2023-10-19 20:48:01,185 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,185 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(32001, 128) (position_embeddings): Embedding(512, 128) (token_type_embeddings): Embedding(2, 128) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-1): 2 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=128, out_features=128, bias=True) (key): Linear(in_features=128, out_features=128, bias=True) (value): Linear(in_features=128, out_features=128, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=128, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=128, out_features=512, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=512, out_features=128, bias=True) (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=128, out_features=128, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=128, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-19 20:48:01,185 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,185 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 2023-10-19 20:48:01,185 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,185 Train: 7142 sentences 2023-10-19 20:48:01,185 (train_with_dev=False, train_with_test=False) 2023-10-19 20:48:01,185 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,185 Training Params: 2023-10-19 20:48:01,186 - learning_rate: "5e-05" 2023-10-19 20:48:01,186 - mini_batch_size: "8" 2023-10-19 20:48:01,186 - max_epochs: "10" 2023-10-19 20:48:01,186 - shuffle: "True" 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 Plugins: 2023-10-19 20:48:01,186 - TensorboardLogger 2023-10-19 20:48:01,186 - LinearScheduler | warmup_fraction: '0.1' 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 Final evaluation on model from best epoch (best-model.pt) 2023-10-19 20:48:01,186 - metric: "('micro avg', 'f1-score')" 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 Computation: 2023-10-19 20:48:01,186 - compute on device: cuda:0 2023-10-19 20:48:01,186 - embedding storage: none 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 Model training base path: "hmbench-newseye/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:01,186 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-19 20:48:03,348 epoch 1 - iter 89/893 - loss 3.31788487 - time (sec): 2.16 - samples/sec: 11311.02 - lr: 0.000005 - momentum: 0.000000 2023-10-19 20:48:05,631 epoch 1 - iter 178/893 - loss 3.00220485 - time (sec): 4.44 - samples/sec: 11232.29 - lr: 0.000010 - momentum: 0.000000 2023-10-19 20:48:07,923 epoch 1 - iter 267/893 - loss 2.52761114 - time (sec): 6.74 - samples/sec: 11190.98 - lr: 0.000015 - momentum: 0.000000 2023-10-19 20:48:10,295 epoch 1 - iter 356/893 - loss 2.08600669 - time (sec): 9.11 - samples/sec: 11235.03 - lr: 0.000020 - momentum: 0.000000 2023-10-19 20:48:12,581 epoch 1 - iter 445/893 - loss 1.83262530 - time (sec): 11.39 - samples/sec: 11102.08 - lr: 0.000025 - momentum: 0.000000 2023-10-19 20:48:15,291 epoch 1 - iter 534/893 - loss 1.65539925 - time (sec): 14.10 - samples/sec: 10637.43 - lr: 0.000030 - momentum: 0.000000 2023-10-19 20:48:17,545 epoch 1 - iter 623/893 - loss 1.51778252 - time (sec): 16.36 - samples/sec: 10597.71 - lr: 0.000035 - momentum: 0.000000 2023-10-19 20:48:19,817 epoch 1 - iter 712/893 - loss 1.39687716 - time (sec): 18.63 - samples/sec: 10637.02 - lr: 0.000040 - momentum: 0.000000 2023-10-19 20:48:22,116 epoch 1 - iter 