| 2023-10-13 12:41:31,655 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 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=21, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-13 12:41:31,656 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences | |
| - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator | |
| 2023-10-13 12:41:31,656 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 Train: 3575 sentences | |
| 2023-10-13 12:41:31,656 (train_with_dev=False, train_with_test=False) | |
| 2023-10-13 12:41:31,656 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 Training Params: | |
| 2023-10-13 12:41:31,656 - learning_rate: "3e-05" | |
| 2023-10-13 12:41:31,656 - mini_batch_size: "8" | |
| 2023-10-13 12:41:31,656 - max_epochs: "10" | |
| 2023-10-13 12:41:31,656 - shuffle: "True" | |
| 2023-10-13 12:41:31,656 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 Plugins: | |
| 2023-10-13 12:41:31,656 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-13 12:41:31,656 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,656 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-13 12:41:31,657 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-13 12:41:31,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,657 Computation: | |
| 2023-10-13 12:41:31,657 - compute on device: cuda:0 | |
| 2023-10-13 12:41:31,657 - embedding storage: none | |
| 2023-10-13 12:41:31,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,657 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3" | |
| 2023-10-13 12:41:31,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:31,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:41:34,383 epoch 1 - iter 44/447 - loss 2.86252177 - time (sec): 2.73 - samples/sec: 3026.88 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:41:37,304 epoch 1 - iter 88/447 - loss 2.08375789 - time (sec): 5.65 - samples/sec: 3029.24 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:41:39,959 epoch 1 - iter 132/447 - loss 1.59708301 - time (sec): 8.30 - samples/sec: 3013.37 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:41:42,919 epoch 1 - iter 176/447 - loss 1.27742097 - time (sec): 11.26 - samples/sec: 3070.12 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:41:45,863 epoch 1 - iter 220/447 - loss 1.09002352 - time (sec): 14.21 - samples/sec: 3048.86 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:41:48,731 epoch 1 - iter 264/447 - loss 0.96186677 - time (sec): 17.07 - samples/sec: 3044.44 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:41:51,437 epoch 1 - iter 308/447 - loss 0.87551581 - time (sec): 19.78 - samples/sec: 3033.31 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:41:54,379 epoch 1 - iter 352/447 - loss 0.81029456 - time (sec): 22.72 - samples/sec: 2996.66 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:41:57,492 epoch 1 - iter 396/447 - loss 0.74501254 - time (sec): 25.83 - samples/sec: 2986.20 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:42:00,160 epoch 1 - iter 440/447 - loss 0.69746191 - time (sec): 28.50 - samples/sec: 2996.51 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:42:00,562 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:42:00,563 EPOCH 1 done: loss 0.6927 - lr: 0.000029 | |
| 2023-10-13 12:42:05,856 DEV : loss 0.19505180418491364 - f1-score (micro avg) 0.6267 | |
| 2023-10-13 12:42:05,882 saving best model | |
| 2023-10-13 12:42:06,236 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:42:09,017 epoch 2 - iter 44/447 - loss 0.21696724 - time (sec): 2.78 - samples/sec: 2924.66 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 12:42:11,819 epoch 2 - iter 88/447 - loss 0.20487957 - time (sec): 5.58 - samples/sec: 2946.26 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:42:14,636 epoch 2 - iter 132/447 - loss 0.19251978 - time (sec): 8.40 - samples/sec: 2958.63 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:42:17,702 epoch 2 - iter 176/447 - loss 0.17906352 - time (sec): 11.46 - samples/sec: 2901.31 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:42:20,409 epoch 