2023-10-11 16:06:44,778 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,780 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 16:06:44,780 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,781 MultiCorpus: 7142 train + 698 dev + 2570 test sentences - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator 2023-10-11 16:06:44,781 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,781 Train: 7142 sentences 2023-10-11 16:06:44,781 (train_with_dev=False, train_with_test=False) 2023-10-11 16:06:44,781 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,781 Training Params: 2023-10-11 16:06:44,781 - learning_rate: "0.00016" 2023-10-11 16:06:44,781 - mini_batch_size: "8" 2023-10-11 16:06:44,781 - max_epochs: "10" 2023-10-11 16:06:44,781 - shuffle: "True" 2023-10-11 16:06:44,781 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,781 Plugins: 2023-10-11 16:06:44,782 - TensorboardLogger 2023-10-11 16:06:44,782 - LinearScheduler | warmup_fraction: '0.1' 2023-10-11 16:06:44,782 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,782 Final evaluation on model from best epoch (best-model.pt) 2023-10-11 16:06:44,782 - metric: "('micro avg', 'f1-score')" 2023-10-11 16:06:44,782 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,782 Computation: 2023-10-11 16:06:44,782 - compute on device: cuda:0 2023-10-11 16:06:44,782 - embedding storage: none 2023-10-11 16:06:44,782 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,782 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4" 2023-10-11 16:06:44,782 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,782 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:06:44,782 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-11 16:07:34,507 epoch 1 - iter 89/893 - loss 2.83150238 - time (sec): 49.72 - samples/sec: 469.42 - lr: 0.000016 - momentum: 0.000000 2023-10-11 16:08:24,856 epoch 1 - iter 178/893 - loss 2.73922034 - time (sec): 100.07 - samples/sec: 478.79 - lr: 0.000032 - momentum: 0.000000 2023-10-11 16:09:15,427 epoch 1 - iter 267/893 - loss 2.54002172 - time (sec): 150.64 - samples/sec: 478.44 - lr: 0.000048 - momentum: 0.000000 2023-10-11 16:10:07,265 epoch 1 - iter 356/893 - loss 2.31635472 - time (sec): 202.48 - samples/sec: 478.37 - lr: 0.000064 - momentum: 0.000000 2023-10-11 16:10:59,517 epoch 1 - iter 445/893 - loss 2.08548476 - time (sec): 254.73 - samples/sec: 472.32 - lr: 0.000080 - momentum: 0.000000 2023-10-11 16:11:54,697 epoch 1 - iter 534/893 - loss 1.85181034 - time (sec): 309.91 - samples/sec: 472.21 - lr: 0.000095 - momentum: 0.000000 2023-10-11 16:12:50,107 epoch 1 - iter 623/893 - loss 1.65665303 - time (sec): 365.32 - samples/sec: 473.67 - lr: 0.000111 - momentum: 0.000000 2023-10-11 16:13:44,323 epoch 1 - iter 712/893 - loss 1.50734839 - time (sec): 419.54 - samples/sec: 472.30 - lr: 0.000127 - momentum: 0.000000 2023-10-11 16:14:37,947 epoch 1 - iter 801/893 - loss 1.38574381 - time (sec): 473.16 - samples/sec: 469.95 - lr: 0.000143 - momentum: 0.000000 2023-10-11 16:15:34,092 epoch 1 - iter 890/893 - loss 1.27856971 - time (sec): 529.31 - samples/sec: 468.60 - lr: 0.000159 - momentum: 0.000000 2023-10-11 16:15:36,427 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:15:36,427 EPOCH 1 done: loss 1.2758 - lr: 0.000159 2023-10-11 16:15:58,475 DEV : loss 