801/893 - loss 1.30006566 - time (sec): 20.93 - samples/sec: 10682.81 - lr: 0.000045 - momentum: 0.000000 2023-10-19 20:48:24,252 epoch 1 - iter 890/893 - loss 1.22292912 - time (sec): 23.07 - samples/sec: 10766.58 - lr: 0.000050 - momentum: 0.000000 2023-10-19 20:48:24,309 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:24,309 EPOCH 1 done: loss 1.2221 - lr: 0.000050 2023-10-19 20:48:25,279 DEV : loss 0.31805744767189026 - f1-score (micro avg) 0.1418 2023-10-19 20:48:25,292 saving best model 2023-10-19 20:48:25,326 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:27,490 epoch 2 - iter 89/893 - loss 0.44839489 - time (sec): 2.16 - samples/sec: 12092.79 - lr: 0.000049 - momentum: 0.000000 2023-10-19 20:48:29,745 epoch 2 - iter 178/893 - loss 0.45190939 - time (sec): 4.42 - samples/sec: 11426.89 - lr: 0.000049 - momentum: 0.000000 2023-10-19 20:48:32,002 epoch 2 - iter 267/893 - loss 0.43792015 - time (sec): 6.68 - samples/sec: 11102.76 - lr: 0.000048 - momentum: 0.000000 2023-10-19 20:48:34,385 epoch 2 - iter 356/893 - loss 0.43913817 - time (sec): 9.06 - samples/sec: 11081.95 - lr: 0.000048 - momentum: 0.000000 2023-10-19 20:48:36,597 epoch 2 - iter 445/893 - loss 0.42993763 - time (sec): 11.27 - samples/sec: 11119.56 - lr: 0.000047 - momentum: 0.000000 2023-10-19 20:48:38,813 epoch 2 - iter 534/893 - loss 0.43160330 - time (sec): 13.49 - samples/sec: 11151.24 - lr: 0.000047 - momentum: 0.000000 2023-10-19 20:48:41,016 epoch 2 - iter 623/893 - loss 0.42662350 - time (sec): 15.69 - samples/sec: 11100.26 - lr: 0.000046 - momentum: 0.000000 2023-10-19 20:48:43,209 epoch 2 - iter 712/893 - loss 0.42179532 - time (sec): 17.88 - samples/sec: 11134.96 - lr: 0.000046 - momentum: 0.000000 2023-10-19 20:48:45,471 epoch 2 - iter 801/893 - loss 0.41961257 - time (sec): 20.14 - samples/sec: 11127.37 - lr: 0.000045 - momentum: 0.000000 2023-10-19 20:48:47,717 epoch 2 - iter 890/893 - loss 0.41430294 - time (sec): 22.39 - samples/sec: 11086.08 - lr: 0.000044 - momentum: 0.000000 2023-10-19 20:48:47,788 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:47,789 EPOCH 2 done: loss 0.4144 - lr: 0.000044 2023-10-19 20:48:50,606 DEV : loss 0.2429792881011963 - f1-score (micro avg) 0.4008 2023-10-19 20:48:50,619 saving best model 2023-10-19 20:48:50,656 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:48:52,917 epoch 3 - iter 89/893 - loss 0.34817659 - time (sec): 2.26 - samples/sec: 10227.98 - lr: 0.000044 - momentum: 0.000000 2023-10-19 20:48:55,197 epoch 3 - iter 178/893 - loss 0.34419809 - time (sec): 4.54 - samples/sec: 10597.89 - lr: 0.000043 - momentum: 0.000000 2023-10-19 20:48:57,484 epoch 3 - iter 267/893 - loss 0.34570190 - time (sec): 6.83 - samples/sec: 10723.08 - lr: 0.000043 - momentum: 0.000000 2023-10-19 20:48:59,738 epoch 3 - iter 356/893 - loss 0.35673527 - time (sec): 9.08 - samples/sec: 10912.76 - lr: 0.000042 - momentum: 0.000000 2023-10-19 20:49:01,958 epoch 3 - iter 445/893 - loss 0.35787478 - time (sec): 11.30 - samples/sec: 10929.46 - lr: 0.000042 - momentum: 0.000000 2023-10-19 20:49:04,293 epoch 3 - iter 534/893 - loss 0.34975742 - time (sec): 13.64 - samples/sec: 10906.45 - lr: 0.000041 - momentum: 0.000000 2023-10-19 20:49:06,578 epoch 3 - iter 623/893 - loss 0.34623751 - time (sec): 15.92 - samples/sec: 10910.39 - lr: 0.000041 - momentum: 0.000000 2023-10-19 20:49:08,859 epoch 3 - iter 712/893 - loss 0.34105492 - time (sec): 18.20 - samples/sec: 10933.97 - lr: 0.000040 - momentum: 0.000000 2023-10-19 20:49:11,148 epoch 3 - iter 801/893 - loss 0.33634117 - time (sec): 20.49 - samples/sec: 10927.23 - lr: 0.000039 - momentum: 0.000000 2023-10-19 20:49:13,428 epoch 3 - iter 890/893 - loss 0.33396920 - time (sec): 22.77 - samples/sec: 10869.31 - lr: 0.000039 - momentum: 0.000000 2023-10-19 20:49:13,514 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:49:13,514 EPOCH 3 done: loss 0.3337 - lr: 0.000039 2023-10-19 20:49:16,351 DEV : loss 0.21542218327522278 - f1-score (micro avg) 0.4361 2023-10-19 20:49:16,366 saving best model 2023-10-19 20:49:16,401 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:49:18,738 epoch 4 - iter 89/893 - loss 0.30607610 - time (sec): 2.34 - samples/sec: 10539.75 - lr: 0.000038 - momentum: 0.000000 2023-10-19 20:49:21,031 epoch 4 - iter 178/893 - loss 0.30905617 - time (sec): 4.63 - samples/sec: 10680.88 - lr: 0.000038 - momentum: 0.000000 2023-10-19 20:49:23,318 epoch 4 - iter 267/893 - loss 0.29857101 - time (sec): 6.92 - samples/sec: 10720.72 - lr: 0.000037 - momentum: 0.000000 2023-10-19 20:49:25,577 epoch 4 - iter 356/893 - loss 0.29476408 - time (sec): 9.17 - samples/sec: 10783.77 - lr: 0.000037 - momentum: 0.000000 2023-10-19 20:49:27,831 epoch 4 - iter 445/893 - loss 0.29327210 - time (sec): 11.43 - samples/sec: 10831.79 - lr: 0.000036 - momentum: 0.000000 2023-10-19 20:49:30,154 epoch 4 - iter 534/893 - loss 0.29218114 - time (sec): 13.75 - samples/sec: 10873.04 - lr: 0.000036 - momentum: 0.000000 2023-10-19 20:49:32,370 epoch 4 - iter 623/893 - loss 0.29353192 - time (sec): 15.97 - samples/sec: 10834.37 - lr: 0.000035 - momentum: 0.000000 2023-10-19 20:49:34,613 epoch 4 - iter 712/893 - loss 0.29422867 - time (sec): 18.21 - samples/sec: 10899.70 - lr: 0.000034 - momentum: 0.000000 2023-10-19 20:49:36,847 epoch 4 - iter 801/893 - loss 0.29355665 - time (sec): 20.45 - samples/sec: 10841.59 - lr: 0.000034 - momentum: 0.000000 2023-10-19 20:49:39,153 epoch 4 - iter 890/893 - loss 0.29116940 - time (sec): 22.75 - samples/sec: 10896.21 - lr: 0.000033 - momentum: 0.000000 2023-10-19 20:49:39,226 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:49:39,227 EPOCH 4 done: loss 0.2910 - lr: 0.000033 2023-10-19 20:49:41,610 DEV : loss 0.20385973155498505 - f1-score (micro avg) 0.4589 2023-10-19 20:49:41,624 saving best model 2023-10-19 20:49:41,659 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:49:43,712 epoch 5 - iter 89/893 - loss 0.25219515 - time (sec): 2.05 - samples/sec: 11876.44 - lr: 0.000033 - momentum: 0.000000 2023-10-19 20:49:45,968 epoch 5 - iter 178/893 - loss 0.26468865 - time (sec): 4.31 - samples/sec: 11290.43 - lr: 0.000032 - momentum: 0.000000 2023-10-19 20:49:48,261 epoch 5 - iter 267/893 - loss 0.27013532 - time (sec): 6.60 - samples/sec: 11053.64 - lr: 0.000032 - momentum: 0.000000 2023-10-19 20:49:50,577 epoch 5 - iter 356/893 - loss 0.26947573 - time (sec): 8.92 - samples/sec: 11028.19 - lr: 0.000031 - momentum: 0.000000 2023-10-19 20:49:52,776 epoch 5 - iter 445/893 - loss 0.27194910 - time (sec): 11.12 - samples/sec: 11145.21 - lr: 0.000031 - momentum: 0.000000 2023-10-19 20:49:54,655 epoch 5 - iter 534/893 - loss 0.26870618 - time (sec): 13.00 - samples/sec: 11451.82 - lr: 0.000030 - momentum: 0.000000 2023-10-19 20:49:56,476 epoch 5 - iter 623/893 - loss 0.26893292 - time (sec): 14.82 - samples/sec: 11703.51 - lr: 0.000029 - momentum: 0.000000 2023-10-19 20:49:58,730 epoch 5 - iter 712/893 - loss 0.26943529 - time (sec): 17.07 - samples/sec: 11640.91 - lr: 0.000029 - momentum: 0.000000 2023-10-19 20:50:01,091 epoch 5 - iter 801/893 - loss 0.26558780 - time (sec): 19.43 - samples/sec: 11484.67 - lr: 0.000028 - momentum: 0.000000 2023-10-19 20:50:03,363 epoch 5 - iter 890/893 - loss 0.26382300 - time (sec): 21.70 - samples/sec: 11422.87 - lr: 0.000028 - momentum: 0.000000 2023-10-19 20:50:03,435 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:03,435 EPOCH 5 done: loss 0.2639 - lr: 0.000028 2023-10-19 20:50:06,328 DEV : loss 0.19422198832035065 - f1-score (micro avg) 0.4997 2023-10-19 20:50:06,349 saving best model 2023-10-19 20:50:06,385 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:08,645 epoch 6 - iter 89/893 - loss 0.24073724 - time (sec): 2.26 - samples/sec: 11025.22 - lr: 0.000027 - momentum: 0.000000 2023-10-19 20:50:10,868 epoch 6 - iter 178/893 - loss 0.23869287 - time (sec): 4.48 - samples/sec: 10742.28 - lr: 0.000027 - momentum: 0.000000 2023-10-19 20:50:13,108 epoch 6 - iter 267/893 - loss 0.23653702 - time (sec): 6.72 - samples/sec: 10700.65 - lr: 0.000026 - momentum: 0.000000 2023-10-19 20:50:15,366 epoch 6 - iter 356/893 - loss 0.23682304 - time (sec): 8.98 - samples/sec: 10740.98 - lr: 0.000026 - momentum: 0.000000 2023-10-19 20:50:17,617 epoch 6 - iter 445/893 - loss 0.23901517 - time (sec): 11.23 - samples/sec: 10742.46 - lr: 0.000025 - momentum: 0.000000 2023-10-19 20:50:19,888 epoch 6 - iter 534/893 - loss 0.24103990 - time (sec): 13.50 - samples/sec: 10774.77 - lr: 0.000024 - momentum: 0.000000 2023-10-19 20:50:22,139 epoch 6 - iter 623/893 - loss 0.24250770 - time (sec): 15.75 - samples/sec: 10852.32 - lr: 0.000024 - momentum: 0.000000 2023-10-19 20:50:24,407 epoch 6 - iter 712/893 - loss 0.24499793 - time (sec): 18.02 - samples/sec: 10936.70 - lr: 0.000023 - momentum: 0.000000 2023-10-19 20:50:26,664 epoch 6 - iter 801/893 - loss 0.24412070 - time (sec): 20.28 - samples/sec: 10977.66 - lr: 0.000023 - momentum: 0.000000 2023-10-19 20:50:28,983 epoch 6 - iter 890/893 - loss 0.24378175 - time (sec): 22.60 - samples/sec: 10963.58 - lr: 0.000022 - momentum: 0.000000 2023-10-19 20:50:29,062 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:29,062 EPOCH 6 done: loss 0.2434 - lr: 0.000022 2023-10-19 20:50:31,413 DEV : loss 0.1892632693052292 - f1-score (micro avg) 0.5121 2023-10-19 20:50:31,426 saving best model 2023-10-19 20:50:31,462 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:34,181 epoch 7 - iter 89/893 - loss 0.21102632 - time (sec): 2.72 - samples/sec: 8398.67 - lr: 0.000022 - momentum: 0.000000 2023-10-19 20:50:36,427 epoch 7 - iter 178/893 - loss 0.22949609 - time (sec): 4.96 - samples/sec: 9490.59 - lr: 0.000021 - momentum: 0.000000 2023-10-19 20:50:38,659 epoch 7 - iter 267/893 - loss 0.22714217 - time (sec): 7.20 - samples/sec: 10067.28 - lr: 0.000021 - momentum: 0.000000 2023-10-19 20:50:40,892 epoch 7 - iter 356/893 - loss 0.22411998 - time (sec): 9.43 - samples/sec: 10212.94 - lr: 0.000020 - momentum: 0.000000 2023-10-19 20:50:43,256 epoch 7 - iter 445/893 - loss 0.22268983 - time (sec): 11.79 - samples/sec: 10343.03 - lr: 0.000019 - momentum: 0.000000 2023-10-19 20:50:45,358 epoch 7 - iter 534/893 - loss 0.22316819 - time (sec): 13.90 - samples/sec: 10467.56 - lr: 