2 - iter 220/447 - loss 0.17778899 - time (sec): 14.17 - samples/sec: 2925.37 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:42:23,246 epoch 2 - iter 264/447 - loss 0.17239185 - time (sec): 17.01 - samples/sec: 2930.20 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:42:25,994 epoch 2 - iter 308/447 - loss 0.17298603 - time (sec): 19.76 - samples/sec: 2936.17 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:42:29,071 epoch 2 - iter 352/447 - loss 0.16720639 - time (sec): 22.83 - samples/sec: 2937.10 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:42:31,912 epoch 2 - iter 396/447 - loss 0.16724068 - time (sec): 25.67 - samples/sec: 2988.06 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:42:34,661 epoch 2 - iter 440/447 - loss 0.16528204 - time (sec): 28.42 - samples/sec: 3000.44 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:42:35,128 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:42:35,128 EPOCH 2 done: loss 0.1650 - lr: 0.000027 | |
| 2023-10-13 12:42:43,746 DEV : loss 0.14146439731121063 - f1-score (micro avg) 0.6657 | |
| 2023-10-13 12:42:43,775 saving best model | |
| 2023-10-13 12:42:44,193 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:42:46,901 epoch 3 - iter 44/447 - loss 0.10202154 - time (sec): 2.71 - samples/sec: 3013.94 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:42:49,545 epoch 3 - iter 88/447 - loss 0.09540967 - time (sec): 5.35 - samples/sec: 2993.34 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:42:52,480 epoch 3 - iter 132/447 - loss 0.09869171 - time (sec): 8.28 - samples/sec: 2988.36 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:42:55,163 epoch 3 - iter 176/447 - loss 0.10009313 - time (sec): 10.97 - samples/sec: 3015.11 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:42:57,900 epoch 3 - iter 220/447 - loss 0.10031670 - time (sec): 13.70 - samples/sec: 2996.70 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:43:00,688 epoch 3 - iter 264/447 - loss 0.09570394 - time (sec): 16.49 - samples/sec: 3021.04 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:43:03,521 epoch 3 - iter 308/447 - loss 0.09263551 - time (sec): 19.32 - samples/sec: 3015.52 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:43:06,404 epoch 3 - iter 352/447 - loss 0.09283022 - time (sec): 22.21 - samples/sec: 3003.11 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:43:09,295 epoch 3 - iter 396/447 - loss 0.08983680 - time (sec): 25.10 - samples/sec: 3009.43 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:43:12,073 epoch 3 - iter 440/447 - loss 0.09082591 - time (sec): 27.88 - samples/sec: 3018.36 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:43:12,851 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:43:12,851 EPOCH 3 done: loss 0.0897 - lr: 0.000023 | |
| 2023-10-13 12:43:22,018 DEV : loss 0.1257464587688446 - f1-score (micro avg) 0.7384 | |
| 2023-10-13 12:43:22,051 saving best model | |
| 2023-10-13 12:43:22,459 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:43:25,496 epoch 4 - iter 44/447 - loss 0.06020034 - time (sec): 3.04 - samples/sec: 2684.53 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:43:28,410 epoch 4 - iter 88/447 - loss 0.06022402 - time (sec): 5.95 - samples/sec: 2785.84 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:43:31,098 epoch 4 - iter 132/447 - loss 0.05742048 - time (sec): 8.64 - samples/sec: 2869.36 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:43:34,344 epoch 4 - iter 176/447 - loss 0.05712786 - time (sec): 11.88 - samples/sec: 2929.12 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:43:37,147 epoch 4 - iter 220/447 - loss 0.05283087 - time (sec): 14.69 - samples/sec: 2943.10 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:43:40,002 epoch 4 - iter 264/447 - loss 0.05318470 - time (sec): 17.54 - samples/sec: 2944.15 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:43:42,801 epoch 4 - iter 308/447 - loss 0.05227936 - time (sec): 20.34 - samples/sec: 2954.65 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:43:45,653 epoch 4 - iter 352/447 - loss 0.05160845 - time (sec): 23.19 - samples/sec: 2949.49 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:43:48,609 epoch 4 - iter 396/447 - loss 0.05224703 - time (sec): 26.15 - samples/sec: 2957.04 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:43:51,250 epoch 4 - iter 440/447 - loss 0.05330500 - time (sec): 28.79 - samples/sec: 2965.16 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:43:51,663 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:43:51,663 EPOCH 4 done: loss 0.0535 - lr: 0.000020 | |