0.26407888531684875 - f1-score (micro avg) 0.2839 2023-10-11 16:15:58,513 saving best model 2023-10-11 16:15:59,455 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:16:53,512 epoch 2 - iter 89/893 - loss 0.30599282 - time (sec): 54.06 - samples/sec: 475.97 - lr: 0.000158 - momentum: 0.000000 2023-10-11 16:17:48,903 epoch 2 - iter 178/893 - loss 0.30164929 - time (sec): 109.45 - samples/sec: 465.09 - lr: 0.000156 - momentum: 0.000000 2023-10-11 16:18:45,107 epoch 2 - iter 267/893 - loss 0.27983412 - time (sec): 165.65 - samples/sec: 468.00 - lr: 0.000155 - momentum: 0.000000 2023-10-11 16:19:37,291 epoch 2 - iter 356/893 - loss 0.26596551 - time (sec): 217.83 - samples/sec: 462.78 - lr: 0.000153 - momentum: 0.000000 2023-10-11 16:20:32,275 epoch 2 - iter 445/893 - loss 0.24908676 - time (sec): 272.82 - samples/sec: 459.68 - lr: 0.000151 - momentum: 0.000000 2023-10-11 16:21:26,899 epoch 2 - iter 534/893 - loss 0.23391028 - time (sec): 327.44 - samples/sec: 457.37 - lr: 0.000149 - momentum: 0.000000 2023-10-11 16:22:17,982 epoch 2 - iter 623/893 - loss 0.22443918 - time (sec): 378.52 - samples/sec: 459.29 - lr: 0.000148 - momentum: 0.000000 2023-10-11 16:23:07,317 epoch 2 - iter 712/893 - loss 0.21432762 - time (sec): 427.86 - samples/sec: 463.46 - lr: 0.000146 - momentum: 0.000000 2023-10-11 16:23:54,170 epoch 2 - iter 801/893 - loss 0.20470001 - time (sec): 474.71 - samples/sec: 467.17 - lr: 0.000144 - momentum: 0.000000 2023-10-11 16:24:43,031 epoch 2 - iter 890/893 - loss 0.19693577 - time (sec): 523.57 - samples/sec: 472.97 - lr: 0.000142 - momentum: 0.000000 2023-10-11 16:24:44,752 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:24:44,752 EPOCH 2 done: loss 0.1964 - lr: 0.000142 2023-10-11 16:25:05,266 DEV : loss 0.10753299295902252 - f1-score (micro avg) 0.7415 2023-10-11 16:25:05,296 saving best model 2023-10-11 16:25:08,063 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:25:56,375 epoch 3 - iter 89/893 - loss 0.09369444 - time (sec): 48.31 - samples/sec: 513.41 - lr: 0.000140 - momentum: 0.000000 2023-10-11 16:26:45,041 epoch 3 - iter 178/893 - loss 0.09463543 - time (sec): 96.97 - samples/sec: 514.91 - lr: 0.000139 - momentum: 0.000000 2023-10-11 16:27:32,896 epoch 3 - iter 267/893 - loss 0.08742843 - time (sec): 144.83 - samples/sec: 511.93 - lr: 0.000137 - momentum: 0.000000 2023-10-11 16:28:21,237 epoch 3 - iter 356/893 - loss 0.08324272 - time (sec): 193.17 - samples/sec: 508.09 - lr: 0.000135 - momentum: 0.000000 2023-10-11 16:29:09,547 epoch 3 - iter 445/893 - loss 0.08475619 - time (sec): 241.48 - samples/sec: 508.37 - lr: 0.000133 - momentum: 0.000000 2023-10-11 16:29:59,366 epoch 3 - iter 534/893 - loss 0.08485703 - time (sec): 291.30 - samples/sec: 506.26 - lr: 0.000132 - momentum: 0.000000 2023-10-11 16:30:48,049 epoch 3 - iter 623/893 - loss 0.08297446 - time (sec): 339.98 - samples/sec: 505.75 - lr: 0.000130 - momentum: 0.000000 2023-10-11 16:31:36,824 epoch 3 - iter 712/893 - loss 0.08307353 - time (sec): 388.76 - samples/sec: 507.19 - lr: 0.000128 - momentum: 0.000000 2023-10-11 16:32:26,257 epoch 3 - iter 801/893 - loss 0.08179325 - time (sec): 438.19 - samples/sec: 507.93 - lr: 0.000126 - momentum: 0.000000 2023-10-11 16:33:14,792 epoch 3 - iter 890/893 - loss 0.08102671 - time (sec): 486.73 - samples/sec: 509.04 - lr: 0.000125 - momentum: 0.000000 2023-10-11 16:33:16,459 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:33:16,460 EPOCH 3 done: loss 0.0812 - lr: 0.000125 2023-10-11 16:33:38,017 DEV : loss 0.11330673843622208 - f1-score (micro avg) 0.7545 2023-10-11 16:33:38,056 saving best model 2023-10-11 16:33:40,713 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:34:31,047 epoch 4 - iter 89/893 - loss 0.05729569 - time (sec): 50.33 - samples/sec: 489.85 - lr: 0.000123 - momentum: 0.000000 2023-10-11 16:35:20,311 epoch 4 - iter 178/893 - loss 0.05027581 - time (sec): 99.59 - samples/sec: 498.88 - lr: 0.000121 - momentum: 0.000000 2023-10-11 16:36:10,722 epoch 4 - iter 267/893 - loss 0.05117128 - time (sec): 150.00 - samples/sec: 506.36 - lr: 0.000119 - momentum: 0.000000 2023-10-11 16:37:00,183 epoch 4 - iter 356/893 - loss 0.05260726 - time (sec): 199.47 - samples/sec: 504.60 - lr: 0.000117 - momentum: 0.000000 2023-10-11 16:37:48,423 epoch 4 - iter 445/893 - loss 0.05204046 - time (sec): 247.71 - samples/sec: 503.98 - lr: 0.000116 - momentum: 0.000000 2023-10-11 16:38:37,235 epoch 4 - iter 534/893 - loss 0.05076359 - time (sec): 296.52 - samples/sec: 505.45 - lr: 0.000114 - momentum: 0.000000 2023-10-11 16:39:27,083 epoch 4 - iter 623/893 - loss 0.05138865 - time (sec): 346.37 - samples/sec: 509.10 - lr: 0.000112 - momentum: 0.000000 2023-10-11 16:40:17,785 epoch 4 - iter 712/893 - loss 0.05173834 - time (sec): 397.07 - samples/sec: 502.80 - lr: 0.000110 - momentum: 0.000000 2023-10-11 16:41:07,370 epoch 4 - iter 801/893 - loss 0.05192190 - time (sec): 446.65 - samples/sec: 500.87 - lr: 0.000109 - momentum: 0.000000 2023-10-11 16:41:57,636 epoch 4 - iter 890/893 - loss 0.05095217 - time (sec): 496.92 - samples/sec: 499.12 - lr: 0.000107 - momentum: 0.000000 2023-10-11 16:41:59,142 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:41:59,143 EPOCH 4 done: loss 0.0509 - lr: 0.000107 2023-10-11 16:42:20,924 DEV : loss 0.13362975418567657 - f1-score (micro avg) 0.7709 2023-10-11 16:42:20,958 saving best model 2023-10-11 16:42:23,554 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:43:11,098 epoch 5 - iter 89/893 - loss 0.03653288 - time (sec): 47.54 - samples/sec: 500.14 - lr: 0.000105 - momentum: 0.000000 2023-10-11 16:44:00,117 epoch 5 - iter 178/893 - loss 0.03595217 - time (sec): 96.56 - samples/sec: 485.02 - lr: 0.000103 - momentum: 0.000000 2023-10-11 16:44:49,513 epoch 5 - iter 267/893 - loss 0.03646653 - time (sec): 145.95 - samples/sec: 502.95 - lr: 0.000101 - momentum: 0.000000 2023-10-11 16:45:39,118 epoch 5 - iter 356/893 - loss 0.03750037 - time (sec): 195.56 - samples/sec: 505.26 - lr: 0.000100 - momentum: 0.000000 2023-10-11 16:46:28,017 epoch 5 - iter 445/893 - loss 0.03695092 - time (sec): 244.46 - samples/sec: 503.19 - lr: 0.000098 - momentum: 0.000000 2023-10-11 16:47:16,434 epoch 5 - iter 534/893 - loss 0.03608794 - time (sec): 292.88 - samples/sec: 499.94 - lr: 0.000096 - momentum: 0.000000 2023-10-11 16:48:07,153 epoch 5 - iter 623/893 - loss 0.03592828 - time (sec): 343.59 - samples/sec: 502.61 - lr: 0.000094 - momentum: 0.000000 2023-10-11 16:48:56,551 epoch 5 - iter 712/893 - loss 0.03600119 - time (sec): 392.99 - samples/sec: 500.53 - lr: 0.000093 - momentum: 0.000000 2023-10-11 16:49:47,849 epoch 5 - iter 801/893 - loss 0.03656926 - time (sec): 444.29 - samples/sec: 500.02 - lr: 0.000091 - momentum: 0.000000 2023-10-11 16:50:39,277 epoch 5 - iter 890/893 - loss 0.03689169 - time (sec): 495.72 - samples/sec: 499.79 - lr: 0.000089 - momentum: 0.000000 2023-10-11 16:50:40,983 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:50:40,984 EPOCH 5 done: loss 0.0370 - lr: 0.000089 2023-10-11 16:51:02,760 DEV : loss 0.1409212052822113 - f1-score (micro avg) 0.7971 2023-10-11 16:51:02,797 saving best model 2023-10-11 16:51:05,388 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:51:56,629 epoch 6 - iter 89/893 - loss 0.03093811 - time (sec): 51.24 - samples/sec: 490.34 - lr: 0.000087 - momentum: 0.000000 2023-10-11 16:52:48,003 epoch 6 - iter 178/893 - loss 0.02884945 - time (sec): 102.61 - samples/sec: 483.47 - lr: 0.000085 - momentum: 0.000000 2023-10-11 16:53:37,672 epoch 6 - iter 267/893 - loss 0.02709581 - time (sec): 152.28 - samples/sec: 483.70 - lr: 0.000084 - momentum: 0.000000 2023-10-11 16:54:28,141 epoch 6 - iter 356/893 - loss 0.02801270 - time (sec): 202.75 - samples/sec: 486.61 - lr: 0.000082 - momentum: 0.000000 2023-10-11 16:55:18,447 epoch 6 - iter 445/893 - loss 0.02788463 - time (sec): 253.05 - samples/sec: 486.04 - lr: 0.000080 - momentum: 0.000000 2023-10-11 16:56:09,874 epoch 6 - iter 534/893 - loss 0.02959514 - time (sec): 304.48 - samples/sec: 487.32 - lr: 0.000078 - momentum: 0.000000 2023-10-11 16:57:00,389 epoch 6 - iter 623/893 - loss 0.02989629 - time (sec): 355.00 - samples/sec: 488.21 - lr: 0.000077 - momentum: 0.000000 2023-10-11 16:57:51,530 epoch 6 - iter 712/893 - loss 0.02978443 - time (sec): 406.14 - samples/sec: 487.85 - lr: 0.000075 - momentum: 0.000000 2023-10-11 16:58:41,657 epoch 6 - iter 801/893 - loss 0.02942788 - time (sec): 456.26 - samples/sec: 488.38 - lr: 0.000073 - momentum: 0.000000 2023-10-11 16:59:32,962 epoch 6 - iter 890/893 - loss 0.02909718 - time (sec): 507.57 - samples/sec: 488.53 - lr: 0.000071 - momentum: 0.000000 2023-10-11 16:59:34,505 ---------------------------------------------------------------------------------------------------- 2023-10-11 16:59:34,505 EPOCH 6 done: loss 0.0292 - lr: 0.000071 2023-10-11 16:59:56,468 DEV : loss 0.16406482458114624 - f1-score (micro avg) 0.7936 2023-10-11 16:59:56,500 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:00:44,851 epoch 7 - iter 89/893 - loss 0.01961977 - time (sec): 48.35 - samples/sec: 500.18 - lr: 0.000069 - momentum: 0.000000 2023-10-11 17:01:34,295 epoch 7 - iter 178/893 - loss 0.02220619 - time (sec): 97.79 - samples/sec: 498.70 - lr: 0.000068 - momentum: 0.000000 2023-10-11 17:02:22,358 epoch 7 - iter 267/893 - loss 0.02475739 - time (sec): 145.86 - samples/sec: 499.63 - lr: 0.000066 - momentum: 0.000000 2023-10-11 17:03:11,495 epoch 7 - iter 356/893 - loss 0.02271444 - time (sec): 194.99 - samples/sec: 505.50 - lr: 0.000064 - momentum: 0.000000 2023-10-11 17:04:00,355 epoch 7 - iter 445/893 - loss 0.02294087 - time (sec): 243.85 - samples/sec: 508.07 - lr: 0.000062 - momentum: 0.000000 2023-10-11 17:04:49,325 epoch 7 - iter 534/893 - loss 0.02184486 - time (sec): 292.82 - samples/sec: 507.52 - lr: 0.000061 - momentum: 0.000000 2023-10-11 17:05:38,345 epoch 7 - iter 623/893 - loss 0.02120379 - time (sec): 341.84 - samples/sec: 506.40 - lr: 