| 2023-10-14 10:57:26,424 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,425 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() | |
| )" | |
| 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,425 MultiCorpus: 5777 train + 722 dev + 723 test sentences | |
| - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl | |
| 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,425 Train: 5777 sentences | |
| 2023-10-14 10:57:26,425 (train_with_dev=False, train_with_test=False) | |
| 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,425 Training Params: | |
| 2023-10-14 10:57:26,425 - learning_rate: "3e-05" | |
| 2023-10-14 10:57:26,425 - mini_batch_size: "8" | |
| 2023-10-14 10:57:26,425 - max_epochs: "10" | |
| 2023-10-14 10:57:26,425 - shuffle: "True" | |
| 2023-10-14 10:57:26,425 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,426 Plugins: | |
| 2023-10-14 10:57:26,426 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,426 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-14 10:57:26,426 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,426 Computation: | |
| 2023-10-14 10:57:26,426 - compute on device: cuda:0 | |
| 2023-10-14 10:57:26,426 - embedding storage: none | |
| 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,426 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:26,426 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:57:32,692 epoch 1 - iter 72/723 - loss 2.31506993 - time (sec): 6.26 - samples/sec: 2976.58 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-14 10:57:38,328 epoch 1 - iter 144/723 - loss 1.40098098 - time (sec): 11.90 - samples/sec: 3035.24 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-14 10:57:44,275 epoch 1 - iter 216/723 - loss 1.02739094 - time (sec): 17.85 - samples/sec: 2985.78 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-14 10:57:49,976 epoch 1 - iter 288/723 - loss 0.82673104 - time (sec): 23.55 - samples/sec: 2980.75 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-14 10:57:55,969 epoch 1 - iter 360/723 - loss 0.69531849 - time (sec): 29.54 - samples/sec: 2988.43 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-14 10:58:01,677 epoch 1 - iter 432/723 - loss 0.60767546 - time (sec): 35.25 - samples/sec: 3009.88 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-14 10:58:07,438 epoch 1 - iter 504/723 - loss 0.54162968 - time (sec): 41.01 - samples/sec: 3014.13 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-14 10:58:14,071 epoch 1 - iter 576/723 - loss 0.48974416 - time (sec): 47.64 - samples/sec: 2993.71 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-14 10:58:20,287 epoch 1 - iter 648/723 - loss 0.45020073 - time (sec): 53.86 - samples/sec: 2969.41 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-14 10:58:25,550 epoch 1 - iter 720/723 - loss 0.42217115 - time (sec): 59.12 - samples/sec: 2971.06 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-14 10:58:25,760 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:58:25,760 EPOCH 1 done: loss 0.4213 - lr: 0.000030 | |
| 2023-10-14 10:58:28,750 DEV : loss 0.12855297327041626 - f1-score (micro avg) 0.7041 | |
| 2023-10-14 10:58:28,768 saving best model | |
| 2023-10-14 10:58:29,152 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:58:34,794 epoch 2 - iter 72/723 - loss 0.12156124 - time (sec): 5.64 - samples/sec: 2875.61 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-14 10:58:40,804 epoch 2 - iter 144/723 - loss 0.11146254 - time (sec): 11.65 - samples/sec: 2900.46 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-14 10:58:46,896 epoch 2 - iter 216/723 - loss 0.11686624 - time (sec): 17.74 - samples/sec: 2917.23 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-14 10:58:53,571 epoch 2 - iter 288/723 - loss 0.11075586 - time (sec): 24.42 - samples/sec: 2902.96 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-14 10:58:59,700 epoch 2 - iter 360/723 - loss 0.10651151 - time (sec): 30.55 - samples/sec: 2907.89 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-14 10:59:05,443 epoch 2 - iter 432/723 - loss 0.10541663 - time (sec): 36.29 - samples/sec: 2909.61 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-14 10:59:10,993 epoch 2 - iter 504/723 - loss 0.10566055 - time (sec): 41.84 - samples/sec: 2918.83 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-14 10:59:16,688 epoch 2 - iter 576/723 - loss 0.10315725 - time (sec): 47.53 - samples/sec: 2933.43 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-14 10:59:22,656 epoch 2 - iter 648/723 - loss 0.10168054 - time (sec): 53.50 - samples/sec: 2939.05 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-14 10:59:28,642 epoch 2 - iter 720/723 - loss 0.10158311 - time (sec): 59.49 - samples/sec: 2953.47 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-14 10:59:28,880 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:59:28,880 EPOCH 2 done: loss 0.1014 - lr: 0.000027 | |
| 2023-10-14 10:59:32,771 DEV : loss 0.0938434973359108 - f1-score (micro avg) 0.7002 | |
| 2023-10-14 10:59:32,788 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 10:59:38,964 epoch 3 - iter 72/723 - loss 0.06077009 - time (sec): 6.17 - samples/sec: 2935.87 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-14 10:59:45,122 epoch 3 - iter 144/723 - loss 0.06201343 - time (sec): 12.33 - samples/sec: 2887.51 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-14 10:59:50,971 epoch 3 - iter 216/723 - loss 0.06525859 - time (sec): 18.18 - samples/sec: 2855.52 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-14 10:59:56,686 epoch 3 - iter 288/723 - loss 0.06305458 - time (sec): 23.90 - samples/sec: 2897.57 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-14 11:00:02,887 epoch 3 - iter 360/723 - loss 0.06172832 - time (sec): 30.10 - samples/sec: 2913.59 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-14 11:00:08,780 epoch 3 - iter 432/723 - loss 0.06330693 - time (sec): 35.99 - samples/sec: 2921.37 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-14 11:00:15,222 epoch 3 - iter 504/723 - loss 0.06448096 - time (sec): 42.43 - samples/sec: 2919.30 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-14 11:00:20,867 epoch 3 - iter 576/723 - loss 0.06382838 - time (sec): 48.08 - samples/sec: 2918.70 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-14 11:00:26,929 epoch 3 - iter 648/723 - loss 0.06273635 - time (sec): 54.14 - samples/sec: 2908.32 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-14 11:00:33,161 epoch 3 - iter 720/723 - loss 0.06321271 - time (sec): 60.37 - samples/sec: 2905.22 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-14 11:00:33,487 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:00:33,488 EPOCH 3 done: loss 0.0631 - lr: 0.000023 | |
| 2023-10-14 11:00:36,981 DEV : loss 0.08630853146314621 - f1-score (micro avg) 0.8069 | |
| 2023-10-14 11:00:36,999 saving best model | |
| 2023-10-14 11:00:37,532 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:00:43,552 epoch 4 - iter 72/723 - loss 0.03595234 - time (sec): 6.02 - samples/sec: 2919.12 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-14 11:00:49,964 epoch 4 - iter 144/723 - loss 0.04721867 - time (sec): 12.43 - samples/sec: 2884.50 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-14 11:00:56,286 epoch 4 - iter 216/723 - loss 0.04688850 - time (sec): 18.75 - samples/sec: 2815.40 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-14 11:01:02,694 epoch 4 - iter 288/723 - loss 0.04288223 - time (sec): 25.16 - samples/sec: 2808.48 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-14 11:01:08,221 epoch 4 - iter 360/723 - loss 0.04159157 - time (sec): 30.69 - samples/sec: 2840.38 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-14 11:01:14,265 epoch 4 - iter 432/723 - loss 0.04113805 - time (sec): 36.73 - samples/sec: 2877.65 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-14 11:01:20,221 epoch 4 - iter 504/723 - loss 0.04112768 - time (sec): 42.69 - samples/sec: 2875.66 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-14 11:01:26,212 epoch 4 - iter 576/723 - loss 0.04139046 - time (sec): 48.68 - samples/sec: 2886.33 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-14 11:01:32,305 epoch 4 - iter 648/723 - loss 0.04073847 - time (sec): 54.77 - samples/sec: 2895.25 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-14 11:01:38,302 epoch 4 - iter 720/723 - loss 0.04096798 - time (sec): 60.77 - samples/sec: 2889.77 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-14 11:01:38,500 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:01:38,501 EPOCH 4 done: loss 0.0412 - lr: 0.000020 | |
