| 2023-10-10 18:16:34,319 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,322 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() | |
| )" | |
| 2023-10-10 18:16:34,322 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,322 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 | |
| 2023-10-10 18:16:34,322 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,322 Train: 20847 sentences | |
| 2023-10-10 18:16:34,322 (train_with_dev=False, train_with_test=False) | |
| 2023-10-10 18:16:34,322 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,322 Training Params: | |
| 2023-10-10 18:16:34,322 - learning_rate: "0.00015" | |
| 2023-10-10 18:16:34,323 - mini_batch_size: "4" | |
| 2023-10-10 18:16:34,323 - max_epochs: "10" | |
| 2023-10-10 18:16:34,323 - shuffle: "True" | |
| 2023-10-10 18:16:34,323 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,323 Plugins: | |
| 2023-10-10 18:16:34,323 - TensorboardLogger | |
| 2023-10-10 18:16:34,323 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-10 18:16:34,323 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,323 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-10 18:16:34,323 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-10 18:16:34,323 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,323 Computation: | |
| 2023-10-10 18:16:34,323 - compute on device: cuda:0 | |
| 2023-10-10 18:16:34,323 - embedding storage: none | |
| 2023-10-10 18:16:34,323 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,324 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-10 18:16:34,324 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,324 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:16:34,324 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-10 18:19:07,343 epoch 1 - iter 521/5212 - loss 2.81016252 - time (sec): 153.02 - samples/sec: 234.74 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-10 18:21:43,109 epoch 1 - iter 1042/5212 - loss 2.37850019 - time (sec): 308.78 - samples/sec: 233.59 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-10 18:24:21,241 epoch 1 - iter 1563/5212 - loss 1.84782296 - time (sec): 466.92 - samples/sec: 234.30 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-10 18:27:01,083 epoch 1 - iter 2084/5212 - loss 1.48063193 - time (sec): 626.76 - samples/sec: 237.27 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-10 18:29:38,764 epoch 1 - iter 2605/5212 - loss 1.27253415 - time (sec): 784.44 - samples/sec: 236.54 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-10 18:32:17,153 epoch 1 - iter 3126/5212 - loss 1.11681269 - time (sec): 942.83 - samples/sec: 237.00 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-10 18:34:54,795 epoch 1 - iter 3647/5212 - loss 1.00835432 - time (sec): 1100.47 - samples/sec: 236.27 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-10 18:37:32,394 epoch 1 - iter 4168/5212 - loss 0.91865937 - time (sec): 1258.07 - samples/sec: 234.97 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-10 18:40:00,428 epoch 1 - iter 4689/5212 - loss 0.84845181 - time (sec): 1406.10 - samples/sec: 234.64 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-10 18:42:26,316 epoch 1 - iter 5210/5212 - loss 0.78330933 - time (sec): 1551.99 - samples/sec: 236.66 - lr: 0.000150 - momentum: 0.000000 | |
| 2023-10-10 18:42:26,799 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:42:26,800 EPOCH 1 done: loss 0.7830 - lr: 0.000150 | |
| 2023-10-10 18:43:02,191 DEV : loss 0.14865536987781525 - f1-score (micro avg) 0.2591 | |
