| 2023-10-17 17:44:48,166 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,168 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): ElectraModel( | |
| (embeddings): ElectraEmbeddings( | |
| (word_embeddings): Embedding(32001, 768) | |
| (position_embeddings): Embedding(512, 768) | |
| (token_type_embeddings): Embedding(2, 768) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): ElectraEncoder( | |
| (layer): ModuleList( | |
| (0-11): 12 x ElectraLayer( | |
| (attention): ElectraAttention( | |
| (self): ElectraSelfAttention( | |
| (query): Linear(in_features=768, out_features=768, bias=True) | |
| (key): Linear(in_features=768, out_features=768, bias=True) | |
| (value): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (output): ElectraSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): ElectraIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): ElectraOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=768, out_features=21, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-17 17:44:48,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,168 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 | |
| 2023-10-17 17:44:48,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,168 Train: 3575 sentences | |
| 2023-10-17 17:44:48,168 (train_with_dev=False, train_with_test=False) | |
| 2023-10-17 17:44:48,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,168 Training Params: | |
| 2023-10-17 17:44:48,168 - learning_rate: "5e-05" | |
| 2023-10-17 17:44:48,168 - mini_batch_size: "8" | |
| 2023-10-17 17:44:48,168 - max_epochs: "10" | |
| 2023-10-17 17:44:48,169 - shuffle: "True" | |
| 2023-10-17 17:44:48,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,169 Plugins: | |
| 2023-10-17 17:44:48,169 - TensorboardLogger | |
| 2023-10-17 17:44:48,169 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-17 17:44:48,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,169 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-17 17:44:48,169 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-17 17:44:48,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,169 Computation: | |
| 2023-10-17 17:44:48,169 - compute on device: cuda:0 | |
| 2023-10-17 17:44:48,169 - embedding storage: none | |
| 2023-10-17 17:44:48,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,169 Model training base path: "hmbench-hipe2020/de-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-17 17:44:48,169 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,170 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:44:48,170 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-17 17:44:52,654 epoch 1 - iter 44/447 - loss 3.53247519 - time (sec): 4.48 - samples/sec: 1962.45 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 17:44:57,042 epoch 1 - iter 88/447 - loss 2.31874710 - time (sec): 8.87 - samples/sec: 1971.29 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 17:45:01,148 epoch 1 - iter 132/447 - loss 1.75427682 - time (sec): 12.98 - samples/sec: 1973.78 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 17:45:05,253 epoch 1 - iter 176/447 - loss 1.42658690 - time (sec): 17.08 - samples/sec: 1990.33 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 17:45:09,368 epoch 1 - iter 220/447 - loss 1.21599396 - time (sec): 21.20 - samples/sec: 1982.09 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 17:45:13,538 epoch 1 - iter 264/447 - loss 1.06151430 - time (sec): 25.37 - samples/sec: 1993.82 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 17:45:17,496 epoch 1 - iter 308/447 - loss 0.94782877 - time (sec): 29.32 - samples/sec: 2013.04 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-17 17:45:21,524 epoch 1 - iter 352/447 - loss 0.86170498 - time (sec): 33.35 - samples/sec: 2021.61 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-17 17:45:26,668 epoch 1 - iter 396/447 - loss 0.77972033 - time (sec): 38.50 - samples/sec: 2006.47 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-17 17:45:30,614 epoch 1 - iter 440/447 - loss 0.72870630 - time (sec): 42.44 - samples/sec: 2009.37 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-17 17:45:31,257 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:45:31,257 EPOCH 1 done: loss 0.7216 - lr: 0.000049 | |
| 2023-10-17 17:45:37,295 DEV : loss 0.17193150520324707 - f1-score (micro avg) 0.6114 | |
