| 2023-10-12 02:19:10,693 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,695 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() | |
| )" | |
| 2023-10-12 02:19:10,696 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,696 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences | |
| - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator | |
| 2023-10-12 02:19:10,696 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,696 Train: 20847 sentences | |
| 2023-10-12 02:19:10,696 (train_with_dev=False, train_with_test=False) | |
| 2023-10-12 02:19:10,696 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,696 Training Params: | |
| 2023-10-12 02:19:10,696 - learning_rate: "0.00015" | |
| 2023-10-12 02:19:10,696 - mini_batch_size: "4" | |
| 2023-10-12 02:19:10,696 - max_epochs: "10" | |
| 2023-10-12 02:19:10,696 - shuffle: "True" | |
| 2023-10-12 02:19:10,696 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,696 Plugins: | |
| 2023-10-12 02:19:10,697 - TensorboardLogger | |
| 2023-10-12 02:19:10,697 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-12 02:19:10,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,697 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-12 02:19:10,697 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-12 02:19:10,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,697 Computation: | |
| 2023-10-12 02:19:10,697 - compute on device: cuda:0 | |
| 2023-10-12 02:19:10,697 - embedding storage: none | |
| 2023-10-12 02:19:10,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,697 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-12 02:19:10,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:19:10,697 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-12 02:21:27,177 epoch 1 - iter 521/5212 - loss 2.79668095 - time (sec): 136.48 - samples/sec: 242.74 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-12 02:23:45,273 epoch 1 - iter 1042/5212 - loss 2.35843978 - time (sec): 274.57 - samples/sec: 247.36 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-12 02:26:04,245 epoch 1 - iter 1563/5212 - loss 1.80796410 - time (sec): 413.55 - samples/sec: 254.16 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-12 02:28:22,402 epoch 1 - iter 2084/5212 - loss 1.45533862 - time (sec): 551.70 - samples/sec: 257.38 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-12 02:30:41,547 epoch 1 - iter 2605/5212 - loss 1.24266578 - time (sec): 690.85 - samples/sec: 260.63 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-12 02:32:58,209 epoch 1 - iter 3126/5212 - loss 1.09834367 - time (sec): 827.51 - samples/sec: 259.96 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-12 02:35:17,942 epoch 1 - iter 3647/5212 - loss 0.98084623 - time (sec): 967.24 - samples/sec: 261.96 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-12 02:37:35,810 epoch 1 - iter 4168/5212 - loss 0.88950826 - time (sec): 1105.11 - samples/sec: 262.06 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-12 02:39:57,111 epoch 1 - iter 4689/5212 - loss 0.80746283 - time (sec): 1246.41 - samples/sec: 264.35 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-12 02:42:16,837 epoch 1 - iter 5210/5212 - loss 0.74371560 - time (sec): 1386.14 - samples/sec: 264.92 - lr: 0.000150 - momentum: 0.000000 | |
| 2023-10-12 02:42:17,401 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:42:17,402 EPOCH 1 done: loss 0.7433 - lr: 0.000150 | |
| 2023-10-12 02:42:52,120 DEV : loss 0.12636248767375946 - f1-score (micro avg) 0.2776 | |
