| 2023-10-17 13:35:36,187 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,189 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=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-17 13:35:36,189 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,189 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences | |
| - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator | |
| 2023-10-17 13:35:36,189 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,189 Train: 20847 sentences | |
| 2023-10-17 13:35:36,189 (train_with_dev=False, train_with_test=False) | |
| 2023-10-17 13:35:36,189 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,189 Training Params: | |
| 2023-10-17 13:35:36,189 - learning_rate: "3e-05" | |
| 2023-10-17 13:35:36,189 - mini_batch_size: "8" | |
| 2023-10-17 13:35:36,190 - max_epochs: "10" | |
| 2023-10-17 13:35:36,190 - shuffle: "True" | |
| 2023-10-17 13:35:36,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,190 Plugins: | |
| 2023-10-17 13:35:36,190 - TensorboardLogger | |
| 2023-10-17 13:35:36,190 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-17 13:35:36,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,190 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-17 13:35:36,190 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-17 13:35:36,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,190 Computation: | |
| 2023-10-17 13:35:36,190 - compute on device: cuda:0 | |
| 2023-10-17 13:35:36,190 - embedding storage: none | |
| 2023-10-17 13:35:36,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,190 Model training base path: "hmbench-newseye/de-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-17 13:35:36,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,191 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:35:36,191 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-17 13:36:03,247 epoch 1 - iter 260/2606 - loss 2.23417308 - time (sec): 27.05 - samples/sec: 1321.16 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 13:36:32,899 epoch 1 - iter 520/2606 - loss 1.31459848 - time (sec): 56.71 - samples/sec: 1299.74 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 13:37:00,740 epoch 1 - iter 780/2606 - loss 0.98767282 - time (sec): 84.55 - samples/sec: 1322.73 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 13:37:28,917 epoch 1 - iter 1040/2606 - loss 0.80515632 - time (sec): 112.72 - samples/sec: 1326.87 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 13:37:57,443 epoch 1 - iter 1300/2606 - loss 0.69065513 - time (sec): 141.25 - samples/sec: 1325.36 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 13:38:24,254 epoch 1 - iter 1560/2606 - loss 0.62104172 - time (sec): 168.06 - samples/sec: 1325.26 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:38:49,550 epoch 1 - iter 1820/2606 - loss 0.56057291 - time (sec): 193.36 - samples/sec: 1349.16 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:39:15,981 epoch 1 - iter 2080/2606 - loss 0.51882473 - time (sec): 219.79 - samples/sec: 1343.75 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:39:42,923 epoch 1 - iter 2340/2606 - loss 0.48259607 - time (sec): 246.73 - samples/sec: 1345.69 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:40:08,974 epoch 1 - iter 2600/2606 - loss 0.45516666 - time (sec): 272.78 - samples/sec: 1344.72 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 13:40:09,555 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:40:09,555 EPOCH 1 done: loss 0.4546 - lr: 0.000030 | |
| 2023-10-17 13:40:17,382 DEV : loss 0.12860068678855896 - f1-score (micro avg) 0.2748 | |
| 2023-10-17 13:40:17,440 saving best model | |