| 2023-10-10 18:43:02,244 saving best model | |
| 2023-10-10 18:43:03,204 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 18:45:23,109 epoch 2 - iter 521/5212 - loss 0.18267050 - time (sec): 139.90 - samples/sec: 250.67 - lr: 0.000148 - momentum: 0.000000 | |
| 2023-10-10 18:47:50,051 epoch 2 - iter 1042/5212 - loss 0.18153459 - time (sec): 286.84 - samples/sec: 247.64 - lr: 0.000147 - momentum: 0.000000 | |
| 2023-10-10 18:50:09,700 epoch 2 - iter 1563/5212 - loss 0.17717219 - time (sec): 426.49 - samples/sec: 252.57 - lr: 0.000145 - momentum: 0.000000 | |
| 2023-10-10 18:52:31,360 epoch 2 - iter 2084/5212 - loss 0.17309531 - time (sec): 568.15 - samples/sec: 251.18 - lr: 0.000143 - momentum: 0.000000 | |
| 2023-10-10 18:54:52,430 epoch 2 - iter 2605/5212 - loss 0.16981704 - time (sec): 709.22 - samples/sec: 253.68 - lr: 0.000142 - momentum: 0.000000 | |
| 2023-10-10 18:57:12,245 epoch 2 - iter 3126/5212 - loss 0.16298321 - time (sec): 849.04 - samples/sec: 256.48 - lr: 0.000140 - momentum: 0.000000 | |
| 2023-10-10 18:59:31,681 epoch 2 - iter 3647/5212 - loss 0.16081033 - time (sec): 988.47 - samples/sec: 258.82 - lr: 0.000138 - momentum: 0.000000 | |
| 2023-10-10 19:01:48,864 epoch 2 - iter 4168/5212 - loss 0.15845681 - time (sec): 1125.66 - samples/sec: 258.75 - lr: 0.000137 - momentum: 0.000000 | |
| 2023-10-10 19:04:06,270 epoch 2 - iter 4689/5212 - loss 0.15711708 - time (sec): 1263.06 - samples/sec: 258.81 - lr: 0.000135 - momentum: 0.000000 | |
| 2023-10-10 19:06:27,101 epoch 2 - iter 5210/5212 - loss 0.15390781 - time (sec): 1403.89 - samples/sec: 261.55 - lr: 0.000133 - momentum: 0.000000 | |
| 2023-10-10 19:06:27,658 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:06:27,658 EPOCH 2 done: loss 0.1539 - lr: 0.000133 | |
| 2023-10-10 19:07:07,419 DEV : loss 0.12877270579338074 - f1-score (micro avg) 0.3352 | |
| 2023-10-10 19:07:07,472 saving best model | |
| 2023-10-10 19:07:10,082 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:09:26,104 epoch 3 - iter 521/5212 - loss 0.09359735 - time (sec): 136.02 - samples/sec: 262.05 - lr: 0.000132 - momentum: 0.000000 | |
| 2023-10-10 19:11:43,039 epoch 3 - iter 1042/5212 - loss 0.09901175 - time (sec): 272.95 - samples/sec: 266.45 - lr: 0.000130 - momentum: 0.000000 | |
| 2023-10-10 19:14:01,365 epoch 3 - iter 1563/5212 - loss 0.10198032 - time (sec): 411.28 - samples/sec: 267.17 - lr: 0.000128 - momentum: 0.000000 | |
| 2023-10-10 19:16:17,180 epoch 3 - iter 2084/5212 - loss 0.10644730 - time (sec): 547.09 - samples/sec: 266.24 - lr: 0.000127 - momentum: 0.000000 | |
| 2023-10-10 19:18:36,955 epoch 3 - iter 2605/5212 - loss 0.10979467 - time (sec): 686.87 - samples/sec: 270.23 - lr: 0.000125 - momentum: 0.000000 | |
| 2023-10-10 19:20:57,796 epoch 3 - iter 3126/5212 - loss 0.10703931 - time (sec): 827.71 - samples/sec: 269.96 - lr: 0.000123 - momentum: 0.000000 | |
| 2023-10-10 19:23:16,304 epoch 3 - iter 3647/5212 - loss 0.10648081 - time (sec): 966.22 - samples/sec: 269.63 - lr: 0.000122 - momentum: 0.000000 | |
| 2023-10-10 19:25:35,494 epoch 3 - iter 4168/5212 - loss 0.10571107 - time (sec): 1105.41 - samples/sec: 268.40 - lr: 0.000120 - momentum: 0.000000 | |
| 2023-10-10 19:27:52,717 epoch 3 - iter 4689/5212 - loss 0.10470171 - time (sec): 1242.63 - samples/sec: 265.47 - lr: 0.000118 - momentum: 0.000000 | |
| 2023-10-10 19:30:15,588 epoch 3 - iter 5210/5212 - loss 0.10374270 - time (sec): 1385.50 - samples/sec: 265.05 - lr: 0.000117 - momentum: 0.000000 | |
