| 2023-10-17 13:37:25,646 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,647 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): ElectraModel( | |
| (embeddings): ElectraEmbeddings( | |
| (word_embeddings): Embedding(32001, 768) | |
| (position_embeddings): Embedding(512, 768) | |
| (token_type_embeddings): Embedding(2, 768) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): ElectraEncoder( | |
| (layer): ModuleList( | |
| (0-11): 12 x ElectraLayer( | |
| (attention): ElectraAttention( | |
| (self): ElectraSelfAttention( | |
| (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): ElectraSelfOutput( | |
| (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): ElectraIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): ElectraOutput( | |
| (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) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=768, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-17 13:37:25,647 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,647 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 | |
| 2023-10-17 13:37:25,647 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Train: 7142 sentences | |
| 2023-10-17 13:37:25,648 (train_with_dev=False, train_with_test=False) | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Training Params: | |
| 2023-10-17 13:37:25,648 - learning_rate: "3e-05" | |
| 2023-10-17 13:37:25,648 - mini_batch_size: "4" | |
| 2023-10-17 13:37:25,648 - max_epochs: "10" | |
| 2023-10-17 13:37:25,648 - shuffle: "True" | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Plugins: | |
| 2023-10-17 13:37:25,648 - TensorboardLogger | |
| 2023-10-17 13:37:25,648 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-17 13:37:25,648 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Computation: | |
| 2023-10-17 13:37:25,648 - compute on device: cuda:0 | |
| 2023-10-17 13:37:25,648 - embedding storage: none | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:37:25,648 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-17 13:37:34,100 epoch 1 - iter 178/1786 - loss 2.71850894 - time (sec): 8.45 - samples/sec: 2722.97 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 13:37:43,271 epoch 1 - iter 356/1786 - loss 1.55627930 - time (sec): 17.62 - samples/sec: 2818.11 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 13:37:52,167 epoch 1 - iter 534/1786 - loss 1.17692252 - time (sec): 26.52 - samples/sec: 2810.54 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 13:38:01,158 epoch 1 - iter 712/1786 - loss 0.95620126 - time (sec): 35.51 - samples/sec: 2847.03 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 13:38:09,947 epoch 1 - iter 890/1786 - loss 0.81990248 - time (sec): 44.30 - samples/sec: 2817.71 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 13:38:18,765 epoch 1 - iter 1068/1786 - loss 0.72732692 - time (sec): 53.12 - samples/sec: 2789.30 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:38:27,632 epoch 1 - iter 1246/1786 - loss 0.64882748 - time (sec): 61.98 - samples/sec: 2788.02 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:38:36,725 epoch 1 - iter 1424/1786 - loss 0.58363820 - time (sec): 71.08 - samples/sec: 2803.30 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:38:45,495 epoch 1 - iter 1602/1786 - loss 0.53997715 - time (sec): 79.85 - samples/sec: 2791.05 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:38:54,216 epoch 1 - iter 1780/1786 - loss 0.49959069 - time (sec): 88.57 - samples/sec: 2802.19 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 13:38:54,485 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:38:54,486 EPOCH 1 done: loss 0.4990 - lr: 0.000030 | |
| 2023-10-17 13:38:57,577 DEV : loss 0.11935114115476608 - f1-score (micro avg) 0.7585 | |
| 2023-10-17 13:38:57,593 saving best model | |