| 2023-10-13 12:44:00,532 DEV : loss 0.14387159049510956 - f1-score (micro avg) 0.7677 | |
| 2023-10-13 12:44:00,559 saving best model | |
| 2023-10-13 12:44:00,978 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:44:03,885 epoch 5 - iter 44/447 - loss 0.04206078 - time (sec): 2.91 - samples/sec: 2913.13 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:44:06,598 epoch 5 - iter 88/447 - loss 0.03343777 - time (sec): 5.62 - samples/sec: 2946.94 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:44:09,347 epoch 5 - iter 132/447 - loss 0.03138511 - time (sec): 8.37 - samples/sec: 2986.43 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:44:12,075 epoch 5 - iter 176/447 - loss 0.03300357 - time (sec): 11.10 - samples/sec: 2967.47 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:44:15,150 epoch 5 - iter 220/447 - loss 0.03298000 - time (sec): 14.17 - samples/sec: 2974.89 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:44:17,806 epoch 5 - iter 264/447 - loss 0.03394846 - time (sec): 16.83 - samples/sec: 2980.12 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:44:20,488 epoch 5 - iter 308/447 - loss 0.03350148 - time (sec): 19.51 - samples/sec: 2994.44 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:44:23,656 epoch 5 - iter 352/447 - loss 0.03406508 - time (sec): 22.68 - samples/sec: 3003.88 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:44:26,614 epoch 5 - iter 396/447 - loss 0.03446389 - time (sec): 25.63 - samples/sec: 3012.96 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:44:29,587 epoch 5 - iter 440/447 - loss 0.03444354 - time (sec): 28.61 - samples/sec: 2983.31 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:44:29,981 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:44:29,981 EPOCH 5 done: loss 0.0340 - lr: 0.000017 | |
| 2023-10-13 12:44:38,623 DEV : loss 0.16171278059482574 - f1-score (micro avg) 0.7783 | |
| 2023-10-13 12:44:38,651 saving best model | |
| 2023-10-13 12:44:39,090 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:44:42,275 epoch 6 - iter 44/447 - loss 0.02199878 - time (sec): 3.18 - samples/sec: 3022.58 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:44:45,000 epoch 6 - iter 88/447 - loss 0.01791271 - time (sec): 5.91 - samples/sec: 3016.28 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:44:47,967 epoch 6 - iter 132/447 - loss 0.01908459 - time (sec): 8.88 - samples/sec: 3001.10 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:44:50,765 epoch 6 - iter 176/447 - loss 0.02371749 - time (sec): 11.67 - samples/sec: 3033.68 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:44:53,605 epoch 6 - iter 220/447 - loss 0.02416858 - time (sec): 14.51 - samples/sec: 3058.44 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:44:56,283 epoch 6 - iter 264/447 - loss 0.02335894 - time (sec): 17.19 - samples/sec: 3043.29 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:44:59,072 epoch 6 - iter 308/447 - loss 0.02268652 - time (sec): 19.98 - samples/sec: 3016.88 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:45:01,767 epoch 6 - iter 352/447 - loss 0.02424439 - time (sec): 22.68 - samples/sec: 3009.53 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:45:04,569 epoch 6 - iter 396/447 - loss 0.02494761 - time (sec): 25.48 - samples/sec: 3020.77 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:45:07,181 epoch 6 - iter 440/447 - loss 0.02450484 - time (sec): 28.09 - samples/sec: 3020.74 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:45:07,807 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:45:07,807 EPOCH 6 done: loss 0.0244 - lr: 0.000013 | |
