| 2023-10-16 22:13:33,990 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,991 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() | |
| )" | |
| 2023-10-16 22:13:33,991 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,991 MultiCorpus: 6183 train + 680 dev + 2113 test sentences | |
| - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator | |
| 2023-10-16 22:13:33,991 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,991 Train: 6183 sentences | |
| 2023-10-16 22:13:33,991 (train_with_dev=False, train_with_test=False) | |
| 2023-10-16 22:13:33,991 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,991 Training Params: | |
| 2023-10-16 22:13:33,992 - learning_rate: "3e-05" | |
| 2023-10-16 22:13:33,992 - mini_batch_size: "4" | |
| 2023-10-16 22:13:33,992 - max_epochs: "10" | |
| 2023-10-16 22:13:33,992 - shuffle: "True" | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,992 Plugins: | |
| 2023-10-16 22:13:33,992 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,992 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-16 22:13:33,992 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,992 Computation: | |
| 2023-10-16 22:13:33,992 - compute on device: cuda:0 | |
| 2023-10-16 22:13:33,992 - embedding storage: none | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,992 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3" | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:33,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:13:41,036 epoch 1 - iter 154/1546 - loss 1.89169363 - time (sec): 7.04 - samples/sec: 1856.25 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 22:13:47,976 epoch 1 - iter 308/1546 - loss 1.11226889 - time (sec): 13.98 - samples/sec: 1798.15 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 22:13:54,826 epoch 1 - iter 462/1546 - loss 0.79922596 - time (sec): 20.83 - samples/sec: 1834.89 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 22:14:01,596 epoch 1 - iter 616/1546 - loss 0.63837166 - time (sec): 27.60 - samples/sec: 1822.95 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 22:14:08,419 epoch 1 - iter 770/1546 - loss 0.53584832 - time (sec): 34.43 - samples/sec: 1820.11 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 22:14:15,218 epoch 1 - iter 924/1546 - loss 0.47208580 - time (sec): 41.22 - samples/sec: 1803.72 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 22:14:21,994 epoch 1 - iter 1078/1546 - loss 0.42137026 - time (sec): 48.00 - samples/sec: 1805.09 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 22:14:28,765 epoch 1 - iter 1232/1546 - loss 0.38283230 - time (sec): 54.77 - samples/sec: 1806.79 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 22:14:35,670 epoch 1 - iter 1386/1546 - loss 0.35327797 - time (sec): 61.68 - samples/sec: 1808.79 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 22:14:42,498 epoch 1 - iter 1540/1546 - loss 0.32990339 - time (sec): 68.51 - samples/sec: 1808.50 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 22:14:42,763 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:14:42,763 EPOCH 1 done: loss 0.3292 - lr: 0.000030 | |
| 2023-10-16 22:14:44,800 DEV : loss 0.06861560046672821 - f1-score (micro avg) 0.7008 | |
| 2023-10-16 22:14:44,813 saving best model | |
| 2023-10-16 22:14:45,144 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:14:51,840 epoch 2 - iter 154/1546 - loss 0.10594349 - time (sec): 6.70 - samples/sec: 1833.60 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 22:14:58,684 epoch 2 - iter 308/1546 - loss 0.09339103 - time (sec): 13.54 - samples/sec: 1878.86 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 22:15:05,450 epoch 2 - iter 462/1546 - loss 0.09323722 - time (sec): 20.30 - samples/sec: 1823.80 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 22:15:12,348 epoch 2 - iter 616/1546 - loss 0.08968150 - time (sec): 27.20 - samples/sec: 1814.58 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 22:15:19,198 epoch 2 - iter 770/1546 - loss 0.09342737 - time (sec): 34.05 - samples/sec: 1798.90 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 22:15:26,224 epoch 2 - iter 924/1546 - loss 0.08829761 - time (sec): 41.08 - samples/sec: 1820.13 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 22:15:33,082 epoch 2 - iter 1078/1546 - loss 0.08746364 - time (sec): 47.94 - samples/sec: 1810.86 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 22:15:39,985 epoch 2 - iter 1232/1546 - loss 0.08799371 - time (sec): 54.84 - samples/sec: 1803.28 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 22:15:46,769 epoch 2 - iter 1386/1546 - loss 0.08684286 - time (sec): 61.62 - samples/sec: 1799.34 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 22:15:53,640 epoch 2 - iter 1540/1546 - loss 0.08551341 - time (sec): 68.49 - samples/sec: 1808.43 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 22:15:53,897 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:15:53,897 EPOCH 2 done: loss 0.0854 - lr: 0.000027 | |
| 2023-10-16 22:15:55,946 DEV : loss 0.061695486307144165 - f1-score (micro avg) 0.7438 | |
| 2023-10-16 22:15:55,958 saving best model | |
| 2023-10-16 22:15:56,336 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:16:03,116 epoch 3 - iter 154/1546 - loss 0.05352142 - time (sec): 6.78 - samples/sec: 1856.48 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 22:16:10,018 epoch 3 - iter 308/1546 - loss 0.05381165 - time (sec): 13.68 - samples/sec: 1831.30 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 22:16:16,836 epoch 3 - iter 462/1546 - loss 0.05138306 - time (sec): 20.50 - samples/sec: 1818.79 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 22:16:23,700 epoch 3 - iter 616/1546 - loss 0.05202487 - time (sec): 27.36 - samples/sec: 1799.35 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 22:16:30,519 epoch 3 - iter 770/1546 - loss 0.05270760 - time (sec): 34.18 - samples/sec: 1781.49 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 22:16:37,305 epoch 3 - iter 924/1546 - loss 0.05313926 - time (sec): 40.97 - samples/sec: 1769.51 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 22:16:44,171 epoch 3 - iter 1078/1546 - loss 0.05425075 - time (sec): 47.83 - samples/sec: 1774.25 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 22:16:51,082 epoch 3 - iter 1232/1546 - loss 0.05410382 - time (sec): 54.75 - samples/sec: 1782.41 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 22:16:57,962 epoch 3 - iter 1386/1546 - loss 0.05476332 - time (sec): 61.62 - samples/sec: 1789.99 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 22:17:04,918 epoch 3 - iter 1540/1546 - loss 0.05489666 - time (sec): 68.58 - samples/sec: 1805.90 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 22:17:05,181 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:17:05,181 EPOCH 3 done: loss 0.0547 - lr: 0.000023 | |
| 2023-10-16 22:17:07,529 DEV : loss 0.061977606266736984 - f1-score (micro avg) 0.7663 | |
| 2023-10-16 22:17:07,541 saving best model | |
| 2023-10-16 22:17:08,003 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:17:14,831 epoch 4 - iter 154/1546 - loss 0.03348088 - time (sec): 6.82 - samples/sec: 1727.26 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 22:17:21,744 epoch 4 - iter 308/1546 - loss 0.03828523 - time (sec): 13.73 - samples/sec: 1842.44 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 22:17:28,540 epoch 4 - iter 462/1546 - loss 0.03893862 - time (sec): 20.53 - samples/sec: 1804.31 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 22:17:35,383 epoch 4 - iter 616/1546 - loss 0.03748261 - time (sec): 27.37 - samples/sec: 1791.42 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 22:17:42,259 epoch 4 - iter 770/1546 - loss 0.03477433 - time (sec): 34.25 - samples/sec: 1810.90 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 22:17:49,167 epoch 4 - iter 924/1546 - loss 0.03501152 - time (sec): 41.15 - samples/sec: 1825.66 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 22:17:55,980 epoch 4 - iter 1078/1546 - loss 0.03703932 - time (sec): 47.97 - samples/sec: 1801.41 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 22:18:02,902 epoch 4 - iter 1232/1546 - loss 0.03858821 - time (sec): 54.89 - samples/sec: 1807.98 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 22:18:09,684 epoch 4 - iter 1386/1546 - loss 0.03914559 - time (sec): 61.67 - samples/sec: 1802.93 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 22:18:17,009 epoch 4 - iter 1540/1546 - loss 0.03933452 - time (sec): 69.00 - samples/sec: 1796.19 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 22:18:17,268 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:18:17,269 EPOCH 4 done: loss 0.0394 - lr: 0.000020 | |
