2023-10-25 21:05:32,624 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(64001, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 MultiCorpus: 1085 train + 148 dev + 364 test sentences - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Train: 1085 sentences 2023-10-25 21:05:32,625 (train_with_dev=False, train_with_test=False) 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Training Params: 2023-10-25 21:05:32,625 - learning_rate: "5e-05" 2023-10-25 21:05:32,625 - mini_batch_size: "4" 2023-10-25 21:05:32,625 - max_epochs: "10" 2023-10-25 21:05:32,625 - shuffle: "True" 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Plugins: 2023-10-25 21:05:32,625 - TensorboardLogger 2023-10-25 21:05:32,625 - LinearScheduler | warmup_fraction: '0.1' 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Final evaluation on model from best epoch (best-model.pt) 2023-10-25 21:05:32,625 - metric: "('micro avg', 'f1-score')" 2023-10-25 21:05:32,625 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,625 Computation: 2023-10-25 21:05:32,625 - compute on device: cuda:0 2023-10-25 21:05:32,625 - embedding storage: none 2023-10-25 21:05:32,626 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,626 Model training base path: "hmbench-newseye/sv-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2" 2023-10-25 21:05:32,626 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,626 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:32,626 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-25 21:05:34,162 epoch 1 - iter 27/272 - loss 2.73607380 - time (sec): 1.54 - samples/sec: 3350.45 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:05:35,786 epoch 1 - iter 54/272 - loss 1.84896315 - time (sec): 3.16 - samples/sec: 3383.35 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:05:37,361 epoch 1 - iter 81/272 - loss 1.37269521 - time (sec): 4.73 - samples/sec: 3351.43 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:05:38,976 epoch 1 - iter 108/272 - loss 1.10859483 - time (sec): 6.35 - samples/sec: 3413.69 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:05:40,561 epoch 1 - iter 135/272 - loss 0.95656040 - time (sec): 7.93 - samples/sec: 3364.00 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:05:42,187 epoch 1 - iter 162/272 - loss 0.83486853 - time (sec): 9.56 - samples/sec: 3338.14 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:05:43,828 epoch 1 - iter 189/272 - loss 0.73760380 - time (sec): 11.20 - samples/sec: 3341.22 - lr: 0.000035 - momentum: 0.000000 2023-10-25 21:05:45,392 epoch 1 - iter 216/272 - loss 0.67349066 - time (sec): 12.77 - samples/sec: 3329.37 - lr: 0.000040 - momentum: 0.000000 2023-10-25 21:05:46,811 epoch 1 - iter 243/272 - loss 0.63006514 - time (sec): 14.18 - samples/sec: 3314.75 - lr: 0.000044 - momentum: 0.000000 2023-10-25 21:05:48,248 epoch 1 - iter 270/272 - loss 0.58984536 - time (sec): 15.62 - samples/sec: 3318.65 - lr: 0.000049 - momentum: 0.000000 2023-10-25 21:05:48,340 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:48,340 EPOCH 1 done: loss 0.5882 - lr: 0.000049 2023-10-25 21:05:49,100 DEV : loss 0.13279995322227478 - f1-score (micro avg) 0.6869 2023-10-25 21:05:49,109 saving best model 2023-10-25 21:05:49,629 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:05:50,990 epoch 2 - iter 27/272 - loss 0.16014777 - time (sec): 1.36 - samples/sec: 3565.41 - lr: 0.000049 - momentum: 0.000000 2023-10-25 21:05:52,395 epoch 2 - iter 54/272 - loss 0.14356362 - time (sec): 2.76 - samples/sec: 3582.12 - lr: 0.000049 - momentum: 0.000000 2023-10-25 21:05:53,853 epoch 2 - iter 81/272 - loss 0.14683167 - time (sec): 4.22 - samples/sec: 3572.70 - lr: 0.000048 - momentum: 0.000000 2023-10-25 21:05:55,348 epoch 2 - iter 108/272 - loss 0.14493448 - time (sec): 5.72 - samples/sec: 3574.26 - lr: 0.000048 - momentum: 0.000000 2023-10-25 21:05:56,817 epoch 2 - iter 135/272 - loss 0.14341750 - time (sec): 7.19 - samples/sec: 