| 2023-10-14 11:01:42,023 DEV : loss 0.08693055063486099 - f1-score (micro avg) 0.8288 | |
| 2023-10-14 11:01:42,042 saving best model | |
| 2023-10-14 11:01:42,516 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:01:48,799 epoch 5 - iter 72/723 - loss 0.03262287 - time (sec): 6.28 - samples/sec: 2932.38 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-14 11:01:54,248 epoch 5 - iter 144/723 - loss 0.03076967 - time (sec): 11.73 - samples/sec: 3021.49 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-14 11:02:00,487 epoch 5 - iter 216/723 - loss 0.02828212 - time (sec): 17.97 - samples/sec: 3012.82 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-14 11:02:06,303 epoch 5 - iter 288/723 - loss 0.03125818 - time (sec): 23.78 - samples/sec: 2978.07 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-14 11:02:11,988 epoch 5 - iter 360/723 - loss 0.02911501 - time (sec): 29.47 - samples/sec: 2974.33 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-14 11:02:17,343 epoch 5 - iter 432/723 - loss 0.02975581 - time (sec): 34.82 - samples/sec: 2979.90 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-14 11:02:23,371 epoch 5 - iter 504/723 - loss 0.02903271 - time (sec): 40.85 - samples/sec: 2985.46 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-14 11:02:29,523 epoch 5 - iter 576/723 - loss 0.03013901 - time (sec): 47.00 - samples/sec: 2972.90 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-14 11:02:35,695 epoch 5 - iter 648/723 - loss 0.03126828 - time (sec): 53.18 - samples/sec: 2976.24 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-14 11:02:41,551 epoch 5 - iter 720/723 - loss 0.03072279 - time (sec): 59.03 - samples/sec: 2976.35 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-14 11:02:41,720 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:02:41,721 EPOCH 5 done: loss 0.0308 - lr: 0.000017 | |
| 2023-10-14 11:02:45,637 DEV : loss 0.13127633929252625 - f1-score (micro avg) 0.8044 | |
| 2023-10-14 11:02:45,653 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:02:51,633 epoch 6 - iter 72/723 - loss 0.01842860 - time (sec): 5.98 - samples/sec: 2902.58 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-14 11:02:57,718 epoch 6 - iter 144/723 - loss 0.02104149 - time (sec): 12.06 - samples/sec: 2907.63 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-14 11:03:03,381 epoch 6 - iter 216/723 - loss 0.02082948 - time (sec): 17.73 - samples/sec: 2961.34 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-14 11:03:09,572 epoch 6 - iter 288/723 - loss 0.02184263 - time (sec): 23.92 - samples/sec: 2949.13 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-14 11:03:15,404 epoch 6 - iter 360/723 - loss 0.02072094 - time (sec): 29.75 - samples/sec: 2938.63 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-14 11:03:21,504 epoch 6 - iter 432/723 - loss 0.02069835 - time (sec): 35.85 - samples/sec: 2930.06 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-14 11:03:27,963 epoch 6 - iter 504/723 - loss 0.02129765 - time (sec): 42.31 - samples/sec: 2904.18 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-14 11:03:34,651 epoch 6 - iter 576/723 - loss 0.02050509 - time (sec): 49.00 - samples/sec: 2903.26 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-14 11:03:40,533 epoch 6 - iter 648/723 - loss 0.02175670 - time (sec): 54.88 - samples/sec: 2899.49 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-14 11:03:46,229 epoch 6 - iter 720/723 - loss 0.02231132 - time (sec): 60.57 - samples/sec: 2901.48 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-14 11:03:46,397 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:03:46,397 EPOCH 6 done: loss 0.0223 - lr: 0.000013 | |
