2023-10-12 01:27:16,208 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,210 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 01:27:16,210 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,210 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 2023-10-12 01:27:16,210 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,210 Train: 7142 sentences 2023-10-12 01:27:16,211 (train_with_dev=False, train_with_test=False) 2023-10-12 01:27:16,211 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,211 Training Params: 2023-10-12 01:27:16,211 - learning_rate: "0.00016" 2023-10-12 01:27:16,211 - mini_batch_size: "4" 2023-10-12 01:27:16,211 - max_epochs: "10" 2023-10-12 01:27:16,211 - shuffle: "True" 2023-10-12 01:27:16,211 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,211 Plugins: 2023-10-12 01:27:16,211 - TensorboardLogger 2023-10-12 01:27:16,211 - LinearScheduler | warmup_fraction: '0.1' 2023-10-12 01:27:16,211 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,211 Final evaluation on model from best epoch (best-model.pt) 2023-10-12 01:27:16,211 - metric: "('micro avg', 'f1-score')" 2023-10-12 01:27:16,211 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,212 Computation: 2023-10-12 01:27:16,212 - compute on device: cuda:0 2023-10-12 01:27:16,212 - embedding storage: none 2023-10-12 01:27:16,212 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,212 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5" 2023-10-12 01:27:16,212 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,212 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:27:16,212 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-12 01:28:09,890 epoch 1 - iter 178/1786 - loss 2.80507616 - time (sec): 53.68 - samples/sec: 504.62 - lr: 0.000016 - momentum: 0.000000 2023-10-12 01:29:02,680 epoch 1 - iter 356/1786 - loss 2.63434995 - time (sec): 106.47 - samples/sec: 496.51 - lr: 0.000032 - momentum: 0.000000 2023-10-12 01:29:57,335 epoch 1 - iter 534/1786 - loss 2.33660891 - time (sec): 161.12 - samples/sec: 494.59 - lr: 0.000048 - momentum: 0.000000 2023-10-12 01:30:49,640 epoch 1 - iter 712/1786 - loss 2.05010669 - time (sec): 213.43 - samples/sec: 491.15 - lr: 0.000064 - momentum: 0.000000 2023-10-12 01:31:40,298 epoch 1 - iter 890/1786 - loss 1.79302625 - time (sec): 264.08 - samples/sec: 489.91 - lr: 0.000080 - momentum: 0.000000 2023-10-12 01:32:31,757 epoch 1 - iter 1068/1786 - loss 1.60141163 - time (sec): 315.54 - samples/sec: 484.97 - lr: 0.000096 - momentum: 0.000000 2023-10-12 01:33:23,002 epoch 1 - iter 1246/1786 - loss 1.43923476 - time (sec): 366.79 - samples/sec: 482.67 - lr: 0.000112 - momentum: 0.000000 2023-10-12 01:34:17,377 epoch 1 - iter 1424/1786 - loss 1.31726143 - time (sec): 421.16 - samples/sec: 473.61 - lr: 0.000127 - momentum: 0.000000 2023-10-12 01:35:08,642 epoch 1 - iter 1602/1786 - loss 1.20681398 - time (sec): 472.43 - samples/sec: 473.34 - lr: 0.000143 - momentum: 0.000000 2023-10-12 01:35:58,724 epoch 1 - iter 1780/1786 - loss 1.11235817 - time (sec): 522.51 - samples/sec: 474.81 - lr: 0.000159 - momentum: 0.000000 2023-10-12 01:36:00,213 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:36:00,213 EPOCH 1 done: loss 1.1097 - lr: 0.000159 2023-10-12 01:36:18,867 DEV : loss 0.16794759035110474 - f1-score (micro avg) 0.5603 2023-10-12 01:36:18,895 saving best model 