3607.07 - lr: 0.000047 - momentum: 0.000000 2023-10-25 21:05:58,276 epoch 2 - iter 162/272 - loss 0.13309816 - time (sec): 8.65 - samples/sec: 3590.36 - lr: 0.000047 - momentum: 0.000000 2023-10-25 21:05:59,691 epoch 2 - iter 189/272 - loss 0.13345670 - time (sec): 10.06 - samples/sec: 3575.57 - lr: 0.000046 - momentum: 0.000000 2023-10-25 21:06:01,102 epoch 2 - iter 216/272 - loss 0.12800000 - time (sec): 11.47 - samples/sec: 3563.37 - lr: 0.000046 - momentum: 0.000000 2023-10-25 21:06:02,569 epoch 2 - iter 243/272 - loss 0.12507971 - time (sec): 12.94 - samples/sec: 3603.58 - lr: 0.000045 - momentum: 0.000000 2023-10-25 21:06:03,956 epoch 2 - iter 270/272 - loss 0.12227685 - time (sec): 14.33 - samples/sec: 3615.14 - lr: 0.000045 - momentum: 0.000000 2023-10-25 21:06:04,048 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:04,049 EPOCH 2 done: loss 0.1240 - lr: 0.000045 2023-10-25 21:06:05,740 DEV : loss 0.11688721925020218 - f1-score (micro avg) 0.7738 2023-10-25 21:06:05,750 saving best model 2023-10-25 21:06:06,490 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:07,926 epoch 3 - iter 27/272 - loss 0.05272744 - time (sec): 1.43 - samples/sec: 3684.31 - lr: 0.000044 - momentum: 0.000000 2023-10-25 21:06:09,417 epoch 3 - iter 54/272 - loss 0.06315361 - time (sec): 2.92 - samples/sec: 3609.68 - lr: 0.000043 - momentum: 0.000000 2023-10-25 21:06:10,876 epoch 3 - iter 81/272 - loss 0.06829449 - time (sec): 4.38 - samples/sec: 3616.16 - lr: 0.000043 - momentum: 0.000000 2023-10-25 21:06:12,390 epoch 3 - iter 108/272 - loss 0.07194428 - time (sec): 5.90 - samples/sec: 3559.81 - lr: 0.000042 - momentum: 0.000000 2023-10-25 21:06:13,842 epoch 3 - iter 135/272 - loss 0.07458317 - time (sec): 7.35 - samples/sec: 3480.29 - lr: 0.000042 - momentum: 0.000000 2023-10-25 21:06:15,350 epoch 3 - iter 162/272 - loss 0.07504227 - time (sec): 8.86 - samples/sec: 3434.72 - lr: 0.000041 - momentum: 0.000000 2023-10-25 21:06:16,845 epoch 3 - iter 189/272 - loss 0.07582093 - time (sec): 10.35 - samples/sec: 3415.59 - lr: 0.000041 - momentum: 0.000000 2023-10-25 21:06:18,396 epoch 3 - iter 216/272 - loss 0.07747465 - time (sec): 11.90 - samples/sec: 3445.16 - lr: 0.000040 - momentum: 0.000000 2023-10-25 21:06:19,917 epoch 3 - iter 243/272 - loss 0.07480487 - time (sec): 13.42 - samples/sec: 3420.94 - lr: 0.000040 - momentum: 0.000000 2023-10-25 21:06:21,418 epoch 3 - iter 270/272 - loss 0.07056925 - time (sec): 14.93 - samples/sec: 3458.13 - lr: 0.000039 - momentum: 0.000000 2023-10-25 21:06:21,550 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:21,550 EPOCH 3 done: loss 0.0709 - lr: 0.000039 2023-10-25 21:06:22,770 DEV : loss 0.11765002459287643 - f1-score (micro avg) 0.7812 2023-10-25 21:06:22,777 saving best model 2023-10-25 21:06:23,502 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:25,013 epoch 4 - iter 27/272 - loss 0.06309874 - time (sec): 1.51 - samples/sec: 3903.99 - lr: 0.000038 - momentum: 0.000000 2023-10-25 21:06:26,492 epoch 4 - iter 54/272 - loss 0.05289798 - time (sec): 2.99 - samples/sec: 3515.76 - lr: 0.000038 - momentum: 0.000000 2023-10-25 21:06:28,004 epoch 4 - iter 81/272 - loss 0.04365696 - time (sec): 4.50 - samples/sec: 3405.93 - lr: 0.000037 - momentum: 0.000000 2023-10-25 21:06:29,539 epoch 4 - iter 108/272 - loss 0.03848979 - time (sec): 6.04 - samples/sec: 3480.18 - lr: 0.000037 - momentum: 0.000000 2023-10-25 21:06:31,122 epoch 4 - iter 135/272 - loss 0.04217353 - time (sec): 7.62 - samples/sec: 3449.76 - lr: 0.000036 - momentum: 0.000000 2023-10-25 21:06:32,608 epoch 4 - iter 162/272 - loss 0.04215972 - time (sec): 9.10 - samples/sec: 3391.67 - lr: 0.000036 - momentum: 0.000000 2023-10-25 21:06:34,094 epoch 4 - iter 189/272 - loss 0.04333349 - time (sec): 10.59 - samples/sec: 3427.56 - lr: 0.000035 - momentum: 0.000000 2023-10-25 21:06:35,607 epoch 4 - iter 216/272 - loss 0.04312284 - time (sec): 12.10 - samples/sec: 3392.55 - lr: 0.000034 - momentum: 0.000000 2023-10-25 21:06:37,160 epoch 4 - iter 243/272 - loss 0.04177106 - time (sec): 13.66 - samples/sec: 3420.69 - lr: 0.000034 - momentum: 0.000000 2023-10-25 21:06:38,661 epoch 4 - iter 270/272 - loss 0.04273552 - time (sec): 15.16 - samples/sec: 3398.72 - lr: 0.000033 - momentum: 0.000000 2023-10-25 21:06:38,769 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:38,769 EPOCH 4 done: loss 0.0429 - lr: 0.000033 2023-10-25 21:06:39,974 DEV : loss 0.14461365342140198 - f1-score (micro avg) 0.7817 2023-10-25 21:06:39,982 saving best model 2023-10-25 21:06:40,659 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:42,161 epoch 5 - iter 27/272 - loss 0.03578132 - time (sec): 1.50 - samples/sec: 2946.96 - lr: 0.000033 - momentum: 0.000000 2023-10-25 21:06:43,729 epoch 5 - iter 54/272 - loss 0.03982125 - time (sec): 3.07 - samples/sec: 3388.05 - lr: 0.000032 - momentum: 0.000000 2023-10-25 21:06:45,201 epoch 5 - iter 81/272 - loss 0.04578774 - time (sec): 4.54 - samples/sec: 3182.37 - lr: 0.000032 - momentum: 0.000000 2023-10-25 21:06:46,722 epoch 5 - iter 108/272 - loss 0.03957806 - time (sec): 6.06 - samples/sec: 3235.40 - lr: 0.000031 - momentum: 0.000000 2023-10-25 21:06:48,179 epoch 5 - iter 135/272 - loss 0.03548740 - time (sec): 7.52 - samples/sec: 3194.94 - lr: 0.000031 - momentum: 0.000000 2023-10-25 21:06:49,746 epoch 5 - iter 162/272 - loss 0.03664451 - time (sec): 9.08 - samples/sec: 3246.35 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:06:51,313 epoch 5 - iter 189/272 - loss 0.03629779 - time (sec): 10.65 - samples/sec: 3276.18 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:06:52,866 epoch 5 - iter 216/272 - loss 0.03416917 - time (sec): 12.20 - samples/sec: 3298.02 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:06:54,354 epoch 5 - iter 243/272 - loss 0.03457437 - time (sec): 13.69 - samples/sec: 3311.03 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:06:55,840 epoch 5 - iter 270/272 - loss 0.03329317 - time (sec): 15.18 - samples/sec: 3405.57 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:06:55,935 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:55,935 EPOCH 5 done: loss 0.0332 - lr: 0.000028 2023-10-25 21:06:57,114 DEV : loss 0.15152905881404877 - f1-score (micro avg) 0.7956 2023-10-25 21:06:57,121 saving best model 2023-10-25 21:06:57,844 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:06:59,315 epoch 6 - iter 27/272 - loss 0.02662659 - time (sec): 1.47 - samples/sec: 3739.49 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:07:01,237 epoch 6 - iter 54/272 - loss 0.02520854 - time (sec): 3.39 - samples/sec: 3147.08 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:07:02,760 epoch 6 - iter 81/272 - loss 0.02763396 - time (sec): 4.91 - samples/sec: 3276.21 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:07:04,284 epoch 6 - iter 108/272 - loss 0.02306852 - time (sec): 6.44 - samples/sec: 3290.51 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:07:05,826 epoch 6 - iter 135/272 - loss 0.02493619 - time (sec): 7.98 - samples/sec: 3360.42 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:07:07,346 epoch 6 - iter 162/272 - loss 0.02506762 - time (sec): 9.50 - samples/sec: 3355.79 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:07:08,822 epoch 6 - iter 189/272 - loss 0.02389920 - time (sec): 10.97 - samples/sec: 3399.74 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:07:10,291 epoch 6 - iter 216/272 - loss 0.02348636 - time (sec): 12.44 - samples/sec: 3400.09 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:07:11,766 epoch 6 - iter 243/272 - loss 0.02385580 - time (sec): 13.92 - samples/sec: 3412.25 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:07:13,222 epoch 6 - iter 270/272 - loss 0.02429412 - time (sec): 15.37 - samples/sec: 3368.14 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:07:13,322 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:13,323 EPOCH 