| 2022-01-16 18:38:17,520 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,523 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): RobertaModel( | |
| (embeddings): RobertaEmbeddings( | |
| (word_embeddings): Embedding(32768, 768, padding_idx=1) | |
| (position_embeddings): Embedding(514, 768, padding_idx=1) | |
| (token_type_embeddings): Embedding(1, 768) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): RobertaEncoder( | |
| (layer): ModuleList( | |
| (0): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (1): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (2): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (3): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (4): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (5): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (6): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (7): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (8): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (9): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (10): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (11): RobertaLayer( | |
| (attention): RobertaAttention( | |
| (self): RobertaSelfAttention( | |
| (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): RobertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): RobertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| ) | |
| (output): RobertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): RobertaPooler( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (activation): Tanh() | |
| ) | |
| ) | |
| ) | |
| (word_dropout): WordDropout(p=0.05) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=768, out_features=51, bias=True) | |
| (beta): 1.0 | |
| (weights): None | |
| (weight_tensor) None | |
| )" | |
| 2022-01-16 18:38:17,526 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,526 Corpus: "Corpus: 5642 train + 195 dev + 649 test sentences" | |
| 2022-01-16 18:38:17,526 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,527 Parameters: | |
| 2022-01-16 18:38:17,527 - learning_rate: "5e-06" | |
| 2022-01-16 18:38:17,527 - mini_batch_size: "32" | |
| 2022-01-16 18:38:17,527 - patience: "3" | |
| 2022-01-16 18:38:17,528 - anneal_factor: "0.5" | |
| 2022-01-16 18:38:17,528 - max_epochs: "10" | |
| 2022-01-16 18:38:17,528 - shuffle: "True" | |
| 2022-01-16 18:38:17,528 - train_with_dev: "False" | |
| 2022-01-16 18:38:17,529 - batch_growth_annealing: "False" | |
| 2022-01-16 18:38:17,529 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,529 Model training base path: "resources/taggers/pos-transformer" | |
| 2022-01-16 18:38:17,530 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,530 Device: cuda:0 | |
| 2022-01-16 18:38:17,530 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:17,530 Embeddings storage mode: none | |
| 2022-01-16 18:38:17,534 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:38:34,359 epoch 1 - iter 17/177 - loss 4.21719545 - samples/sec: 32.34 - lr: 0.000000 | |
| 2022-01-16 18:38:49,400 epoch 1 - iter 34/177 - loss 4.19345430 - samples/sec: 36.17 - lr: 0.000001 | |
| 2022-01-16 18:39:05,256 epoch 1 - iter 51/177 - loss 4.15633603 - samples/sec: 34.31 - lr: 0.000001 | |
