss 0.00434558 - time (sec): 1.31 - samples/sec: 3611.31 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:11:54,797 epoch 9 - iter 58/292 - loss 0.00498322 - time (sec): 2.58 - samples/sec: 3555.16 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:11:56,049 epoch 9 - iter 87/292 - loss 0.00460247 - time (sec): 3.83 - samples/sec: 3395.57 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 21:11:57,416 epoch 9 - iter 116/292 - loss 0.00516195 - time (sec): 5.20 - samples/sec: 3445.47 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:11:58,788 epoch 9 - iter 145/292 - loss 0.00698851 - time (sec): 6.57 - samples/sec: 3472.08 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:12:00,089 epoch 9 - iter 174/292 - loss 0.00881053 - time (sec): 7.87 - samples/sec: 3480.23 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 21:12:01,400 epoch 9 - iter 203/292 - loss 0.00796033 - time (sec): 9.18 - samples/sec: 3444.05 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:12:02,643 epoch 9 - iter 232/292 - loss 0.00873789 - time (sec): 10.42 - samples/sec: 3416.19 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:12:03,950 epoch 9 - iter 261/292 - loss 0.00842511 - time (sec): 11.73 - samples/sec: 3381.93 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 21:12:05,264 epoch 9 - iter 290/292 - loss 0.00863425 - time (sec): 13.04 - samples/sec: 3387.58 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:12:05,342 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:12:05,342 EPOCH 9 done: loss 0.0086 - lr: 0.000003 | |
| 2023-10-25 21:12:06,262 DEV : loss 0.1723966747522354 - f1-score (micro avg) 0.7479 | |
| 2023-10-25 21:12:06,266 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:12:07,519 epoch 10 - iter 29/292 - loss 0.00123487 - time (sec): 1.25 - samples/sec: 3447.36 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:12:08,777 epoch 10 - iter 58/292 - loss 0.00792751 - time (sec): 2.51 - samples/sec: 3468.05 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 21:12:10,077 epoch 10 - iter 87/292 - loss 0.01303484 - time (sec): 3.81 - samples/sec: 3461.35 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:12:11,290 epoch 10 - iter 116/292 - loss 0.01034843 - time (sec): 5.02 - samples/sec: 3469.73 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:12:12,506 epoch 10 - iter 145/292 - loss 0.00886202 - time (sec): 6.24 - samples/sec: 3469.77 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 21:12:13,730 epoch 10 - iter 174/292 - loss 0.00848014 - time (sec): 7.46 - samples/sec: 3435.23 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:12:15,122 epoch 10 - iter 203/292 - loss 0.00876510 - time (sec): 8.86 - samples/sec: 3504.95 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:12:16,413 epoch 10 - iter 232/292 - loss 0.00952739 - time (sec): 10.15 - samples/sec: 3475.76 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 21:12:17,803 epoch 10 - iter 261/292 - loss 0.00885530 - time (sec): 11.54 - samples/sec: 3462.61 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 21:12:19,084 epoch 10 - iter 290/292 - loss 0.00831916 - time (sec): 12.82 - samples/sec: 3445.59 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 21:12:19,171 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:12:19,171 EPOCH 10 done: loss 0.0083 - lr: 0.000000 | |
| 2023-10-25 21:12:20,093 DEV : loss 0.17783689498901367 - f1-score (micro avg) 0.7511 | |
| 2023-10-25 21:12:20,562 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 21:12:20,563 Loading model from best epoch ... | |
| 2023-10-25 21:12:22,175 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 21:12:23,911 | |
| Results: | |
| - F-score (micro) 0.7554 | |
| - F-score (macro) 0.6669 | |
| - Accuracy 0.6324 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.7733 0.8333 0.8022 348 | |
| LOC 0.7063 0.8199 0.7589 261 | |
| ORG 0.4583 0.4231 0.4400 52 | |
| HumanProd 0.6154 0.7273 0.6667 22 | |
| micro avg 0.7207 0.7936 0.7554 683 | |
| macro avg 0.6383 0.7009 0.6669 683 | |
| weighted avg 0.7186 0.7936 0.7537 683 | |
| 2023-10-25 21:12:23,911 ---------------------------------------------------------------------------------------------------- | |