| 2023-10-10 19:30:16,133 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:30:16,133 EPOCH 3 done: loss 0.1037 - lr: 0.000117 | |
| 2023-10-10 19:30:54,988 DEV : loss 0.17644649744033813 - f1-score (micro avg) 0.3625 | |
| 2023-10-10 19:30:55,039 saving best model | |
| 2023-10-10 19:30:57,643 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:33:24,242 epoch 4 - iter 521/5212 - loss 0.06203084 - time (sec): 146.59 - samples/sec: 251.99 - lr: 0.000115 - momentum: 0.000000 | |
| 2023-10-10 19:35:52,103 epoch 4 - iter 1042/5212 - loss 0.07415039 - time (sec): 294.45 - samples/sec: 260.15 - lr: 0.000113 - momentum: 0.000000 | |
| 2023-10-10 19:38:15,428 epoch 4 - iter 1563/5212 - loss 0.07137797 - time (sec): 437.78 - samples/sec: 257.60 - lr: 0.000112 - momentum: 0.000000 | |
| 2023-10-10 19:40:37,400 epoch 4 - iter 2084/5212 - loss 0.07121646 - time (sec): 579.75 - samples/sec: 256.34 - lr: 0.000110 - momentum: 0.000000 | |
| 2023-10-10 19:43:03,829 epoch 4 - iter 2605/5212 - loss 0.06855636 - time (sec): 726.18 - samples/sec: 255.84 - lr: 0.000108 - momentum: 0.000000 | |
| 2023-10-10 19:45:28,588 epoch 4 - iter 3126/5212 - loss 0.07215692 - time (sec): 870.94 - samples/sec: 255.84 - lr: 0.000107 - momentum: 0.000000 | |
| 2023-10-10 19:47:53,752 epoch 4 - iter 3647/5212 - loss 0.07245324 - time (sec): 1016.10 - samples/sec: 256.59 - lr: 0.000105 - momentum: 0.000000 | |
| 2023-10-10 19:50:17,550 epoch 4 - iter 4168/5212 - loss 0.07195821 - time (sec): 1159.90 - samples/sec: 254.60 - lr: 0.000103 - momentum: 0.000000 | |
| 2023-10-10 19:52:43,827 epoch 4 - iter 4689/5212 - loss 0.07354350 - time (sec): 1306.18 - samples/sec: 254.26 - lr: 0.000102 - momentum: 0.000000 | |
| 2023-10-10 19:55:06,401 epoch 4 - iter 5210/5212 - loss 0.07389688 - time (sec): 1448.75 - samples/sec: 253.55 - lr: 0.000100 - momentum: 0.000000 | |
| 2023-10-10 19:55:06,857 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:55:06,857 EPOCH 4 done: loss 0.0739 - lr: 0.000100 | |
| 2023-10-10 19:55:48,160 DEV : loss 0.3035148084163666 - f1-score (micro avg) 0.3425 | |
| 2023-10-10 19:55:48,214 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 19:58:10,368 epoch 5 - iter 521/5212 - loss 0.04380137 - time (sec): 142.15 - samples/sec: 241.95 - lr: 0.000098 - momentum: 0.000000 | |
| 2023-10-10 20:00:32,233 epoch 5 - iter 1042/5212 - loss 0.04814301 - time (sec): 284.02 - samples/sec: 247.92 - lr: 0.000097 - momentum: 0.000000 | |
| 2023-10-10 20:02:56,247 epoch 5 - iter 1563/5212 - loss 0.04738308 - time (sec): 428.03 - samples/sec: 254.16 - lr: 0.000095 - momentum: 0.000000 | |
| 2023-10-10 20:05:18,346 epoch 5 - iter 2084/5212 - loss 0.04616747 - time (sec): 570.13 - samples/sec: 254.93 - lr: 0.000093 - momentum: 0.000000 | |
| 2023-10-10 20:07:43,212 epoch 5 - iter 2605/5212 - loss 0.04857278 - time (sec): 715.00 - samples/sec: 255.31 - lr: 0.000092 - momentum: 0.000000 | |
| 2023-10-10 20:10:05,909 epoch 5 - iter 3126/5212 - loss 0.04886862 - time (sec): 857.69 - samples/sec: 255.88 - lr: 0.000090 - momentum: 0.000000 | |
| 2023-10-10 20:12:25,629 epoch 5 - iter 3647/5212 - loss 0.04941233 - time (sec): 997.41 - samples/sec: 255.51 - lr: 0.000088 - momentum: 0.000000 | |
| 2023-10-10 20:15:05,698 epoch 5 - iter 4168/5212 - loss 0.04979465 - time (sec): 1157.48 - samples/sec: 253.62 - lr: 0.000087 - momentum: 0.000000 | |
| 2023-10-10 20:17:41,182 epoch 5 - iter 4689/5212 - loss 0.04912451 - time (sec): 1312.97 - samples/sec: 252.86 - lr: 0.000085 - momentum: 0.000000 | |