| 2023-10-17 13:40:17,985 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:40:45,255 epoch 2 - iter 260/2606 - loss 0.17952761 - time (sec): 27.27 - samples/sec: 1355.53 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-17 13:41:12,418 epoch 2 - iter 520/2606 - loss 0.17895549 - time (sec): 54.43 - samples/sec: 1353.01 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:41:39,526 epoch 2 - iter 780/2606 - loss 0.17220507 - time (sec): 81.54 - samples/sec: 1357.16 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:42:06,127 epoch 2 - iter 1040/2606 - loss 0.16936553 - time (sec): 108.14 - samples/sec: 1359.56 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-17 13:42:34,312 epoch 2 - iter 1300/2606 - loss 0.16526069 - time (sec): 136.32 - samples/sec: 1362.58 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:43:01,989 epoch 2 - iter 1560/2606 - loss 0.16534493 - time (sec): 164.00 - samples/sec: 1355.56 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:43:28,081 epoch 2 - iter 1820/2606 - loss 0.16526124 - time (sec): 190.09 - samples/sec: 1355.72 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-17 13:43:55,714 epoch 2 - iter 2080/2606 - loss 0.15960605 - time (sec): 217.73 - samples/sec: 1351.07 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:44:23,104 epoch 2 - iter 2340/2606 - loss 0.15681530 - time (sec): 245.12 - samples/sec: 1351.33 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:44:50,177 epoch 2 - iter 2600/2606 - loss 0.15743046 - time (sec): 272.19 - samples/sec: 1347.84 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-17 13:44:50,695 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:44:50,696 EPOCH 2 done: loss 0.1573 - lr: 0.000027 | |
| 2023-10-17 13:45:02,783 DEV : loss 0.18409471213817596 - f1-score (micro avg) 0.2931 | |
| 2023-10-17 13:45:02,846 saving best model | |
| 2023-10-17 13:45:04,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:45:31,452 epoch 3 - iter 260/2606 - loss 0.12993628 - time (sec): 27.17 - samples/sec: 1373.20 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:45:56,995 epoch 3 - iter 520/2606 - loss 0.12197809 - time (sec): 52.72 - samples/sec: 1378.15 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:46:23,269 epoch 3 - iter 780/2606 - loss 0.11338115 - time (sec): 78.99 - samples/sec: 1349.83 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-17 13:46:51,571 epoch 3 - iter 1040/2606 - loss 0.11312511 - time (sec): 107.29 - samples/sec: 1356.63 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:47:19,624 epoch 3 - iter 1300/2606 - loss 0.11104534 - time (sec): 135.35 - samples/sec: 1356.87 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:47:47,029 epoch 3 - iter 1560/2606 - loss 0.10839794 - time (sec): 162.75 - samples/sec: 1358.23 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-17 13:48:13,376 epoch 3 - iter 1820/2606 - loss 0.10823766 - time (sec): 189.10 - samples/sec: 1345.95 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:48:41,227 epoch 3 - iter 2080/2606 - loss 0.10901249 - time (sec): 216.95 - samples/sec: 1347.31 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:49:08,936 epoch 3 - iter 2340/2606 - loss 0.10998392 - time (sec): 244.66 - samples/sec: 1343.61 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-17 13:49:35,404 epoch 3 - iter 2600/2606 - loss 0.11017179 - time (sec): 271.13 - samples/sec: 1351.05 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:49:36,143 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:49:36,143 EPOCH 3 done: loss 0.1099 - lr: 0.000023 | |
| 2023-10-17 13:49:47,073 DEV : loss 0.19609655439853668 - f1-score (micro avg) 0.3532 | |
| 2023-10-17 13:49:47,126 saving best model | |
| 2023-10-17 13:49:48,541 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:50:16,836 epoch 4 - iter 260/2606 - loss 0.06308814 - time (sec): 28.29 - samples/sec: 1315.79 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:50:44,010 epoch 4 - iter 520/2606 - loss 0.06791172 - time (sec): 55.46 - samples/sec: 1324.28 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-17 13:51:10,499 epoch 4 - iter 780/2606 - loss 0.06827024 - time (sec): 81.95 - samples/sec: 1332.80 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:51:37,765 epoch 4 - iter 1040/2606 - loss 0.07212856 - time (sec): 109.22 - samples/sec: 1329.72 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:52:04,808 epoch 4 - iter 1300/2606 - loss 0.07317313 - time (sec): 136.26 - samples/sec: 1346.19 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-17 13:52:31,550 epoch 4 - iter 1560/2606 - loss 0.07332059 - time (sec): 163.00 - samples/sec: 1346.05 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:52:59,143 epoch 4 - iter 1820/2606 - loss 0.07391824 - time (sec): 190.60 - samples/sec: 1345.38 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:53:26,459 epoch 4 - iter 2080/2606 - loss 0.07288315 - time (sec): 217.91 - samples/sec: 1337.06 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-17 13:53:54,604 epoch 4 - iter 2340/2606 - loss 0.07585692 - time (sec): 246.06 - samples/sec: 1341.65 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:54:21,565 epoch 4 - iter 2600/2606 - loss 0.07713987 - time (sec): 273.02 - samples/sec: 1342.54 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:54:22,228 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:54:22,228 EPOCH 4 done: loss 0.0771 - lr: 0.000020 | |
