| 2022-10-26 15:28:10,168 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,173 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): XLMRobertaModel( | |
| (embeddings): RobertaEmbeddings( | |
| (word_embeddings): Embedding(250002, 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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (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) | |
| (embedding2nn): Linear(in_features=768, out_features=768, bias=True) | |
| (rnn): LSTM(768, 256, batch_first=True, bidirectional=True) | |
| (linear): Linear(in_features=512, out_features=15, bias=True) | |
| (loss_function): ViterbiLoss() | |
| (crf): CRF() | |
| )" | |
| 2022-10-26 15:28:10,176 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,180 Corpus: "Corpus: 8551 train + 1425 dev + 1425 test sentences" | |
| 2022-10-26 15:28:10,182 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,184 Parameters: | |
| 2022-10-26 15:28:10,186 - learning_rate: "0.010000" | |
| 2022-10-26 15:28:10,187 - mini_batch_size: "8" | |
| 2022-10-26 15:28:10,188 - patience: "3" | |
| 2022-10-26 15:28:10,189 - anneal_factor: "0.5" | |
| 2022-10-26 15:28:10,191 - max_epochs: "10" | |
| 2022-10-26 15:28:10,192 - shuffle: "True" | |
| 2022-10-26 15:28:10,193 - train_with_dev: "False" | |
| 2022-10-26 15:28:10,194 - batch_growth_annealing: "False" | |
| 2022-10-26 15:28:10,196 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,197 Model training base path: "/content/model/xlmr_ner" | |
| 2022-10-26 15:28:10,198 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,199 Device: cuda:0 | |
| 2022-10-26 15:28:10,201 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:28:10,202 Embeddings storage mode: none | |
| 2022-10-26 15:28:10,203 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:30:29,962 epoch 1 - iter 106/1069 - loss 0.55101171 - samples/sec: 6.07 - lr: 0.010000 | |
| 2022-10-26 15:32:28,714 epoch 1 - iter 212/1069 - loss 0.35636418 - samples/sec: 7.14 - lr: 0.010000 | |
| 2022-10-26 15:34:23,625 epoch 1 - iter 318/1069 - loss 0.28047260 - samples/sec: 7.38 - lr: 0.010000 | |
| 2022-10-26 15:36:24,015 epoch 1 - iter 424/1069 - loss 0.23890211 - samples/sec: 7.04 - lr: 0.010000 | |
| 2022-10-26 15:38:21,987 epoch 1 - iter 530/1069 - loss 0.21322222 - samples/sec: 7.19 - lr: 0.010000 | |
| 2022-10-26 15:40:22,521 epoch 1 - iter 636/1069 - loss 0.19431796 - samples/sec: 7.04 - lr: 0.010000 | |
| 2022-10-26 15:42:18,754 epoch 1 - iter 742/1069 - loss 0.18084010 - samples/sec: 7.30 - lr: 0.010000 | |
| 2022-10-26 15:44:18,344 epoch 1 - iter 848/1069 - loss 0.16975329 - samples/sec: 7.09 - lr: 0.010000 | |
| 2022-10-26 15:46:14,738 epoch 1 - iter 954/1069 - loss 0.16158584 - samples/sec: 7.29 - lr: 0.010000 | |
| 2022-10-26 15:48:14,067 epoch 1 - iter 1060/1069 - loss 0.15491697 - samples/sec: 7.11 - lr: 0.010000 | |
| 2022-10-26 15:48:24,569 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:48:24,577 EPOCH 1 done: loss 0.1543 - lr 0.010000 | |
| 2022-10-26 15:50:17,480 Evaluating as a multi-label problem: False | |