| 2023-10-17 13:38:57,942 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:39:07,045 epoch 2 - iter 178/1786 - loss 0.14207407 - time (sec): 9.10 - samples/sec: 2671.63 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 13:39:15,919 epoch 2 - iter 356/1786 - loss 0.13038487 - time (sec): 17.98 - samples/sec: 2672.23 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:39:24,577 epoch 2 - iter 534/1786 - loss 0.12627094 - time (sec): 26.63 - samples/sec: 2624.39 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:39:33,752 epoch 2 - iter 712/1786 - loss 0.12554020 - time (sec): 35.81 - samples/sec: 2655.03 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:39:42,945 epoch 2 - iter 890/1786 - loss 0.12266313 - time (sec): 45.00 - samples/sec: 2698.72 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:39:52,042 epoch 2 - iter 1068/1786 - loss 0.12077729 - time (sec): 54.10 - samples/sec: 2723.97 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:40:01,199 epoch 2 - iter 1246/1786 - loss 0.11775653 - time (sec): 63.26 - samples/sec: 2764.92 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:40:09,971 epoch 2 - iter 1424/1786 - loss 0.12007377 - time (sec): 72.03 - samples/sec: 2776.20 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:40:18,872 epoch 2 - iter 1602/1786 - loss 0.11957108 - time (sec): 80.93 - samples/sec: 2773.10 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:40:27,656 epoch 2 - iter 1780/1786 - loss 0.11971143 - time (sec): 89.71 - samples/sec: 2765.64 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:40:27,939 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:40:27,940 EPOCH 2 done: loss 0.1195 - lr: 0.000027 | |
| 2023-10-17 13:40:32,630 DEV : loss 0.10125791281461716 - f1-score (micro avg) 0.8144 | |
| 2023-10-17 13:40:32,647 saving best model | |
| 2023-10-17 13:40:33,095 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:40:41,901 epoch 3 - iter 178/1786 - loss 0.07418424 - time (sec): 8.80 - samples/sec: 2788.17 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:40:50,945 epoch 3 - iter 356/1786 - loss 0.07570841 - time (sec): 17.84 - samples/sec: 2768.58 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:40:59,866 epoch 3 - iter 534/1786 - loss 0.07399212 - time (sec): 26.77 - samples/sec: 2788.93 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:41:08,915 epoch 3 - iter 712/1786 - loss 0.07427751 - time (sec): 35.82 - samples/sec: 2747.68 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:41:17,929 epoch 3 - iter 890/1786 - loss 0.07471144 - time (sec): 44.83 - samples/sec: 2766.71 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:41:26,655 epoch 3 - iter 1068/1786 - loss 0.07885766 - time (sec): 53.56 - samples/sec: 2762.94 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:41:35,429 epoch 3 - iter 1246/1786 - loss 0.08022104 - time (sec): 62.33 - samples/sec: 2764.62 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:41:44,429 epoch 3 - iter 1424/1786 - loss 0.07990684 - time (sec): 71.33 - samples/sec: 2780.82 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:41:53,237 epoch 3 - iter 1602/1786 - loss 0.08236359 - time (sec): 80.14 - samples/sec: 2781.43 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:42:02,129 epoch 3 - iter 1780/1786 - loss 0.08175112 - time (sec): 89.03 - samples/sec: 2781.51 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:42:02,455 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:42:02,456 EPOCH 3 done: loss 0.0818 - lr: 0.000023 | |
| 2023-10-17 13:42:06,658 DEV : loss 0.13423609733581543 - f1-score (micro avg) 0.7967 | |
| 2023-10-17 13:42:06,675 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:42:15,751 epoch 4 - iter 178/1786 - loss 0.05237935 - time (sec): 9.07 - samples/sec: 2848.98 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:42:24,779 epoch 4 - iter 356/1786 - loss 0.05659288 - time (sec): 18.10 - samples/sec: 2788.78 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:42:33,850 epoch 4 - iter 534/1786 - loss 0.05742020 - time (sec): 27.17 - samples/sec: 2798.72 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:42:43,043 epoch 4 - iter 712/1786 - loss 0.05968286 - time (sec): 36.37 - samples/sec: 2804.23 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:42:51,955 epoch 4 - iter 890/1786 - loss 0.06046189 - time (sec): 45.28 - samples/sec: 2778.04 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:43:01,044 epoch 4 - iter 1068/1786 - loss 0.05955440 - time (sec): 54.37 - samples/sec: 2780.27 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:43:10,485 