| 2023-10-12 02:42:52,174 saving best model | |
| 2023-10-12 02:42:53,044 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 02:45:11,088 epoch 2 - iter 521/5212 - loss 0.17686924 - time (sec): 138.04 - samples/sec: 262.94 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-12 02:47:30,960 epoch 2 - iter 1042/5212 - loss 0.15464838 - time (sec): 277.91 - samples/sec: 267.07 - lr: 0.000147 - momentum: 0.000000 | |
| 2023-10-12 02:49:50,501 epoch 2 - iter 1563/5212 - loss 0.15677141 - time (sec): 417.45 - samples/sec: 261.48 - lr: 0.000145 - momentum: 0.000000 | |
| 2023-10-12 02:52:16,593 epoch 2 - iter 2084/5212 - loss 0.15541495 - time (sec): 563.55 - samples/sec: 263.09 - lr: 0.000143 - momentum: 0.000000 | |
| 2023-10-12 02:54:38,805 epoch 2 - iter 2605/5212 - loss 0.15288785 - time (sec): 705.76 - samples/sec: 261.65 - lr: 0.000142 - momentum: 0.000000 | |
| 2023-10-12 02:56:58,256 epoch 2 - iter 3126/5212 - loss 0.15178570 - time (sec): 845.21 - samples/sec: 258.20 - lr: 0.000140 - momentum: 0.000000 | |
| 2023-10-12 02:59:16,224 epoch 2 - iter 3647/5212 - loss 0.15320470 - time (sec): 983.18 - samples/sec: 254.99 - lr: 0.000138 - momentum: 0.000000 | |
| 2023-10-12 03:01:39,472 epoch 2 - iter 4168/5212 - loss 0.14998413 - time (sec): 1126.43 - samples/sec: 256.63 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-12 03:04:04,945 epoch 2 - iter 4689/5212 - loss 0.14664370 - time (sec): 1271.90 - samples/sec: 259.73 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-12 03:06:26,106 epoch 2 - iter 5210/5212 - loss 0.14460575 - time (sec): 1413.06 - samples/sec: 259.97 - lr: 0.000133 - momentum: 0.000000 | |
| 2023-10-12 03:06:26,551 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:06:26,552 EPOCH 2 done: loss 0.1446 - lr: 0.000133 | |
| 2023-10-12 03:07:05,601 DEV : loss 0.14167223870754242 - f1-score (micro avg) 0.3339 | |
| 2023-10-12 03:07:05,653 saving best model | |
| 2023-10-12 03:07:08,265 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:09:23,808 epoch 3 - iter 521/5212 - loss 0.10082822 - time (sec): 135.54 - samples/sec: 254.81 - lr: 0.000132 - momentum: 0.000000 | |
| 2023-10-12 03:11:37,979 epoch 3 - iter 1042/5212 - loss 0.09748085 - time (sec): 269.71 - samples/sec: 250.08 - lr: 0.000130 - momentum: 0.000000 | |
| 2023-10-12 03:13:58,974 epoch 3 - iter 1563/5212 - loss 0.09985524 - time (sec): 410.70 - samples/sec: 264.24 - lr: 0.000128 - momentum: 0.000000 | |
| 2023-10-12 03:16:15,894 epoch 3 - iter 2084/5212 - loss 0.09997962 - time (sec): 547.62 - samples/sec: 263.03 - lr: 0.000127 - momentum: 0.000000 | |
| 2023-10-12 03:18:33,733 epoch 3 - iter 2605/5212 - loss 0.09885177 - time (sec): 685.46 - samples/sec: 261.01 - lr: 0.000125 - momentum: 0.000000 | |
| 2023-10-12 03:20:55,408 epoch 3 - iter 3126/5212 - loss 0.09501689 - time (sec): 827.14 - samples/sec: 264.99 - lr: 0.000123 - momentum: 0.000000 | |
| 2023-10-12 03:23:18,463 epoch 3 - iter 3647/5212 - loss 0.09597740 - time (sec): 970.19 - samples/sec: 267.70 - lr: 0.000122 - momentum: 0.000000 | |
| 2023-10-12 03:25:40,852 epoch 3 - iter 4168/5212 - loss 0.09794887 - time (sec): 1112.58 - samples/sec: 263.01 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-12 03:28:07,225 epoch 3 - iter 4689/5212 - loss 0.09904610 - time (sec): 1258.96 - samples/sec: 261.73 - lr: 0.000118 - momentum: 0.000000 | |
| 2023-10-12 03:30:34,184 epoch 3 - iter 5210/5212 - loss 0.09816484 - time (sec): 1405.91 - samples/sec: 261.22 - lr: 0.000117 - momentum: 0.000000 | |