| 2023-10-10 20:20:07,092 epoch 5 - iter 5210/5212 - loss 0.04860995 - time (sec): 1458.88 - samples/sec: 251.66 - lr: 0.000083 - momentum: 0.000000 | |
| 2023-10-10 20:20:07,750 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 20:20:07,750 EPOCH 5 done: loss 0.0486 - lr: 0.000083 | |
| 2023-10-10 20:20:50,848 DEV : loss 0.3323441743850708 - f1-score (micro avg) 0.3643 | |
| 2023-10-10 20:20:50,907 saving best model | |
| 2023-10-10 20:20:53,520 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 20:23:09,350 epoch 6 - iter 521/5212 - loss 0.03801063 - time (sec): 135.83 - samples/sec: 258.05 - lr: 0.000082 - momentum: 0.000000 | |
| 2023-10-10 20:25:25,462 epoch 6 - iter 1042/5212 - loss 0.03880207 - time (sec): 271.94 - samples/sec: 257.64 - lr: 0.000080 - momentum: 0.000000 | |
| 2023-10-10 20:27:44,831 epoch 6 - iter 1563/5212 - loss 0.03989806 - time (sec): 411.31 - samples/sec: 263.80 - lr: 0.000078 - momentum: 0.000000 | |
| 2023-10-10 20:30:04,726 epoch 6 - iter 2084/5212 - loss 0.04174066 - time (sec): 551.20 - samples/sec: 266.80 - lr: 0.000077 - momentum: 0.000000 | |
| 2023-10-10 20:32:24,885 epoch 6 - iter 2605/5212 - loss 0.03948956 - time (sec): 691.36 - samples/sec: 269.01 - lr: 0.000075 - momentum: 0.000000 | |
| 2023-10-10 20:34:43,523 epoch 6 - iter 3126/5212 - loss 0.03830296 - time (sec): 830.00 - samples/sec: 267.05 - lr: 0.000073 - momentum: 0.000000 | |
| 2023-10-10 20:37:01,312 epoch 6 - iter 3647/5212 - loss 0.03849237 - time (sec): 967.79 - samples/sec: 266.77 - lr: 0.000072 - momentum: 0.000000 | |
| 2023-10-10 20:39:20,384 epoch 6 - iter 4168/5212 - loss 0.03899918 - time (sec): 1106.86 - samples/sec: 266.65 - lr: 0.000070 - momentum: 0.000000 | |
| 2023-10-10 20:41:38,051 epoch 6 - iter 4689/5212 - loss 0.03784107 - time (sec): 1244.53 - samples/sec: 264.93 - lr: 0.000068 - momentum: 0.000000 | |
| 2023-10-10 20:43:57,783 epoch 6 - iter 5210/5212 - loss 0.03843205 - time (sec): 1384.26 - samples/sec: 265.36 - lr: 0.000067 - momentum: 0.000000 | |
| 2023-10-10 20:43:58,230 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 20:43:58,230 EPOCH 6 done: loss 0.0384 - lr: 0.000067 | |
| 2023-10-10 20:44:38,095 DEV : loss 0.3565690815448761 - f1-score (micro avg) 0.3714 | |
| 2023-10-10 20:44:38,147 saving best model | |
| 2023-10-10 20:44:40,782 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 20:47:08,584 epoch 7 - iter 521/5212 - loss 0.02908204 - time (sec): 147.80 - samples/sec: 240.48 - lr: 0.000065 - momentum: 0.000000 | |
| 2023-10-10 20:49:40,233 epoch 7 - iter 1042/5212 - loss 0.02884625 - time (sec): 299.45 - samples/sec: 238.89 - lr: 0.000063 - momentum: 0.000000 | |
| 2023-10-10 20:52:06,698 epoch 7 - iter 1563/5212 - loss 0.02640536 - time (sec): 445.91 - samples/sec: 242.78 - lr: 0.000062 - momentum: 0.000000 | |
| 2023-10-10 20:54:30,081 epoch 7 - iter 2084/5212 - loss 0.02749239 - time (sec): 589.29 - samples/sec: 245.46 - lr: 0.000060 - momentum: 0.000000 | |
| 2023-10-10 20:56:53,383 epoch 7 - iter 2605/5212 - loss 0.02821275 - time (sec): 732.59 - samples/sec: 249.90 - lr: 0.000058 - momentum: 0.000000 | |
| 2023-10-10 20:59:13,941 epoch 7 - iter 3126/5212 - loss 0.02790788 - time (sec): 873.15 - samples/sec: 251.10 - lr: 0.000057 - momentum: 0.000000 | |
| 2023-10-10 21:01:41,509 epoch 7 - iter 3647/5212 - loss 0.02797348 - time (sec): 1020.72 - samples/sec: 250.96 - lr: 0.000055 - momentum: 0.000000 | |
| 2023-10-10 21:04:04,892 epoch 7 - iter 4168/5212 - loss 0.02744857 - time (sec): 1164.10 - samples/sec: 249.39 - lr: 0.000053 - momentum: 0.000000 | |