| 2023-10-17 13:54:33,663 DEV : loss 0.30645275115966797 - f1-score (micro avg) 0.3598 | |
| 2023-10-17 13:54:33,734 saving best model | |
| 2023-10-17 13:54:35,171 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:55:03,942 epoch 5 - iter 260/2606 - loss 0.04419626 - time (sec): 28.77 - samples/sec: 1307.72 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-17 13:55:32,835 epoch 5 - iter 520/2606 - loss 0.05530628 - time (sec): 57.66 - samples/sec: 1297.68 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:56:01,826 epoch 5 - iter 780/2606 - loss 0.05500235 - time (sec): 86.65 - samples/sec: 1315.68 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:56:30,166 epoch 5 - iter 1040/2606 - loss 0.05623530 - time (sec): 114.99 - samples/sec: 1325.25 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-17 13:56:57,009 epoch 5 - iter 1300/2606 - loss 0.05618477 - time (sec): 141.83 - samples/sec: 1328.11 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:57:24,218 epoch 5 - iter 1560/2606 - loss 0.05551545 - time (sec): 169.04 - samples/sec: 1335.73 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:57:50,677 epoch 5 - iter 1820/2606 - loss 0.05518345 - time (sec): 195.50 - samples/sec: 1342.10 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-17 13:58:16,808 epoch 5 - iter 2080/2606 - loss 0.05536442 - time (sec): 221.63 - samples/sec: 1340.42 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:58:43,597 epoch 5 - iter 2340/2606 - loss 0.05513621 - time (sec): 248.42 - samples/sec: 1338.91 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:59:10,177 epoch 5 - iter 2600/2606 - loss 0.05550176 - time (sec): 275.00 - samples/sec: 1333.24 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-17 13:59:10,813 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:59:10,813 EPOCH 5 done: loss 0.0556 - lr: 0.000017 | |
| 2023-10-17 13:59:22,685 DEV : loss 0.38089314103126526 - f1-score (micro avg) 0.3689 | |
| 2023-10-17 13:59:22,758 saving best model | |
| 2023-10-17 13:59:24,340 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 13:59:51,579 epoch 6 - iter 260/2606 - loss 0.03634504 - time (sec): 27.24 - samples/sec: 1312.08 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 14:00:19,359 epoch 6 - iter 520/2606 - loss 0.03563055 - time (sec): 55.02 - samples/sec: 1327.10 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 14:00:45,525 epoch 6 - iter 780/2606 - loss 0.03541167 - time (sec): 81.18 - samples/sec: 1310.80 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-17 14:01:13,098 epoch 6 - iter 1040/2606 - loss 0.03662892 - time (sec): 108.75 - samples/sec: 1311.16 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 14:01:41,840 epoch 6 - iter 1300/2606 - loss 0.03651950 - time (sec): 137.50 - samples/sec: 1316.73 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 14:02:09,866 epoch 6 - iter 1560/2606 - loss 0.03685032 - time (sec): 165.52 - samples/sec: 1311.43 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-17 14:02:36,853 epoch 6 - iter 1820/2606 - loss 0.03810930 - time (sec): 192.51 - samples/sec: 1319.49 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 14:03:04,418 epoch 6 - iter 2080/2606 - loss 0.03893618 - time (sec): 220.07 - samples/sec: 1331.23 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 14:03:30,680 epoch 6 - iter 2340/2606 - loss 0.03981572 - time (sec): 246.34 - samples/sec: 1332.99 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-17 14:03:58,240 epoch 6 - iter 2600/2606 - loss 0.03973320 - time (sec): 273.90 - samples/sec: 1337.44 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 14:03:59,011 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:03:59,011 EPOCH 6 done: loss 0.0397 - lr: 0.000013 | |