| 2023-10-12 03:30:34,730 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:30:34,731 EPOCH 3 done: loss 0.0981 - lr: 0.000117 | |
| 2023-10-12 03:31:15,205 DEV : loss 0.2600547671318054 - f1-score (micro avg) 0.3618 | |
| 2023-10-12 03:31:15,262 saving best model | |
| 2023-10-12 03:31:17,833 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:33:41,338 epoch 4 - iter 521/5212 - loss 0.06934331 - time (sec): 143.50 - samples/sec: 252.51 - lr: 0.000115 - momentum: 0.000000 | |
| 2023-10-12 03:36:05,531 epoch 4 - iter 1042/5212 - loss 0.07084255 - time (sec): 287.69 - samples/sec: 257.98 - lr: 0.000113 - momentum: 0.000000 | |
| 2023-10-12 03:38:29,695 epoch 4 - iter 1563/5212 - loss 0.06755543 - time (sec): 431.86 - samples/sec: 261.22 - lr: 0.000112 - momentum: 0.000000 | |
| 2023-10-12 03:40:54,693 epoch 4 - iter 2084/5212 - loss 0.06616838 - time (sec): 576.86 - samples/sec: 261.23 - lr: 0.000110 - momentum: 0.000000 | |
| 2023-10-12 03:43:17,865 epoch 4 - iter 2605/5212 - loss 0.06558266 - time (sec): 720.03 - samples/sec: 258.99 - lr: 0.000108 - momentum: 0.000000 | |
| 2023-10-12 03:45:41,115 epoch 4 - iter 3126/5212 - loss 0.06435344 - time (sec): 863.28 - samples/sec: 259.71 - lr: 0.000107 - momentum: 0.000000 | |
| 2023-10-12 03:48:03,559 epoch 4 - iter 3647/5212 - loss 0.06413682 - time (sec): 1005.72 - samples/sec: 259.21 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-12 03:50:23,570 epoch 4 - iter 4168/5212 - loss 0.06596094 - time (sec): 1145.73 - samples/sec: 257.63 - lr: 0.000103 - momentum: 0.000000 | |
| 2023-10-12 03:52:45,111 epoch 4 - iter 4689/5212 - loss 0.06614742 - time (sec): 1287.27 - samples/sec: 258.43 - lr: 0.000102 - momentum: 0.000000 | |
| 2023-10-12 03:55:04,718 epoch 4 - iter 5210/5212 - loss 0.06648116 - time (sec): 1426.88 - samples/sec: 257.45 - lr: 0.000100 - momentum: 0.000000 | |
| 2023-10-12 03:55:05,152 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:55:05,153 EPOCH 4 done: loss 0.0665 - lr: 0.000100 | |
| 2023-10-12 03:55:45,279 DEV : loss 0.3101561367511749 - f1-score (micro avg) 0.3555 | |
| 2023-10-12 03:55:45,331 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 03:58:04,744 epoch 5 - iter 521/5212 - loss 0.04426358 - time (sec): 139.41 - samples/sec: 259.84 - lr: 0.000098 - momentum: 0.000000 | |
| 2023-10-12 04:00:24,001 epoch 5 - iter 1042/5212 - loss 0.04273940 - time (sec): 278.67 - samples/sec: 260.65 - lr: 0.000097 - momentum: 0.000000 | |
| 2023-10-12 04:02:41,900 epoch 5 - iter 1563/5212 - loss 0.04286202 - time (sec): 416.57 - samples/sec: 256.36 - lr: 0.000095 - momentum: 0.000000 | |
| 2023-10-12 04:05:03,372 epoch 5 - iter 2084/5212 - loss 0.04497980 - time (sec): 558.04 - samples/sec: 260.25 - lr: 0.000093 - momentum: 0.000000 | |
| 2023-10-12 04:07:19,613 epoch 5 - iter 2605/5212 - loss 0.04479044 - time (sec): 694.28 - samples/sec: 258.94 - lr: 0.000092 - momentum: 0.000000 | |
| 2023-10-12 04:09:43,942 epoch 5 - iter 3126/5212 - loss 0.04436294 - time (sec): 838.61 - samples/sec: 258.68 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-12 04:12:11,821 epoch 5 - iter 3647/5212 - loss 0.04380965 - time (sec): 986.49 - samples/sec: 258.99 - lr: 0.000088 - momentum: 0.000000 | |
| 2023-10-12 04:14:37,186 epoch 5 - iter 4168/5212 - loss 0.04530386 - time (sec): 1131.85 - samples/sec: 258.58 - lr: 0.000087 - momentum: 0.000000 | |
| 2023-10-12 04:17:02,247 epoch 5 - iter 4689/5212 - loss 0.04665775 - time (sec): 1276.91 - samples/sec: 257.28 - lr: 0.000085 - momentum: 0.000000 | |
