2022-08-07 16:00:48,261 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,267 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): WordEmbeddings('fa') (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) (list_embedding_2): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) ) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (embedding2nn): Linear(in_features=4396, out_features=4396, bias=True) (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True) (linear): Linear(in_features=512, out_features=32, bias=True) (beta): 1.0 (weights): None (weight_tensor) None )" 2022-08-07 16:00:48,272 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,276 Corpus: "Corpus: 24000 train + 3000 dev + 3000 test sentences" 2022-08-07 16:00:48,281 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,282 Parameters: 2022-08-07 16:00:48,285 - learning_rate: "0.1" 2022-08-07 16:00:48,289 - mini_batch_size: "8" 2022-08-07 16:00:48,293 - patience: "3" 2022-08-07 16:00:48,295 - anneal_factor: "0.5" 2022-08-07 16:00:48,296 - max_epochs: "5" 2022-08-07 16:00:48,297 - shuffle: "True" 2022-08-07 16:00:48,300 - train_with_dev: "False" 2022-08-07 16:00:48,301 - batch_growth_annealing: "False" 2022-08-07 16:00:48,303 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,306 Model training base path: "/content/drive/MyDrive/project/data/pos/model2" 2022-08-07 16:00:48,309 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,316 Device: cuda:0 2022-08-07 16:00:48,317 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:00:48,318 Embeddings storage mode: none 2022-08-07 16:00:48,337 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:02:01,728 epoch 1 - iter 300/3000 - loss 0.75227154 - samples/sec: 32.71 - lr: 0.100000 2022-08-07 16:03:44,240 epoch 1 - iter 600/3000 - loss 0.54616157 - samples/sec: 23.58 - lr: 0.100000 2022-08-07 16:05:07,940 epoch 1 - iter 900/3000 - loss 0.46940731 - samples/sec: 28.91 - lr: 0.100000 2022-08-07 16:06:48,542 epoch 1 - iter 1200/3000 - loss 0.41914715 - samples/sec: 24.03 - lr: 0.100000 2022-08-07 16:08:31,313 epoch 1 - iter 1500/3000 - loss 0.38015901 - samples/sec: 23.52 - lr: 0.100000 2022-08-07 16:10:05,508 epoch 1 - iter 1800/3000 - loss 0.35604709 - samples/sec: 25.67 - lr: 0.100000 2022-08-07 16:11:31,898 epoch 1 - iter 2100/3000 - loss 0.33691470 - samples/sec: 28.01 - lr: 0.100000 2022-08-07 16:13:00,338 epoch 1 - iter 2400/3000 - loss 0.32109903 - samples/sec: 27.35 - lr: 0.100000 2022-08-07 16:14:32,548 epoch 1 - iter 2700/3000 - loss 0.31528796 - samples/sec: 26.23 - lr: 0.100000 2022-08-07 16:16:09,123 epoch 1 - iter 3000/3000 - loss 0.30213703 - samples/sec: 25.03 - lr: 0.100000 2022-08-07 16:16:09,831 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:16:09,836 EPOCH 1 done: loss 0.3021 - lr 0.1000000 2022-08-07 16:21:08,895 DEV : loss 0.1289350390434265 - f1-score (micro avg) 0.9601 2022-08-07 16:21:08,937 BAD EPOCHS (no improvement): 0 2022-08-07 16:21:10,769 saving best model 2022-08-07 16:21:12,532 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:22:54,846 epoch 2 - iter 300/3000 - loss 0.21020090 - samples/sec: 23.46 - lr: 0.100000 2022-08-07 16:24:33,507 epoch 2 - iter 600/3000 - loss 0.20664426 - samples/sec: 24.50 - lr: 0.100000 2022-08-07 16:26:17,056 epoch 2 - iter 900/3000 - loss 0.20271364 - samples/sec: 23.33 - lr: 0.100000 2022-08-07 16:27:59,228 epoch 2 - iter 1200/3000 - loss 