2023-10-12 01:36:19,749 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:37:09,858 epoch 2 - iter 178/1786 - loss 0.18793256 - time (sec): 50.11 - samples/sec: 498.04 - lr: 0.000158 - momentum: 0.000000 2023-10-12 01:38:01,560 epoch 2 - iter 356/1786 - loss 0.17776196 - time (sec): 101.81 - samples/sec: 494.37 - lr: 0.000156 - momentum: 0.000000 2023-10-12 01:38:56,898 epoch 2 - iter 534/1786 - loss 0.16472447 - time (sec): 157.15 - samples/sec: 479.83 - lr: 0.000155 - momentum: 0.000000 2023-10-12 01:39:48,644 epoch 2 - iter 712/1786 - loss 0.15515345 - time (sec): 208.89 - samples/sec: 477.97 - lr: 0.000153 - momentum: 0.000000 2023-10-12 01:40:41,766 epoch 2 - iter 890/1786 - loss 0.14481166 - time (sec): 262.02 - samples/sec: 481.61 - lr: 0.000151 - momentum: 0.000000 2023-10-12 01:41:32,730 epoch 2 - iter 1068/1786 - loss 0.14070964 - time (sec): 312.98 - samples/sec: 477.58 - lr: 0.000149 - momentum: 0.000000 2023-10-12 01:42:23,528 epoch 2 - iter 1246/1786 - loss 0.13733140 - time (sec): 363.78 - samples/sec: 476.43 - lr: 0.000148 - momentum: 0.000000 2023-10-12 01:43:15,628 epoch 2 - iter 1424/1786 - loss 0.13364336 - time (sec): 415.88 - samples/sec: 477.99 - lr: 0.000146 - momentum: 0.000000 2023-10-12 01:44:06,504 epoch 2 - iter 1602/1786 - loss 0.13125581 - time (sec): 466.75 - samples/sec: 477.55 - lr: 0.000144 - momentum: 0.000000 2023-10-12 01:45:00,109 epoch 2 - iter 1780/1786 - loss 0.12730060 - time (sec): 520.36 - samples/sec: 475.83 - lr: 0.000142 - momentum: 0.000000 2023-10-12 01:45:02,016 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:45:02,016 EPOCH 2 done: loss 0.1274 - lr: 0.000142 2023-10-12 01:45:24,922 DEV : loss 0.10730913281440735 - f1-score (micro avg) 0.7623 2023-10-12 01:45:24,958 saving best model 2023-10-12 01:45:28,483 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:46:20,438 epoch 3 - iter 178/1786 - loss 0.06605315 - time (sec): 51.95 - samples/sec: 472.43 - lr: 0.000140 - momentum: 0.000000 2023-10-12 01:47:12,817 epoch 3 - iter 356/1786 - loss 0.06559355 - time (sec): 104.33 - samples/sec: 480.15 - lr: 0.000139 - momentum: 0.000000 2023-10-12 01:48:08,733 epoch 3 - iter 534/1786 - loss 0.06577095 - time (sec): 160.24 - samples/sec: 460.10 - lr: 0.000137 - momentum: 0.000000 2023-10-12 01:48:58,726 epoch 3 - iter 712/1786 - loss 0.06735674 - time (sec): 210.24 - samples/sec: 465.58 - lr: 0.000135 - momentum: 0.000000 2023-10-12 01:49:49,932 epoch 3 - iter 890/1786 - loss 0.06649420 - time (sec): 261.44 - samples/sec: 472.92 - lr: 0.000133 - momentum: 0.000000 2023-10-12 01:50:47,225 epoch 3 - iter 1068/1786 - loss 0.06836462 - time (sec): 318.74 - samples/sec: 468.44 - lr: 0.000132 - momentum: 0.000000 2023-10-12 01:51:41,972 epoch 3 - iter 1246/1786 - loss 0.06785363 - time (sec): 373.48 - samples/sec: 466.11 - lr: 0.000130 - momentum: 0.000000 2023-10-12 01:52:37,998 epoch 3 - iter 1424/1786 - loss 0.06934020 - time (sec): 429.51 - samples/sec: 459.33 - lr: 0.000128 - momentum: 0.000000 2023-10-12 01:53:30,221 epoch 3 - iter 1602/1786 - loss 0.07082217 - time (sec): 481.73 - samples/sec: 460.16 - lr: 0.000126 - momentum: 0.000000 2023-10-12 01:54:22,946 epoch 3 - iter 1780/1786 - loss 0.06960424 - time (sec): 534.46 - samples/sec: 463.92 - lr: 0.000125 - momentum: 0.000000 2023-10-12 01:54:24,559 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:54:24,559 