| 2023-10-12 04:19:31,906 epoch 5 - iter 5210/5212 - loss 0.04638068 - time (sec): 1426.57 - samples/sec: 257.51 - lr: 0.000083 - momentum: 0.000000 | |
| 2023-10-12 04:19:32,352 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 04:19:32,353 EPOCH 5 done: loss 0.0464 - lr: 0.000083 | |
| 2023-10-12 04:20:13,213 DEV : loss 0.3113304078578949 - f1-score (micro avg) 0.4003 | |
| 2023-10-12 04:20:13,271 saving best model | |
| 2023-10-12 04:20:14,217 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 04:22:41,995 epoch 6 - iter 521/5212 - loss 0.02551644 - time (sec): 147.78 - samples/sec: 258.80 - lr: 0.000082 - momentum: 0.000000 | |
| 2023-10-12 04:25:08,081 epoch 6 - iter 1042/5212 - loss 0.02937621 - time (sec): 293.86 - samples/sec: 259.22 - lr: 0.000080 - momentum: 0.000000 | |
| 2023-10-12 04:27:33,632 epoch 6 - iter 1563/5212 - loss 0.03011832 - time (sec): 439.41 - samples/sec: 254.55 - lr: 0.000078 - momentum: 0.000000 | |
| 2023-10-12 04:29:57,124 epoch 6 - iter 2084/5212 - loss 0.03067784 - time (sec): 582.90 - samples/sec: 252.43 - lr: 0.000077 - momentum: 0.000000 | |
| 2023-10-12 04:32:22,709 epoch 6 - iter 2605/5212 - loss 0.03033894 - time (sec): 728.49 - samples/sec: 256.01 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-12 04:34:46,393 epoch 6 - iter 3126/5212 - loss 0.03057800 - time (sec): 872.17 - samples/sec: 256.98 - lr: 0.000073 - momentum: 0.000000 | |
| 2023-10-12 04:37:07,410 epoch 6 - iter 3647/5212 - loss 0.03169587 - time (sec): 1013.19 - samples/sec: 256.43 - lr: 0.000072 - momentum: 0.000000 | |
| 2023-10-12 04:39:27,981 epoch 6 - iter 4168/5212 - loss 0.03188952 - time (sec): 1153.76 - samples/sec: 256.11 - lr: 0.000070 - momentum: 0.000000 | |
| 2023-10-12 04:41:50,066 epoch 6 - iter 4689/5212 - loss 0.03265092 - time (sec): 1295.85 - samples/sec: 256.18 - lr: 0.000068 - momentum: 0.000000 | |
| 2023-10-12 04:44:09,929 epoch 6 - iter 5210/5212 - loss 0.03297566 - time (sec): 1435.71 - samples/sec: 255.87 - lr: 0.000067 - momentum: 0.000000 | |
| 2023-10-12 04:44:10,371 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 04:44:10,371 EPOCH 6 done: loss 0.0330 - lr: 0.000067 | |
| 2023-10-12 04:44:50,633 DEV : loss 0.40718281269073486 - f1-score (micro avg) 0.3971 | |
| 2023-10-12 04:44:50,685 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 04:47:11,977 epoch 7 - iter 521/5212 - loss 0.02552297 - time (sec): 141.29 - samples/sec: 259.67 - lr: 0.000065 - momentum: 0.000000 | |
| 2023-10-12 04:49:32,146 epoch 7 - iter 1042/5212 - loss 0.02437180 - time (sec): 281.46 - samples/sec: 271.70 - lr: 0.000063 - momentum: 0.000000 | |
| 2023-10-12 04:51:48,960 epoch 7 - iter 1563/5212 - loss 0.02514062 - time (sec): 418.27 - samples/sec: 267.26 - lr: 0.000062 - momentum: 0.000000 | |
| 2023-10-12 04:54:07,753 epoch 7 - iter 2084/5212 - loss 0.02516668 - time (sec): 557.07 - samples/sec: 267.87 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-12 04:56:27,890 epoch 7 - iter 2605/5212 - loss 0.02469412 - time (sec): 697.20 - samples/sec: 265.74 - lr: 0.000058 - momentum: 0.000000 | |
| 2023-10-12 04:58:49,869 epoch 7 - iter 3126/5212 - loss 0.02456715 - time (sec): 839.18 - samples/sec: 265.93 - lr: 0.000057 - momentum: 0.000000 | |
| 2023-10-12 05:01:09,634 epoch 7 - iter 3647/5212 - loss 0.02477855 - time (sec): 978.95 - samples/sec: 263.65 - lr: 0.000055 - momentum: 0.000000 | |
| 2023-10-12 05:03:36,700 epoch 7 - iter 4168/5212 - loss 0.02394687 - time (sec): 1126.01 - samples/sec: 262.72 - lr: 0.000053 - momentum: 0.000000 | |