| 2022-10-26 15:50:17,512 DEV : loss 0.060714565217494965 - f1-score (micro avg) 0.7908 | |
| 2022-10-26 15:50:17,553 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 15:50:17,554 saving best model | |
| 2022-10-26 15:50:23,470 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 15:52:24,219 epoch 2 - iter 106/1069 - loss 0.08869057 - samples/sec: 7.02 - lr: 0.010000 | |
| 2022-10-26 15:54:21,594 epoch 2 - iter 212/1069 - loss 0.08600343 - samples/sec: 7.23 - lr: 0.010000 | |
| 2022-10-26 15:56:19,809 epoch 2 - iter 318/1069 - loss 0.08546665 - samples/sec: 7.17 - lr: 0.010000 | |
| 2022-10-26 15:58:17,214 epoch 2 - iter 424/1069 - loss 0.08476718 - samples/sec: 7.22 - lr: 0.010000 | |
| 2022-10-26 16:00:16,114 epoch 2 - iter 530/1069 - loss 0.08542624 - samples/sec: 7.13 - lr: 0.010000 | |
| 2022-10-26 16:02:13,540 epoch 2 - iter 636/1069 - loss 0.08522910 - samples/sec: 7.22 - lr: 0.010000 | |
| 2022-10-26 16:04:12,854 epoch 2 - iter 742/1069 - loss 0.08502467 - samples/sec: 7.11 - lr: 0.010000 | |
| 2022-10-26 16:06:13,219 epoch 2 - iter 848/1069 - loss 0.08373459 - samples/sec: 7.05 - lr: 0.010000 | |
| 2022-10-26 16:08:09,808 epoch 2 - iter 954/1069 - loss 0.08316639 - samples/sec: 7.27 - lr: 0.010000 | |
| 2022-10-26 16:10:11,036 epoch 2 - iter 1060/1069 - loss 0.08215396 - samples/sec: 7.00 - lr: 0.010000 | |
| 2022-10-26 16:10:21,246 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:10:21,249 EPOCH 2 done: loss 0.0821 - lr 0.010000 | |
| 2022-10-26 16:12:13,875 Evaluating as a multi-label problem: False | |
| 2022-10-26 16:12:13,905 DEV : loss 0.05180404335260391 - f1-score (micro avg) 0.8408 | |
| 2022-10-26 16:12:13,947 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 16:12:13,948 saving best model | |
| 2022-10-26 16:12:19,344 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:14:19,879 epoch 3 - iter 106/1069 - loss 0.06627178 - samples/sec: 7.04 - lr: 0.010000 | |
| 2022-10-26 16:16:18,272 epoch 3 - iter 212/1069 - loss 0.07094348 - samples/sec: 7.16 - lr: 0.010000 | |
| 2022-10-26 16:18:18,453 epoch 3 - iter 318/1069 - loss 0.07194093 - samples/sec: 7.06 - lr: 0.010000 | |
| 2022-10-26 16:20:15,802 epoch 3 - iter 424/1069 - loss 0.07242840 - samples/sec: 7.23 - lr: 0.010000 | |
| 2022-10-26 16:22:12,248 epoch 3 - iter 530/1069 - loss 0.07171872 - samples/sec: 7.28 - lr: 0.010000 | |
| 2022-10-26 16:24:12,231 epoch 3 - iter 636/1069 - loss 0.07162092 - samples/sec: 7.07 - lr: 0.010000 | |
| 2022-10-26 16:26:10,382 epoch 3 - iter 742/1069 - loss 0.07130310 - samples/sec: 7.18 - lr: 0.010000 | |
| 2022-10-26 16:28:08,953 epoch 3 - iter 848/1069 - loss 0.07050136 - samples/sec: 7.15 - lr: 0.010000 | |
| 2022-10-26 16:30:09,728 epoch 3 - iter 954/1069 - loss 0.07070517 - samples/sec: 7.02 - lr: 0.010000 | |
| 2022-10-26 16:32:08,721 epoch 3 - iter 1060/1069 - loss 0.07033198 - samples/sec: 7.13 - lr: 0.010000 | |
| 2022-10-26 16:32:18,654 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:32:18,656 EPOCH 3 done: loss 0.0702 - lr 0.010000 | |
