| 2022-11-29 19:55:19,593 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,594 Model: "SequenceTagger( | |
| (embeddings): StackedEmbeddings( | |
| (list_embedding_0): FlairEmbeddings( | |
| (lm): LanguageModel( | |
| (drop): Dropout(p=0.25, inplace=False) | |
| (encoder): Embedding(275, 100) | |
| (rnn): LSTM(100, 2048) | |
| (decoder): Linear(in_features=2048, out_features=275, bias=True) | |
| ) | |
| ) | |
| (list_embedding_1): FlairEmbeddings( | |
| (lm): LanguageModel( | |
| (drop): Dropout(p=0.25, inplace=False) | |
| (encoder): Embedding(275, 100) | |
| (rnn): LSTM(100, 2048) | |
| (decoder): Linear(in_features=2048, out_features=275, bias=True) | |
| ) | |
| ) | |
| ) | |
| (word_dropout): WordDropout(p=0.05) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (embedding2nn): Linear(in_features=4096, out_features=4096, bias=True) | |
| (rnn): LSTM(4096, 256, batch_first=True, bidirectional=True) | |
| (linear): Linear(in_features=512, out_features=15, bias=True) | |
| (loss_function): ViterbiLoss() | |
| (crf): CRF() | |
| )" | |
| 2022-11-29 19:55:19,595 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,597 Corpus: "Corpus: 3200 train + 401 dev + 401 test sentences" | |
| 2022-11-29 19:55:19,597 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,599 Parameters: | |
| 2022-11-29 19:55:19,600 - learning_rate: "0.100000" | |
| 2022-11-29 19:55:19,601 - mini_batch_size: "32" | |
| 2022-11-29 19:55:19,602 - patience: "3" | |
| 2022-11-29 19:55:19,603 - anneal_factor: "0.5" | |
| 2022-11-29 19:55:19,604 - max_epochs: "10" | |
| 2022-11-29 19:55:19,605 - shuffle: "True" | |
| 2022-11-29 19:55:19,606 - train_with_dev: "False" | |
| 2022-11-29 19:55:19,607 - batch_growth_annealing: "False" | |
| 2022-11-29 19:55:19,609 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,610 Model training base path: "models\ner_models\flair" | |
| 2022-11-29 19:55:19,611 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,612 Device: cuda:0 | |
| 2022-11-29 19:55:19,613 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:19,615 Embeddings storage mode: gpu | |
| 2022-11-29 19:55:19,616 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:20,794 epoch 1 - iter 10/100 - loss 2.51444360 - samples/sec: 271.88 - lr: 0.100000 | |
| 2022-11-29 19:55:21,564 epoch 1 - iter 20/100 - loss 2.26125464 - samples/sec: 416.67 - lr: 0.100000 | |
| 2022-11-29 19:55:22,363 epoch 1 - iter 30/100 - loss 2.07498067 - samples/sec: 401.50 - lr: 0.100000 | |
| 2022-11-29 19:55:23,306 epoch 1 - iter 40/100 - loss 1.93443117 - samples/sec: 340.06 - lr: 0.100000 | |
| 2022-11-29 19:55:24,072 epoch 1 - iter 50/100 - loss 1.83384382 - samples/sec: 418.85 - lr: 0.100000 | |
| 2022-11-29 19:55:24,832 epoch 1 - iter 60/100 - loss 1.74987443 - samples/sec: 422.16 - lr: 0.100000 | |
| 2022-11-29 19:55:25,602 epoch 1 - iter 70/100 - loss 1.65976205 - samples/sec: 417.21 - lr: 0.100000 | |
| 2022-11-29 19:55:26,397 epoch 1 - iter 80/100 - loss 1.58696204 - samples/sec: 403.53 - lr: 0.100000 | |
