2023-10-17 18:51:00,787 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): ElectraModel( (embeddings): ElectraEmbeddings( (word_embeddings): Embedding(32001, 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): ElectraEncoder( (layer): ModuleList( (0-11): 12 x ElectraLayer( (attention): ElectraAttention( (self): ElectraSelfAttention( (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): ElectraSelfOutput( (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): ElectraIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): ElectraOutput( (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) ) ) ) ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 MultiCorpus: 5777 train + 722 dev + 723 test sentences - NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Train: 5777 sentences 2023-10-17 18:51:00,788 (train_with_dev=False, train_with_test=False) 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Training Params: 2023-10-17 18:51:00,788 - learning_rate: "3e-05" 2023-10-17 18:51:00,788 - mini_batch_size: "8" 2023-10-17 18:51:00,788 - max_epochs: "10" 2023-10-17 18:51:00,788 - shuffle: "True" 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Plugins: 2023-10-17 18:51:00,788 - TensorboardLogger 2023-10-17 18:51:00,788 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 18:51:00,788 - metric: "('micro avg', 'f1-score')" 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Computation: 2023-10-17 18:51:00,788 - compute on device: cuda:0 2023-10-17 18:51:00,788 - embedding storage: none 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,788 Model training base path: "hmbench-icdar/nl-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-17 18:51:00,788 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,789 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:00,789 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 18:51:05,999 epoch 1 - iter 72/723 - loss 2.73572129 - time (sec): 5.21 - samples/sec: 3228.40 - lr: 0.000003 - momentum: 0.000000 2023-10-17 18:51:11,090 epoch 1 - iter 144/723 - loss 1.71636677 - time (sec): 10.30 - samples/sec: 3297.56 - lr: 0.000006 - momentum: 0.000000 2023-10-17 18:51:16,224 epoch 1 - iter 216/723 - loss 1.21015689 - time (sec): 15.43 - samples/sec: 3319.86 - lr: 0.000009 - momentum: 0.000000 2023-10-17 18:51:21,390 epoch 1 - iter 288/723 - loss 0.94420268 - time (sec): 20.60 - samples/sec: 3347.59 - lr: 0.000012 - momentum: 0.000000 2023-10-17 18:51:26,193 epoch 1 - iter 360/723 - loss 0.78590918 - time (sec): 25.40 - samples/sec: 3395.27 - lr: 0.000015 - momentum: 0.000000 2023-10-17 18:51:31,477 epoch 1 - iter 432/723 - loss 0.67292077 - time (sec): 30.69 - samples/sec: 3400.44 - lr: 0.000018 - momentum: 0.000000 2023-10-17 18:51:36,674 epoch 1 - iter 504/723 - loss 0.59756926 - time (sec): 35.88 - samples/sec: 3400.07 - lr: 0.000021 - momentum: 0.000000 2023-10-17 18:51:42,043 epoch 1 - iter 576/723 - loss 0.53717802 - time (sec): 41.25 - samples/sec: 3387.98 - lr: 0.000024 - momentum: 0.000000 2023-10-17 18:51:47,518 epoch 1 - iter 648/723 - loss 0.48924915 - time (sec): 46.73 - samples/sec: 3371.37 - lr: 0.000027 - momentum: 0.000000 2023-10-17 18:51:52,896 epoch 1 - iter 720/723 - loss 0.45323298 - time (sec): 52.11 - samples/sec: 3367.96 - lr: 0.000030 - momentum: 0.000000 2023-10-17 18:51:53,104 