| 2023-10-11 02:58:49,354 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,356 Model: "SequenceTagger( | |
| (embeddings): ByT5Embeddings( | |
| (model): T5EncoderModel( | |
| (shared): Embedding(384, 1472) | |
| (encoder): T5Stack( | |
| (embed_tokens): Embedding(384, 1472) | |
| (block): ModuleList( | |
| (0): T5Block( | |
| (layer): ModuleList( | |
| (0): T5LayerSelfAttention( | |
| (SelfAttention): T5Attention( | |
| (q): Linear(in_features=1472, out_features=384, bias=False) | |
| (k): Linear(in_features=1472, out_features=384, bias=False) | |
| (v): Linear(in_features=1472, out_features=384, bias=False) | |
| (o): Linear(in_features=384, out_features=1472, bias=False) | |
| (relative_attention_bias): Embedding(32, 6) | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (1): T5LayerFF( | |
| (DenseReluDense): T5DenseGatedActDense( | |
| (wi_0): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wi_1): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wo): Linear(in_features=3584, out_features=1472, bias=False) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| (act): NewGELUActivation() | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| (1-11): 11 x T5Block( | |
| (layer): ModuleList( | |
| (0): T5LayerSelfAttention( | |
| (SelfAttention): T5Attention( | |
| (q): Linear(in_features=1472, out_features=384, bias=False) | |
| (k): Linear(in_features=1472, out_features=384, bias=False) | |
| (v): Linear(in_features=1472, out_features=384, bias=False) | |
| (o): Linear(in_features=384, out_features=1472, bias=False) | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (1): T5LayerFF( | |
| (DenseReluDense): T5DenseGatedActDense( | |
| (wi_0): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wi_1): Linear(in_features=1472, out_features=3584, bias=False) | |
| (wo): Linear(in_features=3584, out_features=1472, bias=False) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| (act): NewGELUActivation() | |
| ) | |
| (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=1472, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-11 02:58:49,356 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,356 MultiCorpus: 1166 train + 165 dev + 415 test sentences | |
| - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator | |
| 2023-10-11 02:58:49,357 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,357 Train: 1166 sentences | |
| 2023-10-11 02:58:49,357 (train_with_dev=False, train_with_test=False) | |
| 2023-10-11 02:58:49,357 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,357 Training Params: | |
| 2023-10-11 02:58:49,357 - learning_rate: "0.00016" | |
| 2023-10-11 02:58:49,357 - mini_batch_size: "8" | |
| 2023-10-11 02:58:49,357 - max_epochs: "10" | |
| 2023-10-11 02:58:49,357 - shuffle: "True" | |
| 2023-10-11 02:58:49,357 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,357 Plugins: | |
| 2023-10-11 02:58:49,357 - TensorboardLogger | |
| 2023-10-11 02:58:49,357 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-11 02:58:49,357 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,357 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-11 02:58:49,357 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-11 02:58:49,358 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,358 Computation: | |
| 2023-10-11 02:58:49,358 - compute on device: cuda:0 | |
| 2023-10-11 02:58:49,358 - embedding storage: none | |
| 2023-10-11 02:58:49,358 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,358 Model training base path: "hmbench-newseye/fi-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5" | |