| 2022-01-16 18:39:19,936 epoch 1 - iter 68/177 - loss 4.11811385 - samples/sec: 37.07 - lr: 0.000002 | |
| 2022-01-16 18:39:35,631 epoch 1 - iter 85/177 - loss 4.06705216 - samples/sec: 34.68 - lr: 0.000002 | |
| 2022-01-16 18:39:49,539 epoch 1 - iter 102/177 - loss 4.01162833 - samples/sec: 39.12 - lr: 0.000003 | |
| 2022-01-16 18:40:04,517 epoch 1 - iter 119/177 - loss 3.95117440 - samples/sec: 36.33 - lr: 0.000003 | |
| 2022-01-16 18:40:18,637 epoch 1 - iter 136/177 - loss 3.88391044 - samples/sec: 38.53 - lr: 0.000004 | |
| 2022-01-16 18:40:34,602 epoch 1 - iter 153/177 - loss 3.78662706 - samples/sec: 34.08 - lr: 0.000004 | |
| 2022-01-16 18:40:50,297 epoch 1 - iter 170/177 - loss 3.66565316 - samples/sec: 34.67 - lr: 0.000005 | |
| 2022-01-16 18:40:55,405 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:40:55,406 EPOCH 1 done: loss 3.6331 - lr 0.0000050 | |
| 2022-01-16 18:41:01,071 DEV : loss 2.0775277614593506 - f1-score (micro avg) 0.5698 | |
| 2022-01-16 18:41:01,073 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:41:01,075 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:41:14,873 epoch 2 - iter 17/177 - loss 2.20805337 - samples/sec: 39.44 - lr: 0.000005 | |
| 2022-01-16 18:41:29,867 epoch 2 - iter 34/177 - loss 1.96658974 - samples/sec: 36.29 - lr: 0.000005 | |
| 2022-01-16 18:41:45,607 epoch 2 - iter 51/177 - loss 1.75508128 - samples/sec: 34.57 - lr: 0.000005 | |
| 2022-01-16 18:42:01,386 epoch 2 - iter 68/177 - loss 1.58575541 - samples/sec: 34.48 - lr: 0.000005 | |
| 2022-01-16 18:42:16,804 epoch 2 - iter 85/177 - loss 1.45429547 - samples/sec: 35.29 - lr: 0.000005 | |
| 2022-01-16 18:42:32,178 epoch 2 - iter 102/177 - loss 1.34526502 - samples/sec: 35.39 - lr: 0.000005 | |
| 2022-01-16 18:42:48,735 epoch 2 - iter 119/177 - loss 1.23724431 - samples/sec: 32.86 - lr: 0.000005 | |
| 2022-01-16 18:43:03,310 epoch 2 - iter 136/177 - loss 1.16223838 - samples/sec: 37.33 - lr: 0.000005 | |
| 2022-01-16 18:43:18,304 epoch 2 - iter 153/177 - loss 1.09870495 - samples/sec: 36.29 - lr: 0.000005 | |
| 2022-01-16 18:43:34,956 epoch 2 - iter 170/177 - loss 1.03855466 - samples/sec: 32.67 - lr: 0.000004 | |
| 2022-01-16 18:43:40,722 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:43:40,723 EPOCH 2 done: loss 1.0198 - lr 0.0000044 | |
| 2022-01-16 18:43:46,405 DEV : loss 0.23464356362819672 - f1-score (micro avg) 0.9443 | |
| 2022-01-16 18:43:46,407 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:43:46,408 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:44:01,387 epoch 3 - iter 17/177 - loss 0.46476740 - samples/sec: 36.33 - lr: 0.000004 | |
| 2022-01-16 18:44:17,394 epoch 3 - iter 34/177 - loss 0.46233323 - samples/sec: 33.99 - lr: 0.000004 | |
| 2022-01-16 18:44:32,304 epoch 3 - iter 51/177 - loss 0.45235428 - samples/sec: 36.49 - lr: 0.000004 | |
| 2022-01-16 18:44:46,826 epoch 3 - iter 68/177 - loss 0.44547326 - samples/sec: 37.47 - lr: 0.000004 | |
| 2022-01-16 18:45:03,857 epoch 3 - iter 85/177 - loss 0.43503033 - samples/sec: 31.95 - lr: 0.000004 | |
| 2022-01-16 18:45:20,043 epoch 3 - iter 102/177 - loss 0.42734805 - samples/sec: 33.63 - lr: 0.000004 | |
| 2022-01-16 18:45:36,060 epoch 3 - iter 119/177 - loss 0.42237100 - samples/sec: 33.97 - lr: 0.000004 | |