| 2023-10-17 17:45:37,350 saving best model | |
| 2023-10-17 17:45:37,922 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:45:42,340 epoch 2 - iter 44/447 - loss 0.19212755 - time (sec): 4.42 - samples/sec: 1736.07 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-17 17:45:46,547 epoch 2 - iter 88/447 - loss 0.18829141 - time (sec): 8.62 - samples/sec: 1909.97 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-17 17:45:50,480 epoch 2 - iter 132/447 - loss 0.17009265 - time (sec): 12.56 - samples/sec: 1969.56 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-17 17:45:54,515 epoch 2 - iter 176/447 - loss 0.16569554 - time (sec): 16.59 - samples/sec: 2009.28 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-17 17:45:58,711 epoch 2 - iter 220/447 - loss 0.15747248 - time (sec): 20.79 - samples/sec: 2044.40 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-17 17:46:02,697 epoch 2 - iter 264/447 - loss 0.15702120 - time (sec): 24.77 - samples/sec: 2040.25 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-17 17:46:06,765 epoch 2 - iter 308/447 - loss 0.15884489 - time (sec): 28.84 - samples/sec: 2057.88 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-17 17:46:10,920 epoch 2 - iter 352/447 - loss 0.15406260 - time (sec): 33.00 - samples/sec: 2059.91 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-17 17:46:15,205 epoch 2 - iter 396/447 - loss 0.14959137 - time (sec): 37.28 - samples/sec: 2071.69 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-17 17:46:19,041 epoch 2 - iter 440/447 - loss 0.14633762 - time (sec): 41.12 - samples/sec: 2075.89 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-17 17:46:19,629 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:46:19,629 EPOCH 2 done: loss 0.1456 - lr: 0.000045 | |
| 2023-10-17 17:46:30,392 DEV : loss 0.14541815221309662 - f1-score (micro avg) 0.6911 | |
| 2023-10-17 17:46:30,444 saving best model | |
| 2023-10-17 17:46:31,832 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:46:35,959 epoch 3 - iter 44/447 - loss 0.09885066 - time (sec): 4.12 - samples/sec: 1899.38 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-17 17:46:39,789 epoch 3 - iter 88/447 - loss 0.08670680 - time (sec): 7.95 - samples/sec: 1930.35 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-17 17:46:43,693 epoch 3 - iter 132/447 - loss 0.08996882 - time (sec): 11.86 - samples/sec: 1948.35 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-17 17:46:47,693 epoch 3 - iter 176/447 - loss 0.08967241 - time (sec): 15.86 - samples/sec: 1982.97 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-17 17:46:51,668 epoch 3 - iter 220/447 - loss 0.08753443 - time (sec): 19.83 - samples/sec: 2017.86 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-17 17:46:55,783 epoch 3 - iter 264/447 - loss 0.08959986 - time (sec): 23.95 - samples/sec: 2032.83 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-17 17:47:00,057 epoch 3 - iter 308/447 - loss 0.08503972 - time (sec): 28.22 - samples/sec: 2050.68 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-17 17:47:04,697 epoch 3 - iter 352/447 - loss 0.08384251 - time (sec): 32.86 - samples/sec: 2062.53 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-17 17:47:08,798 epoch 3 - iter 396/447 - loss 0.08475909 - time (sec): 36.96 - samples/sec: 2064.75 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-17 17:47:12,845 epoch 3 - iter 440/447 - loss 0.08292084 - time (sec): 41.01 - samples/sec: 2081.99 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-17 17:47:13,443 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:47:13,444 EPOCH 3 done: loss 0.0828 - lr: 0.000039 | |
| 2023-10-17 17:47:24,567 DEV : loss 0.18581056594848633 - f1-score (micro avg) 0.734 | |
| 2023-10-17 17:47:24,619 saving best model | |