| 2023-10-13 12:45:16,042 DEV : loss 0.17539535462856293 - f1-score (micro avg) 0.7765 | |
| 2023-10-13 12:45:16,072 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:45:19,440 epoch 7 - iter 44/447 - loss 0.01357369 - time (sec): 3.37 - samples/sec: 2730.95 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:45:22,746 epoch 7 - iter 88/447 - loss 0.01403634 - time (sec): 6.67 - samples/sec: 2843.58 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:45:25,595 epoch 7 - iter 132/447 - loss 0.01539611 - time (sec): 9.52 - samples/sec: 2903.29 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:45:28,448 epoch 7 - iter 176/447 - loss 0.01710186 - time (sec): 12.38 - samples/sec: 2971.82 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:45:31,550 epoch 7 - iter 220/447 - loss 0.01634294 - time (sec): 15.48 - samples/sec: 2950.74 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:45:34,209 epoch 7 - iter 264/447 - loss 0.01602423 - time (sec): 18.14 - samples/sec: 2939.09 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:45:36,766 epoch 7 - iter 308/447 - loss 0.01550904 - time (sec): 20.69 - samples/sec: 2950.56 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:45:39,529 epoch 7 - iter 352/447 - loss 0.01612650 - time (sec): 23.46 - samples/sec: 2954.33 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:45:42,057 epoch 7 - iter 396/447 - loss 0.01558782 - time (sec): 25.98 - samples/sec: 2967.20 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:45:44,752 epoch 7 - iter 440/447 - loss 0.01530422 - time (sec): 28.68 - samples/sec: 2965.04 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:45:45,265 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:45:45,265 EPOCH 7 done: loss 0.0152 - lr: 0.000010 | |
| 2023-10-13 12:45:53,827 DEV : loss 0.18422970175743103 - f1-score (micro avg) 0.7758 | |
| 2023-10-13 12:45:53,857 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:45:56,629 epoch 8 - iter 44/447 - loss 0.00798763 - time (sec): 2.77 - samples/sec: 3120.73 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:45:59,360 epoch 8 - iter 88/447 - loss 0.00777261 - time (sec): 5.50 - samples/sec: 3050.62 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:46:03,058 epoch 8 - iter 132/447 - loss 0.00764591 - time (sec): 9.20 - samples/sec: 2909.35 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:46:06,043 epoch 8 - iter 176/447 - loss 0.00851798 - time (sec): 12.18 - samples/sec: 2888.94 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:46:08,754 epoch 8 - iter 220/447 - loss 0.00875116 - time (sec): 14.90 - samples/sec: 2926.03 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:46:11,769 epoch 8 - iter 264/447 - loss 0.00970690 - time (sec): 17.91 - samples/sec: 2922.91 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:46:14,532 epoch 8 - iter 308/447 - loss 0.00932691 - time (sec): 20.67 - samples/sec: 2946.73 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:46:17,149 epoch 8 - iter 352/447 - loss 0.00886594 - time (sec): 23.29 - samples/sec: 2958.75 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:46:19,955 epoch 8 - iter 396/447 - loss 0.00894768 - time (sec): 26.10 - samples/sec: 2967.27 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:46:22,571 epoch 8 - iter 440/447 - loss 0.00921656 - time (sec): 28.71 - samples/sec: 2972.85 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:46:22,952 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:46:22,952 EPOCH 8 done: loss 0.0093 - lr: 0.000007 | |
| 2023-10-13 12:46:31,318 DEV : loss 0.2055710256099701 - f1-score (micro avg) 0.7846 | |
| 2023-10-13 12:46:31,349 saving best model | |