0.000019 - momentum: 0.000000 2023-10-19 20:50:47,671 epoch 7 - iter 623/893 - loss 0.22564912 - time (sec): 16.21 - samples/sec: 10384.21 - lr: 0.000018 - momentum: 0.000000 2023-10-19 20:50:50,031 epoch 7 - iter 712/893 - loss 0.22635014 - time (sec): 18.57 - samples/sec: 10611.04 - lr: 0.000018 - momentum: 0.000000 2023-10-19 20:50:52,321 epoch 7 - iter 801/893 - loss 0.22691152 - time (sec): 20.86 - samples/sec: 10735.21 - lr: 0.000017 - momentum: 0.000000 2023-10-19 20:50:54,568 epoch 7 - iter 890/893 - loss 0.22770015 - time (sec): 23.10 - samples/sec: 10734.74 - lr: 0.000017 - momentum: 0.000000 2023-10-19 20:50:54,639 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:54,639 EPOCH 7 done: loss 0.2274 - lr: 0.000017 2023-10-19 20:50:56,997 DEV : loss 0.18830116093158722 - f1-score (micro avg) 0.5288 2023-10-19 20:50:57,012 saving best model 2023-10-19 20:50:57,045 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:50:59,273 epoch 8 - iter 89/893 - loss 0.19916108 - time (sec): 2.23 - samples/sec: 10591.83 - lr: 0.000016 - momentum: 0.000000 2023-10-19 20:51:01,665 epoch 8 - iter 178/893 - loss 0.21119586 - time (sec): 4.62 - samples/sec: 10765.32 - lr: 0.000016 - momentum: 0.000000 2023-10-19 20:51:03,984 epoch 8 - iter 267/893 - loss 0.21979280 - time (sec): 6.94 - samples/sec: 10695.84 - lr: 0.000015 - momentum: 0.000000 2023-10-19 20:51:06,299 epoch 8 - iter 356/893 - loss 0.21652746 - time (sec): 9.25 - samples/sec: 10710.65 - lr: 0.000014 - momentum: 0.000000 2023-10-19 20:51:08,560 epoch 8 - iter 445/893 - loss 0.22339324 - time (sec): 11.51 - samples/sec: 10656.55 - lr: 0.000014 - momentum: 0.000000 2023-10-19 20:51:10,778 epoch 8 - iter 534/893 - loss 0.22339473 - time (sec): 13.73 - samples/sec: 10739.10 - lr: 0.000013 - momentum: 0.000000 2023-10-19 20:51:13,062 epoch 8 - iter 623/893 - loss 0.22183591 - time (sec): 16.02 - samples/sec: 10697.70 - lr: 0.000013 - momentum: 0.000000 2023-10-19 20:51:15,299 epoch 8 - iter 712/893 - loss 0.21960297 - time (sec): 18.25 - samples/sec: 10714.14 - lr: 0.000012 - momentum: 0.000000 2023-10-19 20:51:17,631 epoch 8 - iter 801/893 - loss 0.22015520 - time (sec): 20.59 - samples/sec: 10823.07 - lr: 0.000012 - momentum: 0.000000 2023-10-19 20:51:19,875 epoch 8 - iter 890/893 - loss 0.21912504 - time (sec): 22.83 - samples/sec: 10865.83 - lr: 0.000011 - momentum: 0.000000 2023-10-19 20:51:19,943 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:51:19,943 EPOCH 8 done: loss 0.2197 - lr: 0.000011 2023-10-19 20:51:22,761 DEV : loss 0.18704333901405334 - f1-score (micro avg) 0.5266 2023-10-19 20:51:22,774 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:51:25,008 epoch 9 - iter 89/893 - loss 0.22409608 - time (sec): 2.23 - samples/sec: 10905.75 - lr: 0.000011 - momentum: 0.000000 2023-10-19 20:51:27,275 epoch 9 - iter 178/893 - loss 0.21545283 - time (sec): 4.50 - samples/sec: 10899.19 - lr: 0.000010 - momentum: 0.000000 2023-10-19 20:51:29,461 epoch 9 - iter 267/893 - loss 0.22001584 - time (sec): 6.69 - samples/sec: 10951.22 - lr: 0.000009 - momentum: 0.000000 2023-10-19 20:51:31,753 epoch 9 - iter 356/893 - loss 0.22377309 - time (sec): 8.98 - samples/sec: 10998.28 - lr: 0.000009 - momentum: 0.000000 2023-10-19 20:51:34,036 epoch 9 - iter 445/893 - loss 0.22189321 - time (sec): 11.26 - samples/sec: 11162.57 - lr: 0.000008 - momentum: 0.000000 2023-10-19 20:51:36,280 epoch 9 - iter 534/893 - loss 0.21704728 - time (sec): 13.50 - samples/sec: 11064.48 - lr: 0.000008 - momentum: 0.000000 2023-10-19 20:51:38,531 epoch 9 - iter 623/893 - loss 0.21551115 - time (sec): 15.76 - samples/sec: 11080.72 - lr: 0.000007 - momentum: 0.000000 2023-10-19 20:51:40,762 epoch 9 - iter 712/893 - loss 0.21465534 - time (sec): 17.99 - samples/sec: 11037.93 - lr: 0.000007 - momentum: 0.000000 2023-10-19 20:51:43,131 epoch 9 - iter 801/893 - loss 0.21346279 - time (sec): 20.36 - samples/sec: 10987.62 - lr: 0.000006 - momentum: 0.000000 2023-10-19 20:51:45,480 epoch 9 - iter 890/893 - loss 0.21290729 - time (sec): 22.70 - samples/sec: 10917.64 - lr: 0.000006 - momentum: 0.000000 2023-10-19 20:51:45,552 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:51:45,552 EPOCH 9 done: loss 0.2128 - lr: 0.000006 2023-10-19 20:51:47,908 DEV : loss 0.18491902947425842 - f1-score (micro avg) 0.5228 2023-10-19 20:51:47,922 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:51:50,065 epoch 10 - iter 89/893 - loss 0.19783421 - time (sec): 2.14 - samples/sec: 12237.43 - lr: 0.000005 - momentum: 0.000000 2023-10-19 20:51:52,362 epoch 10 - iter 178/893 - loss 0.19939993 - time (sec): 4.44 - samples/sec: 11666.48 - lr: 0.000004 - momentum: 0.000000 2023-10-19 20:51:55,147 epoch 10 - iter 267/893 - loss 0.20297035 - time (sec): 7.22 - samples/sec: 10795.24 - lr: 0.000004 - momentum: 0.000000 2023-10-19 20:51:57,423 epoch 10 - iter 356/893 - loss 0.20510717 - time (sec): 9.50 - samples/sec: 10783.91 - lr: 0.000003 - momentum: 0.000000 2023-10-19 20:51:59,726 epoch 10 - iter 445/893 - loss 0.20467266 - time (sec): 11.80 - samples/sec: 10781.26 - lr: 0.000003 - momentum: 0.000000 2023-10-19 20:52:01,947 epoch 10 - iter 534/893 - loss 0.20537293 - time (sec): 14.02 - samples/sec: 10806.69 - lr: 0.000002 - momentum: 0.000000 2023-10-19 20:52:04,321 epoch 10 - iter 623/893 - loss 0.20318246 - time (sec): 16.40 - samples/sec: 10731.05 - lr: 0.000002 - momentum: 0.000000 2023-10-19 20:52:06,590 epoch 10 - iter 712/893 - loss 0.20334880 - time (sec): 18.67 - samples/sec: 10705.27 - lr: 0.000001 - momentum: 0.000000 2023-10-19 20:52:08,869 epoch 10 - iter 801/893 - loss 0.20499649 - time (sec): 20.95 - samples/sec: 10675.00 - lr: 0.000001 - momentum: 0.000000 2023-10-19 20:52:11,144 epoch 10 - iter 890/893 - loss 0.20573355 - time (sec): 23.22 - samples/sec: 10664.10 - lr: 0.000000 - momentum: 0.000000 2023-10-19 20:52:11,215 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:52:11,215 EPOCH 10 done: loss 0.2059 - lr: 0.000000 2023-10-19 20:52:13,598 DEV : loss 0.18561328947544098 - f1-score (micro avg) 0.5273 2023-10-19 20:52:13,641 ---------------------------------------------------------------------------------------------------- 2023-10-19 20:52:13,642 Loading model from best epoch ... 2023-10-19 20:52:13,719 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 2023-10-19 20:52:18,262 Results: - F-score (micro) 0.4184 - F-score (macro) 0.256 - Accuracy 0.2729 By class: precision recall f1-score support LOC 0.4267 0.4968 0.4591 1095 PER 0.4579 0.4733 0.4655 1012 ORG 0.1359 0.0784 0.0995 357 HumanProd 0.0000 0.0000 0.0000 33 micro avg 0.4159 0.4209 0.4184 2497 macro avg 0.2551 0.2621 0.2560 2497 weighted avg 0.3921 0.4209 0.4042 2497 2023-10-19 20:52:18,262 ----------------------------------------------------------------------------------------------------