| 2023-10-11 02:58:49,358 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,358 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 02:58:49,358 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-11 02:58:58,372 epoch 1 - iter 14/146 - loss 2.82156082 - time (sec): 9.01 - samples/sec: 470.06 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-11 02:59:06,819 epoch 1 - iter 28/146 - loss 2.81328962 - time (sec): 17.46 - samples/sec: 467.31 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-11 02:59:15,404 epoch 1 - iter 42/146 - loss 2.80236709 - time (sec): 26.04 - samples/sec: 455.58 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-11 02:59:25,395 epoch 1 - iter 56/146 - loss 2.77560051 - time (sec): 36.03 - samples/sec: 460.86 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-11 02:59:34,647 epoch 1 - iter 70/146 - loss 2.73504332 - time (sec): 45.29 - samples/sec: 461.08 - lr: 0.000076 - momentum: 0.000000 | |
| 2023-10-11 02:59:44,395 epoch 1 - iter 84/146 - loss 2.66639699 - time (sec): 55.04 - samples/sec: 469.45 - lr: 0.000091 - momentum: 0.000000 | |
| 2023-10-11 02:59:53,771 epoch 1 - iter 98/146 - loss 2.58655394 - time (sec): 64.41 - samples/sec: 475.67 - lr: 0.000106 - momentum: 0.000000 | |
| 2023-10-11 03:00:02,636 epoch 1 - iter 112/146 - loss 2.52547117 - time (sec): 73.28 - samples/sec: 475.66 - lr: 0.000122 - momentum: 0.000000 | |
| 2023-10-11 03:00:12,006 epoch 1 - iter 126/146 - loss 2.43072282 - time (sec): 82.65 - samples/sec: 478.44 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-11 03:00:20,230 epoch 1 - iter 140/146 - loss 2.35869693 - time (sec): 90.87 - samples/sec: 475.36 - lr: 0.000152 - momentum: 0.000000 | |
| 2023-10-11 03:00:23,460 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:00:23,460 EPOCH 1 done: loss 2.3326 - lr: 0.000152 | |
| 2023-10-11 03:00:28,956 DEV : loss 1.2650508880615234 - f1-score (micro avg) 0.0 | |
| 2023-10-11 03:00:28,965 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:00:37,834 epoch 2 - iter 14/146 - loss 1.28850784 - time (sec): 8.87 - samples/sec: 483.05 - lr: 0.000158 - momentum: 0.000000 | |
| 2023-10-11 03:00:46,400 epoch 2 - iter 28/146 - loss 1.17475751 - time (sec): 17.43 - samples/sec: 488.80 - lr: 0.000157 - momentum: 0.000000 | |
| 2023-10-11 03:00:54,577 epoch 2 - iter 42/146 - loss 1.11252222 - time (sec): 25.61 - samples/sec: 479.42 - lr: 0.000155 - momentum: 0.000000 | |
| 2023-10-11 03:01:03,357 epoch 2 - iter 56/146 - loss 1.00841290 - time (sec): 34.39 - samples/sec: 481.62 - lr: 0.000153 - momentum: 0.000000 | |
| 2023-10-11 03:01:12,621 epoch 2 - iter 70/146 - loss 0.99023783 - time (sec): 43.65 - samples/sec: 488.32 - lr: 0.000152 - momentum: 0.000000 | |
| 2023-10-11 03:01:21,672 epoch 2 - iter 84/146 - loss 0.92269285 - time (sec): 52.71 - samples/sec: 490.27 - lr: 0.000150 - momentum: 0.000000 | |
| 2023-10-11 03:01:30,342 epoch 2 - iter 98/146 - loss 0.87677537 - time (sec): 61.38 - samples/sec: 490.80 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-11 03:01:38,580 epoch 2 - iter 112/146 - loss 0.83163574 - time (sec): 69.61 - samples/sec: 487.38 - lr: 0.000147 - momentum: 0.000000 | |
| 2023-10-11 03:01:46,714 epoch 2 - iter 126/146 - loss 0.81335178 - time (sec): 77.75 - samples/sec: 483.21 - lr: 0.000145 - momentum: 0.000000 | |
| 2023-10-11 03:01:56,170 epoch 2 - iter 140/146 - loss 0.78893900 - time (sec): 87.20 - samples/sec: 486.32 - lr: 0.000143 - momentum: 0.000000 | |
| 2023-10-11 03:02:00,042 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:02:00,043 EPOCH 2 done: loss 0.7803 - lr: 0.000143 | |
| 2023-10-11 03:02:05,705 DEV : loss 0.4041951894760132 - f1-score (micro avg) 0.0 | |
| 2023-10-11 03:02:05,714 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:02:14,895 epoch 3 - iter 14/146 - loss 0.53427675 - time (sec): 9.18 - samples/sec: 419.66 - lr: 0.000141 - momentum: 0.000000 | |
| 2023-10-11 03:02:23,336 epoch 3 - iter 28/146 - loss 0.51483169 - time (sec): 17.62 - samples/sec: 439.28 - lr: 0.000139 - momentum: 0.000000 | |