EPOCH 3 done: loss 0.0697 - lr: 0.000125 2023-10-12 01:54:46,566 DEV : loss 0.12944428622722626 - f1-score (micro avg) 0.7745 2023-10-12 01:54:46,599 saving best model 2023-10-12 01:54:49,221 ---------------------------------------------------------------------------------------------------- 2023-10-12 01:55:44,398 epoch 4 - iter 178/1786 - loss 0.05577792 - time (sec): 55.17 - samples/sec: 486.15 - lr: 0.000123 - momentum: 0.000000 2023-10-12 01:56:39,284 epoch 4 - iter 356/1786 - loss 0.05538162 - time (sec): 110.06 - samples/sec: 461.68 - lr: 0.000121 - momentum: 0.000000 2023-10-12 01:57:31,509 epoch 4 - iter 534/1786 - loss 0.05233655 - time (sec): 162.28 - samples/sec: 467.73 - lr: 0.000119 - momentum: 0.000000 2023-10-12 01:58:23,217 epoch 4 - iter 712/1786 - loss 0.05259265 - time (sec): 213.99 - samples/sec: 469.50 - lr: 0.000117 - momentum: 0.000000 2023-10-12 01:59:13,920 epoch 4 - iter 890/1786 - loss 0.05373927 - time (sec): 264.69 - samples/sec: 468.70 - lr: 0.000116 - momentum: 0.000000 2023-10-12 02:00:06,383 epoch 4 - iter 1068/1786 - loss 0.05149636 - time (sec): 317.16 - samples/sec: 470.91 - lr: 0.000114 - momentum: 0.000000 2023-10-12 02:00:58,159 epoch 4 - iter 1246/1786 - loss 0.05173730 - time (sec): 368.93 - samples/sec: 469.30 - lr: 0.000112 - momentum: 0.000000 2023-10-12 02:01:49,397 epoch 4 - iter 1424/1786 - loss 0.05114958 - time (sec): 420.17 - samples/sec: 469.30 - lr: 0.000110 - momentum: 0.000000 2023-10-12 02:02:43,021 epoch 4 - iter 1602/1786 - loss 0.05108851 - time (sec): 473.80 - samples/sec: 472.58 - lr: 0.000109 - momentum: 0.000000 2023-10-12 02:03:34,895 epoch 4 - iter 1780/1786 - loss 0.05115817 - time (sec): 525.67 - samples/sec: 471.96 - lr: 0.000107 - momentum: 0.000000 2023-10-12 02:03:36,495 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:03:36,495 EPOCH 4 done: loss 0.0511 - lr: 0.000107 2023-10-12 02:03:56,766 DEV : loss 0.14689753949642181 - f1-score (micro avg) 0.785 2023-10-12 02:03:56,795 saving best model 2023-10-12 02:03:59,385 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:04:55,346 epoch 5 - iter 178/1786 - loss 0.03039438 - time (sec): 55.96 - samples/sec: 438.41 - lr: 0.000105 - momentum: 0.000000 2023-10-12 02:05:49,136 epoch 5 - iter 356/1786 - loss 0.03164096 - time (sec): 109.75 - samples/sec: 446.21 - lr: 0.000103 - momentum: 0.000000 2023-10-12 02:06:42,843 epoch 5 - iter 534/1786 - loss 0.03344778 - time (sec): 163.45 - samples/sec: 454.29 - lr: 0.000101 - momentum: 0.000000 2023-10-12 02:07:35,189 epoch 5 - iter 712/1786 - loss 0.03152047 - time (sec): 215.80 - samples/sec: 452.98 - lr: 0.000100 - momentum: 0.000000 2023-10-12 02:08:30,997 epoch 5 - iter 890/1786 - loss 0.03361595 - time (sec): 271.61 - samples/sec: 448.12 - lr: 0.000098 - momentum: 0.000000 2023-10-12 02:09:23,289 epoch 5 - iter 1068/1786 - loss 0.03322299 - time (sec): 323.90 - samples/sec: 451.89 - lr: 0.000096 - momentum: 0.000000 2023-10-12 02:10:20,398 epoch 5 - iter 1246/1786 - loss 0.03402782 - time (sec): 381.01 - samples/sec: 455.72 - lr: 0.000094 - momentum: 0.000000 2023-10-12 02:11:15,612 epoch 5 - iter 1424/1786 - loss 0.03608606 - time (sec): 436.22 - samples/sec: 454.93 - lr: 0.000093 - momentum: 0.000000 2023-10-12 02:12:11,200 epoch 5 - iter 1602/1786 - loss 0.03657160 - time (sec): 491.81 - samples/sec: 453.93 - lr: 0.000091 - momentum: 0.000000 2023-10-12 02:13:08,194 epoch 5 - iter 1780/1786 - loss 0.03671433 - time (sec): 548.80 - samples/sec: 452.08 - lr: 0.000089 - momentum: 0.000000 2023-10-12 02:13:09,930 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:13:09,931 EPOCH 5 done: loss 0.0367 - lr: 0.000089 2023-10-12 02:13:31,881 DEV : loss 0.16735321283340454 - f1-score (micro avg) 0.7933 2023-10-12 02:13:31,912 saving best model 2023-10-12 02:13:34,616 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:14:29,724 epoch 6 - iter 178/1786 - loss 0.02884951 - time (sec): 55.10 - samples/sec: 466.32 - lr: 0.000087 - momentum: 0.000000 2023-10-12 02:15:24,276 epoch 6 - iter 356/1786 - loss 0.02844827 - time (sec): 109.66 - samples/sec: 454.00 - lr: 0.000085 - momentum: 0.000000 2023-10-12 02:16:22,180 epoch 6 - iter 534/1786 - loss 0.02788463 - time (sec): 167.56 - samples/sec: 460.39 - lr: 0.000084 - momentum: 0.000000 2023-10-12 02:17:16,497 epoch 6 - iter 712/1786 - loss 0.02904160 - time (sec): 221.88 - samples/sec: 456.66 - lr: 0.000082 - momentum: 0.000000 2023-10-12 02:18:11,328 epoch 6 - iter 890/1786 - loss 0.02995947 - time (sec): 276.71 - samples/sec: 460.24 - lr: 0.000080 - momentum: 0.000000 2023-10-12 02:19:04,986 epoch 6 - iter 1068/1786 - loss 0.02837177 - time (sec): 330.37 - samples/sec: 459.17 - lr: 0.000078 - momentum: 0.000000 2023-10-12 02:19:57,466 epoch 6 - iter 1246/1786 - loss 0.02758746 - time (sec): 382.85 - samples/sec: 459.16 - lr: 0.000077 - momentum: 0.000000 2023-10-12 02:20:51,594 epoch 6 - iter 1424/1786 - loss 0.02731558 - time (sec): 436.97 - samples/sec: 459.85 - lr: 0.000075 - momentum: 0.000000 2023-10-12 02:21:44,296 epoch 6 - iter 1602/1786 - loss 0.02738174 - time (sec): 489.68 - samples/sec: 457.93 - lr: 0.000073 - momentum: 0.000000 2023-10-12 02:22:38,466 epoch 6 - iter 1780/1786 - loss 0.02826862 - time (sec): 543.85 - samples/sec: 455.42 - lr: 0.000071 - momentum: 0.000000 2023-10-12 02:22:40,335 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:22:40,335 EPOCH 6 done: loss 0.0282 - lr: 0.000071 2023-10-12 02:23:01,339 DEV : loss 0.1834934949874878 - f1-score (micro avg) 0.7908 2023-10-12 02:23:01,368 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:23:53,130 epoch 7 - iter 178/1786 - loss 0.02428625 - time (sec): 51.76 - samples/sec: 464.99 - lr: 0.000069 - momentum: 0.000000 2023-10-12 02:24:46,191 epoch 7 - iter 356/1786 - loss 0.01938786 - time (sec): 104.82 - samples/sec: 475.11 - lr: 0.000068 - momentum: 0.000000 2023-10-12 02:25:37,985 epoch 7 - iter 534/1786 - loss 0.01994075 - time (sec): 156.62 - samples/sec: 470.15 - lr: 0.000066 - momentum: 0.000000 2023-10-12 02:26:35,234 epoch 7 - iter 712/1786 - loss 0.01883127 - time (sec): 213.86 - samples/sec: 465.13 - lr: 0.000064 - momentum: 0.000000 2023-10-12 02:27:29,734 epoch 7 - iter 890/1786 - loss 0.02000040 - time (sec): 268.36 - samples/sec: 463.92 - lr: 0.000062 - momentum: 0.000000 2023-10-12 02:28:24,145 epoch 7 - iter 1068/1786 - loss 0.01988337 - time (sec): 322.78 - samples/sec: 462.58 - lr: 0.000061 - momentum: 0.000000 2023-10-12 02:29:21,554 epoch 7 - iter 1246/1786 - loss 0.01983623 - time (sec): 380.18 - samples/sec: 456.64 - lr: 0.000059 - momentum: 0.000000 2023-10-12 02:30:17,602 epoch 7 - iter 1424/1786 - loss 0.02013644 - time (sec): 436.23 - samples/sec: 453.81 - lr: 0.000057 - momentum: 0.000000 2023-10-12 02:31:11,178 epoch 7 - iter 1602/1786 - loss 0.02053497 - time (sec): 489.81 - samples/sec: 455.80 - lr: 0.000055 - momentum: 0.000000 2023-10-12 02:32:06,509 epoch 7 - iter 1780/1786 - loss 0.02060644 - time (sec): 545.14 - samples/sec: 454.69 - lr: 0.000053 - momentum: 0.000000 2023-10-12 02:32:08,217 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:32:08,217 EPOCH 7 done: loss 0.0206 - lr: 0.000053 2023-10-12 02:32:28,843 DEV : loss 0.2012760192155838 - f1-score (micro avg) 0.7981 2023-10-12 02:32:28,873 saving best model 2023-10-12 02:32:31,463 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:33:25,115 epoch 8 - iter 178/1786 - loss 0.02276046 - time (sec): 53.65 - samples/sec: 466.86 - lr: 0.000052 - momentum: 0.000000 2023-10-12 02:34:19,214 epoch 8 - iter 356/1786 - loss 0.01914228 - time (sec): 107.75 - samples/sec: 466.87 - lr: 0.000050 - momentum: 0.000000 2023-10-12 02:35:14,651 epoch 8 - iter 534/1786 - loss 0.01763461 - time (sec): 163.18 - samples/sec: 460.55 - lr: 0.000048 - momentum: 0.000000 2023-10-12 02:36:05,888 epoch 8 - iter 712/1786 - loss 0.01587325 - time (sec): 214.42 - samples/sec: 458.52 - lr: 0.000046 - momentum: 0.000000 2023-10-12 02:36:56,876 epoch 8 - iter 890/1786 - loss 0.01556667 - time (sec): 265.41 - samples/sec: 459.13 - lr: 0.000044 - momentum: 0.000000 2023-10-12 02:37:50,858 epoch 8 - iter 1068/1786 - loss 0.01500536 - time (sec): 319.39 - samples/sec: 463.67 - lr: 0.000043 - momentum: 0.000000 2023-10-12 02:38:42,418 epoch 8 - iter 1246/1786 - loss 0.01483176 - time (sec): 370.95 - samples/sec: 461.23 - lr: 0.000041 - momentum: 0.000000 2023-10-12 02:39:40,033 epoch 8 - iter 1424/1786 - loss 0.01450104 - time (sec): 428.57 - samples/sec: 460.07 - lr: 0.000039 - momentum: 0.000000 2023-10-12 02:40:35,374 epoch 8 - iter 1602/1786 - loss 0.01489033 - time (sec): 483.91 - samples/sec: 461.79 - lr: 0.000037 - momentum: 0.000000 2023-10-12 02:41:31,522 epoch 8 - iter 1780/1786 - loss 0.01450964 - time (sec): 540.05 - samples/sec: 459.03 - lr: 0.000036 - momentum: 0.000000 2023-10-12 02:41:33,346 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:41:33,347 EPOCH 8 done: loss 0.0145 - lr: 0.000036 2023-10-12 02:41:58,535 DEV : loss 0.2015368640422821 - f1-score (micro avg) 0.8133 2023-10-12 02:41:58,571 saving best model 2023-10-12 02:42:01,252 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:42:54,587 epoch 9 - iter 178/1786 - loss 0.00872584 - time (sec): 53.33 - samples/sec: 486.07 - lr: 0.000034 - momentum: 0.000000 2023-10-12 02:43:46,379 epoch 9 - iter 356/1786 - loss 0.00630023 - time (sec): 105.12 - samples/sec: 475.08 - lr: 0.000032 - momentum: 0.000000 2023-10-12 02:44:39,948 epoch 9 - iter 534/1786 - loss 0.00756441 - time (sec): 158.69 - samples/sec: 468.31 - lr: 0.000030 - momentum: 0.000000 2023-10-12 02:45:33,997 epoch 9 - iter 712/1786 - loss 0.00695522 - time (sec): 212.74 - samples/sec: 462.16 - lr: 0.000028 - momentum: 0.000000 2023-10-12 02:46:30,457 epoch 9 - iter 890/1786 - loss 0.00724403 - time (sec): 269.20 - samples/sec: 456.26 - lr: 0.000027 - momentum: 0.000000 2023-10-12 02:47:24,449 epoch 9 - iter 1068/1786 - loss 0.00812575 - time (sec): 323.19 - samples/sec: 461.32 - lr: 0.000025 - momentum: 0.000000 2023-10-12 02:48:19,624 epoch 9 - iter 1246/1786 - loss 0.00937037 - time (sec): 378.37 - samples/sec: 463.37 - lr: 0.000023 - momentum: 0.000000 2023-10-12 02:49:13,453 epoch 9 - iter 1424/1786 - loss 0.00930061 - time (sec): 432.20 - samples/sec: 463.61 - lr: 0.000021 - momentum: 0.000000 2023-10-12 02:50:05,206 epoch 9 - iter 1602/1786 - loss 0.00976462 - time (sec): 483.95 - samples/sec: 463.83 - lr: 0.000020 - momentum: 0.000000 2023-10-12 02:51:00,266 epoch 9 - iter 1780/1786 - loss 0.00953790 - time (sec): 539.01 - samples/sec: 460.23 - lr: 0.000018 - momentum: 0.000000 2023-10-12 02:51:02,088 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:51:02,088 EPOCH 9 done: loss 0.0095 - lr: 0.000018 2023-10-12 02:51:23,679 DEV : loss 0.2193737030029297 - f1-score (micro avg) 0.8133 2023-10-12 02:51:23,714 ---------------------------------------------------------------------------------------------------- 2023-10-12 02:52:17,448 epoch 10 - iter 178/1786 - loss 0.00370170 - time (sec): 53.73 - samples/sec: 469.72 - lr: 0.000016 - momentum: 0.000000 2023-10-12 02:53:11,609 epoch 10 - iter 356/1786 - loss 0.00580275 - time (sec): 107.89 - samples/sec: 468.02 - lr: 0.000014 - momentum: 0.000000 2023-10-12 02:54:05,708 epoch 10 - iter 534/1786 - loss 0.00544806 - time (sec): 161.99 - samples/sec: 468.33 - lr: 0.000012 - momentum: 0.000000 2023-10-12 02:54:59,294 epoch 10 - iter 712/1786 - loss 0.00565942 - time (sec): 215.58 - samples/sec: 469.11 - lr: 0.000011 - momentum: 0.000000 2023-10-12 02:55:53,156 epoch 10 - iter 890/1786 - loss 0.00585775 - time (sec): 269.44 - samples/sec: 469.40 - lr: 0.000009 - momentum: 0.000000 2023-10-12 02:56:46,577 epoch 10 - iter 1068/1786 - loss 0.00554799 - time (sec): 322.86 - samples/sec: 466.02 - lr: 0.000007 - momentum: 0.000000 2023-10-12 02:57:40,202 epoch 10 - iter 1246/1786 - loss 0.00618939 - time (sec): 376.49 - samples/sec: 468.04 - lr: 0.000005 - momentum: 0.000000 2023-10-12 02:58:32,386 epoch 10 - iter 1424/1786 - loss 0.00593712 - time (sec): 428.67 - samples/sec: 467.12 - lr: 0.000004 - momentum: 0.000000 2023-10-12 02:59:24,805 epoch 10 - iter 1602/1786 - loss 0.00597006 - time (sec): 481.09 - samples/sec: 466.32 - lr: 0.000002 - momentum: 0.000000 2023-10-12 03:00:19,462 epoch 10 - iter 1780/1786 - loss 0.00611996 - time (sec): 535.75 - samples/sec: 463.25 - lr: 0.000000 - momentum: 0.000000 2023-10-12 03:00:20,969 ---------------------------------------------------------------------------------------------------- 2023-10-12 03:00:20,970 EPOCH 10 done: loss 0.0061 - lr: 0.000000 2023-10-12 03:00:43,438 DEV : loss 0.2238311767578125 - f1-score (micro avg) 0.8046 2023-10-12 03:00:44,547 ---------------------------------------------------------------------------------------------------- 2023-10-12 03:00:44,549 Loading model from best epoch ... 2023-10-12 03:00:48,482 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 2023-10-12 03:02:01,023 Results: - F-score (micro) 0.7119 - F-score (macro) 0.6266 - Accuracy 0.5663 By class: precision recall f1-score support LOC 0.7359 0.7251 0.7305 1095 PER 0.7911 0.7747 0.7828 1012 ORG 0.4551 0.5826 0.5111 357 HumanProd 0.4000 0.6061 0.4819 33 micro avg 0.7008 0.7233 0.7119 2497 macro avg 0.5955 0.6721 0.6266 2497 weighted avg 0.7137 0.7233 0.7170 2497 2023-10-12 03:02:01,023 ----------------------------------------------------------------------------------------------------