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:51:53,105 EPOCH 1 done: loss 0.4518 - lr: 0.000030 2023-10-17 18:51:55,813 DEV : loss 0.08571955561637878 - f1-score (micro avg) 0.7621 2023-10-17 18:51:55,830 saving best model 2023-10-17 18:51:56,351 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:52:01,211 epoch 2 - iter 72/723 - loss 0.09693799 - time (sec): 4.86 - samples/sec: 3414.32 - lr: 0.000030 - momentum: 0.000000 2023-10-17 18:52:06,705 epoch 2 - iter 144/723 - loss 0.09826077 - time (sec): 10.35 - samples/sec: 3307.89 - lr: 0.000029 - momentum: 0.000000 2023-10-17 18:52:11,708 epoch 2 - iter 216/723 - loss 0.09503249 - time (sec): 15.36 - samples/sec: 3367.88 - lr: 0.000029 - momentum: 0.000000 2023-10-17 18:52:16,853 epoch 2 - iter 288/723 - loss 0.09891978 - time (sec): 20.50 - samples/sec: 3362.88 - lr: 0.000029 - momentum: 0.000000 2023-10-17 18:52:22,304 epoch 2 - iter 360/723 - loss 0.09274178 - time (sec): 25.95 - samples/sec: 3380.81 - lr: 0.000028 - momentum: 0.000000 2023-10-17 18:52:27,873 epoch 2 - iter 432/723 - loss 0.08927963 - time (sec): 31.52 - samples/sec: 3401.91 - lr: 0.000028 - momentum: 0.000000 2023-10-17 18:52:32,724 epoch 2 - iter 504/723 - loss 0.09084334 - time (sec): 36.37 - samples/sec: 3381.60 - lr: 0.000028 - momentum: 0.000000 2023-10-17 18:52:38,353 epoch 2 - iter 576/723 - loss 0.09010738 - time (sec): 42.00 - samples/sec: 3375.35 - lr: 0.000027 - momentum: 0.000000 2023-10-17 18:52:43,617 epoch 2 - iter 648/723 - loss 0.08974176 - time (sec): 47.26 - samples/sec: 3348.81 - lr: 0.000027 - momentum: 0.000000 2023-10-17 18:52:48,857 epoch 2 - iter 720/723 - loss 0.08723098 - time (sec): 52.50 - samples/sec: 3343.67 - lr: 0.000027 - momentum: 0.000000 2023-10-17 18:52:49,018 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:52:49,019 EPOCH 2 done: loss 0.0871 - lr: 0.000027 2023-10-17 18:52:52,238 DEV : loss 0.05628642439842224 - f1-score (micro avg) 0.8664 2023-10-17 18:52:52,255 saving best model 2023-10-17 18:52:52,649 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:52:58,275 epoch 3 - iter 72/723 - loss 0.06108376 - time (sec): 5.62 - samples/sec: 3077.41 - lr: 0.000026 - momentum: 0.000000 2023-10-17 18:53:03,076 epoch 3 - iter 144/723 - loss 0.06229454 - time (sec): 10.43 - samples/sec: 3255.95 - lr: 0.000026 - momentum: 0.000000 2023-10-17 18:53:08,621 epoch 3 - iter 216/723 - loss 0.06269040 - time (sec): 15.97 - samples/sec: 3261.71 - lr: 0.000026 - momentum: 0.000000 2023-10-17 18:53:14,059 epoch 3 - iter 288/723 - loss 0.05782452 - time (sec): 21.41 - samples/sec: 3273.63 - lr: 0.000025 - momentum: 0.000000 2023-10-17 18:53:19,376 epoch 3 - iter 360/723 - loss 0.05776341 - time (sec): 26.73 - samples/sec: 3305.07 - lr: 0.000025 - momentum: 0.000000 2023-10-17 18:53:24,787 epoch 3 - iter 432/723 - loss 0.06112370 - time (sec): 32.14 - samples/sec: 3288.95 - lr: 0.000025 - momentum: 0.000000 2023-10-17 18:53:29,871 epoch 3 - iter 504/723 - loss 0.06276585 - time (sec): 37.22 - samples/sec: 3302.02 - lr: 0.000024 - momentum: 0.000000 2023-10-17 18:53:35,301 epoch 3 - iter 576/723 - loss 0.06100180 - time (sec): 42.65 - samples/sec: 3318.70 - lr: 0.000024 - momentum: 0.000000 2023-10-17 18:53:40,357 epoch 3 - iter 648/723 - loss 0.06087244 - time (sec): 47.71 - samples/sec: 3323.88 - lr: 0.000024 - momentum: 0.000000 2023-10-17 18:53:45,595 epoch 3 - iter 720/723 - loss 0.06067803 - time (sec): 52.94 - samples/sec: 3316.47 - lr: 0.000023 - momentum: 0.000000 2023-10-17 18:53:45,776 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:53:45,777 EPOCH 3 done: loss 0.0606 - lr: 0.000023 2023-10-17 18:53:48,994 DEV : loss 0.061015695333480835 - f1-score (micro avg) 0.8829 2023-10-17 18:53:49,010 saving best model 2023-10-17 18:53:49,436 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:53:54,717 epoch 4 - iter 72/723 - loss 0.04387038 - time (sec): 5.28 - samples/sec: 3489.64 - lr: 0.000023 - momentum: 0.000000 2023-10-17 18:54:00,075 epoch 4 - iter 144/723 - loss 0.03977768 - time (sec): 10.64 - samples/sec: 3434.68 - lr: 0.000023 - momentum: 0.000000 2023-10-17 18:54:05,046 epoch 4 - iter 216/723 - loss 0.04174055 - time (sec): 15.61 - samples/sec: 3394.76 - lr: 0.000022 - momentum: 0.000000 2023-10-17 18:54:10,454 epoch 4 - iter 288/723 - loss 0.04295155 - time (sec): 21.02 - samples/sec: 3371.28 - lr: 0.000022 - momentum: 0.000000 2023-10-17 18:54:15,352 epoch 4 - iter 360/723 - loss 0.04184529 - time (sec): 25.92 - samples/sec: 3370.25 - lr: 0.000022 - momentum: 0.000000 2023-10-17 18:54:20,627 epoch 4 - iter 432/723 - loss 0.04216528 - time (sec): 31.19 - samples/sec: 3358.32 - lr: 0.000021 - momentum: 0.000000 2023-10-17 18:54:25,677 epoch 4 - iter 504/723 - loss 0.04174425 - time (sec): 36.24 - samples/sec: 3385.08 - lr: 0.000021 - momentum: 0.000000 2023-10-17 18:54:31,093 epoch 4 - iter 576/723 - loss 0.04256262 - time (sec): 41.66 - samples/sec: 3366.49 - lr: 0.000021 - momentum: 0.000000 2023-10-17 18:54:36,301 epoch 4 - iter 648/723 - loss 0.04255295 - time (sec): 46.86 - samples/sec: 3360.15 - lr: 0.000020 - momentum: 0.000000 2023-10-17 18:54:41,551 epoch 4 - iter 720/723 - loss 0.04326055 - time (sec): 52.11 - samples/sec: 3372.14 - lr: 0.000020 - momentum: 0.000000 2023-10-17 18:54:41,713 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:54:41,713 EPOCH 4 done: loss 0.0432 - lr: 0.000020 2023-10-17 18:54:45,293 DEV : loss 0.07059507817029953 - f1-score (micro avg) 0.8652 2023-10-17 18:54:45,309 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:54:50,582 epoch 5 - iter 72/723 - loss 0.03765946 - time (sec): 5.27 - samples/sec: 3200.52 - lr: 0.000020 - momentum: 0.000000 2023-10-17 18:54:55,435 epoch 5 - iter 144/723 - loss 0.03376893 - time (sec): 10.12 - samples/sec: 3269.32 - lr: 0.000019 - momentum: 0.000000 2023-10-17 18:55:01,381 epoch 5 - iter 216/723 - loss 0.03444213 - time (sec): 16.07 - samples/sec: 3259.98 - lr: 0.000019 - momentum: 0.000000 2023-10-17 18:55:06,439 epoch 5 - iter 288/723 - loss 0.03201450 - time (sec): 21.13 - samples/sec: 3278.94 - lr: 0.000019 - momentum: 0.000000 2023-10-17 18:55:11,837 epoch 5 - iter 360/723 - loss 0.03021248 - time (sec): 26.53 - samples/sec: 3268.82 - lr: 0.000018 - momentum: 0.000000 2023-10-17 18:55:17,061 epoch 5 - iter 432/723 - loss 0.03095066 - time (sec): 31.75 - samples/sec: 3295.73 - lr: 0.000018 - momentum: 0.000000 2023-10-17 18:55:22,311 epoch 5 - iter 504/723 - loss 0.03205508 - time (sec): 37.00 - samples/sec: 3318.65 - lr: 0.000018 - momentum: 0.000000 2023-10-17 18:55:27,501 epoch 5 - iter 576/723 - loss 0.03251336 - time (sec): 42.19 - samples/sec: 3325.42 - lr: 