| 2023-10-16 22:18:19,309 DEV : loss 0.0907452255487442 - f1-score (micro avg) 0.7805 | |
| 2023-10-16 22:18:19,323 saving best model | |
| 2023-10-16 22:18:19,768 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:18:26,862 epoch 5 - iter 154/1546 - loss 0.02856650 - time (sec): 7.09 - samples/sec: 1732.93 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 22:18:33,828 epoch 5 - iter 308/1546 - loss 0.02438427 - time (sec): 14.06 - samples/sec: 1786.41 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 22:18:40,685 epoch 5 - iter 462/1546 - loss 0.02487299 - time (sec): 20.91 - samples/sec: 1793.05 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 22:18:47,656 epoch 5 - iter 616/1546 - loss 0.02566201 - time (sec): 27.88 - samples/sec: 1825.66 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 22:18:54,562 epoch 5 - iter 770/1546 - loss 0.02715511 - time (sec): 34.79 - samples/sec: 1825.46 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 22:19:01,378 epoch 5 - iter 924/1546 - loss 0.02767064 - time (sec): 41.61 - samples/sec: 1828.00 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 22:19:08,338 epoch 5 - iter 1078/1546 - loss 0.02706633 - time (sec): 48.57 - samples/sec: 1834.40 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 22:19:15,159 epoch 5 - iter 1232/1546 - loss 0.02726636 - time (sec): 55.39 - samples/sec: 1823.57 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 22:19:21,988 epoch 5 - iter 1386/1546 - loss 0.02736502 - time (sec): 62.22 - samples/sec: 1808.57 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 22:19:28,800 epoch 5 - iter 1540/1546 - loss 0.02672501 - time (sec): 69.03 - samples/sec: 1795.96 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 22:19:29,066 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:19:29,067 EPOCH 5 done: loss 0.0267 - lr: 0.000017 | |
| 2023-10-16 22:19:31,125 DEV : loss 0.10103413462638855 - f1-score (micro avg) 0.7935 | |
| 2023-10-16 22:19:31,138 saving best model | |
| 2023-10-16 22:19:31,609 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:19:38,520 epoch 6 - iter 154/1546 - loss 0.01115327 - time (sec): 6.91 - samples/sec: 1773.33 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 22:19:45,472 epoch 6 - iter 308/1546 - loss 0.01261767 - time (sec): 13.86 - samples/sec: 1775.01 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 22:19:52,353 epoch 6 - iter 462/1546 - loss 0.01720971 - time (sec): 20.74 - samples/sec: 1760.86 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 22:19:59,182 epoch 6 - iter 616/1546 - loss 0.01700125 - time (sec): 27.57 - samples/sec: 1789.53 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 22:20:05,960 epoch 6 - iter 770/1546 - loss 0.01632348 - time (sec): 34.35 - samples/sec: 1797.98 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 22:20:12,846 epoch 6 - iter 924/1546 - loss 0.01648542 - time (sec): 41.23 - samples/sec: 1822.12 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 22:20:19,764 epoch 6 - iter 1078/1546 - loss 0.01624090 - time (sec): 48.15 - samples/sec: 1807.88 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 22:20:26,576 epoch 6 - iter 1232/1546 - loss 0.01732653 - time (sec): 54.96 - samples/sec: 1798.95 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 22:20:33,435 epoch 6 - iter 1386/1546 - loss 0.01698745 - time (sec): 61.82 - samples/sec: 1796.22 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 22:20:40,310 epoch 6 - iter 1540/1546 - loss 0.01680358 - time (sec): 68.70 - samples/sec: 1804.24 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 22:20:40,579 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:20:40,579 EPOCH 6 done: loss 0.0168 - lr: 0.000013 | |