| 2023-10-12 05:06:00,466 epoch 7 - iter 4689/5212 - loss 0.02356060 - time (sec): 1269.78 - samples/sec: 260.95 - lr: 0.000052 - momentum: 0.000000 | |
| 2023-10-12 05:08:24,608 epoch 7 - iter 5210/5212 - loss 0.02323141 - time (sec): 1413.92 - samples/sec: 259.78 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-12 05:08:25,097 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:08:25,097 EPOCH 7 done: loss 0.0232 - lr: 0.000050 | |
| 2023-10-12 05:09:04,885 DEV : loss 0.408222496509552 - f1-score (micro avg) 0.4035 | |
| 2023-10-12 05:09:04,936 saving best model | |
| 2023-10-12 05:09:07,501 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:11:27,797 epoch 8 - iter 521/5212 - loss 0.01537607 - time (sec): 140.29 - samples/sec: 265.21 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-12 05:13:47,952 epoch 8 - iter 1042/5212 - loss 0.01753064 - time (sec): 280.45 - samples/sec: 271.51 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-12 05:16:11,225 epoch 8 - iter 1563/5212 - loss 0.01696724 - time (sec): 423.72 - samples/sec: 277.40 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-12 05:18:28,174 epoch 8 - iter 2084/5212 - loss 0.01669412 - time (sec): 560.67 - samples/sec: 273.78 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-12 05:20:45,546 epoch 8 - iter 2605/5212 - loss 0.01686027 - time (sec): 698.04 - samples/sec: 269.95 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-12 05:23:00,618 epoch 8 - iter 3126/5212 - loss 0.01671656 - time (sec): 833.11 - samples/sec: 267.12 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-12 05:25:16,222 epoch 8 - iter 3647/5212 - loss 0.01600572 - time (sec): 968.72 - samples/sec: 265.31 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-12 05:27:35,013 epoch 8 - iter 4168/5212 - loss 0.01597679 - time (sec): 1107.51 - samples/sec: 264.67 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-12 05:29:56,551 epoch 8 - iter 4689/5212 - loss 0.01553053 - time (sec): 1249.05 - samples/sec: 263.33 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-12 05:32:19,571 epoch 8 - iter 5210/5212 - loss 0.01639257 - time (sec): 1392.06 - samples/sec: 263.90 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-12 05:32:19,996 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:32:19,997 EPOCH 8 done: loss 0.0164 - lr: 0.000033 | |
| 2023-10-12 05:32:58,072 DEV : loss 0.4335840940475464 - f1-score (micro avg) 0.404 | |
| 2023-10-12 05:32:58,123 saving best model | |
| 2023-10-12 05:33:00,797 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:35:21,905 epoch 9 - iter 521/5212 - loss 0.01030793 - time (sec): 141.10 - samples/sec: 275.22 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-12 05:37:41,298 epoch 9 - iter 1042/5212 - loss 0.01148381 - time (sec): 280.50 - samples/sec: 274.31 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-12 05:40:02,195 epoch 9 - iter 1563/5212 - loss 0.01090402 - time (sec): 421.39 - samples/sec: 263.64 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-12 05:42:24,062 epoch 9 - iter 2084/5212 - loss 0.01205054 - time (sec): 563.26 - samples/sec: 259.21 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-12 05:44:49,921 epoch 9 - iter 2605/5212 - loss 0.01249863 - time (sec): 709.12 - samples/sec: 258.65 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-12 05:47:12,873 epoch 9 - iter 3126/5212 - loss 0.01215066 - time (sec): 852.07 - samples/sec: 258.10 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-12 