0.000017 - momentum: 0.000000 2023-10-17 18:55:32,561 epoch 5 - iter 648/723 - loss 0.03276588 - time (sec): 47.25 - samples/sec: 3329.33 - lr: 0.000017 - momentum: 0.000000 2023-10-17 18:55:37,985 epoch 5 - iter 720/723 - loss 0.03238438 - time (sec): 52.67 - samples/sec: 3338.39 - lr: 0.000017 - momentum: 0.000000 2023-10-17 18:55:38,138 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:55:38,139 EPOCH 5 done: loss 0.0324 - lr: 0.000017 2023-10-17 18:55:41,451 DEV : loss 0.07911184430122375 - f1-score (micro avg) 0.8697 2023-10-17 18:55:41,469 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:55:46,855 epoch 6 - iter 72/723 - loss 0.01939646 - time (sec): 5.38 - samples/sec: 3381.89 - lr: 0.000016 - momentum: 0.000000 2023-10-17 18:55:52,097 epoch 6 - iter 144/723 - loss 0.02202295 - time (sec): 10.63 - samples/sec: 3376.79 - lr: 0.000016 - momentum: 0.000000 2023-10-17 18:55:57,312 epoch 6 - iter 216/723 - loss 0.02329081 - time (sec): 15.84 - samples/sec: 3385.10 - lr: 0.000016 - momentum: 0.000000 2023-10-17 18:56:03,153 epoch 6 - iter 288/723 - loss 0.02709437 - time (sec): 21.68 - samples/sec: 3284.61 - lr: 0.000015 - momentum: 0.000000 2023-10-17 18:56:08,569 epoch 6 - iter 360/723 - loss 0.02852147 - time (sec): 27.10 - samples/sec: 3309.49 - lr: 0.000015 - momentum: 0.000000 2023-10-17 18:56:13,824 epoch 6 - iter 432/723 - loss 0.02704669 - time (sec): 32.35 - samples/sec: 3322.19 - lr: 0.000015 - momentum: 0.000000 2023-10-17 18:56:18,846 epoch 6 - iter 504/723 - loss 0.02698341 - time (sec): 37.38 - samples/sec: 3337.29 - lr: 0.000014 - momentum: 0.000000 2023-10-17 18:56:23,707 epoch 6 - iter 576/723 - loss 0.02713054 - time (sec): 42.24 - samples/sec: 3344.91 - lr: 0.000014 - momentum: 0.000000 2023-10-17 18:56:28,856 epoch 6 - iter 648/723 - loss 0.02638017 - time (sec): 47.38 - samples/sec: 3348.51 - lr: 0.000014 - momentum: 0.000000 2023-10-17 18:56:33,896 epoch 6 - iter 720/723 - loss 0.02633353 - time (sec): 52.43 - samples/sec: 3352.29 - lr: 0.000013 - momentum: 0.000000 2023-10-17 18:56:34,069 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:56:34,069 EPOCH 6 done: loss 0.0263 - lr: 0.000013 2023-10-17 18:56:37,242 DEV : loss 0.08742444217205048 - f1-score (micro avg) 0.8809 2023-10-17 18:56:37,259 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:56:42,537 epoch 7 - iter 72/723 - loss 0.01010414 - time (sec): 5.28 - samples/sec: 3350.03 - lr: 0.000013 - momentum: 0.000000 2023-10-17 18:56:47,589 epoch 7 - iter 144/723 - loss 0.01933338 - time (sec): 10.33 - samples/sec: 3321.61 - lr: 0.000013 - momentum: 0.000000 2023-10-17 18:56:53,263 epoch 7 - iter 216/723 - loss 0.01860946 - time (sec): 16.00 - samples/sec: 3316.41 - lr: 0.000012 - momentum: 0.000000 2023-10-17 18:56:58,767 epoch 7 - iter 288/723 - loss 0.01987477 - time (sec): 21.51 - samples/sec: 3329.15 - lr: 0.000012 - momentum: 0.000000 2023-10-17 18:57:04,192 epoch 7 - iter 360/723 - loss 0.01978143 - time (sec): 26.93 - samples/sec: 3325.96 - lr: 0.000012 - momentum: 0.000000 2023-10-17 18:57:09,642 epoch 7 - iter 432/723 - loss 0.02023814 - time (sec): 32.38 - samples/sec: 3310.10 - lr: 0.000011 - momentum: 0.000000 2023-10-17 18:57:14,799 epoch 7 - iter 504/723 - loss 0.01949429 - time (sec): 37.54 - samples/sec: 3315.96 - lr: 0.000011 - momentum: 0.000000 2023-10-17 