| 2022-10-26 16:34:10,956 Evaluating as a multi-label problem: False | |
| 2022-10-26 16:34:10,986 DEV : loss 0.04575943946838379 - f1-score (micro avg) 0.8693 | |
| 2022-10-26 16:34:11,026 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 16:34:11,029 saving best model | |
| 2022-10-26 16:34:16,564 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:36:12,350 epoch 4 - iter 106/1069 - loss 0.06432601 - samples/sec: 7.32 - lr: 0.010000 | |
| 2022-10-26 16:38:08,474 epoch 4 - iter 212/1069 - loss 0.06376094 - samples/sec: 7.30 - lr: 0.010000 | |
| 2022-10-26 16:40:03,219 epoch 4 - iter 318/1069 - loss 0.06273795 - samples/sec: 7.39 - lr: 0.010000 | |
| 2022-10-26 16:41:59,110 epoch 4 - iter 424/1069 - loss 0.06153989 - samples/sec: 7.32 - lr: 0.010000 | |
| 2022-10-26 16:43:57,347 epoch 4 - iter 530/1069 - loss 0.06137878 - samples/sec: 7.17 - lr: 0.010000 | |
| 2022-10-26 16:45:55,146 epoch 4 - iter 636/1069 - loss 0.06072772 - samples/sec: 7.20 - lr: 0.010000 | |
| 2022-10-26 16:47:53,049 epoch 4 - iter 742/1069 - loss 0.06031769 - samples/sec: 7.19 - lr: 0.010000 | |
| 2022-10-26 16:49:50,705 epoch 4 - iter 848/1069 - loss 0.06084099 - samples/sec: 7.21 - lr: 0.010000 | |
| 2022-10-26 16:51:49,833 epoch 4 - iter 954/1069 - loss 0.06096388 - samples/sec: 7.12 - lr: 0.010000 | |
| 2022-10-26 16:53:45,640 epoch 4 - iter 1060/1069 - loss 0.06061743 - samples/sec: 7.32 - lr: 0.010000 | |
| 2022-10-26 16:53:54,974 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:53:54,976 EPOCH 4 done: loss 0.0606 - lr 0.010000 | |
| 2022-10-26 16:55:45,518 Evaluating as a multi-label problem: False | |
| 2022-10-26 16:55:45,548 DEV : loss 0.04747875779867172 - f1-score (micro avg) 0.8627 | |
| 2022-10-26 16:55:45,589 BAD EPOCHS (no improvement): 1 | |
| 2022-10-26 16:55:45,590 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 16:57:41,259 epoch 5 - iter 106/1069 - loss 0.05285565 - samples/sec: 7.33 - lr: 0.010000 | |
| 2022-10-26 16:59:40,296 epoch 5 - iter 212/1069 - loss 0.05049977 - samples/sec: 7.12 - lr: 0.010000 | |
| 2022-10-26 17:01:35,184 epoch 5 - iter 318/1069 - loss 0.05297933 - samples/sec: 7.38 - lr: 0.010000 | |
| 2022-10-26 17:03:34,028 epoch 5 - iter 424/1069 - loss 0.05293744 - samples/sec: 7.14 - lr: 0.010000 | |
| 2022-10-26 17:05:29,295 epoch 5 - iter 530/1069 - loss 0.05359386 - samples/sec: 7.36 - lr: 0.010000 | |
| 2022-10-26 17:07:25,593 epoch 5 - iter 636/1069 - loss 0.05307424 - samples/sec: 7.29 - lr: 0.010000 | |
| 2022-10-26 17:09:22,893 epoch 5 - iter 742/1069 - loss 0.05323355 - samples/sec: 7.23 - lr: 0.010000 | |
| 2022-10-26 17:11:22,602 epoch 5 - iter 848/1069 - loss 0.05272547 - samples/sec: 7.08 - lr: 0.010000 | |
| 2022-10-26 17:13:22,960 epoch 5 - iter 954/1069 - loss 0.05280553 - samples/sec: 7.05 - lr: 0.010000 | |
| 2022-10-26 17:15:20,527 epoch 5 - iter 1060/1069 - loss 0.05265360 - samples/sec: 7.21 - lr: 0.010000 | |
| 2022-10-26 17:15:29,931 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 17:15:29,932 EPOCH 5 done: loss 0.0526 - lr 0.010000 | |
