| 2023-10-13 21:37:23,966 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,967 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (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): 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=13, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-13 21:37:23,967 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,967 MultiCorpus: 7936 train + 992 dev + 992 test sentences | |
| - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr | |
| 2023-10-13 21:37:23,967 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,967 Train: 7936 sentences | |
| 2023-10-13 21:37:23,967 (train_with_dev=False, train_with_test=False) | |
| 2023-10-13 21:37:23,967 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,967 Training Params: | |
| 2023-10-13 21:37:23,967 - learning_rate: "5e-05" | |
| 2023-10-13 21:37:23,968 - mini_batch_size: "8" | |
| 2023-10-13 21:37:23,968 - max_epochs: "10" | |
| 2023-10-13 21:37:23,968 - shuffle: "True" | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,968 Plugins: | |
| 2023-10-13 21:37:23,968 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,968 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-13 21:37:23,968 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,968 Computation: | |
| 2023-10-13 21:37:23,968 - compute on device: cuda:0 | |
| 2023-10-13 21:37:23,968 - embedding storage: none | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,968 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:23,968 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:37:29,890 epoch 1 - iter 99/992 - loss 1.91261312 - time (sec): 5.92 - samples/sec: 2715.74 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 21:37:35,908 epoch 1 - iter 198/992 - loss 1.13445665 - time (sec): 11.94 - samples/sec: 2727.88 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 21:37:41,742 epoch 1 - iter 297/992 - loss 0.83788670 - time (sec): 17.77 - samples/sec: 2767.01 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 21:37:47,441 epoch 1 - iter 396/992 - loss 0.67694574 - time (sec): 23.47 - samples/sec: 2781.96 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 21:37:53,157 epoch 1 - iter 495/992 - loss 0.57510064 - time (sec): 29.19 - samples/sec: 2789.19 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 21:37:58,914 epoch 1 - iter 594/992 - loss 0.50275111 - time (sec): 34.95 - samples/sec: 2791.76 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 21:38:04,772 epoch 1 - iter 693/992 - loss 0.44880423 - time (sec): 40.80 - samples/sec: 2810.61 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-13 21:38:11,079 epoch 1 - iter 792/992 - loss 0.40684356 - time (sec): 47.11 - samples/sec: 2805.49 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-13 21:38:16,874 epoch 1 - iter 891/992 - loss 0.37820026 - time (sec): 52.90 - samples/sec: 2799.25 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-13 21:38:22,677 epoch 1 - iter 990/992 - loss 0.35457753 - time (sec): 58.71 - samples/sec: 2789.75 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-13 21:38:22,786 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:38:22,786 EPOCH 1 done: loss 0.3542 - lr: 0.000050 | |
| 2023-10-13 21:38:26,277 DEV : loss 0.09475447982549667 - f1-score (micro avg) 0.6958 | |
| 2023-10-13 21:38:26,298 saving best model | |
| 2023-10-13 21:38:26,721 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:38:32,814 epoch 2 - iter 99/992 - loss 0.12012769 - time (sec): 6.09 - samples/sec: 2833.35 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-13 21:38:38,526 epoch 2 - iter 198/992 - loss 0.11631000 - time (sec): 11.80 - samples/sec: 2746.93 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-13 21:38:44,938 epoch 2 - iter 297/992 - loss 0.11145763 - time (sec): 18.22 - samples/sec: 2735.81 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-13 21:38:50,662 epoch 2 - iter 396/992 - loss 0.10881005 - time (sec): 23.94 - samples/sec: 2704.74 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-13 21:38:56,694 epoch 2 - iter 495/992 - loss 0.10846778 - time (sec): 29.97 - samples/sec: 2740.68 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-13 21:39:02,441 epoch 2 - iter 594/992 - loss 0.10567866 - time (sec): 35.72 - samples/sec: 2746.65 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-13 21:39:08,610 epoch 2 - iter 693/992 - loss 0.10559153 - time (sec): 41.89 - samples/sec: 2735.81 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-13 21:39:14,672 epoch 2 - iter 792/992 - loss 0.10488835 - time (sec): 47.95 - samples/sec: 2722.53 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-13 21:39:20,740 epoch 2 - iter 891/992 - loss 0.10442316 - time (sec): 54.02 - samples/sec: 2727.03 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-13 21:39:26,803 epoch 2 - iter 990/992 - loss 0.10329774 - time (sec): 60.08 - samples/sec: 2725.62 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-13 21:39:26,916 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:39:26,916 EPOCH 2 done: loss 0.1033 - lr: 0.000044 | |
| 2023-10-13 21:39:30,341 DEV : loss 0.08205121755599976 - f1-score (micro avg) 0.7129 | |
| 2023-10-13 21:39:30,361 saving best model | |
| 2023-10-13 21:39:30,861 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:39:36,742 epoch 3 - iter 99/992 - loss 0.07082610 - time (sec): 5.87 - samples/sec: 2789.48 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-13 21:39:42,620 epoch 3 - iter 198/992 - loss 0.06839682 - time (sec): 11.75 - samples/sec: 2713.38 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-13 21:39:48,758 epoch 3 - iter 297/992 - loss 0.06531227 - time (sec): 17.89 - samples/sec: 2763.77 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-13 21:39:54,864 epoch 3 - iter 396/992 - loss 0.06974993 - time (sec): 23.99 - samples/sec: 2777.66 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-13 21:40:00,891 epoch 3 - iter 495/992 - loss 0.07037521 - time (sec): 30.02 - samples/sec: 2759.70 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-13 21:40:06,636 epoch 3 - iter 594/992 - loss 0.07072352 - time (sec): 35.77 - samples/sec: 2768.83 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-13 21:40:12,505 epoch 3 - iter 693/992 - loss 0.07026764 - time (sec): 41.64 - samples/sec: 2761.90 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-13 21:40:18,247 epoch 3 - iter 792/992 - loss 0.07260697 - time (sec): 47.38 - samples/sec: 2775.98 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-13 21:40:24,041 epoch 3 - iter 891/992 - loss 0.07256251 - time (sec): 53.17 - samples/sec: 2778.82 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-13 21:40:29,699 epoch 3 - iter 990/992 - loss 0.07225091 - time (sec): 58.83 - samples/sec: 2780.17 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-13 21:40:29,815 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:40:29,815 EPOCH 3 done: loss 0.0722 - lr: 0.000039 | |
| 2023-10-13 21:40:33,902 DEV : loss 0.10864556580781937 - f1-score (micro avg) 0.758 | |
| 2023-10-13 21:40:33,939 saving best model | |
| 2023-10-13 21:40:34,466 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:40:40,380 epoch 4 - iter 99/992 - loss 0.05150709 - time (sec): 5.91 - samples/sec: 2796.73 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-13 21:40:46,332 epoch 4 - iter 198/992 - loss 0.04958120 - time (sec): 11.86 - samples/sec: 2802.33 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-13 21:40:51,999 epoch 4 - iter 297/992 - loss 0.05125257 - time (sec): 17.53 - samples/sec: 2798.55 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-13 21:40:57,854 epoch 4 - iter 396/992 - loss 0.04922105 - time (sec): 23.39 - samples/sec: 2782.53 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-13 21:41:03,725 epoch 4 - iter 495/992 - loss 0.04834639 - time (sec): 29.26 - samples/sec: 2783.19 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-13 21:41:09,534 epoch 4 - iter 594/992 - loss 0.04924934 - time (sec): 35.07 - samples/sec: 2786.12 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-13 21:41:15,489 epoch 4 - iter 693/992 - loss 0.04948734 - time (sec): 41.02 - samples/sec: 2778.64 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-13 21:41:21,333 epoch 4 - iter 792/992 - loss 0.04908108 - time (sec): 46.87 - samples/sec: 2773.09 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-13 21:41:27,452 epoch 4 - iter 891/992 - loss 0.04886483 - time (sec): 52.98 - samples/sec: 2765.98 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-13 21:41:33,681 epoch 4 - iter 990/992 - loss 0.04998705 - time (sec): 59.21 - samples/sec: 2765.34 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-13 21:41:33,794 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:41:33,794 EPOCH 4 done: loss 0.0499 - lr: 0.000033 | |