| 2022-11-29 19:55:27,195 epoch 1 - iter 90/100 - loss 1.52321554 - samples/sec: 402.52 - lr: 0.100000 | |
| 2022-11-29 19:55:27,983 epoch 1 - iter 100/100 - loss 1.46963711 - samples/sec: 407.12 - lr: 0.100000 | |
| 2022-11-29 19:55:27,984 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:27,985 EPOCH 1 done: loss 1.4696 - lr 0.100000 | |
| 2022-11-29 19:55:29,146 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:29,162 DEV : loss 0.743816614151001 - f1-score (micro avg) 0.6684 | |
| 2022-11-29 19:55:29,170 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:55:29,172 saving best model | |
| 2022-11-29 19:55:29,960 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:30,227 epoch 2 - iter 10/100 - loss 0.93122098 - samples/sec: 1212.12 - lr: 0.100000 | |
| 2022-11-29 19:55:30,472 epoch 2 - iter 20/100 - loss 0.93700673 - samples/sec: 1311.46 - lr: 0.100000 | |
| 2022-11-29 19:55:30,716 epoch 2 - iter 30/100 - loss 0.92589947 - samples/sec: 1322.31 - lr: 0.100000 | |
| 2022-11-29 19:55:30,964 epoch 2 - iter 40/100 - loss 0.91140916 - samples/sec: 1300.83 - lr: 0.100000 | |
| 2022-11-29 19:55:31,218 epoch 2 - iter 50/100 - loss 0.90092413 - samples/sec: 1269.84 - lr: 0.100000 | |
| 2022-11-29 19:55:31,469 epoch 2 - iter 60/100 - loss 0.88857491 - samples/sec: 1295.53 - lr: 0.100000 | |
| 2022-11-29 19:55:31,718 epoch 2 - iter 70/100 - loss 0.88186254 - samples/sec: 1300.81 - lr: 0.100000 | |
| 2022-11-29 19:55:31,978 epoch 2 - iter 80/100 - loss 0.86919368 - samples/sec: 1240.31 - lr: 0.100000 | |
| 2022-11-29 19:55:32,230 epoch 2 - iter 90/100 - loss 0.86008132 - samples/sec: 1274.91 - lr: 0.100000 | |
| 2022-11-29 19:55:32,469 epoch 2 - iter 100/100 - loss 0.84783343 - samples/sec: 1355.94 - lr: 0.100000 | |
| 2022-11-29 19:55:32,471 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:32,472 EPOCH 2 done: loss 0.8478 - lr 0.100000 | |
| 2022-11-29 19:55:32,906 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:32,918 DEV : loss 0.5499727725982666 - f1-score (micro avg) 0.7507 | |
| 2022-11-29 19:55:32,925 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:55:32,927 saving best model | |
| 2022-11-29 19:55:33,696 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:33,939 epoch 3 - iter 10/100 - loss 0.74651710 - samples/sec: 1338.91 - lr: 0.100000 | |
| 2022-11-29 19:55:34,185 epoch 3 - iter 20/100 - loss 0.72825161 - samples/sec: 1311.48 - lr: 0.100000 | |
| 2022-11-29 19:55:34,437 epoch 3 - iter 30/100 - loss 0.72226545 - samples/sec: 1280.02 - lr: 0.100000 | |
| 2022-11-29 19:55:34,684 epoch 3 - iter 40/100 - loss 0.71087911 - samples/sec: 1311.47 - lr: 0.100000 | |
| 2022-11-29 19:55:34,928 epoch 3 - iter 50/100 - loss 0.71147087 - samples/sec: 1322.32 - lr: 0.100000 | |
| 2022-11-29 19:55:35,170 epoch 3 - iter 60/100 - loss 0.70753659 - samples/sec: 1338.93 - lr: 0.100000 | |
| 2022-11-29 19:55:35,418 epoch 3 - iter 70/100 - loss 0.70232310 - samples/sec: 1300.81 - lr: 0.100000 | |
| 2022-11-29 19:55:35,677 epoch 3 - iter 80/100 - loss 0.69187476 - samples/sec: 1245.14 - lr: 0.100000 | |