| 2023-10-10 21:06:29,203 epoch 7 - iter 4689/5212 - loss 0.02707634 - time (sec): 1308.42 - samples/sec: 250.75 - lr: 0.000052 - momentum: 0.000000 | |
| 2023-10-10 21:08:56,201 epoch 7 - iter 5210/5212 - loss 0.02676611 - time (sec): 1455.41 - samples/sec: 252.36 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-10 21:08:56,684 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 21:08:56,684 EPOCH 7 done: loss 0.0268 - lr: 0.000050 | |
| 2023-10-10 21:09:37,357 DEV : loss 0.3953941762447357 - f1-score (micro avg) 0.3828 | |
| 2023-10-10 21:09:37,410 saving best model | |
| 2023-10-10 21:09:40,213 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 21:12:07,582 epoch 8 - iter 521/5212 - loss 0.01910833 - time (sec): 147.37 - samples/sec: 271.50 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-10 21:14:32,536 epoch 8 - iter 1042/5212 - loss 0.01909368 - time (sec): 292.32 - samples/sec: 264.69 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-10 21:16:51,098 epoch 8 - iter 1563/5212 - loss 0.01709210 - time (sec): 430.88 - samples/sec: 260.58 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-10 21:19:14,800 epoch 8 - iter 2084/5212 - loss 0.01718362 - time (sec): 574.58 - samples/sec: 261.23 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-10 21:21:38,710 epoch 8 - iter 2605/5212 - loss 0.01688276 - time (sec): 718.49 - samples/sec: 258.02 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-10 21:24:04,659 epoch 8 - iter 3126/5212 - loss 0.01713091 - time (sec): 864.44 - samples/sec: 258.08 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-10 21:26:29,475 epoch 8 - iter 3647/5212 - loss 0.01686813 - time (sec): 1009.26 - samples/sec: 256.90 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-10 21:28:50,236 epoch 8 - iter 4168/5212 - loss 0.01687351 - time (sec): 1150.02 - samples/sec: 255.80 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-10 21:31:09,057 epoch 8 - iter 4689/5212 - loss 0.01695639 - time (sec): 1288.84 - samples/sec: 254.16 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-10 21:33:31,826 epoch 8 - iter 5210/5212 - loss 0.01809776 - time (sec): 1431.61 - samples/sec: 256.63 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-10 21:33:32,225 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 21:33:32,225 EPOCH 8 done: loss 0.0181 - lr: 0.000033 | |
| 2023-10-10 21:34:11,286 DEV : loss 0.43120914697647095 - f1-score (micro avg) 0.3696 | |
| 2023-10-10 21:34:11,342 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 21:36:31,101 epoch 9 - iter 521/5212 - loss 0.01561923 - time (sec): 139.76 - samples/sec: 266.39 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-10 21:38:54,563 epoch 9 - iter 1042/5212 - loss 0.01332527 - time (sec): 283.22 - samples/sec: 273.46 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-10 21:41:13,645 epoch 9 - iter 1563/5212 - loss 0.01209082 - time (sec): 422.30 - samples/sec: 268.95 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-10 21:43:28,845 epoch 9 - iter 2084/5212 - loss 0.01241173 - time (sec): 557.50 - samples/sec: 263.32 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-10 21:45:49,437 epoch 9 - iter 2605/5212 - loss 0.01364082 - time (sec): 698.09 - samples/sec: 265.94 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-10 21:48:05,337 epoch 9 - iter 3126/5212 - loss 0.01364299 - time (sec): 833.99 - samples/sec: 264.53 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-10 