| 2023-10-13 21:41:37,201 DEV : loss 0.11597025394439697 - f1-score (micro avg) 0.7675 | |
| 2023-10-13 21:41:37,222 saving best model | |
| 2023-10-13 21:41:37,774 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:41:43,640 epoch 5 - iter 99/992 - loss 0.04977178 - time (sec): 5.86 - samples/sec: 2838.67 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-13 21:41:49,509 epoch 5 - iter 198/992 - loss 0.04238345 - time (sec): 11.73 - samples/sec: 2809.15 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-13 21:41:55,313 epoch 5 - iter 297/992 - loss 0.04044960 - time (sec): 17.53 - samples/sec: 2809.68 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-13 21:42:01,758 epoch 5 - iter 396/992 - loss 0.03908575 - time (sec): 23.98 - samples/sec: 2780.41 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-13 21:42:07,657 epoch 5 - iter 495/992 - loss 0.03930276 - time (sec): 29.88 - samples/sec: 2784.79 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-13 21:42:13,458 epoch 5 - iter 594/992 - loss 0.03815672 - time (sec): 35.68 - samples/sec: 2781.01 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 21:42:19,292 epoch 5 - iter 693/992 - loss 0.03945281 - time (sec): 41.51 - samples/sec: 2772.88 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 21:42:24,988 epoch 5 - iter 792/992 - loss 0.03857818 - time (sec): 47.21 - samples/sec: 2787.22 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 21:42:31,007 epoch 5 - iter 891/992 - loss 0.03988100 - time (sec): 53.23 - samples/sec: 2781.52 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 21:42:36,766 epoch 5 - iter 990/992 - loss 0.04081192 - time (sec): 58.99 - samples/sec: 2773.33 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 21:42:36,903 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:42:36,903 EPOCH 5 done: loss 0.0408 - lr: 0.000028 | |
| 2023-10-13 21:42:41,542 DEV : loss 0.13763324916362762 - f1-score (micro avg) 0.7589 | |
| 2023-10-13 21:42:41,580 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:42:47,750 epoch 6 - iter 99/992 - loss 0.02422172 - time (sec): 6.17 - samples/sec: 2688.98 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 21:42:53,863 epoch 6 - iter 198/992 - loss 0.02863550 - time (sec): 12.28 - samples/sec: 2753.55 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 21:42:59,675 epoch 6 - iter 297/992 - loss 0.02988685 - time (sec): 18.09 - samples/sec: 2746.20 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 21:43:05,663 epoch 6 - iter 396/992 - loss 0.02833903 - time (sec): 24.08 - samples/sec: 2722.44 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 21:43:11,508 epoch 6 - iter 495/992 - loss 0.02786454 - time (sec): 29.93 - samples/sec: 2737.83 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 21:43:17,288 epoch 6 - iter 594/992 - loss 0.02866839 - time (sec): 35.71 - samples/sec: 2755.15 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 21:43:23,408 epoch 6 - iter 693/992 - loss 0.02806587 - time (sec): 41.83 - samples/sec: 2753.01 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 21:43:29,273 epoch 6 - iter 792/992 - loss 0.02884132 - time (sec): 47.69 - samples/sec: 2755.88 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 21:43:35,118 epoch 6 - iter 891/992 - loss 0.02883868 - time (sec): 53.54 - samples/sec: 2753.63 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 21:43:41,138 epoch 6 - iter 990/992 - loss 0.02863755 - time (sec): 59.56 - samples/sec: 2748.21 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 21:43:41,250 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:43:41,250 EPOCH 6 done: loss 0.0288 - lr: 0.000022 | |