epoch 4 - iter 1246/1786 - loss 0.06002902 - time (sec): 63.81 - samples/sec: 2761.07 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:43:19,111 epoch 4 - iter 1424/1786 - loss 0.05900004 - time (sec): 72.43 - samples/sec: 2757.94 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:43:27,980 epoch 4 - iter 1602/1786 - loss 0.05891783 - time (sec): 81.30 - samples/sec: 2755.96 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:43:36,698 epoch 4 - iter 1780/1786 - loss 0.05878177 - time (sec): 90.02 - samples/sec: 2752.92 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:43:37,002 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:43:37,002 EPOCH 4 done: loss 0.0589 - lr: 0.000020 | |
| 2023-10-17 13:43:41,153 DEV : loss 0.16494163870811462 - f1-score (micro avg) 0.8072 | |
| 2023-10-17 13:43:41,170 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:43:50,053 epoch 5 - iter 178/1786 - loss 0.03097896 - time (sec): 8.88 - samples/sec: 2793.19 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:43:59,171 epoch 5 - iter 356/1786 - loss 0.04055098 - time (sec): 18.00 - samples/sec: 2843.10 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:44:08,165 epoch 5 - iter 534/1786 - loss 0.04153062 - time (sec): 26.99 - samples/sec: 2845.05 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:44:17,143 epoch 5 - iter 712/1786 - loss 0.04112747 - time (sec): 35.97 - samples/sec: 2818.48 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:44:25,980 epoch 5 - iter 890/1786 - loss 0.04124960 - time (sec): 44.81 - samples/sec: 2781.91 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:44:34,928 epoch 5 - iter 1068/1786 - loss 0.04175525 - time (sec): 53.76 - samples/sec: 2789.27 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:44:43,746 epoch 5 - iter 1246/1786 - loss 0.04113562 - time (sec): 62.57 - samples/sec: 2782.73 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:44:52,508 epoch 5 - iter 1424/1786 - loss 0.04207320 - time (sec): 71.34 - samples/sec: 2788.00 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:45:01,198 epoch 5 - iter 1602/1786 - loss 0.04173442 - time (sec): 80.03 - samples/sec: 2765.43 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:45:10,322 epoch 5 - iter 1780/1786 - loss 0.04286181 - time (sec): 89.15 - samples/sec: 2780.12 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:45:10,633 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:45:10,633 EPOCH 5 done: loss 0.0427 - lr: 0.000017 | |
| 2023-10-17 13:45:15,433 DEV : loss 0.1587299257516861 - f1-score (micro avg) 0.8108 | |
| 2023-10-17 13:45:15,450 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:45:24,076 epoch 6 - iter 178/1786 - loss 0.03579067 - time (sec): 8.62 - samples/sec: 2905.87 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 13:45:33,127 epoch 6 - iter 356/1786 - loss 0.02738996 - time (sec): 17.68 - samples/sec: 2901.45 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 13:45:42,026 epoch 6 - iter 534/1786 - loss 0.02902765 - time (sec): 26.57 - samples/sec: 2826.40 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 13:45:51,006 epoch 6 - iter 712/1786 - loss 0.02908652 - time (sec): 35.56 - samples/sec: 2805.29 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 13:45:59,947 epoch 6 - iter 890/1786 - loss 0.02990502 - time (sec): 44.50 - samples/sec: 2780.47 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 13:46:08,899 epoch 6 - iter 1068/1786 - loss 0.02956431 - time (sec): 53.45 - samples/sec: 2767.16 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 13:46:17,597 epoch 6 - iter 1246/1786 - loss 0.03095708 - time (sec): 62.15 - samples/sec: 2779.68 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 13:46:26,112 epoch 6 - iter 1424/1786 - loss 0.03171714 - time (sec): 70.66 - samples/sec: 2810.15 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 13:46:34,499 epoch 6 - iter 1602/1786 - loss 0.03200171 - time (sec): 79.05 - samples/sec: 2818.22 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 13:46:42,973 epoch 6 - iter 1780/1786 - loss 0.03147805 - time (sec): 87.52 - samples/sec: 2831.86 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 13:46:43,268 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:46:43,269 EPOCH 6 done: loss 0.0315 - lr: 0.000013 | |