21:50:21,661 epoch 9 - iter 3647/5212 - loss 0.01332216 - time (sec): 970.32 - samples/sec: 263.44 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-10 21:52:40,256 epoch 9 - iter 4168/5212 - loss 0.01347305 - time (sec): 1108.91 - samples/sec: 263.38 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-10 21:54:57,896 epoch 9 - iter 4689/5212 - loss 0.01371078 - time (sec): 1246.55 - samples/sec: 263.47 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-10 21:57:17,103 epoch 9 - iter 5210/5212 - loss 0.01386029 - time (sec): 1385.76 - samples/sec: 265.11 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-10 21:57:17,512 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 21:57:17,513 EPOCH 9 done: loss 0.0139 - lr: 0.000017 | |
| 2023-10-10 21:57:57,009 DEV : loss 0.4479060769081116 - f1-score (micro avg) 0.3711 | |
| 2023-10-10 21:57:57,061 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 22:00:15,416 epoch 10 - iter 521/5212 - loss 0.00629895 - time (sec): 138.35 - samples/sec: 276.50 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-10 22:02:36,634 epoch 10 - iter 1042/5212 - loss 0.00672524 - time (sec): 279.57 - samples/sec: 275.18 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-10 22:04:56,189 epoch 10 - iter 1563/5212 - loss 0.00663359 - time (sec): 419.13 - samples/sec: 269.04 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-10 22:07:12,353 epoch 10 - iter 2084/5212 - loss 0.00656745 - time (sec): 555.29 - samples/sec: 261.90 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-10 22:09:33,505 epoch 10 - iter 2605/5212 - loss 0.00650200 - time (sec): 696.44 - samples/sec: 262.57 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-10 22:11:53,364 epoch 10 - iter 3126/5212 - loss 0.00681067 - time (sec): 836.30 - samples/sec: 261.75 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-10 22:14:13,626 epoch 10 - iter 3647/5212 - loss 0.00705344 - time (sec): 976.56 - samples/sec: 262.86 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-10 22:16:35,112 epoch 10 - iter 4168/5212 - loss 0.00721008 - time (sec): 1118.05 - samples/sec: 264.27 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-10 22:18:53,877 epoch 10 - iter 4689/5212 - loss 0.00709267 - time (sec): 1256.81 - samples/sec: 263.70 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-10 22:21:11,081 epoch 10 - iter 5210/5212 - loss 0.00738418 - time (sec): 1394.02 - samples/sec: 263.35 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-10 22:21:11,710 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 22:21:11,710 EPOCH 10 done: loss 0.0074 - lr: 0.000000 | |
| 2023-10-10 22:21:50,043 DEV : loss 0.47310149669647217 - f1-score (micro avg) 0.3745 | |
| 2023-10-10 22:21:51,032 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-10 22:21:51,034 Loading model from best epoch ... | |
| 2023-10-10 22:21:56,334 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 | |
| 2023-10-10 22:23:37,448 | |
| Results: | |
| - F-score (micro) 0.4359 | |
| - F-score (macro) 0.2967 | |
| - Accuracy 0.2824 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.4896 0.5066 0.4980 1214 | |
| PER 0.4020 0.4567 0.4276 808 | |
| ORG 0.2487 0.2748 0.2611 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.4206 0.4523 0.4359 2390 | |
| macro avg 0.2851 0.3095 0.2967 2390 | |
| weighted avg 0.4213 0.4523 0.4361 2390 | |
| 2023-10-10 22:23:37,448 ---------------------------------------------------------------------------------------------------- | |