| 2023-10-14 11:03:49,947 DEV : loss 0.12690779566764832 - f1-score (micro avg) 0.8249 | |
| 2023-10-14 11:03:49,964 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:03:56,165 epoch 7 - iter 72/723 - loss 0.00937309 - time (sec): 6.20 - samples/sec: 2830.04 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-14 11:04:02,678 epoch 7 - iter 144/723 - loss 0.01350239 - time (sec): 12.71 - samples/sec: 2882.76 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-14 11:04:08,323 epoch 7 - iter 216/723 - loss 0.01358360 - time (sec): 18.36 - samples/sec: 2915.57 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-14 11:04:15,072 epoch 7 - iter 288/723 - loss 0.01452212 - time (sec): 25.11 - samples/sec: 2864.73 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-14 11:04:20,800 epoch 7 - iter 360/723 - loss 0.01439804 - time (sec): 30.84 - samples/sec: 2869.66 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-14 11:04:26,288 epoch 7 - iter 432/723 - loss 0.01516061 - time (sec): 36.32 - samples/sec: 2893.06 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-14 11:04:32,788 epoch 7 - iter 504/723 - loss 0.01712164 - time (sec): 42.82 - samples/sec: 2892.01 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-14 11:04:38,780 epoch 7 - iter 576/723 - loss 0.01729432 - time (sec): 48.81 - samples/sec: 2906.42 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-14 11:04:44,374 epoch 7 - iter 648/723 - loss 0.01749135 - time (sec): 54.41 - samples/sec: 2924.73 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-14 11:04:50,330 epoch 7 - iter 720/723 - loss 0.01711051 - time (sec): 60.37 - samples/sec: 2912.37 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-14 11:04:50,524 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:04:50,525 EPOCH 7 done: loss 0.0174 - lr: 0.000010 | |
| 2023-10-14 11:04:54,070 DEV : loss 0.15461641550064087 - f1-score (micro avg) 0.8145 | |
| 2023-10-14 11:04:54,088 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:04:59,959 epoch 8 - iter 72/723 - loss 0.01665349 - time (sec): 5.87 - samples/sec: 2848.03 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-14 11:05:06,094 epoch 8 - iter 144/723 - loss 0.01446601 - time (sec): 12.00 - samples/sec: 2881.67 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-14 11:05:12,405 epoch 8 - iter 216/723 - loss 0.01410012 - time (sec): 18.32 - samples/sec: 2857.36 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-14 11:05:18,576 epoch 8 - iter 288/723 - loss 0.01366919 - time (sec): 24.49 - samples/sec: 2845.71 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-14 11:05:24,652 epoch 8 - iter 360/723 - loss 0.01239443 - time (sec): 30.56 - samples/sec: 2877.55 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-14 11:05:30,144 epoch 8 - iter 432/723 - loss 0.01265536 - time (sec): 36.05 - samples/sec: 2900.06 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-14 11:05:36,539 epoch 8 - iter 504/723 - loss 0.01358128 - time (sec): 42.45 - samples/sec: 2891.45 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-14 11:05:42,619 epoch 8 - iter 576/723 - loss 0.01348813 - time (sec): 48.53 - samples/sec: 2893.15 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-14 11:05:48,324 epoch 8 - iter 648/723 - loss 0.01308338 - time (sec): 54.23 - samples/sec: 2905.38 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-14 11:05:54,660 epoch 8 - iter 720/723 - loss 0.01321059 - time (sec): 60.57 - samples/sec: 2896.19 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-14 11:05:54,878 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:05:54,878 EPOCH 8 done: loss 0.0132 - lr: 0.000007 | |
| 2023-10-14 11:05:58,864 DEV : loss 0.150771364569664 - f1-score (micro avg) 0.8431 | |
| 2023-10-14 11:05:58,880 saving best model | |