| 2023-10-17 13:46:47,415 DEV : loss 0.1810143142938614 - f1-score (micro avg) 0.8208 | |
| 2023-10-17 13:46:47,432 saving best model | |
| 2023-10-17 13:46:47,883 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:46:57,010 epoch 7 - iter 178/1786 - loss 0.02252211 - time (sec): 9.12 - samples/sec: 2815.32 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 13:47:06,046 epoch 7 - iter 356/1786 - loss 0.02178416 - time (sec): 18.16 - samples/sec: 2798.52 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 13:47:14,788 epoch 7 - iter 534/1786 - loss 0.02234988 - time (sec): 26.90 - samples/sec: 2783.93 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 13:47:23,746 epoch 7 - iter 712/1786 - loss 0.02071319 - time (sec): 35.86 - samples/sec: 2801.18 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 13:47:32,861 epoch 7 - iter 890/1786 - loss 0.02322136 - time (sec): 44.97 - samples/sec: 2795.86 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 13:47:41,641 epoch 7 - iter 1068/1786 - loss 0.02464583 - time (sec): 53.75 - samples/sec: 2781.24 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 13:47:50,621 epoch 7 - iter 1246/1786 - loss 0.02371729 - time (sec): 62.73 - samples/sec: 2796.27 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 13:47:59,715 epoch 7 - iter 1424/1786 - loss 0.02337745 - time (sec): 71.83 - samples/sec: 2785.17 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 13:48:08,615 epoch 7 - iter 1602/1786 - loss 0.02344878 - time (sec): 80.73 - samples/sec: 2757.92 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 13:48:17,579 epoch 7 - iter 1780/1786 - loss 0.02410208 - time (sec): 89.69 - samples/sec: 2758.99 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 13:48:17,888 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:48:17,889 EPOCH 7 done: loss 0.0240 - lr: 0.000010 | |
| 2023-10-17 13:48:22,557 DEV : loss 0.1769118458032608 - f1-score (micro avg) 0.8345 | |
| 2023-10-17 13:48:22,574 saving best model | |
| 2023-10-17 13:48:23,039 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:48:31,997 epoch 8 - iter 178/1786 - loss 0.01978668 - time (sec): 8.95 - samples/sec: 2710.28 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 13:48:40,923 epoch 8 - iter 356/1786 - loss 0.01795202 - time (sec): 17.88 - samples/sec: 2730.53 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 13:48:49,838 epoch 8 - iter 534/1786 - loss 0.01682048 - time (sec): 26.80 - samples/sec: 2732.43 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 13:48:58,809 epoch 8 - iter 712/1786 - loss 0.01706732 - time (sec): 35.77 - samples/sec: 2726.67 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 13:49:07,606 epoch 8 - iter 890/1786 - loss 0.01709347 - time (sec): 44.56 - samples/sec: 2745.57 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 13:49:16,512 epoch 8 - iter 1068/1786 - loss 0.01748776 - time (sec): 53.47 - samples/sec: 2735.99 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 13:49:25,400 epoch 8 - iter 1246/1786 - loss 0.01730659 - time (sec): 62.36 - samples/sec: 2751.55 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 13:49:34,658 epoch 8 - iter 1424/1786 - loss 0.01760562 - time (sec): 71.62 - samples/sec: 2770.82 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 13:49:43,429 epoch 8 - iter 1602/1786 - loss 0.01761039 - time (sec): 80.39 - samples/sec: 2768.58 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 13:49:52,395 epoch 8 - iter 1780/1786 - loss 0.01754475 - time (sec): 89.35 - samples/sec: 2776.92 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 13:49:52,677 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:49:52,677 EPOCH 8 done: loss 0.0177 - lr: 0.000007 | |
| 2023-10-17 13:49:56,898 DEV : loss 0.196747824549675 - f1-score (micro avg) 0.8165 | |