| 2022-11-29 19:55:35,927 epoch 3 - iter 90/100 - loss 0.69092892 - samples/sec: 1290.33 - lr: 0.100000 | |
| 2022-11-29 19:55:36,165 epoch 3 - iter 100/100 - loss 0.69021075 - samples/sec: 1361.70 - lr: 0.100000 | |
| 2022-11-29 19:55:36,166 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:36,167 EPOCH 3 done: loss 0.6902 - lr 0.100000 | |
| 2022-11-29 19:55:36,608 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:36,619 DEV : loss 0.5182793736457825 - f1-score (micro avg) 0.7469 | |
| 2022-11-29 19:55:36,627 BAD EPOCHS (no improvement): 1 | |
| 2022-11-29 19:55:36,628 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:36,877 epoch 4 - iter 10/100 - loss 0.55811226 - samples/sec: 1295.55 - lr: 0.100000 | |
| 2022-11-29 19:55:37,123 epoch 4 - iter 20/100 - loss 0.59166121 - samples/sec: 1311.48 - lr: 0.100000 | |
| 2022-11-29 19:55:37,384 epoch 4 - iter 30/100 - loss 0.60031438 - samples/sec: 1230.64 - lr: 0.100000 | |
| 2022-11-29 19:55:37,652 epoch 4 - iter 40/100 - loss 0.60103191 - samples/sec: 1221.61 - lr: 0.100000 | |
| 2022-11-29 19:55:37,899 epoch 4 - iter 50/100 - loss 0.60056311 - samples/sec: 1311.47 - lr: 0.100000 | |
| 2022-11-29 19:55:38,157 epoch 4 - iter 60/100 - loss 0.59683237 - samples/sec: 1248.48 - lr: 0.100000 | |
| 2022-11-29 19:55:38,430 epoch 4 - iter 70/100 - loss 0.59160524 - samples/sec: 1180.76 - lr: 0.100000 | |
| 2022-11-29 19:55:38,714 epoch 4 - iter 80/100 - loss 0.58813251 - samples/sec: 1135.77 - lr: 0.100000 | |
| 2022-11-29 19:55:39,030 epoch 4 - iter 90/100 - loss 0.58326348 - samples/sec: 1018.25 - lr: 0.100000 | |
| 2022-11-29 19:55:39,318 epoch 4 - iter 100/100 - loss 0.58096572 - samples/sec: 1123.08 - lr: 0.100000 | |
| 2022-11-29 19:55:39,320 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:39,321 EPOCH 4 done: loss 0.5810 - lr 0.100000 | |
| 2022-11-29 19:55:39,782 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:39,794 DEV : loss 0.4407889246940613 - f1-score (micro avg) 0.7854 | |
| 2022-11-29 19:55:39,802 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:55:39,804 saving best model | |
| 2022-11-29 19:55:40,596 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:40,878 epoch 5 - iter 10/100 - loss 0.56197289 - samples/sec: 1142.34 - lr: 0.100000 | |
| 2022-11-29 19:55:41,139 epoch 5 - iter 20/100 - loss 0.53811764 - samples/sec: 1240.30 - lr: 0.100000 | |
| 2022-11-29 19:55:41,397 epoch 5 - iter 30/100 - loss 0.54474518 - samples/sec: 1253.62 - lr: 0.100000 | |
| 2022-11-29 19:55:41,655 epoch 5 - iter 40/100 - loss 0.55153870 - samples/sec: 1252.74 - lr: 0.100000 | |
| 2022-11-29 19:55:41,904 epoch 5 - iter 50/100 - loss 0.55267311 - samples/sec: 1290.31 - lr: 0.100000 | |
| 2022-11-29 19:55:42,160 epoch 5 - iter 60/100 - loss 0.54321808 - samples/sec: 1259.84 - lr: 0.100000 | |
| 2022-11-29 19:55:42,420 epoch 5 - iter 70/100 - loss 0.53776303 - samples/sec: 1241.33 - lr: 0.100000 | |
| 2022-11-29 19:55:42,676 epoch 5 - iter 80/100 - loss 0.53917517 - samples/sec: 1259.84 - lr: 0.100000 | |
| 2022-11-29 19:55:42,930 epoch 5 - iter 90/100 - loss 0.54249645 - samples/sec: 1269.81 - lr: 0.100000 | |