18:57:19,826 epoch 7 - iter 576/723 - loss 0.01865648 - time (sec): 42.57 - samples/sec: 3327.50 - lr: 0.000011 - momentum: 0.000000 2023-10-17 18:57:24,812 epoch 7 - iter 648/723 - loss 0.01853140 - time (sec): 47.55 - samples/sec: 3333.24 - lr: 0.000010 - momentum: 0.000000 2023-10-17 18:57:30,046 epoch 7 - iter 720/723 - loss 0.01839669 - time (sec): 52.79 - samples/sec: 3328.86 - lr: 0.000010 - momentum: 0.000000 2023-10-17 18:57:30,201 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:57:30,201 EPOCH 7 done: loss 0.0184 - lr: 0.000010 2023-10-17 18:57:33,757 DEV : loss 0.10578546673059464 - f1-score (micro avg) 0.8809 2023-10-17 18:57:33,774 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:57:38,932 epoch 8 - iter 72/723 - loss 0.00841995 - time (sec): 5.16 - samples/sec: 3443.43 - lr: 0.000010 - momentum: 0.000000 2023-10-17 18:57:44,074 epoch 8 - iter 144/723 - loss 0.01229420 - time (sec): 10.30 - samples/sec: 3426.89 - lr: 0.000009 - momentum: 0.000000 2023-10-17 18:57:49,096 epoch 8 - iter 216/723 - loss 0.01355410 - time (sec): 15.32 - samples/sec: 3395.51 - lr: 0.000009 - momentum: 0.000000 2023-10-17 18:57:54,305 epoch 8 - iter 288/723 - loss 0.01394882 - time (sec): 20.53 - samples/sec: 3385.90 - lr: 0.000009 - momentum: 0.000000 2023-10-17 18:57:59,323 epoch 8 - iter 360/723 - loss 0.01323447 - time (sec): 25.55 - samples/sec: 3374.12 - lr: 0.000008 - momentum: 0.000000 2023-10-17 18:58:04,498 epoch 8 - iter 432/723 - loss 0.01270974 - time (sec): 30.72 - samples/sec: 3377.16 - lr: 0.000008 - momentum: 0.000000 2023-10-17 18:58:09,812 epoch 8 - iter 504/723 - loss 0.01255571 - time (sec): 36.04 - samples/sec: 3355.95 - lr: 0.000008 - momentum: 0.000000 2023-10-17 18:58:15,497 epoch 8 - iter 576/723 - loss 0.01341256 - time (sec): 41.72 - samples/sec: 3360.99 - lr: 0.000007 - momentum: 0.000000 2023-10-17 18:58:20,693 epoch 8 - iter 648/723 - loss 0.01371160 - time (sec): 46.92 - samples/sec: 3357.01 - lr: 0.000007 - momentum: 0.000000 2023-10-17 18:58:26,278 epoch 8 - iter 720/723 - loss 0.01394702 - time (sec): 52.50 - samples/sec: 3344.06 - lr: 0.000007 - momentum: 0.000000 2023-10-17 18:58:26,474 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:58:26,474 EPOCH 8 done: loss 0.0139 - lr: 0.000007 2023-10-17 18:58:29,679 DEV : loss 0.11371435225009918 - f1-score (micro avg) 0.8805 2023-10-17 18:58:29,695 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:58:35,055 epoch 9 - iter 72/723 - loss 0.01003235 - time (sec): 5.36 - samples/sec: 3286.54 - lr: 0.000006 - momentum: 0.000000 2023-10-17 18:58:40,336 epoch 9 - iter 144/723 - loss 0.00981857 - time (sec): 10.64 - samples/sec: 3403.11 - lr: 0.000006 - momentum: 0.000000 2023-10-17 18:58:45,118 epoch 9 - iter 216/723 - loss 0.01076096 - time (sec): 15.42 - samples/sec: 3432.99 - lr: 0.000006 - momentum: 0.000000 2023-10-17 18:58:50,044 epoch 9 - iter 288/723 - loss 0.01027158 - time (sec): 20.35 - samples/sec: 3464.18 - lr: 0.000005 - momentum: 0.000000 2023-10-17 18:58:55,521 epoch 9 - iter 360/723 - loss 0.00989332 - time (sec): 25.82 - samples/sec: 3429.30 - lr: 0.000005 - momentum: 0.000000 2023-10-17 18:59:00,544 epoch 9 - iter 432/723 - loss 0.00992623 - time (sec): 30.85 - samples/sec: 3432.33 - lr: 0.000005 - momentum: 0.000000 2023-10-17 18:59:06,392 epoch 9 - iter 