| 2023-10-11 03:02:32,172 epoch 3 - iter 42/146 - loss 0.48292516 - time (sec): 26.46 - samples/sec: 457.02 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-11 03:02:40,999 epoch 3 - iter 56/146 - loss 0.45342483 - time (sec): 35.28 - samples/sec: 471.31 - lr: 0.000136 - momentum: 0.000000 | |
| 2023-10-11 03:02:49,374 epoch 3 - iter 70/146 - loss 0.44551814 - time (sec): 43.66 - samples/sec: 471.21 - lr: 0.000134 - momentum: 0.000000 | |
| 2023-10-11 03:02:57,741 epoch 3 - iter 84/146 - loss 0.42893312 - time (sec): 52.03 - samples/sec: 474.40 - lr: 0.000132 - momentum: 0.000000 | |
| 2023-10-11 03:03:07,151 epoch 3 - iter 98/146 - loss 0.44060923 - time (sec): 61.43 - samples/sec: 482.77 - lr: 0.000131 - momentum: 0.000000 | |
| 2023-10-11 03:03:16,212 epoch 3 - iter 112/146 - loss 0.43204748 - time (sec): 70.50 - samples/sec: 472.07 - lr: 0.000129 - momentum: 0.000000 | |
| 2023-10-11 03:03:26,562 epoch 3 - iter 126/146 - loss 0.42357750 - time (sec): 80.85 - samples/sec: 472.37 - lr: 0.000127 - momentum: 0.000000 | |
| 2023-10-11 03:03:36,128 epoch 3 - iter 140/146 - loss 0.42216673 - time (sec): 90.41 - samples/sec: 470.41 - lr: 0.000125 - momentum: 0.000000 | |
| 2023-10-11 03:03:40,267 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:03:40,268 EPOCH 3 done: loss 0.4162 - lr: 0.000125 | |
| 2023-10-11 03:03:45,974 DEV : loss 0.2766019403934479 - f1-score (micro avg) 0.0 | |
| 2023-10-11 03:03:45,983 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:03:56,215 epoch 4 - iter 14/146 - loss 0.28148112 - time (sec): 10.23 - samples/sec: 450.55 - lr: 0.000123 - momentum: 0.000000 | |
| 2023-10-11 03:04:06,418 epoch 4 - iter 28/146 - loss 0.26087007 - time (sec): 20.43 - samples/sec: 466.80 - lr: 0.000121 - momentum: 0.000000 | |
| 2023-10-11 03:04:15,473 epoch 4 - iter 42/146 - loss 0.27268962 - time (sec): 29.49 - samples/sec: 473.85 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-11 03:04:24,961 epoch 4 - iter 56/146 - loss 0.32422208 - time (sec): 38.98 - samples/sec: 488.20 - lr: 0.000118 - momentum: 0.000000 | |
| 2023-10-11 03:04:33,577 epoch 4 - iter 70/146 - loss 0.32316287 - time (sec): 47.59 - samples/sec: 486.47 - lr: 0.000116 - momentum: 0.000000 | |
| 2023-10-11 03:04:42,328 epoch 4 - iter 84/146 - loss 0.31830554 - time (sec): 56.34 - samples/sec: 483.60 - lr: 0.000115 - momentum: 0.000000 | |
| 2023-10-11 03:04:50,674 epoch 4 - iter 98/146 - loss 0.32027813 - time (sec): 64.69 - samples/sec: 479.57 - lr: 0.000113 - momentum: 0.000000 | |
| 2023-10-11 03:04:59,746 epoch 4 - iter 112/146 - loss 0.30992976 - time (sec): 73.76 - samples/sec: 468.27 - lr: 0.000111 - momentum: 0.000000 | |
| 2023-10-11 03:05:08,557 epoch 4 - iter 126/146 - loss 0.30555368 - time (sec): 82.57 - samples/sec: 469.25 - lr: 0.000109 - momentum: 0.000000 | |
| 2023-10-11 03:05:17,259 epoch 4 - iter 140/146 - loss 0.30611365 - time (sec): 91.27 - samples/sec: 471.05 - lr: 0.000108 - momentum: 0.000000 | |
| 2023-10-11 03:05:20,441 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:05:20,442 EPOCH 4 done: loss 0.3084 - lr: 0.000108 | |
| 2023-10-11 03:05:26,153 DEV : loss 0.2286217361688614 - f1-score (micro avg) 0.3883 | |
| 2023-10-11 03:05:26,162 saving best model | |
| 2023-10-11 03:05:27,094 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:05:36,127 epoch 5 - iter 14/146 - loss 0.25889454 - time (sec): 9.03 - samples/sec: 470.83 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-11 03:05:46,332 epoch 5 - iter 28/146 - loss 0.23524679 - time (sec): 19.24 - samples/sec: 485.34 - lr: 0.000104 - momentum: 0.000000 | |
| 2023-10-11 03:05:55,679 epoch 5 - iter 42/146 - loss 0.24651874 - time (sec): 28.58 - samples/sec: 487.14 - lr: 0.000102 - momentum: 0.000000 | |