| 2023-10-17 14:04:10,365 DEV : loss 0.3598186671733856 - f1-score (micro avg) 0.3703 | |
| 2023-10-17 14:04:10,425 saving best model | |
| 2023-10-17 14:04:11,836 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:04:38,872 epoch 7 - iter 260/2606 - loss 0.02914898 - time (sec): 27.03 - samples/sec: 1354.67 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 14:05:05,820 epoch 7 - iter 520/2606 - loss 0.02552344 - time (sec): 53.98 - samples/sec: 1376.27 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-17 14:05:31,950 epoch 7 - iter 780/2606 - loss 0.02775229 - time (sec): 80.11 - samples/sec: 1363.39 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 14:06:00,317 epoch 7 - iter 1040/2606 - loss 0.02635722 - time (sec): 108.48 - samples/sec: 1364.31 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 14:06:28,215 epoch 7 - iter 1300/2606 - loss 0.02810019 - time (sec): 136.37 - samples/sec: 1367.56 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-17 14:06:55,868 epoch 7 - iter 1560/2606 - loss 0.02775623 - time (sec): 164.03 - samples/sec: 1361.77 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 14:07:24,511 epoch 7 - iter 1820/2606 - loss 0.02725007 - time (sec): 192.67 - samples/sec: 1356.57 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 14:07:52,475 epoch 7 - iter 2080/2606 - loss 0.02794776 - time (sec): 220.64 - samples/sec: 1351.16 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-17 14:08:18,907 epoch 7 - iter 2340/2606 - loss 0.02788600 - time (sec): 247.07 - samples/sec: 1343.37 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 14:08:45,921 epoch 7 - iter 2600/2606 - loss 0.02768411 - time (sec): 274.08 - samples/sec: 1339.04 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 14:08:46,550 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:08:46,550 EPOCH 7 done: loss 0.0277 - lr: 0.000010 | |
| 2023-10-17 14:08:58,244 DEV : loss 0.46519169211387634 - f1-score (micro avg) 0.3683 | |
| 2023-10-17 14:08:58,320 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:09:25,709 epoch 8 - iter 260/2606 - loss 0.01354824 - time (sec): 27.39 - samples/sec: 1270.70 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-17 14:09:52,894 epoch 8 - iter 520/2606 - loss 0.01746483 - time (sec): 54.57 - samples/sec: 1295.91 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 14:10:20,826 epoch 8 - iter 780/2606 - loss 0.01722493 - time (sec): 82.50 - samples/sec: 1322.69 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 14:10:48,150 epoch 8 - iter 1040/2606 - loss 0.01799795 - time (sec): 109.83 - samples/sec: 1318.28 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-17 14:11:14,849 epoch 8 - iter 1300/2606 - loss 0.02034286 - time (sec): 136.53 - samples/sec: 1321.37 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 14:11:41,434 epoch 8 - iter 1560/2606 - loss 0.02094045 - time (sec): 163.11 - samples/sec: 1329.60 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 14:12:08,680 epoch 8 - iter 1820/2606 - loss 0.02116218 - time (sec): 190.36 - samples/sec: 1339.00 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-17 14:12:35,381 epoch 8 - iter 2080/2606 - loss 0.02089064 - time (sec): 217.06 - samples/sec: 1342.79 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 14:13:02,686 epoch 8 - iter 2340/2606 - loss 0.02211357 - time (sec): 244.36 - samples/sec: 1347.86 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 14:13:28,922 epoch 8 - iter 2600/2606 - loss 0.02165998 - time (sec): 270.60 - samples/sec: 1354.06 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-17 14:13:29,518 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:13:29,519 EPOCH 8 done: loss 0.0217 - lr: 0.000007 | |
| 2023-10-17 14:13:41,241 DEV : loss 0.4531383812427521 - f1-score (micro avg) 0.3797 | |
| 2023-10-17 14:13:41,306 saving best model | |