| 2022-01-16 18:45:51,576 epoch 3 - iter 136/177 - loss 0.41700412 - samples/sec: 35.07 - lr: 0.000004 | |
| 2022-01-16 18:46:07,252 epoch 3 - iter 153/177 - loss 0.41455352 - samples/sec: 34.71 - lr: 0.000004 | |
| 2022-01-16 18:46:23,597 epoch 3 - iter 170/177 - loss 0.41134424 - samples/sec: 33.29 - lr: 0.000004 | |
| 2022-01-16 18:46:29,222 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:46:29,223 EPOCH 3 done: loss 0.4103 - lr 0.0000039 | |
| 2022-01-16 18:46:34,899 DEV : loss 0.140821173787117 - f1-score (micro avg) 0.9632 | |
| 2022-01-16 18:46:34,901 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:46:34,902 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:46:49,649 epoch 4 - iter 17/177 - loss 0.34770276 - samples/sec: 36.90 - lr: 0.000004 | |
| 2022-01-16 18:47:05,137 epoch 4 - iter 34/177 - loss 0.34449519 - samples/sec: 35.13 - lr: 0.000004 | |
| 2022-01-16 18:47:20,666 epoch 4 - iter 51/177 - loss 0.35038471 - samples/sec: 35.04 - lr: 0.000004 | |
| 2022-01-16 18:47:35,593 epoch 4 - iter 68/177 - loss 0.34965167 - samples/sec: 36.45 - lr: 0.000004 | |
| 2022-01-16 18:47:51,537 epoch 4 - iter 85/177 - loss 0.35074386 - samples/sec: 34.13 - lr: 0.000004 | |
| 2022-01-16 18:48:06,575 epoch 4 - iter 102/177 - loss 0.34919573 - samples/sec: 36.18 - lr: 0.000004 | |
| 2022-01-16 18:48:22,671 epoch 4 - iter 119/177 - loss 0.34906482 - samples/sec: 33.80 - lr: 0.000004 | |
| 2022-01-16 18:48:38,152 epoch 4 - iter 136/177 - loss 0.34645574 - samples/sec: 35.15 - lr: 0.000003 | |
| 2022-01-16 18:48:53,425 epoch 4 - iter 153/177 - loss 0.34515747 - samples/sec: 35.63 - lr: 0.000003 | |
| 2022-01-16 18:49:08,614 epoch 4 - iter 170/177 - loss 0.34411478 - samples/sec: 35.82 - lr: 0.000003 | |
| 2022-01-16 18:49:14,556 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:49:14,557 EPOCH 4 done: loss 0.3430 - lr 0.0000033 | |
| 2022-01-16 18:49:20,294 DEV : loss 0.11640190333127975 - f1-score (micro avg) 0.9703 | |
| 2022-01-16 18:49:20,297 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:49:20,297 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:49:36,057 epoch 5 - iter 17/177 - loss 0.31027747 - samples/sec: 34.53 - lr: 0.000003 | |
| 2022-01-16 18:49:51,823 epoch 5 - iter 34/177 - loss 0.31176440 - samples/sec: 34.51 - lr: 0.000003 | |
| 2022-01-16 18:50:06,630 epoch 5 - iter 51/177 - loss 0.31452075 - samples/sec: 36.75 - lr: 0.000003 | |
| 2022-01-16 18:50:22,294 epoch 5 - iter 68/177 - loss 0.31209996 - samples/sec: 34.73 - lr: 0.000003 | |
| 2022-01-16 18:50:36,301 epoch 5 - iter 85/177 - loss 0.31357991 - samples/sec: 38.85 - lr: 0.000003 | |
| 2022-01-16 18:50:52,962 epoch 5 - iter 102/177 - loss 0.31496866 - samples/sec: 32.66 - lr: 0.000003 | |
| 2022-01-16 18:51:08,260 epoch 5 - iter 119/177 - loss 0.31294977 - samples/sec: 35.57 - lr: 0.000003 | |
| 2022-01-16 18:51:24,158 epoch 5 - iter 136/177 - loss 0.31189665 - samples/sec: 34.22 - lr: 0.000003 | |
| 2022-01-16 18:51:39,145 epoch 5 - iter 153/177 - loss 0.31138881 - samples/sec: 36.31 - lr: 0.000003 | |
| 2022-01-16 18:51:54,700 epoch 5 - iter 170/177 - loss 0.30960234 - samples/sec: 34.98 - lr: 0.000003 | |
| 2022-01-16 18:51:59,742 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:51:59,743 EPOCH 5 done: loss 0.3098 - lr 0.0000028 | |