| 2022-10-26 17:17:21,728 Evaluating as a multi-label problem: False | |
| 2022-10-26 17:17:21,760 DEV : loss 0.03879784420132637 - f1-score (micro avg) 0.8864 | |
| 2022-10-26 17:17:21,803 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 17:17:21,804 saving best model | |
| 2022-10-26 17:17:27,330 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 17:19:26,401 epoch 6 - iter 106/1069 - loss 0.04801558 - samples/sec: 7.12 - lr: 0.010000 | |
| 2022-10-26 17:21:22,988 epoch 6 - iter 212/1069 - loss 0.05008290 - samples/sec: 7.27 - lr: 0.010000 | |
| 2022-10-26 17:23:16,794 epoch 6 - iter 318/1069 - loss 0.04925649 - samples/sec: 7.45 - lr: 0.010000 | |
| 2022-10-26 17:25:15,532 epoch 6 - iter 424/1069 - loss 0.04786643 - samples/sec: 7.14 - lr: 0.010000 | |
| 2022-10-26 17:27:13,913 epoch 6 - iter 530/1069 - loss 0.04879792 - samples/sec: 7.16 - lr: 0.010000 | |
| 2022-10-26 17:29:10,114 epoch 6 - iter 636/1069 - loss 0.04800786 - samples/sec: 7.30 - lr: 0.010000 | |
| 2022-10-26 17:31:07,810 epoch 6 - iter 742/1069 - loss 0.04755361 - samples/sec: 7.21 - lr: 0.010000 | |
| 2022-10-26 17:33:04,496 epoch 6 - iter 848/1069 - loss 0.04782375 - samples/sec: 7.27 - lr: 0.010000 | |
| 2022-10-26 17:35:05,834 epoch 6 - iter 954/1069 - loss 0.04776160 - samples/sec: 6.99 - lr: 0.010000 | |
| 2022-10-26 17:37:03,878 epoch 6 - iter 1060/1069 - loss 0.04743945 - samples/sec: 7.18 - lr: 0.010000 | |
| 2022-10-26 17:37:14,466 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 17:37:14,468 EPOCH 6 done: loss 0.0475 - lr 0.010000 | |
| 2022-10-26 17:39:07,562 Evaluating as a multi-label problem: False | |
| 2022-10-26 17:39:07,592 DEV : loss 0.03874654322862625 - f1-score (micro avg) 0.8908 | |
| 2022-10-26 17:39:07,633 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 17:39:07,635 saving best model | |
| 2022-10-26 17:39:13,242 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 17:41:11,924 epoch 7 - iter 106/1069 - loss 0.04334369 - samples/sec: 7.15 - lr: 0.010000 | |
| 2022-10-26 17:43:11,382 epoch 7 - iter 212/1069 - loss 0.04192565 - samples/sec: 7.10 - lr: 0.010000 | |
| 2022-10-26 17:45:08,087 epoch 7 - iter 318/1069 - loss 0.04115627 - samples/sec: 7.27 - lr: 0.010000 | |
| 2022-10-26 17:47:06,615 epoch 7 - iter 424/1069 - loss 0.04114928 - samples/sec: 7.16 - lr: 0.010000 | |
| 2022-10-26 17:49:03,863 epoch 7 - iter 530/1069 - loss 0.04105023 - samples/sec: 7.23 - lr: 0.010000 | |
| 2022-10-26 17:51:02,216 epoch 7 - iter 636/1069 - loss 0.04125208 - samples/sec: 7.17 - lr: 0.010000 | |
| 2022-10-26 17:53:04,293 epoch 7 - iter 742/1069 - loss 0.04151765 - samples/sec: 6.95 - lr: 0.010000 | |
| 2022-10-26 17:55:01,446 epoch 7 - iter 848/1069 - loss 0.04170200 - samples/sec: 7.24 - lr: 0.010000 | |
| 2022-10-26 17:56:59,848 epoch 7 - iter 954/1069 - loss 0.04180177 - samples/sec: 7.16 - lr: 0.010000 | |
| 2022-10-26 17:58:56,175 epoch 7 - iter 1060/1069 - loss 0.04203413 - samples/sec: 7.29 - lr: 0.010000 | |