| 2023-10-17 14:13:42,719 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:14:10,088 epoch 9 - iter 260/2606 - loss 0.01374020 - time (sec): 27.36 - samples/sec: 1343.84 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 14:14:37,237 epoch 9 - iter 520/2606 - loss 0.01399606 - time (sec): 54.51 - samples/sec: 1373.81 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 14:15:04,557 epoch 9 - iter 780/2606 - loss 0.01498264 - time (sec): 81.83 - samples/sec: 1362.74 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-17 14:15:30,917 epoch 9 - iter 1040/2606 - loss 0.01450304 - time (sec): 108.19 - samples/sec: 1363.37 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 14:15:57,984 epoch 9 - iter 1300/2606 - loss 0.01479871 - time (sec): 135.26 - samples/sec: 1366.94 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 14:16:25,970 epoch 9 - iter 1560/2606 - loss 0.01476623 - time (sec): 163.25 - samples/sec: 1360.61 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-17 14:16:53,168 epoch 9 - iter 1820/2606 - loss 0.01440257 - time (sec): 190.44 - samples/sec: 1345.53 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 14:17:22,071 epoch 9 - iter 2080/2606 - loss 0.01471634 - time (sec): 219.35 - samples/sec: 1356.71 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 14:17:48,025 epoch 9 - iter 2340/2606 - loss 0.01462050 - time (sec): 245.30 - samples/sec: 1347.69 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-17 14:18:15,810 epoch 9 - iter 2600/2606 - loss 0.01420519 - time (sec): 273.09 - samples/sec: 1343.04 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 14:18:16,365 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:18:16,366 EPOCH 9 done: loss 0.0142 - lr: 0.000003 | |
| 2023-10-17 14:18:29,180 DEV : loss 0.5335880517959595 - f1-score (micro avg) 0.3697 | |
| 2023-10-17 14:18:29,239 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:18:57,797 epoch 10 - iter 260/2606 - loss 0.00788040 - time (sec): 28.56 - samples/sec: 1317.69 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 14:19:25,473 epoch 10 - iter 520/2606 - loss 0.00890575 - time (sec): 56.23 - samples/sec: 1311.63 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-17 14:19:53,273 epoch 10 - iter 780/2606 - loss 0.00939444 - time (sec): 84.03 - samples/sec: 1292.56 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 14:20:21,086 epoch 10 - iter 1040/2606 - loss 0.01008456 - time (sec): 111.84 - samples/sec: 1280.70 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 14:20:50,402 epoch 10 - iter 1300/2606 - loss 0.01075747 - time (sec): 141.16 - samples/sec: 1271.11 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-17 14:21:17,606 epoch 10 - iter 1560/2606 - loss 0.01058155 - time (sec): 168.36 - samples/sec: 1273.33 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 14:21:45,269 epoch 10 - iter 1820/2606 - loss 0.01068539 - time (sec): 196.03 - samples/sec: 1275.73 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 14:22:13,609 epoch 10 - iter 2080/2606 - loss 0.01111151 - time (sec): 224.37 - samples/sec: 1285.50 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-17 14:22:42,423 epoch 10 - iter 2340/2606 - loss 0.01084573 - time (sec): 253.18 - samples/sec: 1298.87 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-17 14:23:09,546 epoch 10 - iter 2600/2606 - loss 0.01098140 - time (sec): 280.30 - samples/sec: 1308.85 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-17 14:23:10,109 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:23:10,109 EPOCH 10 done: loss 0.0110 - lr: 0.000000 | |
| 2023-10-17 14:23:22,465 DEV : loss 0.536491334438324 - f1-score (micro avg) 0.3636 | |
| 2023-10-17 14:23:23,103 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-17 14:23:23,105 Loading model from best epoch ... | |
| 2023-10-17 14:23:25,695 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd | |
| 2023-10-17 14:23:45,508 | |
| Results: | |
| - F-score (micro) 0.4513 | |
| - F-score (macro) 0.3196 | |
| - Accuracy 0.2959 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.4768 0.5074 0.4916 1214 | |
| PER 0.4066 0.5062 0.4509 808 | |
| ORG 0.3068 0.3711 0.3359 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.4230 0.4837 0.4513 2390 | |
| macro avg 0.2975 0.3462 0.3196 2390 | |
| weighted avg 0.4249 0.4837 0.4518 2390 | |
| 2023-10-17 14:23:45,509 ---------------------------------------------------------------------------------------------------- | |