| 2023-10-14 11:05:59,413 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:06:05,658 epoch 9 - iter 72/723 - loss 0.01017083 - time (sec): 6.24 - samples/sec: 2911.90 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-14 11:06:11,476 epoch 9 - iter 144/723 - loss 0.00911976 - time (sec): 12.06 - samples/sec: 2930.73 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-14 11:06:17,232 epoch 9 - iter 216/723 - loss 0.00838583 - time (sec): 17.81 - samples/sec: 2926.00 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-14 11:06:23,911 epoch 9 - iter 288/723 - loss 0.00904573 - time (sec): 24.49 - samples/sec: 2913.97 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-14 11:06:29,832 epoch 9 - iter 360/723 - loss 0.00913035 - time (sec): 30.41 - samples/sec: 2918.84 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-14 11:06:36,140 epoch 9 - iter 432/723 - loss 0.00938579 - time (sec): 36.72 - samples/sec: 2905.02 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-14 11:06:41,831 epoch 9 - iter 504/723 - loss 0.00897802 - time (sec): 42.41 - samples/sec: 2912.32 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-14 11:06:48,221 epoch 9 - iter 576/723 - loss 0.00894380 - time (sec): 48.80 - samples/sec: 2899.02 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-14 11:06:54,001 epoch 9 - iter 648/723 - loss 0.00958515 - time (sec): 54.58 - samples/sec: 2903.49 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-14 11:06:59,830 epoch 9 - iter 720/723 - loss 0.00943720 - time (sec): 60.41 - samples/sec: 2908.78 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-14 11:07:00,059 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:07:00,060 EPOCH 9 done: loss 0.0095 - lr: 0.000003 | |
| 2023-10-14 11:07:03,577 DEV : loss 0.15790636837482452 - f1-score (micro avg) 0.833 | |
| 2023-10-14 11:07:03,592 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:07:09,324 epoch 10 - iter 72/723 - loss 0.00876310 - time (sec): 5.73 - samples/sec: 2913.63 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-14 11:07:15,547 epoch 10 - iter 144/723 - loss 0.00697092 - time (sec): 11.95 - samples/sec: 2905.71 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-14 11:07:22,240 epoch 10 - iter 216/723 - loss 0.00740430 - time (sec): 18.65 - samples/sec: 2809.93 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-14 11:07:28,447 epoch 10 - iter 288/723 - loss 0.00869243 - time (sec): 24.85 - samples/sec: 2862.60 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-14 11:07:34,727 epoch 10 - iter 360/723 - loss 0.00813367 - time (sec): 31.13 - samples/sec: 2878.44 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-14 11:07:40,624 epoch 10 - iter 432/723 - loss 0.00870267 - time (sec): 37.03 - samples/sec: 2886.14 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-14 11:07:46,030 epoch 10 - iter 504/723 - loss 0.00810991 - time (sec): 42.44 - samples/sec: 2888.69 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-14 11:07:51,966 epoch 10 - iter 576/723 - loss 0.00778453 - time (sec): 48.37 - samples/sec: 2879.90 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-14 11:07:58,270 epoch 10 - iter 648/723 - loss 0.00790965 - time (sec): 54.68 - samples/sec: 2885.46 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-14 11:08:04,217 epoch 10 - iter 720/723 - loss 0.00773716 - time (sec): 60.62 - samples/sec: 2894.81 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-14 11:08:04,452 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:08:04,452 EPOCH 10 done: loss 0.0077 - lr: 0.000000 | |
| 2023-10-14 11:08:07,999 DEV : loss 0.16016288101673126 - f1-score (micro avg) 0.8324 | |
| 2023-10-14 11:08:08,469 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-14 11:08:08,470 Loading model from best epoch ... | |
| 2023-10-14 11:08:10,080 SequenceTagger predicts: Dictionary with 13 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 | |
| 2023-10-14 11:08:13,254 | |
| Results: | |
| - F-score (micro) 0.8224 | |
| - F-score (macro) 0.7445 | |
| - Accuracy 0.7093 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.8407 0.8320 0.8363 482 | |
| LOC 0.8710 0.8253 0.8475 458 | |
| ORG 0.5806 0.5217 0.5496 69 | |
| micro avg 0.8376 0.8077 0.8224 1009 | |
| macro avg 0.7641 0.7263 0.7445 1009 | |
| weighted avg 0.8366 0.8077 0.8218 1009 | |
| 2023-10-14 11:08:13,254 ---------------------------------------------------------------------------------------------------- | |