| 2023-10-17 17:47:25,193 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:47:29,381 epoch 4 - iter 44/447 - loss 0.06148268 - time (sec): 4.19 - samples/sec: 2073.49 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-17 17:47:33,657 epoch 4 - iter 88/447 - loss 0.05203947 - time (sec): 8.46 - samples/sec: 2178.64 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-17 17:47:37,580 epoch 4 - iter 132/447 - loss 0.05294970 - time (sec): 12.38 - samples/sec: 2117.16 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-17 17:47:41,665 epoch 4 - iter 176/447 - loss 0.05512343 - time (sec): 16.47 - samples/sec: 2105.44 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-17 17:47:45,882 epoch 4 - iter 220/447 - loss 0.05440061 - time (sec): 20.69 - samples/sec: 2074.33 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-17 17:47:49,882 epoch 4 - iter 264/447 - loss 0.05339531 - time (sec): 24.69 - samples/sec: 2058.85 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-17 17:47:54,034 epoch 4 - iter 308/447 - loss 0.05563142 - time (sec): 28.84 - samples/sec: 2085.27 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-17 17:47:58,609 epoch 4 - iter 352/447 - loss 0.05395382 - time (sec): 33.41 - samples/sec: 2068.53 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-17 17:48:03,017 epoch 4 - iter 396/447 - loss 0.05405361 - time (sec): 37.82 - samples/sec: 2040.32 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-17 17:48:07,090 epoch 4 - iter 440/447 - loss 0.05319170 - time (sec): 41.89 - samples/sec: 2035.85 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-17 17:48:07,746 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:48:07,747 EPOCH 4 done: loss 0.0530 - lr: 0.000033 | |
| 2023-10-17 17:48:19,334 DEV : loss 0.16320064663887024 - f1-score (micro avg) 0.7421 | |
| 2023-10-17 17:48:19,391 saving best model | |
| 2023-10-17 17:48:20,773 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:48:24,936 epoch 5 - iter 44/447 - loss 0.03339435 - time (sec): 4.16 - samples/sec: 2013.95 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-17 17:48:28,768 epoch 5 - iter 88/447 - loss 0.03083486 - time (sec): 7.99 - samples/sec: 2075.68 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-17 17:48:32,818 epoch 5 - iter 132/447 - loss 0.02828933 - time (sec): 12.04 - samples/sec: 2079.53 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-17 17:48:37,063 epoch 5 - iter 176/447 - loss 0.03313995 - time (sec): 16.29 - samples/sec: 2083.18 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-17 17:48:40,905 epoch 5 - iter 220/447 - loss 0.03539016 - time (sec): 20.13 - samples/sec: 2106.25 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-17 17:48:45,061 epoch 5 - iter 264/447 - loss 0.03731012 - time (sec): 24.28 - samples/sec: 2098.95 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 17:48:49,291 epoch 5 - iter 308/447 - loss 0.03763343 - time (sec): 28.51 - samples/sec: 2108.65 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 17:48:53,141 epoch 5 - iter 352/447 - loss 0.03771950 - time (sec): 32.36 - samples/sec: 2119.26 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 17:48:57,037 epoch 5 - iter 396/447 - loss 0.03610494 - time (sec): 36.26 - samples/sec: 2110.09 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 17:49:00,960 epoch 5 - iter 440/447 - loss 0.03458475 - time (sec): 40.18 - samples/sec: 2111.19 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 17:49:01,787 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:49:01,787 EPOCH 5 done: loss 0.0343 - lr: 0.000028 | |
| 2023-10-17 17:49:12,745 DEV : loss 0.2096388190984726 - f1-score (micro avg) 0.7754 | |
| 2023-10-17 17:49:12,801 saving best model | |
| 2023-10-17 17:49:13,365 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:49:17,391 epoch 6 - iter 44/447 - loss 0.02071263 - time (sec): 4.02 - samples/sec: 2067.62 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 17:49:21,287 epoch 6 - iter 88/447 - loss 0.02045657 - time (sec): 7.92 - samples/sec: 2084.19 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 17:49:25,310 epoch 6 - iter 132/447 - loss 0.02395735 - time (sec): 11.94 - samples/sec: 2103.18 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 17:49:29,245 epoch 6 - iter 176/447 - loss 0.02314660 - time (sec): 15.88 - samples/sec: 2120.89 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 17:49:33,267 epoch 6 - iter 220/447 - loss 0.02399633 - time (sec): 19.90 - samples/sec: 2074.51 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 17:49:37,652 epoch 6 - iter 264/447 - loss 0.02289993 - time (sec): 24.28 - samples/sec: 2079.34 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 17:49:41,757 epoch 6 - iter 308/447 - loss 0.02175241 - time (sec): 28.39 - samples/sec: 2078.16 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 17:49:46,034 epoch 6 - iter 352/447 - loss 0.02292294 - time (sec): 32.67 - samples/sec: 2060.30 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 17:49:50,018 epoch 6 - iter 396/447 - loss 0.02332494 - time (sec): 36.65 - samples/sec: 2058.40 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 17:49:54,610 epoch 6 - iter 440/447 - loss 0.02230257 - time (sec): 41.24 - samples/sec: 2069.91 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 17:49:55,243 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:49:55,243 EPOCH 6 done: loss 0.0220 - lr: 0.000022 | |