| 2023-10-13 12:46:31,806 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:46:34,789 epoch 9 - iter 44/447 - loss 0.00632862 - time (sec): 2.98 - samples/sec: 2741.13 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:46:38,014 epoch 9 - iter 88/447 - loss 0.00391427 - time (sec): 6.21 - samples/sec: 2820.55 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:46:40,912 epoch 9 - iter 132/447 - loss 0.00600799 - time (sec): 9.10 - samples/sec: 2861.99 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:46:43,703 epoch 9 - iter 176/447 - loss 0.00647352 - time (sec): 11.90 - samples/sec: 2895.16 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:46:46,314 epoch 9 - iter 220/447 - loss 0.00669799 - time (sec): 14.51 - samples/sec: 2947.90 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:46:48,942 epoch 9 - iter 264/447 - loss 0.00717437 - time (sec): 17.13 - samples/sec: 2969.34 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:46:51,618 epoch 9 - iter 308/447 - loss 0.00633304 - time (sec): 19.81 - samples/sec: 2976.81 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:46:54,292 epoch 9 - iter 352/447 - loss 0.00630348 - time (sec): 22.48 - samples/sec: 2987.23 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:46:57,800 epoch 9 - iter 396/447 - loss 0.00673031 - time (sec): 25.99 - samples/sec: 2963.79 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:47:00,643 epoch 9 - iter 440/447 - loss 0.00677427 - time (sec): 28.84 - samples/sec: 2958.97 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:47:01,044 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:47:01,044 EPOCH 9 done: loss 0.0071 - lr: 0.000003 | |
| 2023-10-13 12:47:09,395 DEV : loss 0.21442939341068268 - f1-score (micro avg) 0.7876 | |
| 2023-10-13 12:47:09,425 saving best model | |
| 2023-10-13 12:47:09,938 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:47:12,923 epoch 10 - iter 44/447 - loss 0.00347708 - time (sec): 2.98 - samples/sec: 3022.65 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:47:15,674 epoch 10 - iter 88/447 - loss 0.00567608 - time (sec): 5.73 - samples/sec: 3017.03 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:47:18,567 epoch 10 - iter 132/447 - loss 0.00482620 - time (sec): 8.63 - samples/sec: 2983.60 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:47:21,552 epoch 10 - iter 176/447 - loss 0.00604485 - time (sec): 11.61 - samples/sec: 2982.37 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:47:24,150 epoch 10 - iter 220/447 - loss 0.00591778 - time (sec): 14.21 - samples/sec: 3001.57 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:47:26,840 epoch 10 - iter 264/447 - loss 0.00602543 - time (sec): 16.90 - samples/sec: 3005.87 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:47:29,663 epoch 10 - iter 308/447 - loss 0.00564455 - time (sec): 19.72 - samples/sec: 3012.65 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:47:33,086 epoch 10 - iter 352/447 - loss 0.00531102 - time (sec): 23.15 - samples/sec: 3009.37 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:47:35,778 epoch 10 - iter 396/447 - loss 0.00565506 - time (sec): 25.84 - samples/sec: 2999.81 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 12:47:38,391 epoch 10 - iter 440/447 - loss 0.00523144 - time (sec): 28.45 - samples/sec: 2997.36 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 12:47:38,804 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:47:38,804 EPOCH 10 done: loss 0.0052 - lr: 0.000000 | |
| 2023-10-13 12:47:47,440 DEV : loss 0.21124331653118134 - f1-score (micro avg) 0.7838 | |
| 2023-10-13 12:47:47,791 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:47:47,792 Loading model from best epoch ... | |
| 2023-10-13 12:47:49,218 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time | |
| 2023-10-13 12:47:54,501 | |
| Results: | |
| - F-score (micro) 0.7514 | |
| - F-score (macro) 0.6796 | |
| - Accuracy 0.6216 | |
| By class: | |
| precision recall f1-score support | |
| loc 0.8401 0.8289 0.8345 596 | |
| pers 0.6885 0.7568 0.7210 333 | |
| org 0.5455 0.5455 0.5455 132 | |
| prod 0.7021 0.5000 0.5841 66 | |
| time 0.6923 0.7347 0.7129 49 | |
| micro avg 0.7485 0.7543 0.7514 1176 | |
| macro avg 0.6937 0.6732 0.6796 1176 | |
| weighted avg 0.7502 0.7543 0.7508 1176 | |
| 2023-10-13 12:47:54,502 ---------------------------------------------------------------------------------------------------- | |