6 done: loss 0.0244 - lr: 0.000022 2023-10-25 21:07:14,531 DEV : loss 0.18444250524044037 - f1-score (micro avg) 0.7963 2023-10-25 21:07:14,540 saving best model 2023-10-25 21:07:15,252 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:16,718 epoch 7 - iter 27/272 - loss 0.01815834 - time (sec): 1.46 - samples/sec: 3493.39 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:07:18,161 epoch 7 - iter 54/272 - loss 0.01737148 - time (sec): 2.91 - samples/sec: 3410.74 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:07:19,666 epoch 7 - iter 81/272 - loss 0.01790111 - time (sec): 4.41 - samples/sec: 3587.60 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:07:21,195 epoch 7 - iter 108/272 - loss 0.01703003 - time (sec): 5.94 - samples/sec: 3629.33 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:07:22,744 epoch 7 - iter 135/272 - loss 0.01648317 - time (sec): 7.49 - samples/sec: 3656.34 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:07:24,329 epoch 7 - iter 162/272 - loss 0.01658498 - time (sec): 9.08 - samples/sec: 3607.73 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:07:25,916 epoch 7 - iter 189/272 - loss 0.01502441 - time (sec): 10.66 - samples/sec: 3532.25 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:07:27,504 epoch 7 - iter 216/272 - loss 0.01519647 - time (sec): 12.25 - samples/sec: 3545.79 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:07:28,965 epoch 7 - iter 243/272 - loss 0.01481267 - time (sec): 13.71 - samples/sec: 3443.69 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:07:30,440 epoch 7 - iter 270/272 - loss 0.01479507 - time (sec): 15.19 - samples/sec: 3401.04 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:07:30,542 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:30,542 EPOCH 7 done: loss 0.0151 - lr: 0.000017 2023-10-25 21:07:31,893 DEV : loss 0.18188118934631348 - f1-score (micro avg) 0.7927 2023-10-25 21:07:31,901 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:33,374 epoch 8 - iter 27/272 - loss 0.01173756 - time (sec): 1.47 - samples/sec: 4062.71 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:07:34,856 epoch 8 - iter 54/272 - loss 0.01144365 - time (sec): 2.95 - samples/sec: 3968.39 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:07:36,354 epoch 8 - iter 81/272 - loss 0.01050122 - time (sec): 4.45 - samples/sec: 3641.66 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:07:37,856 epoch 8 - iter 108/272 - loss 0.00954095 - time (sec): 5.95 - samples/sec: 3611.45 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:07:39,419 epoch 8 - iter 135/272 - loss 0.00867344 - time (sec): 7.52 - samples/sec: 3549.08 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:07:40,932 epoch 8 - iter 162/272 - loss 0.00912422 - time (sec): 9.03 - samples/sec: 3482.29 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:07:42,439 epoch 8 - iter 189/272 - loss 0.00858298 - time (sec): 10.54 - samples/sec: 3441.14 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:07:43,956 epoch 8 - iter 216/272 - loss 0.00857042 - time (sec): 12.05 - samples/sec: 3459.67 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:07:45,546 epoch 8 - iter 243/272 - loss 0.00832379 - time (sec): 13.64 - samples/sec: 3461.18 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:07:47,010 epoch 8 - iter 270/272 - loss 0.00849181 - time (sec): 15.11 - samples/sec: 3423.99 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:07:47,113 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:47,114 EPOCH 8 done: loss 0.0085 - lr: 0.000011 2023-10-25 21:07:48,321 DEV : loss 0.17987406253814697 - f1-score (micro avg) 0.8066 2023-10-25 21:07:48,328 saving best model 2023-10-25 21:07:49,042 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:07:50,558 epoch 9 - iter 27/272 - loss 0.00059481 - time (sec): 1.51 - samples/sec: 3896.57 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:07:52,032 epoch 9 - iter 54/272 - loss 0.00437637 - time (sec): 2.99 - samples/sec: 3442.08 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:07:53,621 epoch 9 - iter 81/272 - loss 0.00449556 - time (sec): 4.58 - samples/sec: 3517.08 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:07:55,148 epoch 9 - iter 108/272 - loss 0.00672091 - time (sec): 6.10 - samples/sec: 3507.96 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:07:56,692 epoch 9 - iter 135/272 - loss 0.00680104 - time (sec): 7.65 - samples/sec: 3455.22 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:07:58,219 epoch 9 - iter 162/272 - loss 0.00656457 - time (sec): 9.17 - samples/sec: 3439.60 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:07:59,678 epoch 9 - iter 189/272 - loss 0.00653987 - time (sec): 10.63 - samples/sec: 3390.09 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:08:01,096 epoch 9 - iter 216/272 - loss 0.00686872 - time (sec): 12.05 - samples/sec: 3379.14 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:08:02,916 epoch 9 - iter 243/272 - loss 0.00643747 - time (sec): 13.87 - samples/sec: 3356.97 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:08:04,356 epoch 9 - iter 270/272 - loss 0.00654410 - time (sec): 15.31 - samples/sec: 3368.70 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:08:04,457 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:08:04,458 EPOCH 9 done: loss 0.0065 - lr: 0.000006 2023-10-25 21:08:05,753 DEV : loss 0.19414767622947693 - f1-score (micro avg) 0.7883 2023-10-25 21:08:05,762 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:08:07,206 epoch 10 - iter 27/272 - loss 0.00949480 - time (sec): 1.44 - samples/sec: 3040.73 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:08:08,729 epoch 10 - iter 54/272 - loss 0.00586201 - time (sec): 2.97 - samples/sec: 3077.41 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:08:10,230 epoch 10 - iter 81/272 - loss 0.00514860 - time (sec): 4.47 - samples/sec: 3137.90 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:08:11,771 epoch 10 - iter 108/272 - loss 0.00510123 - time (sec): 6.01 - samples/sec: 3259.80 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:08:13,329 epoch 10 - iter 135/272 - loss 0.00443539 - time (sec): 7.57 - samples/sec: 3352.04 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:08:14,883 epoch 10 - iter 162/272 - loss 0.00394396 - time (sec): 9.12 - samples/sec: 3329.12 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:08:16,414 epoch 10 - iter 189/272 - loss 0.00341668 - time (sec): 10.65 - samples/sec: 3309.64 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:08:17,913 epoch 10 - iter 216/272 - loss 0.00373493 - time (sec): 12.15 - samples/sec: 3380.68 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:08:19,424 epoch 10 - iter 243/272 - loss 0.00455730 - time (sec): 13.66 - samples/sec: 3379.38 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:08:20,883 epoch 10 - iter 270/272 - loss 0.00439589 - time (sec): 15.12 - samples/sec: 3427.85 - lr: 0.000000 - momentum: 0.000000 2023-10-25 21:08:20,975 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:08:20,976 EPOCH 10 done: loss 0.0044 - lr: 0.000000 2023-10-25 21:08:22,205 DEV : loss 0.19060413539409637 - f1-score (micro avg) 0.7905 2023-10-25 21:08:22,747 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:08:22,749 Loading model from best epoch ... 2023-10-25 21:08:24,708 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG 2023-10-25 21:08:26,730 Results: - F-score (micro) 0.7882 - F-score (macro) 0.7203 - Accuracy 0.6653 By class: precision recall f1-score support LOC 0.8411 0.8654 0.8531 312 PER 0.7206 0.8558 0.7824 208 ORG 0.4355 0.4909 0.4615 55 HumanProd 0.6897 0.9091 0.7843 22 micro avg 0.7511 0.8291 0.7882 597 macro avg 0.6717 0.7803 0.7203 597 weighted avg 0.7562 0.8291 0.7899 597 2023-10-25 21:08:26,730 ----------------------------------------------------------------------------------------------------