| 2023-10-11 03:06:05,138 epoch 5 - iter 56/146 - loss 0.25126634 - time (sec): 38.04 - samples/sec: 487.88 - lr: 0.000100 - momentum: 0.000000 | |
| 2023-10-11 03:06:14,789 epoch 5 - iter 70/146 - loss 0.27351281 - time (sec): 47.69 - samples/sec: 477.24 - lr: 0.000099 - momentum: 0.000000 | |
| 2023-10-11 03:06:23,908 epoch 5 - iter 84/146 - loss 0.26826607 - time (sec): 56.81 - samples/sec: 474.74 - lr: 0.000097 - momentum: 0.000000 | |
| 2023-10-11 03:06:32,783 epoch 5 - iter 98/146 - loss 0.25700981 - time (sec): 65.69 - samples/sec: 472.31 - lr: 0.000095 - momentum: 0.000000 | |
| 2023-10-11 03:06:40,955 epoch 5 - iter 112/146 - loss 0.25377327 - time (sec): 73.86 - samples/sec: 465.88 - lr: 0.000093 - momentum: 0.000000 | |
| 2023-10-11 03:06:50,055 epoch 5 - iter 126/146 - loss 0.24593692 - time (sec): 82.96 - samples/sec: 467.80 - lr: 0.000092 - momentum: 0.000000 | |
| 2023-10-11 03:06:58,949 epoch 5 - iter 140/146 - loss 0.24041901 - time (sec): 91.85 - samples/sec: 467.84 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-11 03:07:02,398 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:07:02,398 EPOCH 5 done: loss 0.2400 - lr: 0.000090 | |
| 2023-10-11 03:07:08,709 DEV : loss 0.1938922256231308 - f1-score (micro avg) 0.5096 | |
| 2023-10-11 03:07:08,719 saving best model | |
| 2023-10-11 03:07:11,341 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:07:19,792 epoch 6 - iter 14/146 - loss 0.17003425 - time (sec): 8.45 - samples/sec: 421.76 - lr: 0.000088 - momentum: 0.000000 | |
| 2023-10-11 03:07:29,419 epoch 6 - iter 28/146 - loss 0.21222092 - time (sec): 18.07 - samples/sec: 424.62 - lr: 0.000086 - momentum: 0.000000 | |
| 2023-10-11 03:07:38,670 epoch 6 - iter 42/146 - loss 0.21021534 - time (sec): 27.32 - samples/sec: 425.94 - lr: 0.000084 - momentum: 0.000000 | |
| 2023-10-11 03:07:48,456 epoch 6 - iter 56/146 - loss 0.20003050 - time (sec): 37.11 - samples/sec: 436.31 - lr: 0.000083 - momentum: 0.000000 | |
| 2023-10-11 03:07:57,208 epoch 6 - iter 70/146 - loss 0.19157871 - time (sec): 45.86 - samples/sec: 443.75 - lr: 0.000081 - momentum: 0.000000 | |
| 2023-10-11 03:08:06,928 epoch 6 - iter 84/146 - loss 0.17913338 - time (sec): 55.58 - samples/sec: 459.50 - lr: 0.000079 - momentum: 0.000000 | |
| 2023-10-11 03:08:15,299 epoch 6 - iter 98/146 - loss 0.17940064 - time (sec): 63.95 - samples/sec: 461.31 - lr: 0.000077 - momentum: 0.000000 | |
| 2023-10-11 03:08:23,678 epoch 6 - iter 112/146 - loss 0.18121308 - time (sec): 72.33 - samples/sec: 463.21 - lr: 0.000076 - momentum: 0.000000 | |
| 2023-10-11 03:08:31,936 epoch 6 - iter 126/146 - loss 0.17892881 - time (sec): 80.59 - samples/sec: 461.57 - lr: 0.000074 - momentum: 0.000000 | |
| 2023-10-11 03:08:41,917 epoch 6 - iter 140/146 - loss 0.18585573 - time (sec): 90.57 - samples/sec: 469.98 - lr: 0.000072 - momentum: 0.000000 | |
| 2023-10-11 03:08:45,984 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:08:45,984 EPOCH 6 done: loss 0.1851 - lr: 0.000072 | |
| 2023-10-11 03:08:51,662 DEV : loss 0.17115506529808044 - f1-score (micro avg) 0.566 | |
| 2023-10-11 03:08:51,671 saving best model | |
| 2023-10-11 03:08:54,392 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:09:04,084 epoch 7 - iter 14/146 - loss 0.15701219 - time (sec): 9.69 - samples/sec: 391.69 - lr: 0.000070 - momentum: 0.000000 | |
| 2023-10-11 03:09:12,536 epoch 7 - iter 28/146 - loss 0.15510761 - time (sec): 18.14 - samples/sec: 423.97 - lr: 0.000068 - momentum: 0.000000 | |
| 2023-10-11 03:09:20,257 epoch 7 - iter 42/146 - loss 0.14629892 - time (sec): 25.86 - samples/sec: 427.51 - lr: 0.000067 - momentum: 0.000000 | |
| 2023-10-11 03:09:30,238 epoch 7 - iter 56/146 - loss 0.14700964 - time (sec): 35.84 - samples/sec: 460.86 - lr: 0.000065 - momentum: 0.000000 | |