05:49:39,769 epoch 9 - iter 3647/5212 - loss 0.01116151 - time (sec): 998.97 - samples/sec: 259.45 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-12 05:52:01,961 epoch 9 - iter 4168/5212 - loss 0.01081521 - time (sec): 1141.16 - samples/sec: 257.32 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-12 05:54:25,864 epoch 9 - iter 4689/5212 - loss 0.01082631 - time (sec): 1285.06 - samples/sec: 256.89 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-12 05:56:50,574 epoch 9 - iter 5210/5212 - loss 0.01087435 - time (sec): 1429.77 - samples/sec: 256.94 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-12 05:56:51,009 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:56:51,009 EPOCH 9 done: loss 0.0109 - lr: 0.000017 | |
| 2023-10-12 05:57:30,552 DEV : loss 0.47575101256370544 - f1-score (micro avg) 0.3948 | |
| 2023-10-12 05:57:30,605 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 05:59:52,416 epoch 10 - iter 521/5212 - loss 0.00498377 - time (sec): 141.81 - samples/sec: 252.02 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-12 06:02:13,588 epoch 10 - iter 1042/5212 - loss 0.00681335 - time (sec): 282.98 - samples/sec: 255.31 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-12 06:04:37,692 epoch 10 - iter 1563/5212 - loss 0.00622093 - time (sec): 427.08 - samples/sec: 259.59 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-12 06:06:59,831 epoch 10 - iter 2084/5212 - loss 0.00629281 - time (sec): 569.22 - samples/sec: 258.24 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-12 06:09:21,704 epoch 10 - iter 2605/5212 - loss 0.00707237 - time (sec): 711.10 - samples/sec: 257.28 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-12 06:11:42,090 epoch 10 - iter 3126/5212 - loss 0.00730698 - time (sec): 851.48 - samples/sec: 256.91 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-12 06:14:01,717 epoch 10 - iter 3647/5212 - loss 0.00730729 - time (sec): 991.11 - samples/sec: 258.63 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-12 06:16:22,285 epoch 10 - iter 4168/5212 - loss 0.00690845 - time (sec): 1131.68 - samples/sec: 260.93 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-12 06:18:43,018 epoch 10 - iter 4689/5212 - loss 0.00696219 - time (sec): 1272.41 - samples/sec: 260.74 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-12 06:21:02,617 epoch 10 - iter 5210/5212 - loss 0.00715270 - time (sec): 1412.01 - samples/sec: 260.15 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-12 06:21:03,064 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 06:21:03,065 EPOCH 10 done: loss 0.0072 - lr: 0.000000 | |
| 2023-10-12 06:21:42,939 DEV : loss 0.4921533763408661 - f1-score (micro avg) 0.3907 | |
| 2023-10-12 06:21:43,893 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-12 06:21:43,895 Loading model from best epoch ... | |
| 2023-10-12 06:21:47,649 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 | |
| 2023-10-12 06:23:28,188 | |
| Results: | |
| - F-score (micro) 0.4681 | |
| - F-score (macro) 0.3274 | |
| - Accuracy 0.3108 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.5033 0.5610 0.5306 1214 | |
| PER 0.4123 0.4567 0.4334 808 | |
| ORG 0.3282 0.3654 0.3458 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.4454 0.4933 0.4681 2390 | |
| macro avg 0.3110 0.3458 0.3274 2390 | |
| weighted avg 0.4435 0.4933 0.4671 2390 | |
| 2023-10-12 06:23:28,188 ---------------------------------------------------------------------------------------------------- | |