| 2022-11-29 19:55:43,190 epoch 5 - iter 100/100 - loss 0.53252619 - samples/sec: 1244.40 - lr: 0.100000 | |
| 2022-11-29 19:55:43,192 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:43,193 EPOCH 5 done: loss 0.5325 - lr 0.100000 | |
| 2022-11-29 19:55:43,645 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:43,657 DEV : loss 0.40568605065345764 - f1-score (micro avg) 0.8008 | |
| 2022-11-29 19:55:43,664 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:55:43,666 saving best model | |
| 2022-11-29 19:55:44,450 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:44,710 epoch 6 - iter 10/100 - loss 0.53370135 - samples/sec: 1240.31 - lr: 0.100000 | |
| 2022-11-29 19:55:44,971 epoch 6 - iter 20/100 - loss 0.52620640 - samples/sec: 1235.53 - lr: 0.100000 | |
| 2022-11-29 19:55:45,226 epoch 6 - iter 30/100 - loss 0.50857884 - samples/sec: 1269.84 - lr: 0.100000 | |
| 2022-11-29 19:55:45,475 epoch 6 - iter 40/100 - loss 0.51512304 - samples/sec: 1300.80 - lr: 0.100000 | |
| 2022-11-29 19:55:45,738 epoch 6 - iter 50/100 - loss 0.50988354 - samples/sec: 1222.70 - lr: 0.100000 | |
| 2022-11-29 19:55:46,009 epoch 6 - iter 60/100 - loss 0.50078065 - samples/sec: 1188.89 - lr: 0.100000 | |
| 2022-11-29 19:55:46,272 epoch 6 - iter 70/100 - loss 0.49147668 - samples/sec: 1235.51 - lr: 0.100000 | |
| 2022-11-29 19:55:46,526 epoch 6 - iter 80/100 - loss 0.49512972 - samples/sec: 1266.58 - lr: 0.100000 | |
| 2022-11-29 19:55:46,774 epoch 6 - iter 90/100 - loss 0.49726457 - samples/sec: 1295.55 - lr: 0.100000 | |
| 2022-11-29 19:55:47,041 epoch 6 - iter 100/100 - loss 0.49195739 - samples/sec: 1205.56 - lr: 0.100000 | |
| 2022-11-29 19:55:47,043 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:47,044 EPOCH 6 done: loss 0.4920 - lr 0.100000 | |
| 2022-11-29 19:55:47,481 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:47,492 DEV : loss 0.410269558429718 - f1-score (micro avg) 0.7988 | |
| 2022-11-29 19:55:47,500 BAD EPOCHS (no improvement): 1 | |
| 2022-11-29 19:55:47,502 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:47,750 epoch 7 - iter 10/100 - loss 0.46687846 - samples/sec: 1295.41 - lr: 0.100000 | |
| 2022-11-29 19:55:48,003 epoch 7 - iter 20/100 - loss 0.49256551 - samples/sec: 1285.09 - lr: 0.100000 | |
| 2022-11-29 19:55:48,257 epoch 7 - iter 30/100 - loss 0.48660147 - samples/sec: 1269.85 - lr: 0.100000 | |
| 2022-11-29 19:55:48,508 epoch 7 - iter 40/100 - loss 0.47807865 - samples/sec: 1285.07 - lr: 0.100000 | |
| 2022-11-29 19:55:48,769 epoch 7 - iter 50/100 - loss 0.46595729 - samples/sec: 1259.41 - lr: 0.100000 | |
| 2022-11-29 19:55:49,026 epoch 7 - iter 60/100 - loss 0.46114198 - samples/sec: 1253.73 - lr: 0.100000 | |
| 2022-11-29 19:55:49,310 epoch 7 - iter 70/100 - loss 0.45048113 - samples/sec: 1132.92 - lr: 0.100000 | |
| 2022-11-29 19:55:49,584 epoch 7 - iter 80/100 - loss 0.45121334 - samples/sec: 1180.54 - lr: 0.100000 | |
| 2022-11-29 19:55:49,855 epoch 7 - iter 90/100 - loss 0.45056088 - samples/sec: 1189.15 - lr: 0.100000 | |