| 2022-01-16 18:52:05,466 DEV : loss 0.10135460644960403 - f1-score (micro avg) 0.9729 | |
| 2022-01-16 18:52:05,468 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:52:05,469 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:52:20,458 epoch 6 - iter 17/177 - loss 0.30154787 - samples/sec: 36.30 - lr: 0.000003 | |
| 2022-01-16 18:52:34,917 epoch 6 - iter 34/177 - loss 0.30197436 - samples/sec: 37.63 - lr: 0.000003 | |
| 2022-01-16 18:52:49,618 epoch 6 - iter 51/177 - loss 0.30167136 - samples/sec: 37.01 - lr: 0.000003 | |
| 2022-01-16 18:53:04,988 epoch 6 - iter 68/177 - loss 0.30196611 - samples/sec: 35.40 - lr: 0.000003 | |
| 2022-01-16 18:53:20,297 epoch 6 - iter 85/177 - loss 0.30182940 - samples/sec: 35.54 - lr: 0.000003 | |
| 2022-01-16 18:53:35,734 epoch 6 - iter 102/177 - loss 0.30003109 - samples/sec: 35.25 - lr: 0.000002 | |
| 2022-01-16 18:53:51,701 epoch 6 - iter 119/177 - loss 0.30091205 - samples/sec: 34.08 - lr: 0.000002 | |
| 2022-01-16 18:54:06,831 epoch 6 - iter 136/177 - loss 0.30099483 - samples/sec: 35.96 - lr: 0.000002 | |
| 2022-01-16 18:54:22,486 epoch 6 - iter 153/177 - loss 0.29848715 - samples/sec: 34.76 - lr: 0.000002 | |
| 2022-01-16 18:54:37,203 epoch 6 - iter 170/177 - loss 0.29689481 - samples/sec: 36.97 - lr: 0.000002 | |
| 2022-01-16 18:54:44,337 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:54:44,338 EPOCH 6 done: loss 0.2966 - lr 0.0000022 | |
| 2022-01-16 18:54:49,620 DEV : loss 0.09480294585227966 - f1-score (micro avg) 0.974 | |
| 2022-01-16 18:54:49,623 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:54:49,623 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:55:05,515 epoch 7 - iter 17/177 - loss 0.28239213 - samples/sec: 34.24 - lr: 0.000002 | |
| 2022-01-16 18:55:20,295 epoch 7 - iter 34/177 - loss 0.28557506 - samples/sec: 36.81 - lr: 0.000002 | |
| 2022-01-16 18:55:35,660 epoch 7 - iter 51/177 - loss 0.28541785 - samples/sec: 35.41 - lr: 0.000002 | |
| 2022-01-16 18:55:51,758 epoch 7 - iter 68/177 - loss 0.29320767 - samples/sec: 33.80 - lr: 0.000002 | |
| 2022-01-16 18:56:06,783 epoch 7 - iter 85/177 - loss 0.29339894 - samples/sec: 36.21 - lr: 0.000002 | |
| 2022-01-16 18:56:22,815 epoch 7 - iter 102/177 - loss 0.29253486 - samples/sec: 33.94 - lr: 0.000002 | |
| 2022-01-16 18:56:39,028 epoch 7 - iter 119/177 - loss 0.29145637 - samples/sec: 33.56 - lr: 0.000002 | |
| 2022-01-16 18:56:54,361 epoch 7 - iter 136/177 - loss 0.29111952 - samples/sec: 35.49 - lr: 0.000002 | |
| 2022-01-16 18:57:09,548 epoch 7 - iter 153/177 - loss 0.29113036 - samples/sec: 35.83 - lr: 0.000002 | |
| 2022-01-16 18:57:23,584 epoch 7 - iter 170/177 - loss 0.29066532 - samples/sec: 38.76 - lr: 0.000002 | |
| 2022-01-16 18:57:29,584 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:57:29,585 EPOCH 7 done: loss 0.2896 - lr 0.0000017 | |
| 2022-01-16 18:57:34,894 DEV : loss 0.09033482521772385 - f1-score (micro avg) 0.9743 | |
| 2022-01-16 18:57:34,896 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 18:57:34,898 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 18:57:50,623 epoch 8 - iter 17/177 - loss 0.28329047 - samples/sec: 34.60 - lr: 0.000002 | |
| 2022-01-16 18:58:06,213 epoch 8 - iter 34/177 - loss 0.28096448 - samples/sec: 34.90 - lr: 0.000002 | |