0.20055706 - samples/sec: 23.65 - lr: 0.100000 2022-08-07 16:29:39,722 epoch 2 - iter 1500/3000 - loss 0.19912427 - samples/sec: 24.05 - lr: 0.100000 2022-08-07 16:31:27,754 epoch 2 - iter 1800/3000 - loss 0.19760227 - samples/sec: 22.36 - lr: 0.100000 2022-08-07 16:33:12,162 epoch 2 - iter 2100/3000 - loss 0.19795635 - samples/sec: 23.14 - lr: 0.100000 2022-08-07 16:34:53,586 epoch 2 - iter 2400/3000 - loss 0.19672791 - samples/sec: 23.84 - lr: 0.100000 2022-08-07 16:36:42,505 epoch 2 - iter 2700/3000 - loss 0.19643492 - samples/sec: 22.19 - lr: 0.100000 2022-08-07 16:38:22,496 epoch 2 - iter 3000/3000 - loss 0.19530593 - samples/sec: 24.17 - lr: 0.100000 2022-08-07 16:38:23,157 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:38:23,162 EPOCH 2 done: loss 0.1953 - lr 0.1000000 2022-08-07 16:43:34,928 DEV : loss 0.10149012506008148 - f1-score (micro avg) 0.9708 2022-08-07 16:43:34,973 BAD EPOCHS (no improvement): 0 2022-08-07 16:43:36,767 saving best model 2022-08-07 16:43:38,486 ---------------------------------------------------------------------------------------------------- 2022-08-07 16:45:23,089 epoch 3 - iter 300/3000 - loss 0.17774341 - samples/sec: 22.95 - lr: 0.100000 2022-08-07 16:47:08,214 epoch 3 - iter 600/3000 - loss 0.17596867 - samples/sec: 22.98 - lr: 0.100000 2022-08-07 16:48:50,711 epoch 3 - iter 900/3000 - loss 0.17436321 - samples/sec: 23.58 - lr: 0.100000 2022-08-07 16:50:35,039 epoch 3 - iter 1200/3000 - loss 0.17306311 - samples/sec: 23.16 - lr: 0.100000 2022-08-07 16:52:20,808 epoch 3 - iter 1500/3000 - loss 0.17261464 - samples/sec: 22.84 - lr: 0.100000 2022-08-07 16:54:02,750 epoch 3 - iter 1800/3000 - loss 0.17438407 - samples/sec: 23.71 - lr: 0.100000 2022-08-07 16:55:42,154 epoch 3 - iter 2100/3000 - loss 0.17363800 - samples/sec: 24.31 - lr: 0.100000 2022-08-07 16:57:21,978 epoch 3 - iter 2400/3000 - loss 0.17156485 - samples/sec: 24.21 - lr: 0.100000 2022-08-07 16:59:05,968 epoch 3 - iter 2700/3000 - loss 0.17042576 - samples/sec: 23.23 - lr: 0.100000 2022-08-07 17:00:46,166 epoch 3 - iter 3000/3000 - loss 0.16937353 - samples/sec: 24.12 - lr: 0.100000 2022-08-07 17:00:46,857 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:00:46,860 EPOCH 3 done: loss 0.1694 - lr 0.1000000 2022-08-07 17:05:58,652 DEV : loss 0.09684865176677704 - f1-score (micro avg) 0.9731 2022-08-07 17:05:58,703 BAD EPOCHS (no improvement): 0 2022-08-07 17:06:00,477 saving best model 2022-08-07 17:06:02,321 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:07:44,646 epoch 4 - iter 300/3000 - loss 0.16212096 - samples/sec: 23.46 - lr: 0.100000 2022-08-07 17:09:25,119 epoch 4 - iter 600/3000 - loss 0.15843816 - samples/sec: 24.05 - lr: 0.100000 2022-08-07 17:11:07,080 epoch 4 - iter 900/3000 - loss 0.15900626 - samples/sec: 23.70 - lr: 0.100000 2022-08-07 17:12:47,149 epoch 4 - iter 1200/3000 - loss 0.15764029 - samples/sec: 24.15 - lr: 0.100000 2022-08-07 17:14:33,737 epoch 4 - iter 1500/3000 - loss 0.16000098 - samples/sec: 22.66 - lr: 0.100000 2022-08-07 17:16:21,024 epoch 4 - iter 1800/3000 - loss 0.15931205 - samples/sec: 22.52 - lr: 0.100000 2022-08-07 17:18:01,785 epoch 4 - iter 2100/3000 - loss 0.15961928 - samples/sec: 23.99 - lr: 0.100000 2022-08-07 17:19:44,524 epoch 4 - iter 2400/3000 - loss 0.15845056 - samples/sec: 23.52 - lr: 0.100000 2022-08-07 17:21:27,429 epoch 4 - iter 2700/3000 - loss 0.15771950 - samples/sec: 