| 2022-11-29 19:55:50,116 epoch 7 - iter 100/100 - loss 0.44944011 - samples/sec: 1233.24 - lr: 0.100000 | |
| 2022-11-29 19:55:50,118 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:50,119 EPOCH 7 done: loss 0.4494 - lr 0.100000 | |
| 2022-11-29 19:55:50,574 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:50,583 DEV : loss 0.4015345871448517 - f1-score (micro avg) 0.8039 | |
| 2022-11-29 19:55:50,591 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:55:50,593 saving best model | |
| 2022-11-29 19:55:51,377 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:51,633 epoch 8 - iter 10/100 - loss 0.39979676 - samples/sec: 1264.81 - lr: 0.100000 | |
| 2022-11-29 19:55:51,891 epoch 8 - iter 20/100 - loss 0.42294478 - samples/sec: 1255.60 - lr: 0.100000 | |
| 2022-11-29 19:55:52,147 epoch 8 - iter 30/100 - loss 0.43561446 - samples/sec: 1256.72 - lr: 0.100000 | |
| 2022-11-29 19:55:52,401 epoch 8 - iter 40/100 - loss 0.42958077 - samples/sec: 1269.11 - lr: 0.100000 | |
| 2022-11-29 19:55:52,651 epoch 8 - iter 50/100 - loss 0.43644458 - samples/sec: 1296.15 - lr: 0.100000 | |
| 2022-11-29 19:55:52,912 epoch 8 - iter 60/100 - loss 0.44022842 - samples/sec: 1235.50 - lr: 0.100000 | |
| 2022-11-29 19:55:53,174 epoch 8 - iter 70/100 - loss 0.43387762 - samples/sec: 1246.46 - lr: 0.100000 | |
| 2022-11-29 19:55:53,429 epoch 8 - iter 80/100 - loss 0.43344584 - samples/sec: 1264.25 - lr: 0.100000 | |
| 2022-11-29 19:55:53,695 epoch 8 - iter 90/100 - loss 0.43213231 - samples/sec: 1211.55 - lr: 0.100000 | |
| 2022-11-29 19:55:53,954 epoch 8 - iter 100/100 - loss 0.43290627 - samples/sec: 1245.12 - lr: 0.100000 | |
| 2022-11-29 19:55:53,956 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:53,957 EPOCH 8 done: loss 0.4329 - lr 0.100000 | |
| 2022-11-29 19:55:54,408 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:54,418 DEV : loss 0.41782504320144653 - f1-score (micro avg) 0.7641 | |
| 2022-11-29 19:55:54,426 BAD EPOCHS (no improvement): 1 | |
| 2022-11-29 19:55:54,428 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:54,679 epoch 9 - iter 10/100 - loss 0.38809048 - samples/sec: 1285.14 - lr: 0.100000 | |
| 2022-11-29 19:55:54,938 epoch 9 - iter 20/100 - loss 0.38429409 - samples/sec: 1256.56 - lr: 0.100000 | |
| 2022-11-29 19:55:55,189 epoch 9 - iter 30/100 - loss 0.39951865 - samples/sec: 1281.77 - lr: 0.100000 | |
| 2022-11-29 19:55:55,448 epoch 9 - iter 40/100 - loss 0.40274063 - samples/sec: 1245.02 - lr: 0.100000 | |
| 2022-11-29 19:55:55,706 epoch 9 - iter 50/100 - loss 0.40411498 - samples/sec: 1258.99 - lr: 0.100000 | |
| 2022-11-29 19:55:55,963 epoch 9 - iter 60/100 - loss 0.40716752 - samples/sec: 1254.85 - lr: 0.100000 | |
| 2022-11-29 19:55:56,215 epoch 9 - iter 70/100 - loss 0.40607803 - samples/sec: 1280.01 - lr: 0.100000 | |
| 2022-11-29 19:55:56,470 epoch 9 - iter 80/100 - loss 0.40395778 - samples/sec: 1261.20 - lr: 0.100000 | |
| 2022-11-29 19:55:56,746 epoch 9 - iter 90/100 - loss 0.40295678 - samples/sec: 1163.38 - lr: 0.100000 | |
| 2022-11-29 19:55:57,000 epoch 9 - iter 100/100 - loss 0.40353242 - samples/sec: 1270.32 - lr: 0.100000 | |