| 2022-01-16 18:58:22,737 epoch 8 - iter 51/177 - loss 0.28201738 - samples/sec: 32.93 - lr: 0.000002 | |
| 2022-01-16 18:58:37,507 epoch 8 - iter 68/177 - loss 0.28137267 - samples/sec: 36.84 - lr: 0.000001 | |
| 2022-01-16 18:58:52,962 epoch 8 - iter 85/177 - loss 0.28405564 - samples/sec: 35.21 - lr: 0.000001 | |
| 2022-01-16 18:59:08,711 epoch 8 - iter 102/177 - loss 0.28496531 - samples/sec: 34.55 - lr: 0.000001 | |
| 2022-01-16 18:59:23,238 epoch 8 - iter 119/177 - loss 0.28466528 - samples/sec: 37.46 - lr: 0.000001 | |
| 2022-01-16 18:59:38,520 epoch 8 - iter 136/177 - loss 0.28246598 - samples/sec: 35.60 - lr: 0.000001 | |
| 2022-01-16 18:59:53,789 epoch 8 - iter 153/177 - loss 0.28078088 - samples/sec: 35.63 - lr: 0.000001 | |
| 2022-01-16 19:00:09,934 epoch 8 - iter 170/177 - loss 0.28075535 - samples/sec: 33.70 - lr: 0.000001 | |
| 2022-01-16 19:00:15,100 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:00:15,101 EPOCH 8 done: loss 0.2814 - lr 0.0000011 | |
| 2022-01-16 19:00:20,403 DEV : loss 0.08581043034791946 - f1-score (micro avg) 0.9745 | |
| 2022-01-16 19:00:20,406 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 19:00:20,406 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:00:36,469 epoch 9 - iter 17/177 - loss 0.27366042 - samples/sec: 33.87 - lr: 0.000001 | |
| 2022-01-16 19:00:51,042 epoch 9 - iter 34/177 - loss 0.27417563 - samples/sec: 37.34 - lr: 0.000001 | |
| 2022-01-16 19:01:06,968 epoch 9 - iter 51/177 - loss 0.27908066 - samples/sec: 34.16 - lr: 0.000001 | |
| 2022-01-16 19:01:21,551 epoch 9 - iter 68/177 - loss 0.27815091 - samples/sec: 37.31 - lr: 0.000001 | |
| 2022-01-16 19:01:38,409 epoch 9 - iter 85/177 - loss 0.27855783 - samples/sec: 32.28 - lr: 0.000001 | |
| 2022-01-16 19:01:53,547 epoch 9 - iter 102/177 - loss 0.28336618 - samples/sec: 35.94 - lr: 0.000001 | |
| 2022-01-16 19:02:09,188 epoch 9 - iter 119/177 - loss 0.28196400 - samples/sec: 34.79 - lr: 0.000001 | |
| 2022-01-16 19:02:25,112 epoch 9 - iter 136/177 - loss 0.28112997 - samples/sec: 34.17 - lr: 0.000001 | |
| 2022-01-16 19:02:41,122 epoch 9 - iter 153/177 - loss 0.28271008 - samples/sec: 33.99 - lr: 0.000001 | |
| 2022-01-16 19:02:57,003 epoch 9 - iter 170/177 - loss 0.28254205 - samples/sec: 34.26 - lr: 0.000001 | |
| 2022-01-16 19:03:02,602 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:03:02,603 EPOCH 9 done: loss 0.2826 - lr 0.0000006 | |
| 2022-01-16 19:03:08,344 DEV : loss 0.08502506464719772 - f1-score (micro avg) 0.974 | |
| 2022-01-16 19:03:08,347 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 19:03:08,348 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:03:22,683 epoch 10 - iter 17/177 - loss 0.29810598 - samples/sec: 37.96 - lr: 0.000001 | |
| 2022-01-16 19:03:38,044 epoch 10 - iter 34/177 - loss 0.29633129 - samples/sec: 35.42 - lr: 0.000000 | |
| 2022-01-16 19:03:54,399 epoch 10 - iter 51/177 - loss 0.28500408 - samples/sec: 33.27 - lr: 0.000000 | |
| 2022-01-16 19:04:09,802 epoch 10 - iter 68/177 - loss 0.28305573 - samples/sec: 35.32 - lr: 0.000000 | |
| 2022-01-16 19:04:25,641 epoch 10 - iter 85/177 - loss 0.28663575 - samples/sec: 34.35 - lr: 0.000000 | |
| 2022-01-16 19:04:40,354 epoch 10 - iter 102/177 - loss 0.28653115 - samples/sec: 36.98 - lr: 0.000000 | |