23.49 - lr: 0.100000 2022-08-07 17:23:10,018 epoch 4 - iter 3000/3000 - loss 0.15777116 - samples/sec: 23.56 - lr: 0.100000 2022-08-07 17:23:10,788 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:23:10,794 EPOCH 4 done: loss 0.1578 - lr 0.1000000 2022-08-07 17:28:23,406 DEV : loss 0.09011354297399521 - f1-score (micro avg) 0.9744 2022-08-07 17:28:23,451 BAD EPOCHS (no improvement): 0 2022-08-07 17:28:25,515 saving best model 2022-08-07 17:28:27,346 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:30:06,455 epoch 5 - iter 300/3000 - loss 0.14466099 - samples/sec: 24.22 - lr: 0.100000 2022-08-07 17:31:44,351 epoch 5 - iter 600/3000 - loss 0.14401223 - samples/sec: 24.70 - lr: 0.100000 2022-08-07 17:33:27,083 epoch 5 - iter 900/3000 - loss 0.14768050 - samples/sec: 23.53 - lr: 0.100000 2022-08-07 17:35:07,577 epoch 5 - iter 1200/3000 - loss 0.14646819 - samples/sec: 24.05 - lr: 0.100000 2022-08-07 17:36:47,275 epoch 5 - iter 1500/3000 - loss 0.14604558 - samples/sec: 24.25 - lr: 0.100000 2022-08-07 17:38:24,129 epoch 5 - iter 1800/3000 - loss 0.14788483 - samples/sec: 24.96 - lr: 0.100000 2022-08-07 17:40:04,518 epoch 5 - iter 2100/3000 - loss 0.14695063 - samples/sec: 24.08 - lr: 0.100000 2022-08-07 17:41:51,964 epoch 5 - iter 2400/3000 - loss 0.14697433 - samples/sec: 22.49 - lr: 0.100000 2022-08-07 17:43:32,173 epoch 5 - iter 2700/3000 - loss 0.14745015 - samples/sec: 24.12 - lr: 0.100000 2022-08-07 17:45:17,557 epoch 5 - iter 3000/3000 - loss 0.14917362 - samples/sec: 22.93 - lr: 0.100000 2022-08-07 17:45:18,255 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:45:18,263 EPOCH 5 done: loss 0.1492 - lr 0.1000000 2022-08-07 17:50:33,128 DEV : loss 0.08973350375890732 - f1-score (micro avg) 0.9746 2022-08-07 17:50:33,176 BAD EPOCHS (no improvement): 0 2022-08-07 17:50:34,869 saving best model 2022-08-07 17:50:38,774 ---------------------------------------------------------------------------------------------------- 2022-08-07 17:50:38,811 loading file /content/drive/MyDrive/project/data/pos/model2/best-model.pt 2022-08-07 17:55:05,420 0.9637 0.9637 0.9637 0.9637 2022-08-07 17:55:05,422 Results: - F-score (micro) 0.9637 - F-score (macro) 0.8989 - Accuracy 0.9637 By class: precision recall f1-score support N_SING 0.9724 0.9521 0.9621 30553 P 0.9577 0.9919 0.9745 9951 DELM 0.9982 0.9996 0.9989 8122 ADJ 0.8768 0.9334 0.9042 7466 CON 0.9905 0.9786 0.9845 6823 N_PL 0.9719 0.9644 0.9681 5163 V_PA 0.9753 0.9756 0.9755 2873 V_PRS 0.9922 0.9852 0.9887 2841 NUM 0.9907 0.9982 0.9944 2232 PRO 0.9823 0.9349 0.9580 2258 DET 0.9429 0.9800 0.9611 1853 CLITIC 1.0000 1.0000 1.0000 1259 V_PP 0.9398 0.9836 0.9612 1158 V_SUB 0.9746 0.9680 0.9713 1031 ADV 0.8180 0.8375 0.8276 880 ADV_TIME 0.9238 0.9673 0.9451 489 V_AUX 0.9947 0.9947 0.9947 379 ADJ_SUP 0.9925 0.9815 0.9870 270 ADJ_CMPR 0.9372 0.9275 0.9323 193 ADV_NEG 0.9071 0.8523 0.8789 149 ADV_I 0.8345 0.8286 0.8315 140 ADJ_INO 0.8846 0.5476 0.6765 168 FW 0.8442 0.5285 0.6500 123 ADV_COMP 0.8072 0.8816 0.8428 76 ADV_LOC 0.9342 0.9726 0.9530 73 V_IMP 0.7826 0.6429 0.7059 56 PREV 0.8276 0.7500 0.7869 32 INT 0.8333 0.4167 0.5556 24 micro avg 0.9637 0.9637 0.9637 86635 macro avg 0.9245 0.8848 0.8989 86635 weighted avg 0.9643 0.9637 0.9637 86635 samples avg 0.9637 0.9637 0.9637 86635 2022-08-07 17:55:05,427 ----------------------------------------------------------------------------------------------------