| 2023-10-11 03:09:39,544 epoch 7 - iter 70/146 - loss 0.14185422 - time (sec): 45.15 - samples/sec: 455.37 - lr: 0.000063 - momentum: 0.000000 | |
| 2023-10-11 03:09:48,635 epoch 7 - iter 84/146 - loss 0.14513215 - time (sec): 54.24 - samples/sec: 454.63 - lr: 0.000061 - momentum: 0.000000 | |
| 2023-10-11 03:09:57,352 epoch 7 - iter 98/146 - loss 0.15171915 - time (sec): 62.96 - samples/sec: 460.05 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-11 03:10:06,611 epoch 7 - iter 112/146 - loss 0.14705100 - time (sec): 72.22 - samples/sec: 460.11 - lr: 0.000058 - momentum: 0.000000 | |
| 2023-10-11 03:10:15,921 epoch 7 - iter 126/146 - loss 0.14658088 - time (sec): 81.53 - samples/sec: 462.85 - lr: 0.000056 - momentum: 0.000000 | |
| 2023-10-11 03:10:25,015 epoch 7 - iter 140/146 - loss 0.14531914 - time (sec): 90.62 - samples/sec: 468.17 - lr: 0.000055 - momentum: 0.000000 | |
| 2023-10-11 03:10:28,779 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:10:28,780 EPOCH 7 done: loss 0.1463 - lr: 0.000055 | |
| 2023-10-11 03:10:34,791 DEV : loss 0.16687284409999847 - f1-score (micro avg) 0.617 | |
| 2023-10-11 03:10:34,801 saving best model | |
| 2023-10-11 03:10:37,399 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:10:45,847 epoch 8 - iter 14/146 - loss 0.12928267 - time (sec): 8.44 - samples/sec: 470.41 - lr: 0.000052 - momentum: 0.000000 | |
| 2023-10-11 03:10:54,199 epoch 8 - iter 28/146 - loss 0.12679178 - time (sec): 16.80 - samples/sec: 486.18 - lr: 0.000051 - momentum: 0.000000 | |
| 2023-10-11 03:11:02,512 epoch 8 - iter 42/146 - loss 0.15315931 - time (sec): 25.11 - samples/sec: 482.89 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-11 03:11:11,760 epoch 8 - iter 56/146 - loss 0.15510801 - time (sec): 34.36 - samples/sec: 496.46 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-11 03:11:21,747 epoch 8 - iter 70/146 - loss 0.14964513 - time (sec): 44.34 - samples/sec: 479.51 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-11 03:11:30,788 epoch 8 - iter 84/146 - loss 0.14419529 - time (sec): 53.38 - samples/sec: 464.01 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-11 03:11:40,389 epoch 8 - iter 98/146 - loss 0.14165465 - time (sec): 62.99 - samples/sec: 468.01 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-11 03:11:49,920 epoch 8 - iter 112/146 - loss 0.13718145 - time (sec): 72.52 - samples/sec: 473.75 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-11 03:11:58,832 epoch 8 - iter 126/146 - loss 0.13421700 - time (sec): 81.43 - samples/sec: 467.13 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-11 03:12:08,287 epoch 8 - iter 140/146 - loss 0.12891406 - time (sec): 90.88 - samples/sec: 468.80 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-11 03:12:12,150 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:12:12,151 EPOCH 8 done: loss 0.1265 - lr: 0.000037 | |
| 2023-10-11 03:12:17,999 DEV : loss 0.15703192353248596 - f1-score (micro avg) 0.6863 | |
| 2023-10-11 03:12:18,008 saving best model | |
| 2023-10-11 03:12:20,577 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:12:29,531 epoch 9 - iter 14/146 - loss 0.10771080 - time (sec): 8.95 - samples/sec: 474.12 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-11 03:12:37,859 epoch 9 - iter 28/146 - loss 0.11279535 - time (sec): 17.28 - samples/sec: 460.24 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-11 03:12:46,480 epoch 9 - iter 42/146 - loss 0.12798759 - time (sec): 25.90 - samples/sec: 467.63 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-11 03:12:54,631 epoch 9 - iter 56/146 - loss 0.13087974 - time (sec): 34.05 - samples/sec: 469.99 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-11 03:13:03,571 epoch 9 - iter 70/146 - loss 0.12721638 - time (sec): 42.99 - samples/sec: 476.40 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-11 03:13:13,313 epoch 9 - iter 84/146 - loss 0.12112059 - time (sec): 52.73 - samples/sec: 482.65 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-11 03:13:21,939 epoch 9 - iter 98/146 - loss 0.11598258 - time (sec): 61.36 - samples/sec: 482.96 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-11 03:13:30,792 epoch 9 - iter 112/146 - loss 0.11367321 - time (sec): 70.21 - samples/sec: 487.31 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-11 03:13:39,412 epoch 9 - iter 126/146 - loss 0.11114356 - time (sec): 78.83 - samples/sec: 491.30 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-11 03:13:48,050 epoch 9 - iter 140/146 - loss 0.11106486 - time (sec): 87.47 - samples/sec: 490.13 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-11 03:13:51,530 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:13:51,530 EPOCH 9 done: loss 0.1103 - lr: 0.000019 | |
| 2023-10-11 03:13:57,096 DEV : loss 0.1517515331506729 - f1-score (micro avg) 0.7064 | |
| 2023-10-11 03:13:57,105 saving best model | |
| 2023-10-11 03:13:59,616 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:14:09,366 epoch 10 - iter 14/146 - loss 0.09910516 - time (sec): 9.75 - samples/sec: 425.43 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-11 03:14:18,329 epoch 10 - iter 28/146 - loss 0.11261227 - time (sec): 18.71 - samples/sec: 425.22 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-11 03:14:28,387 epoch 10 - iter 42/146 - loss 0.10216965 - time (sec): 28.77 - samples/sec: 443.50 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-11 03:14:37,707 epoch 10 - iter 56/146 - loss 0.09500174 - time (sec): 38.09 - samples/sec: 450.40 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-11 03:14:46,518 epoch 10 - iter 70/146 - loss 0.09562900 - time (sec): 46.90 - samples/sec: 454.01 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-11 03:14:55,546 epoch 10 - iter 84/146 - loss 0.09501906 - time (sec): 55.93 - samples/sec: 452.85 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-11 03:15:04,659 epoch 10 - iter 98/146 - loss 0.09724605 - time (sec): 65.04 - samples/sec: 457.59 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-11 03:15:15,133 epoch 10 - iter 112/146 - loss 0.10037234 - time (sec): 75.51 - samples/sec: 450.82 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-11 03:15:24,660 epoch 10 - iter 126/146 - loss 0.10048535 - time (sec): 85.04 - samples/sec: 449.83 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-11 03:15:33,705 epoch 10 - iter 140/146 - loss 0.10441213 - time (sec): 94.08 - samples/sec: 450.04 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-11 03:15:37,647 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:15:37,647 EPOCH 10 done: loss 0.1026 - lr: 0.000002 | |
| 2023-10-11 03:15:43,591 DEV : loss 0.14971290528774261 - f1-score (micro avg) 0.7176 | |
| 2023-10-11 03:15:43,600 saving best model | |
| 2023-10-11 03:15:47,074 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-11 03:15:47,076 Loading model from best epoch ... | |
| 2023-10-11 03:15:51,855 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd | |
| 2023-10-11 03:16:05,085 | |
| Results: | |
| - F-score (micro) 0.6815 | |
| - F-score (macro) 0.6332 | |
| - Accuracy 0.5518 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.7686 0.7730 0.7708 348 | |
| LOC 0.5942 0.7011 0.6432 261 | |
| ORG 0.2763 0.4038 0.3281 52 | |
| HumanProd 0.8095 0.7727 0.7907 22 | |
| micro avg 0.6490 0.7174 0.6815 683 | |
| macro avg 0.6121 0.6627 0.6332 683 | |
| weighted avg 0.6658 0.7174 0.6890 683 | |
| 2023-10-11 03:16:05,086 ---------------------------------------------------------------------------------------------------- | |