| 2022-01-16 19:04:56,702 epoch 10 - iter 119/177 - loss 0.28579694 - samples/sec: 33.28 - lr: 0.000000 | |
| 2022-01-16 19:05:12,070 epoch 10 - iter 136/177 - loss 0.28590446 - samples/sec: 35.40 - lr: 0.000000 | |
| 2022-01-16 19:05:27,377 epoch 10 - iter 153/177 - loss 0.28533742 - samples/sec: 35.55 - lr: 0.000000 | |
| 2022-01-16 19:05:42,603 epoch 10 - iter 170/177 - loss 0.28333786 - samples/sec: 35.73 - lr: 0.000000 | |
| 2022-01-16 19:05:48,443 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:05:48,444 EPOCH 10 done: loss 0.2832 - lr 0.0000000 | |
| 2022-01-16 19:05:54,211 DEV : loss 0.08448906987905502 - f1-score (micro avg) 0.974 | |
| 2022-01-16 19:05:54,214 BAD EPOCHS (no improvement): 4 | |
| 2022-01-16 19:05:55,439 ---------------------------------------------------------------------------------------------------- | |
| 2022-01-16 19:05:55,440 Testing using last state of model ... | |
| 2022-01-16 19:06:15,179 0.9788 0.9788 0.9788 0.9788 | |
| 2022-01-16 19:06:15,180 | |
| Results: | |
| - F-score (micro) 0.9788 | |
| - F-score (macro) 0.7527 | |
| - Accuracy 0.9788 | |
| By class: | |
| precision recall f1-score support | |
| NOMcom 0.9850 0.9840 0.9845 2130 | |
| VERcjg 0.9974 0.9954 0.9964 1535 | |
| PROper 0.9912 0.9920 0.9916 1368 | |
| PONfbl 1.0000 0.9993 0.9996 1341 | |
| PRE 0.9881 0.9955 0.9918 1331 | |
| ADVgen 0.9713 0.9263 0.9483 841 | |
| PONfrt 0.9895 1.0000 0.9947 662 | |
| DETdef 0.9983 0.9983 0.9983 606 | |
| ADJqua 0.9259 0.9500 0.9378 500 | |
| VERinf 0.9920 1.0000 0.9960 497 | |
| DETpos 1.0000 0.9957 0.9979 469 | |
| CONcoo 0.9957 0.9935 0.9946 465 | |
| CONsub 0.9337 0.9409 0.9373 389 | |
| VERppe 0.9659 0.9720 0.9689 321 | |
| ADVneg 0.9476 1.0000 0.9731 271 | |
| PROrel 0.9194 0.9296 0.9245 270 | |
| NOMpro 0.9634 0.9925 0.9777 265 | |
| DETndf 0.9958 0.9715 0.9835 246 | |
| PROind 0.9526 0.9628 0.9577 188 | |
| PRE.DETdef 0.9785 0.9945 0.9864 183 | |
| DETdem 1.0000 0.9806 0.9902 155 | |
| PROdem 0.9675 1.0000 0.9835 119 | |
| PROadv 0.9083 0.9820 0.9437 111 | |
| DETind 0.9223 0.9694 0.9453 98 | |
| VERppa 0.9683 0.9104 0.9385 67 | |
| PROimp 0.8333 0.8333 0.8333 54 | |
| DETcar 0.7381 1.0000 0.8493 31 | |
| INJ 1.0000 0.8571 0.9231 35 | |
| ADJind 0.9310 0.9000 0.9153 30 | |
| PROint 0.6957 0.7273 0.7111 22 | |
| ADJcar 0.8333 0.4762 0.6061 21 | |
| PROcar 0.7333 0.6111 0.6667 18 | |
| PONpga 1.0000 1.0000 1.0000 16 | |
| PROpos 0.9231 0.8571 0.8889 14 | |
| DETrel 0.6364 0.4375 0.5185 16 | |
| DETint 0.4706 0.8000 0.5926 10 | |
| PONpdr 1.0000 1.0000 1.0000 13 | |
| ADJord 0.8889 0.5000 0.6400 16 | |
| ADVint 1.0000 0.8000 0.8889 5 | |
| PONpxx 0.0000 0.0000 0.0000 6 | |
| PRE.PROrel 0.0000 0.0000 0.0000 2 | |
| latin 0.0000 0.0000 0.0000 2 | |
| PROord 0.0000 0.0000 0.0000 1 | |
| PRE.PROdem 0.0000 0.0000 0.0000 1 | |
| PRE.NOMcom 0.0000 0.0000 0.0000 1 | |
| ETR 0.0000 0.0000 0.0000 1 | |
| ADVsub 0.0000 0.0000 0.0000 1 | |
| micro avg 0.9788 0.9788 0.9788 14744 | |
| macro avg 0.7647 0.7497 0.7527 14744 | |
| weighted avg 0.9781 0.9788 0.9782 14744 | |
| samples avg 0.9788 0.9788 0.9788 14744 | |
| 2022-01-16 19:06:15,180 ---------------------------------------------------------------------------------------------------- | |