| 2023-10-16 22:20:42,593 DEV : loss 0.10263525694608688 - f1-score (micro avg) 0.7835 | |
| 2023-10-16 22:20:42,605 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:20:49,419 epoch 7 - iter 154/1546 - loss 0.01073330 - time (sec): 6.81 - samples/sec: 1767.10 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 22:20:56,281 epoch 7 - iter 308/1546 - loss 0.01268942 - time (sec): 13.67 - samples/sec: 1748.02 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 22:21:03,138 epoch 7 - iter 462/1546 - loss 0.01130454 - time (sec): 20.53 - samples/sec: 1771.55 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 22:21:09,977 epoch 7 - iter 616/1546 - loss 0.01193437 - time (sec): 27.37 - samples/sec: 1775.93 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 22:21:16,911 epoch 7 - iter 770/1546 - loss 0.01224745 - time (sec): 34.30 - samples/sec: 1789.42 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 22:21:23,792 epoch 7 - iter 924/1546 - loss 0.01174877 - time (sec): 41.19 - samples/sec: 1805.47 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 22:21:30,715 epoch 7 - iter 1078/1546 - loss 0.01116054 - time (sec): 48.11 - samples/sec: 1807.28 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 22:21:37,649 epoch 7 - iter 1232/1546 - loss 0.01077128 - time (sec): 55.04 - samples/sec: 1799.13 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 22:21:44,464 epoch 7 - iter 1386/1546 - loss 0.01068182 - time (sec): 61.86 - samples/sec: 1799.59 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 22:21:51,345 epoch 7 - iter 1540/1546 - loss 0.01058741 - time (sec): 68.74 - samples/sec: 1803.61 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 22:21:51,605 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:21:51,605 EPOCH 7 done: loss 0.0106 - lr: 0.000010 | |
| 2023-10-16 22:21:53,621 DEV : loss 0.11100788414478302 - f1-score (micro avg) 0.7739 | |
| 2023-10-16 22:21:53,634 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:22:00,462 epoch 8 - iter 154/1546 - loss 0.00712344 - time (sec): 6.83 - samples/sec: 1667.10 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 22:22:07,489 epoch 8 - iter 308/1546 - loss 0.00737014 - time (sec): 13.85 - samples/sec: 1786.24 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 22:22:14,432 epoch 8 - iter 462/1546 - loss 0.00844824 - time (sec): 20.80 - samples/sec: 1819.72 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 22:22:21,368 epoch 8 - iter 616/1546 - loss 0.00756277 - time (sec): 27.73 - samples/sec: 1839.47 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 22:22:28,177 epoch 8 - iter 770/1546 - loss 0.00738767 - time (sec): 34.54 - samples/sec: 1825.85 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 22:22:34,927 epoch 8 - iter 924/1546 - loss 0.00797400 - time (sec): 41.29 - samples/sec: 1809.26 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 22:22:41,703 epoch 8 - iter 1078/1546 - loss 0.00749557 - time (sec): 48.07 - samples/sec: 1812.26 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 22:22:48,592 epoch 8 - iter 1232/1546 - loss 0.00775779 - time (sec): 54.96 - samples/sec: 1814.63 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 22:22:55,442 epoch 8 - iter 1386/1546 - loss 0.00804980 - time (sec): 61.81 - samples/sec: 1814.91 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 22:23:02,230 epoch 8 - iter 1540/1546 - loss 0.00758625 - time (sec): 68.60 - samples/sec: 1804.27 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 22:23:02,492 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:23:02,492 EPOCH 8 done: loss 0.0076 - lr: 0.000007 | |
| 2023-10-16 22:23:04,849 DEV : loss 0.11394007503986359 - f1-score (micro avg) 0.7967 | |
| 2023-10-16 22:23:04,862 saving best model | |