| 2023-10-13 21:43:44,796 DEV : loss 0.17916934192180634 - f1-score (micro avg) 0.7563 | |
| 2023-10-13 21:43:44,821 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:43:50,755 epoch 7 - iter 99/992 - loss 0.01996214 - time (sec): 5.93 - samples/sec: 2590.21 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 21:43:57,458 epoch 7 - iter 198/992 - loss 0.02277291 - time (sec): 12.63 - samples/sec: 2548.91 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 21:44:03,311 epoch 7 - iter 297/992 - loss 0.02180867 - time (sec): 18.49 - samples/sec: 2584.45 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 21:44:09,225 epoch 7 - iter 396/992 - loss 0.02147002 - time (sec): 24.40 - samples/sec: 2621.14 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 21:44:15,345 epoch 7 - iter 495/992 - loss 0.02147281 - time (sec): 30.52 - samples/sec: 2658.70 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 21:44:21,676 epoch 7 - iter 594/992 - loss 0.02278340 - time (sec): 36.85 - samples/sec: 2672.02 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 21:44:27,314 epoch 7 - iter 693/992 - loss 0.02302071 - time (sec): 42.49 - samples/sec: 2675.36 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 21:44:33,289 epoch 7 - iter 792/992 - loss 0.02238297 - time (sec): 48.47 - samples/sec: 2696.36 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 21:44:38,932 epoch 7 - iter 891/992 - loss 0.02194504 - time (sec): 54.11 - samples/sec: 2717.35 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 21:44:44,806 epoch 7 - iter 990/992 - loss 0.02178032 - time (sec): 59.98 - samples/sec: 2727.36 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 21:44:44,942 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:44:44,942 EPOCH 7 done: loss 0.0217 - lr: 0.000017 | |
| 2023-10-13 21:44:48,386 DEV : loss 0.19963695108890533 - f1-score (micro avg) 0.7511 | |
| 2023-10-13 21:44:48,411 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:44:54,512 epoch 8 - iter 99/992 - loss 0.00802566 - time (sec): 6.10 - samples/sec: 2678.43 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 21:45:00,449 epoch 8 - iter 198/992 - loss 0.01380324 - time (sec): 12.04 - samples/sec: 2738.01 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 21:45:06,122 epoch 8 - iter 297/992 - loss 0.01227669 - time (sec): 17.71 - samples/sec: 2757.67 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 21:45:12,518 epoch 8 - iter 396/992 - loss 0.01400552 - time (sec): 24.11 - samples/sec: 2729.93 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 21:45:18,333 epoch 8 - iter 495/992 - loss 0.01475903 - time (sec): 29.92 - samples/sec: 2736.32 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 21:45:23,984 epoch 8 - iter 594/992 - loss 0.01530820 - time (sec): 35.57 - samples/sec: 2735.01 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 21:45:30,069 epoch 8 - iter 693/992 - loss 0.01589983 - time (sec): 41.66 - samples/sec: 2736.91 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 21:45:36,359 epoch 8 - iter 792/992 - loss 0.01603725 - time (sec): 47.95 - samples/sec: 2744.42 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 21:45:42,042 epoch 8 - iter 891/992 - loss 0.01533557 - time (sec): 53.63 - samples/sec: 2749.62 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 21:45:47,922 epoch 8 - iter 990/992 - loss 0.01568211 - time (sec): 59.51 - samples/sec: 2751.39 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 21:45:48,031 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:45:48,031 EPOCH 8 done: loss 0.0157 - lr: 0.000011 | |
| 2023-10-13 21:45:51,538 DEV : loss 0.2106373906135559 - f1-score (micro avg) 0.7511 | |