| 2022-10-26 17:59:05,814 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 17:59:05,816 EPOCH 7 done: loss 0.0420 - lr 0.010000 | |
| 2022-10-26 18:00:59,457 Evaluating as a multi-label problem: False | |
| 2022-10-26 18:00:59,486 DEV : loss 0.04413652420043945 - f1-score (micro avg) 0.8968 | |
| 2022-10-26 18:00:59,527 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 18:00:59,529 saving best model | |
| 2022-10-26 18:01:05,372 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 18:03:03,422 epoch 8 - iter 106/1069 - loss 0.03592615 - samples/sec: 7.18 - lr: 0.010000 | |
| 2022-10-26 18:05:00,466 epoch 8 - iter 212/1069 - loss 0.03676863 - samples/sec: 7.25 - lr: 0.010000 | |
| 2022-10-26 18:06:58,178 epoch 8 - iter 318/1069 - loss 0.03702258 - samples/sec: 7.20 - lr: 0.010000 | |
| 2022-10-26 18:08:55,170 epoch 8 - iter 424/1069 - loss 0.03704658 - samples/sec: 7.25 - lr: 0.010000 | |
| 2022-10-26 18:10:52,222 epoch 8 - iter 530/1069 - loss 0.03711348 - samples/sec: 7.25 - lr: 0.010000 | |
| 2022-10-26 18:12:51,244 epoch 8 - iter 636/1069 - loss 0.03715815 - samples/sec: 7.13 - lr: 0.010000 | |
| 2022-10-26 18:14:50,229 epoch 8 - iter 742/1069 - loss 0.03708747 - samples/sec: 7.13 - lr: 0.010000 | |
| 2022-10-26 18:16:47,946 epoch 8 - iter 848/1069 - loss 0.03734575 - samples/sec: 7.20 - lr: 0.010000 | |
| 2022-10-26 18:18:45,873 epoch 8 - iter 954/1069 - loss 0.03736843 - samples/sec: 7.19 - lr: 0.010000 | |
| 2022-10-26 18:20:43,504 epoch 8 - iter 1060/1069 - loss 0.03737578 - samples/sec: 7.21 - lr: 0.010000 | |
| 2022-10-26 18:20:53,262 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 18:20:53,265 EPOCH 8 done: loss 0.0374 - lr 0.010000 | |
| 2022-10-26 18:22:46,256 Evaluating as a multi-label problem: False | |
| 2022-10-26 18:22:46,293 DEV : loss 0.03726610541343689 - f1-score (micro avg) 0.9117 | |
| 2022-10-26 18:22:46,336 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 18:22:46,337 saving best model | |
| 2022-10-26 18:22:51,847 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 18:24:50,402 epoch 9 - iter 106/1069 - loss 0.03606101 - samples/sec: 7.15 - lr: 0.010000 | |
| 2022-10-26 18:26:47,577 epoch 9 - iter 212/1069 - loss 0.03466163 - samples/sec: 7.24 - lr: 0.010000 | |
| 2022-10-26 18:28:47,029 epoch 9 - iter 318/1069 - loss 0.03420843 - samples/sec: 7.10 - lr: 0.010000 | |
| 2022-10-26 18:30:43,235 epoch 9 - iter 424/1069 - loss 0.03406325 - samples/sec: 7.30 - lr: 0.010000 | |
| 2022-10-26 18:32:41,132 epoch 9 - iter 530/1069 - loss 0.03393077 - samples/sec: 7.19 - lr: 0.010000 | |
| 2022-10-26 18:34:35,953 epoch 9 - iter 636/1069 - loss 0.03438052 - samples/sec: 7.39 - lr: 0.010000 | |
| 2022-10-26 18:36:33,872 epoch 9 - iter 742/1069 - loss 0.03435922 - samples/sec: 7.19 - lr: 0.010000 | |
| 2022-10-26 18:38:30,457 epoch 9 - iter 848/1069 - loss 0.03351594 - samples/sec: 7.27 - lr: 0.010000 | |
| 2022-10-26 18:40:26,775 epoch 9 - iter 954/1069 - loss 0.03363514 - samples/sec: 7.29 - lr: 0.010000 | |
| 2022-10-26 18:42:26,040 epoch 9 - iter 1060/1069 - loss 0.03301736 - samples/sec: 7.11 - lr: 0.010000 | |