| 2023-10-16 22:23:05,318 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:23:12,200 epoch 9 - iter 154/1546 - loss 0.00573957 - time (sec): 6.88 - samples/sec: 1709.00 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 22:23:19,147 epoch 9 - iter 308/1546 - loss 0.00372078 - time (sec): 13.83 - samples/sec: 1719.76 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 22:23:26,016 epoch 9 - iter 462/1546 - loss 0.00469241 - time (sec): 20.70 - samples/sec: 1796.59 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 22:23:32,827 epoch 9 - iter 616/1546 - loss 0.00728260 - time (sec): 27.51 - samples/sec: 1786.73 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 22:23:39,731 epoch 9 - iter 770/1546 - loss 0.00701758 - time (sec): 34.41 - samples/sec: 1807.80 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 22:23:46,646 epoch 9 - iter 924/1546 - loss 0.00660586 - time (sec): 41.33 - samples/sec: 1797.68 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 22:23:53,527 epoch 9 - iter 1078/1546 - loss 0.00602231 - time (sec): 48.21 - samples/sec: 1805.05 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 22:24:00,289 epoch 9 - iter 1232/1546 - loss 0.00642192 - time (sec): 54.97 - samples/sec: 1801.13 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 22:24:07,070 epoch 9 - iter 1386/1546 - loss 0.00626127 - time (sec): 61.75 - samples/sec: 1800.99 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 22:24:13,888 epoch 9 - iter 1540/1546 - loss 0.00615296 - time (sec): 68.57 - samples/sec: 1807.95 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 22:24:14,136 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:24:14,136 EPOCH 9 done: loss 0.0062 - lr: 0.000003 | |
| 2023-10-16 22:24:16,156 DEV : loss 0.11375954002141953 - f1-score (micro avg) 0.7926 | |
| 2023-10-16 22:24:16,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:24:22,994 epoch 10 - iter 154/1546 - loss 0.00252186 - time (sec): 6.82 - samples/sec: 1789.34 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 22:24:29,820 epoch 10 - iter 308/1546 - loss 0.00144410 - time (sec): 13.65 - samples/sec: 1820.64 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 22:24:36,641 epoch 10 - iter 462/1546 - loss 0.00151338 - time (sec): 20.47 - samples/sec: 1816.93 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 22:24:43,491 epoch 10 - iter 616/1546 - loss 0.00162804 - time (sec): 27.32 - samples/sec: 1819.88 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 22:24:50,236 epoch 10 - iter 770/1546 - loss 0.00277701 - time (sec): 34.07 - samples/sec: 1826.95 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 22:24:57,155 epoch 10 - iter 924/1546 - loss 0.00280504 - time (sec): 40.98 - samples/sec: 1826.24 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 22:25:04,089 epoch 10 - iter 1078/1546 - loss 0.00303272 - time (sec): 47.92 - samples/sec: 1814.17 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 22:25:10,898 epoch 10 - iter 1232/1546 - loss 0.00317340 - time (sec): 54.73 - samples/sec: 1815.67 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 22:25:17,708 epoch 10 - iter 1386/1546 - loss 0.00315536 - time (sec): 61.54 - samples/sec: 1813.27 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 22:25:24,582 epoch 10 - iter 1540/1546 - loss 0.00355373 - time (sec): 68.41 - samples/sec: 1811.86 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 22:25:24,842 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:25:24,842 EPOCH 10 done: loss 0.0035 - lr: 0.000000 | |
| 2023-10-16 22:25:26,849 DEV : loss 0.11292611062526703 - f1-score (micro avg) 0.8017 | |
| 2023-10-16 22:25:26,861 saving best model | |
| 2023-10-16 22:25:27,661 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 22:25:27,663 Loading model from best epoch ... | |
| 2023-10-16 22:25:29,149 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET | |
| 2023-10-16 22:25:35,105 | |
| Results: | |
| - F-score (micro) 0.8044 | |
| - F-score (macro) 0.7149 | |
| - Accuracy 0.6943 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.8392 0.8605 0.8497 946 | |
| BUILDING 0.6329 0.5405 0.5831 185 | |
| STREET 0.6774 0.7500 0.7119 56 | |
| micro avg 0.8034 0.8054 0.8044 1187 | |
| macro avg 0.7165 0.7170 0.7149 1187 | |
| weighted avg 0.7994 0.8054 0.8016 1187 | |
| 2023-10-16 22:25:35,105 ---------------------------------------------------------------------------------------------------- | |