| 2022-11-29 19:55:57,002 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:57,003 EPOCH 9 done: loss 0.4035 - lr 0.100000 | |
| 2022-11-29 19:55:57,447 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:55:57,459 DEV : loss 0.40029552578926086 - f1-score (micro avg) 0.7899 | |
| 2022-11-29 19:55:57,468 BAD EPOCHS (no improvement): 2 | |
| 2022-11-29 19:55:57,469 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:57,739 epoch 10 - iter 10/100 - loss 0.38553391 - samples/sec: 1196.01 - lr: 0.100000 | |
| 2022-11-29 19:55:57,984 epoch 10 - iter 20/100 - loss 0.38512171 - samples/sec: 1322.31 - lr: 0.100000 | |
| 2022-11-29 19:55:58,230 epoch 10 - iter 30/100 - loss 0.37342493 - samples/sec: 1316.88 - lr: 0.100000 | |
| 2022-11-29 19:55:58,485 epoch 10 - iter 40/100 - loss 0.38000802 - samples/sec: 1264.83 - lr: 0.100000 | |
| 2022-11-29 19:55:58,728 epoch 10 - iter 50/100 - loss 0.38083797 - samples/sec: 1338.91 - lr: 0.100000 | |
| 2022-11-29 19:55:58,974 epoch 10 - iter 60/100 - loss 0.38513078 - samples/sec: 1316.08 - lr: 0.100000 | |
| 2022-11-29 19:55:59,220 epoch 10 - iter 70/100 - loss 0.38870147 - samples/sec: 1311.48 - lr: 0.100000 | |
| 2022-11-29 19:55:59,477 epoch 10 - iter 80/100 - loss 0.38746411 - samples/sec: 1264.54 - lr: 0.100000 | |
| 2022-11-29 19:55:59,724 epoch 10 - iter 90/100 - loss 0.39006729 - samples/sec: 1316.87 - lr: 0.100000 | |
| 2022-11-29 19:55:59,979 epoch 10 - iter 100/100 - loss 0.39083806 - samples/sec: 1264.82 - lr: 0.100000 | |
| 2022-11-29 19:55:59,981 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:55:59,982 EPOCH 10 done: loss 0.3908 - lr 0.100000 | |
| 2022-11-29 19:56:00,610 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:56:00,621 DEV : loss 0.37994131445884705 - f1-score (micro avg) 0.8077 | |
| 2022-11-29 19:56:00,629 BAD EPOCHS (no improvement): 0 | |
| 2022-11-29 19:56:00,631 saving best model | |
| 2022-11-29 19:56:02,157 ---------------------------------------------------------------------------------------------------- | |
| 2022-11-29 19:56:02,159 loading file models\ner_models\flair\best-model.pt | |
| 2022-11-29 19:56:02,668 SequenceTagger predicts: Dictionary with 15 tags: O, S-Item, B-Item, E-Item, I-Item, S-Activity, B-Activity, E-Activity, I-Activity, S-Observation, B-Observation, E-Observation, I-Observation, <START>, <STOP> | |
| 2022-11-29 19:56:04,074 Evaluating as a multi-label problem: False | |
| 2022-11-29 19:56:04,084 0.7956 0.7771 0.7863 0.6533 | |
| 2022-11-29 19:56:04,086 | |
| Results: | |
| - F-score (micro) 0.7863 | |
| - F-score (macro) 0.8002 | |
| - Accuracy 0.6533 | |
| By class: | |
| precision recall f1-score support | |
| Item 0.7638 0.7555 0.7596 548 | |
| Observation 0.7725 0.7309 0.7512 223 | |
| Activity 0.9005 0.8796 0.8899 216 | |
| micro avg 0.7956 0.7771 0.7863 987 | |
| macro avg 0.8123 0.7887 0.8002 987 | |
| weighted avg 0.7957 0.7771 0.7862 987 | |
| 2022-11-29 19:56:04,087 ---------------------------------------------------------------------------------------------------- | |