0.000059 - momentum: 0.000000 2023-10-11 17:06:29,178 epoch 7 - iter 712/893 - loss 0.02183594 - time (sec): 392.68 - samples/sec: 505.93 - lr: 0.000057 - momentum: 0.000000 2023-10-11 17:07:20,003 epoch 7 - iter 801/893 - loss 0.02155659 - time (sec): 443.50 - samples/sec: 504.30 - lr: 0.000055 - momentum: 0.000000 2023-10-11 17:08:08,594 epoch 7 - iter 890/893 - loss 0.02139749 - time (sec): 492.09 - samples/sec: 503.21 - lr: 0.000053 - momentum: 0.000000 2023-10-11 17:08:10,436 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:08:10,436 EPOCH 7 done: loss 0.0213 - lr: 0.000053 2023-10-11 17:08:31,770 DEV : loss 0.17885611951351166 - f1-score (micro avg) 0.8011 2023-10-11 17:08:31,801 saving best model 2023-10-11 17:08:34,366 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:09:24,407 epoch 8 - iter 89/893 - loss 0.01715620 - time (sec): 50.04 - samples/sec: 493.87 - lr: 0.000052 - momentum: 0.000000 2023-10-11 17:10:16,482 epoch 8 - iter 178/893 - loss 0.02006197 - time (sec): 102.11 - samples/sec: 494.07 - lr: 0.000050 - momentum: 0.000000 2023-10-11 17:11:06,885 epoch 8 - iter 267/893 - loss 0.01738780 - time (sec): 152.52 - samples/sec: 494.04 - lr: 0.000048 - momentum: 0.000000 2023-10-11 17:11:57,243 epoch 8 - iter 356/893 - loss 0.01811409 - time (sec): 202.87 - samples/sec: 494.75 - lr: 0.000046 - momentum: 0.000000 2023-10-11 17:12:46,260 epoch 8 - iter 445/893 - loss 0.01897415 - time (sec): 251.89 - samples/sec: 493.42 - lr: 0.000045 - momentum: 0.000000 2023-10-11 17:13:35,565 epoch 8 - iter 534/893 - loss 0.01805358 - time (sec): 301.20 - samples/sec: 493.49 - lr: 0.000043 - momentum: 0.000000 2023-10-11 17:14:26,683 epoch 8 - iter 623/893 - loss 0.01824633 - time (sec): 352.31 - samples/sec: 494.18 - lr: 0.000041 - momentum: 0.000000 2023-10-11 17:15:14,708 epoch 8 - iter 712/893 - loss 0.01811871 - time (sec): 400.34 - samples/sec: 491.30 - lr: 0.000039 - momentum: 0.000000 2023-10-11 17:16:04,826 epoch 8 - iter 801/893 - loss 0.01785414 - time (sec): 450.46 - samples/sec: 494.17 - lr: 0.000037 - momentum: 0.000000 2023-10-11 17:16:55,097 epoch 8 - iter 890/893 - loss 0.01770565 - time (sec): 500.73 - samples/sec: 495.42 - lr: 0.000036 - momentum: 0.000000 2023-10-11 17:16:56,588 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:16:56,588 EPOCH 8 done: loss 0.0177 - lr: 0.000036 2023-10-11 17:17:18,690 DEV : loss 0.19161181151866913 - f1-score (micro avg) 0.7955 2023-10-11 17:17:18,720 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:18:06,228 epoch 9 - iter 89/893 - loss 0.01298334 - time (sec): 47.51 - samples/sec: 497.93 - lr: 0.000034 - momentum: 0.000000 2023-10-11 17:18:55,387 epoch 9 - iter 178/893 - loss 0.01537760 - time (sec): 96.67 - samples/sec: 511.42 - lr: 0.000032 - momentum: 0.000000 2023-10-11 17:19:45,056 epoch 9 - iter 267/893 - loss 0.01528293 - time (sec): 146.33 - samples/sec: 517.74 - lr: 0.000030 - momentum: 0.000000 2023-10-11 17:20:34,247 epoch 9 - iter 356/893 - loss 0.01453369 - time (sec): 195.53 - samples/sec: 514.55 - lr: 0.000029 - momentum: 0.000000 2023-10-11 17:21:22,221 epoch 9 - iter 445/893 - loss 0.01412119 - time (sec): 243.50 - samples/sec: 512.37 - lr: 0.000027 - momentum: 0.000000 2023-10-11 17:22:10,404 epoch 9 - iter 534/893 - loss 0.01411537 - time (sec): 291.68 - samples/sec: 