| 2022-10-26 18:42:34,477 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 18:42:34,480 EPOCH 9 done: loss 0.0330 - lr 0.010000 | |
| 2022-10-26 18:44:24,572 Evaluating as a multi-label problem: False | |
| 2022-10-26 18:44:24,602 DEV : loss 0.04557322338223457 - f1-score (micro avg) 0.9084 | |
| 2022-10-26 18:44:24,644 BAD EPOCHS (no improvement): 1 | |
| 2022-10-26 18:44:24,646 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 18:46:21,774 epoch 10 - iter 106/1069 - loss 0.02992093 - samples/sec: 7.24 - lr: 0.010000 | |
| 2022-10-26 18:48:20,730 epoch 10 - iter 212/1069 - loss 0.02886380 - samples/sec: 7.13 - lr: 0.010000 | |
| 2022-10-26 18:50:20,679 epoch 10 - iter 318/1069 - loss 0.03109654 - samples/sec: 7.07 - lr: 0.010000 | |
| 2022-10-26 18:52:14,564 epoch 10 - iter 424/1069 - loss 0.03091892 - samples/sec: 7.45 - lr: 0.010000 | |
| 2022-10-26 18:54:14,888 epoch 10 - iter 530/1069 - loss 0.02977117 - samples/sec: 7.05 - lr: 0.010000 | |
| 2022-10-26 18:56:13,992 epoch 10 - iter 636/1069 - loss 0.02969566 - samples/sec: 7.12 - lr: 0.010000 | |
| 2022-10-26 18:58:12,618 epoch 10 - iter 742/1069 - loss 0.02979601 - samples/sec: 7.15 - lr: 0.010000 | |
| 2022-10-26 19:00:10,398 epoch 10 - iter 848/1069 - loss 0.03040781 - samples/sec: 7.20 - lr: 0.010000 | |
| 2022-10-26 19:02:06,063 epoch 10 - iter 954/1069 - loss 0.03029135 - samples/sec: 7.33 - lr: 0.010000 | |
| 2022-10-26 19:04:05,626 epoch 10 - iter 1060/1069 - loss 0.03035206 - samples/sec: 7.09 - lr: 0.010000 | |
| 2022-10-26 19:04:15,538 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 19:04:15,540 EPOCH 10 done: loss 0.0303 - lr 0.010000 | |
| 2022-10-26 19:06:06,586 Evaluating as a multi-label problem: False | |
| 2022-10-26 19:06:06,621 DEV : loss 0.03892701491713524 - f1-score (micro avg) 0.9132 | |
| 2022-10-26 19:06:06,663 BAD EPOCHS (no improvement): 0 | |
| 2022-10-26 19:06:06,665 saving best model | |
| 2022-10-26 19:06:17,597 ---------------------------------------------------------------------------------------------------- | |
| 2022-10-26 19:06:17,723 loading file /content/model/xlmr_ner/best-model.pt | |
| 2022-10-26 19:06:24,597 SequenceTagger predicts: Dictionary with 15 tags: O, S-PER, B-PER, E-PER, I-PER, S-MISC, B-MISC, E-MISC, I-MISC, S-LOC, B-LOC, E-LOC, I-LOC, <START>, <STOP> | |
| 2022-10-26 19:08:17,003 Evaluating as a multi-label problem: False | |
| 2022-10-26 19:08:17,040 0.9053 0.9316 0.9182 0.8955 | |
| 2022-10-26 19:08:17,041 | |
| Results: | |
| - F-score (micro) 0.9182 | |
| - F-score (macro) 0.8875 | |
| - Accuracy 0.8955 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.9339 0.9633 0.9484 2127 | |
| MISC 0.8469 0.9250 0.8842 933 | |
| LOC 0.8955 0.7732 0.8299 388 | |
| micro avg 0.9053 0.9316 0.9182 3448 | |
| macro avg 0.8921 0.8872 0.8875 3448 | |
| weighted avg 0.9060 0.9316 0.9177 3448 | |
| 2022-10-26 19:08:17,045 ---------------------------------------------------------------------------------------------------- | |