| 2023-10-17 17:50:06,845 DEV : loss 0.21218937635421753 - f1-score (micro avg) 0.7601 | |
| 2023-10-17 17:50:06,908 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:50:11,253 epoch 7 - iter 44/447 - loss 0.01285933 - time (sec): 4.34 - samples/sec: 2001.74 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 17:50:15,325 epoch 7 - iter 88/447 - loss 0.01388987 - time (sec): 8.42 - samples/sec: 1998.53 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 17:50:19,308 epoch 7 - iter 132/447 - loss 0.01336076 - time (sec): 12.40 - samples/sec: 2011.61 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 17:50:23,382 epoch 7 - iter 176/447 - loss 0.01261394 - time (sec): 16.47 - samples/sec: 2054.87 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 17:50:27,928 epoch 7 - iter 220/447 - loss 0.01097990 - time (sec): 21.02 - samples/sec: 2053.75 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 17:50:32,045 epoch 7 - iter 264/447 - loss 0.01111927 - time (sec): 25.14 - samples/sec: 2023.76 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 17:50:36,497 epoch 7 - iter 308/447 - loss 0.01188959 - time (sec): 29.59 - samples/sec: 2017.85 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 17:50:40,689 epoch 7 - iter 352/447 - loss 0.01124010 - time (sec): 33.78 - samples/sec: 2016.53 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 17:50:44,831 epoch 7 - iter 396/447 - loss 0.01118474 - time (sec): 37.92 - samples/sec: 2034.64 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 17:50:48,860 epoch 7 - iter 440/447 - loss 0.01185488 - time (sec): 41.95 - samples/sec: 2029.22 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 17:50:49,541 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:50:49,541 EPOCH 7 done: loss 0.0124 - lr: 0.000017 | |
| 2023-10-17 17:51:00,437 DEV : loss 0.24467261135578156 - f1-score (micro avg) 0.7769 | |
| 2023-10-17 17:51:00,497 saving best model | |
| 2023-10-17 17:51:01,918 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:51:05,955 epoch 8 - iter 44/447 - loss 0.00687414 - time (sec): 4.03 - samples/sec: 1946.11 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 17:51:10,038 epoch 8 - iter 88/447 - loss 0.00560644 - time (sec): 8.12 - samples/sec: 1995.96 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 17:51:14,085 epoch 8 - iter 132/447 - loss 0.00512284 - time (sec): 12.16 - samples/sec: 2000.44 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 17:51:18,460 epoch 8 - iter 176/447 - loss 0.00669502 - time (sec): 16.54 - samples/sec: 1975.13 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 17:51:23,370 epoch 8 - iter 220/447 - loss 0.00700551 - time (sec): 21.45 - samples/sec: 1936.87 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 17:51:27,445 epoch 8 - iter 264/447 - loss 0.00799623 - time (sec): 25.52 - samples/sec: 1967.09 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 17:51:31,651 epoch 8 - iter 308/447 - loss 0.00762820 - time (sec): 29.73 - samples/sec: 1973.87 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 17:51:35,768 epoch 8 - iter 352/447 - loss 0.00779306 - time (sec): 33.85 - samples/sec: 1982.90 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 17:51:39,774 epoch 8 - iter 396/447 - loss 0.00777938 - time (sec): 37.85 - samples/sec: 1994.88 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 17:51:44,165 epoch 8 - iter 440/447 - loss 0.00736500 - time (sec): 42.24 - samples/sec: 2019.04 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 17:51:44,797 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:51:44,798 EPOCH 8 done: loss 0.0073 - lr: 0.000011 | |
| 2023-10-17 17:51:55,888 DEV : loss 0.24660176038742065 - f1-score (micro avg) 0.7942 | |
| 2023-10-17 17:51:55,947 saving best model | |