| 2023-10-17 13:49:56,915 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:50:06,356 epoch 9 - iter 178/1786 - loss 0.01085864 - time (sec): 9.44 - samples/sec: 2704.82 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 13:50:15,176 epoch 9 - iter 356/1786 - loss 0.01281656 - time (sec): 18.26 - samples/sec: 2792.68 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 13:50:24,105 epoch 9 - iter 534/1786 - loss 0.01358075 - time (sec): 27.19 - samples/sec: 2767.01 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 13:50:33,067 epoch 9 - iter 712/1786 - loss 0.01366083 - time (sec): 36.15 - samples/sec: 2736.04 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 13:50:41,994 epoch 9 - iter 890/1786 - loss 0.01227376 - time (sec): 45.08 - samples/sec: 2741.15 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 13:50:50,756 epoch 9 - iter 1068/1786 - loss 0.01291585 - time (sec): 53.84 - samples/sec: 2748.82 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 13:51:00,405 epoch 9 - iter 1246/1786 - loss 0.01335982 - time (sec): 63.49 - samples/sec: 2696.12 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 13:51:09,305 epoch 9 - iter 1424/1786 - loss 0.01331987 - time (sec): 72.39 - samples/sec: 2709.60 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 13:51:18,256 epoch 9 - iter 1602/1786 - loss 0.01337922 - time (sec): 81.34 - samples/sec: 2723.43 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 13:51:27,315 epoch 9 - iter 1780/1786 - loss 0.01258706 - time (sec): 90.40 - samples/sec: 2745.83 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 13:51:27,587 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:51:27,587 EPOCH 9 done: loss 0.0126 - lr: 0.000003 | |
| 2023-10-17 13:51:31,855 DEV : loss 0.20930074155330658 - f1-score (micro avg) 0.8232 | |
| 2023-10-17 13:51:31,872 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:51:40,887 epoch 10 - iter 178/1786 - loss 0.00609129 - time (sec): 9.01 - samples/sec: 2824.04 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 13:51:50,023 epoch 10 - iter 356/1786 - loss 0.00715017 - time (sec): 18.15 - samples/sec: 2816.42 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 13:51:58,978 epoch 10 - iter 534/1786 - loss 0.00851154 - time (sec): 27.10 - samples/sec: 2809.81 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 13:52:07,808 epoch 10 - iter 712/1786 - loss 0.00825487 - time (sec): 35.93 - samples/sec: 2767.77 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 13:52:17,310 epoch 10 - iter 890/1786 - loss 0.00855273 - time (sec): 45.44 - samples/sec: 2755.18 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 13:52:26,532 epoch 10 - iter 1068/1786 - loss 0.00838603 - time (sec): 54.66 - samples/sec: 2739.98 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 13:52:35,578 epoch 10 - iter 1246/1786 - loss 0.00840932 - time (sec): 63.70 - samples/sec: 2727.41 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 13:52:44,438 epoch 10 - iter 1424/1786 - loss 0.00809426 - time (sec): 72.56 - samples/sec: 2745.31 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 13:52:53,121 epoch 10 - iter 1602/1786 - loss 0.00816111 - time (sec): 81.25 - samples/sec: 2749.63 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-17 13:53:01,991 epoch 10 - iter 1780/1786 - loss 0.00858460 - time (sec): 90.12 - samples/sec: 2750.90 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-17 13:53:02,263 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:53:02,263 EPOCH 10 done: loss 0.0086 - lr: 0.000000 | |
| 2023-10-17 13:53:06,932 DEV : loss 0.20266954600811005 - f1-score (micro avg) 0.8289 | |
| 2023-10-17 13:53:07,291 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:53:07,292 Loading model from best epoch ... | |
| 2023-10-17 13:53:08,632 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 | |
| 2023-10-17 13:53:18,265 | |
| Results: | |
| - F-score (micro) 0.705 | |
| - F-score (macro) 0.6364 | |
| - Accuracy 0.5623 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7285 0.6959 0.7118 1095 | |
| PER 0.7944 0.7787 0.7864 1012 | |
| ORG 0.4503 0.5714 0.5037 357 | |
| HumanProd 0.4237 0.7576 0.5435 33 | |
| micro avg 0.6976 0.7125 0.7050 2497 | |
| macro avg 0.5992 0.7009 0.6364 2497 | |
| weighted avg 0.7114 0.7125 0.7101 2497 | |
| 2023-10-17 13:53:18,265 ---------------------------------------------------------------------------------------------------- | |