| 2023-10-13 21:45:51,560 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:45:57,529 epoch 9 - iter 99/992 - loss 0.00887012 - time (sec): 5.97 - samples/sec: 2806.18 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 21:46:03,489 epoch 9 - iter 198/992 - loss 0.00899000 - time (sec): 11.93 - samples/sec: 2748.69 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 21:46:09,616 epoch 9 - iter 297/992 - loss 0.00742884 - time (sec): 18.05 - samples/sec: 2694.16 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 21:46:15,833 epoch 9 - iter 396/992 - loss 0.00794530 - time (sec): 24.27 - samples/sec: 2710.07 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 21:46:21,629 epoch 9 - iter 495/992 - loss 0.00848285 - time (sec): 30.07 - samples/sec: 2744.72 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 21:46:27,363 epoch 9 - iter 594/992 - loss 0.00917051 - time (sec): 35.80 - samples/sec: 2739.94 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 21:46:32,985 epoch 9 - iter 693/992 - loss 0.00939071 - time (sec): 41.42 - samples/sec: 2755.95 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 21:46:38,976 epoch 9 - iter 792/992 - loss 0.01010125 - time (sec): 47.41 - samples/sec: 2762.85 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 21:46:45,090 epoch 9 - iter 891/992 - loss 0.01075447 - time (sec): 53.53 - samples/sec: 2761.88 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 21:46:50,883 epoch 9 - iter 990/992 - loss 0.01068750 - time (sec): 59.32 - samples/sec: 2757.49 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 21:46:51,018 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:46:51,018 EPOCH 9 done: loss 0.0107 - lr: 0.000006 | |
| 2023-10-13 21:46:54,559 DEV : loss 0.22137963771820068 - f1-score (micro avg) 0.7493 | |
| 2023-10-13 21:46:54,581 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:47:00,669 epoch 10 - iter 99/992 - loss 0.00885115 - time (sec): 6.09 - samples/sec: 2800.45 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 21:47:06,567 epoch 10 - iter 198/992 - loss 0.00701261 - time (sec): 11.98 - samples/sec: 2734.37 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 21:47:12,490 epoch 10 - iter 297/992 - loss 0.00709994 - time (sec): 17.91 - samples/sec: 2725.21 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 21:47:19,212 epoch 10 - iter 396/992 - loss 0.00713180 - time (sec): 24.63 - samples/sec: 2657.26 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 21:47:24,800 epoch 10 - iter 495/992 - loss 0.00712523 - time (sec): 30.22 - samples/sec: 2696.43 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 21:47:30,657 epoch 10 - iter 594/992 - loss 0.00752130 - time (sec): 36.07 - samples/sec: 2715.70 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 21:47:36,606 epoch 10 - iter 693/992 - loss 0.00756143 - time (sec): 42.02 - samples/sec: 2720.45 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 21:47:42,502 epoch 10 - iter 792/992 - loss 0.00745144 - time (sec): 47.92 - samples/sec: 2732.37 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 21:47:48,499 epoch 10 - iter 891/992 - loss 0.00734368 - time (sec): 53.92 - samples/sec: 2744.99 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 21:47:54,258 epoch 10 - iter 990/992 - loss 0.00723729 - time (sec): 59.68 - samples/sec: 2742.25 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 21:47:54,381 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:47:54,381 EPOCH 10 done: loss 0.0072 - lr: 0.000000 | |
| 2023-10-13 21:47:57,909 DEV : loss 0.22665657103061676 - f1-score (micro avg) 0.7513 | |
| 2023-10-13 21:47:58,383 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 21:47:58,384 Loading model from best epoch ... | |
| 2023-10-13 21:47:59,826 SequenceTagger predicts: Dictionary with 13 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 | |
| 2023-10-13 21:48:03,320 | |
| Results: | |
| - F-score (micro) 0.7736 | |
| - F-score (macro) 0.6613 | |
| - Accuracy 0.6522 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7826 0.8794 0.8282 655 | |
| PER 0.8556 0.6906 0.7643 223 | |
| ORG 0.5968 0.2913 0.3915 127 | |
| micro avg 0.7843 0.7632 0.7736 1005 | |
| macro avg 0.7450 0.6204 0.6613 1005 | |
| weighted avg 0.7753 0.7632 0.7588 1005 | |
| 2023-10-13 21:48:03,320 ---------------------------------------------------------------------------------------------------- | |