| 2023-10-17 17:51:57,324 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:52:01,426 epoch 9 - iter 44/447 - loss 0.00110632 - time (sec): 4.10 - samples/sec: 2054.78 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 17:52:05,847 epoch 9 - iter 88/447 - loss 0.00403816 - time (sec): 8.52 - samples/sec: 2019.00 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 17:52:10,085 epoch 9 - iter 132/447 - loss 0.00297756 - time (sec): 12.76 - samples/sec: 2008.52 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 17:52:14,224 epoch 9 - iter 176/447 - loss 0.00426497 - time (sec): 16.90 - samples/sec: 1986.65 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 17:52:18,201 epoch 9 - iter 220/447 - loss 0.00546162 - time (sec): 20.87 - samples/sec: 2011.65 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 17:52:22,301 epoch 9 - iter 264/447 - loss 0.00547408 - time (sec): 24.97 - samples/sec: 2013.82 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 17:52:26,521 epoch 9 - iter 308/447 - loss 0.00608106 - time (sec): 29.19 - samples/sec: 2030.28 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 17:52:30,568 epoch 9 - iter 352/447 - loss 0.00557877 - time (sec): 33.24 - samples/sec: 2013.26 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 17:52:35,013 epoch 9 - iter 396/447 - loss 0.00549500 - time (sec): 37.68 - samples/sec: 2008.03 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 17:52:39,171 epoch 9 - iter 440/447 - loss 0.00576947 - time (sec): 41.84 - samples/sec: 2020.27 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 17:52:40,081 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:52:40,081 EPOCH 9 done: loss 0.0058 - lr: 0.000006 | |
| 2023-10-17 17:52:51,569 DEV : loss 0.25680792331695557 - f1-score (micro avg) 0.7843 | |
| 2023-10-17 17:52:51,627 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:52:55,624 epoch 10 - iter 44/447 - loss 0.00263929 - time (sec): 3.99 - samples/sec: 2143.95 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 17:52:59,919 epoch 10 - iter 88/447 - loss 0.00194551 - time (sec): 8.29 - samples/sec: 2079.76 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 17:53:04,432 epoch 10 - iter 132/447 - loss 0.00150227 - time (sec): 12.80 - samples/sec: 2103.00 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 17:53:08,350 epoch 10 - iter 176/447 - loss 0.00167795 - time (sec): 16.72 - samples/sec: 2102.13 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 17:53:12,303 epoch 10 - iter 220/447 - loss 0.00167062 - time (sec): 20.67 - samples/sec: 2102.03 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 17:53:16,389 epoch 10 - iter 264/447 - loss 0.00153587 - time (sec): 24.76 - samples/sec: 2086.20 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 17:53:20,663 epoch 10 - iter 308/447 - loss 0.00191345 - time (sec): 29.03 - samples/sec: 2079.68 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 17:53:24,664 epoch 10 - iter 352/447 - loss 0.00211922 - time (sec): 33.04 - samples/sec: 2074.21 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 17:53:28,695 epoch 10 - iter 396/447 - loss 0.00212426 - time (sec): 37.07 - samples/sec: 2075.22 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 17:53:33,014 epoch 10 - iter 440/447 - loss 0.00225616 - time (sec): 41.39 - samples/sec: 2062.25 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-17 17:53:33,689 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:53:33,690 EPOCH 10 done: loss 0.0025 - lr: 0.000000 | |
| 2023-10-17 17:53:45,285 DEV : loss 0.264967143535614 - f1-score (micro avg) 0.7935 | |
| 2023-10-17 17:53:45,874 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 17:53:45,876 Loading model from best epoch ... | |
| 2023-10-17 17:53:48,322 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 | |
| 2023-10-17 17:53:54,591 | |
| Results: | |
| - F-score (micro) 0.7531 | |
| - F-score (macro) 0.6677 | |
| - Accuracy 0.6203 | |
| By class: | |
| precision recall f1-score support | |
| loc 0.8596 0.8523 0.8559 596 | |
| pers 0.6992 0.7538 0.7254 333 | |
| org 0.4615 0.5455 0.5000 132 | |
| prod 0.5806 0.5455 0.5625 66 | |
| time 0.7174 0.6735 0.6947 49 | |
| micro avg 0.7414 0.7653 0.7531 1176 | |
| macro avg 0.6637 0.6741 0.6677 1176 | |
| weighted avg 0.7479 0.7653 0.7558 1176 | |
| 2023-10-17 17:53:54,591 ---------------------------------------------------------------------------------------------------- | |