513.33 - lr: 0.000025 - momentum: 0.000000 2023-10-11 17:22:58,710 epoch 9 - iter 623/893 - loss 0.01468822 - time (sec): 339.99 - samples/sec: 511.65 - lr: 0.000023 - momentum: 0.000000 2023-10-11 17:23:46,894 epoch 9 - iter 712/893 - loss 0.01427033 - time (sec): 388.17 - samples/sec: 510.66 - lr: 0.000022 - momentum: 0.000000 2023-10-11 17:24:35,059 epoch 9 - iter 801/893 - loss 0.01423814 - time (sec): 436.34 - samples/sec: 509.86 - lr: 0.000020 - momentum: 0.000000 2023-10-11 17:25:24,567 epoch 9 - iter 890/893 - loss 0.01466708 - time (sec): 485.85 - samples/sec: 510.22 - lr: 0.000018 - momentum: 0.000000 2023-10-11 17:25:26,215 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:25:26,216 EPOCH 9 done: loss 0.0146 - lr: 0.000018 2023-10-11 17:25:46,751 DEV : loss 0.19483081996440887 - f1-score (micro avg) 0.7944 2023-10-11 17:25:46,781 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:26:35,157 epoch 10 - iter 89/893 - loss 0.01073313 - time (sec): 48.37 - samples/sec: 520.32 - lr: 0.000016 - momentum: 0.000000 2023-10-11 17:27:25,428 epoch 10 - iter 178/893 - loss 0.01013166 - time (sec): 98.65 - samples/sec: 519.71 - lr: 0.000014 - momentum: 0.000000 2023-10-11 17:28:12,901 epoch 10 - iter 267/893 - loss 0.01112463 - time (sec): 146.12 - samples/sec: 510.31 - lr: 0.000013 - momentum: 0.000000 2023-10-11 17:29:01,976 epoch 10 - iter 356/893 - loss 0.01098189 - time (sec): 195.19 - samples/sec: 508.58 - lr: 0.000011 - momentum: 0.000000 2023-10-11 17:29:49,819 epoch 10 - iter 445/893 - loss 0.01066311 - time (sec): 243.04 - samples/sec: 503.61 - lr: 0.000009 - momentum: 0.000000 2023-10-11 17:30:40,164 epoch 10 - iter 534/893 - loss 0.01119657 - time (sec): 293.38 - samples/sec: 508.28 - lr: 0.000007 - momentum: 0.000000 2023-10-11 17:31:28,238 epoch 10 - iter 623/893 - loss 0.01162212 - time (sec): 341.45 - samples/sec: 506.25 - lr: 0.000006 - momentum: 0.000000 2023-10-11 17:32:17,725 epoch 10 - iter 712/893 - loss 0.01182235 - time (sec): 390.94 - samples/sec: 506.41 - lr: 0.000004 - momentum: 0.000000 2023-10-11 17:33:08,012 epoch 10 - iter 801/893 - loss 0.01144574 - time (sec): 441.23 - samples/sec: 506.35 - lr: 0.000002 - momentum: 0.000000 2023-10-11 17:33:58,609 epoch 10 - iter 890/893 - loss 0.01158836 - time (sec): 491.83 - samples/sec: 504.65 - lr: 0.000000 - momentum: 0.000000 2023-10-11 17:34:00,008 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:34:00,008 EPOCH 10 done: loss 0.0116 - lr: 0.000000 2023-10-11 17:34:20,889 DEV : loss 0.19962604343891144 - f1-score (micro avg) 0.7897 2023-10-11 17:34:21,786 ---------------------------------------------------------------------------------------------------- 2023-10-11 17:34:21,788 Loading model from best epoch ... 2023-10-11 17:34:26,562 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-11 17:35:36,469 Results: - F-score (micro) 0.6917 - F-score (macro) 0.5961 - Accuracy 0.545 By class: precision recall f1-score support LOC 0.7070 0.6986 0.7028 1095 PER 0.7745 0.7806 0.7776 1012 ORG 0.4204 0.5770 0.4864 357 HumanProd 0.3276 0.5758 0.4176 33 micro avg 0.6717 0.7129 0.6917 2497 macro avg 0.5574 0.6580 0.5961 2497 weighted avg 0.6884 0.7129 0.6984 2497 2023-10-11 17:35:36,470 ----------------------------------------------------------------------------------------------------