504/723 - loss 0.01080183 - time (sec): 36.70 - samples/sec: 3398.02 - lr: 0.000004 - momentum: 0.000000 2023-10-17 18:59:11,482 epoch 9 - iter 576/723 - loss 0.01069467 - time (sec): 41.79 - samples/sec: 3391.20 - lr: 0.000004 - momentum: 0.000000 2023-10-17 18:59:16,665 epoch 9 - iter 648/723 - loss 0.01087289 - time (sec): 46.97 - samples/sec: 3403.56 - lr: 0.000004 - momentum: 0.000000 2023-10-17 18:59:21,382 epoch 9 - iter 720/723 - loss 0.01170845 - time (sec): 51.69 - samples/sec: 3401.34 - lr: 0.000003 - momentum: 0.000000 2023-10-17 18:59:21,537 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:59:21,537 EPOCH 9 done: loss 0.0117 - lr: 0.000003 2023-10-17 18:59:24,756 DEV : loss 0.11608566343784332 - f1-score (micro avg) 0.8813 2023-10-17 18:59:24,773 ---------------------------------------------------------------------------------------------------- 2023-10-17 18:59:30,213 epoch 10 - iter 72/723 - loss 0.01498393 - time (sec): 5.44 - samples/sec: 3305.97 - lr: 0.000003 - momentum: 0.000000 2023-10-17 18:59:35,092 epoch 10 - iter 144/723 - loss 0.00950962 - time (sec): 10.32 - samples/sec: 3395.52 - lr: 0.000003 - momentum: 0.000000 2023-10-17 18:59:40,454 epoch 10 - iter 216/723 - loss 0.00882272 - time (sec): 15.68 - samples/sec: 3373.59 - lr: 0.000002 - momentum: 0.000000 2023-10-17 18:59:45,853 epoch 10 - iter 288/723 - loss 0.00879424 - time (sec): 21.08 - samples/sec: 3350.52 - lr: 0.000002 - momentum: 0.000000 2023-10-17 18:59:50,927 epoch 10 - iter 360/723 - loss 0.00903110 - time (sec): 26.15 - samples/sec: 3359.20 - lr: 0.000002 - momentum: 0.000000 2023-10-17 18:59:56,386 epoch 10 - iter 432/723 - loss 0.00841109 - time (sec): 31.61 - samples/sec: 3355.32 - lr: 0.000001 - momentum: 0.000000 2023-10-17 19:00:01,775 epoch 10 - iter 504/723 - loss 0.00806376 - time (sec): 37.00 - samples/sec: 3325.43 - lr: 0.000001 - momentum: 0.000000 2023-10-17 19:00:06,842 epoch 10 - iter 576/723 - loss 0.00790310 - time (sec): 42.07 - samples/sec: 3324.90 - lr: 0.000001 - momentum: 0.000000 2023-10-17 19:00:12,025 epoch 10 - iter 648/723 - loss 0.00804525 - time (sec): 47.25 - samples/sec: 3339.23 - lr: 0.000000 - momentum: 0.000000 2023-10-17 19:00:17,383 epoch 10 - iter 720/723 - loss 0.00796014 - time (sec): 52.61 - samples/sec: 3342.17 - lr: 0.000000 - momentum: 0.000000 2023-10-17 19:00:17,532 ---------------------------------------------------------------------------------------------------- 2023-10-17 19:00:17,533 EPOCH 10 done: loss 0.0079 - lr: 0.000000 2023-10-17 19:00:21,687 DEV : loss 0.12145841866731644 - f1-score (micro avg) 0.8792 2023-10-17 19:00:22,119 ---------------------------------------------------------------------------------------------------- 2023-10-17 19:00:22,121 Loading model from best epoch ... 2023-10-17 19:00:23,863 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG 2023-10-17 19:00:27,596 Results: - F-score (micro) 0.8643 - F-score (macro) 0.7231 - Accuracy 0.7673 By class: precision recall f1-score support PER 0.8669 0.8651 0.8660 482 LOC 0.9509 0.8886 0.9187 458 ORG 0.5714 0.2899 0.3846 69 micro avg 0.8941 0.8365 0.8643 1009 macro avg 0.7964 0.6812 0.7231 1009 weighted avg 0.8849 0.8365 0.8570 1009 2023-10-17 19:00:27,596 ----------------------------------------------------------------------------------------------------