2023-10-17 11:21:40,050 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,051 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 11:21:40,051 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,051 MultiCorpus: 7936 train + 992 dev + 992 test sentences - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr 2023-10-17 11:21:40,051 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Train: 7936 sentences 2023-10-17 11:21:40,052 (train_with_dev=False, train_with_test=False) 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Training Params: 2023-10-17 11:21:40,052 - learning_rate: "5e-05" 2023-10-17 11:21:40,052 - mini_batch_size: "8" 2023-10-17 11:21:40,052 - max_epochs: "10" 2023-10-17 11:21:40,052 - shuffle: "True" 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Plugins: 2023-10-17 11:21:40,052 - TensorboardLogger 2023-10-17 11:21:40,052 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 11:21:40,052 - metric: "('micro avg', 'f1-score')" 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Computation: 2023-10-17 11:21:40,052 - compute on device: cuda:0 2023-10-17 11:21:40,052 - embedding storage: none 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:21:40,052 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 11:21:45,743 epoch 1 - iter 99/992 - loss 2.00752470 - time (sec): 5.69 - samples/sec: 2843.79 - lr: 0.000005 - momentum: 0.000000 2023-10-17 11:21:51,606 epoch 1 - iter 198/992 - loss 1.16110239 - time (sec): 11.55 - samples/sec: 2877.04 - lr: 0.000010 - momentum: 0.000000 2023-10-17 11:21:57,730 epoch 1 - iter 297/992 - loss 0.86009292 - time (sec): 17.68 - samples/sec: 2807.08 - lr: 0.000015 - momentum: 0.000000 2023-10-17 11:22:03,344 epoch 1 - iter 396/992 - loss 0.70056867 - time (sec): 23.29 - samples/sec: 2807.69 - lr: 0.000020 - momentum: 0.000000 2023-10-17 11:22:09,204 epoch 1 - iter 495/992 - loss 0.59290102 - time (sec): 29.15 - samples/sec: 2807.40 - lr: 0.000025 - momentum: 0.000000 2023-10-17 11:22:15,182 epoch 1 - iter 594/992 - loss 0.51678549 - time (sec): 35.13 - samples/sec: 2808.16 - lr: 0.000030 - momentum: 0.000000 2023-10-17 11:22:20,959 epoch 1 - iter 693/992 - loss 0.46411643 - time (sec): 40.91 - samples/sec: 2806.30 - lr: 0.000035 - momentum: 0.000000 2023-10-17 11:22:26,973 epoch 1 - iter 792/992 - loss 0.42507693 - time (sec): 46.92 - samples/sec: 2798.32 - lr: 0.000040 - momentum: 0.000000 2023-10-17 11:22:32,888 epoch 1 - iter 891/992 - loss 0.39307524 - time (sec): 52.83 - samples/sec: 2786.92 - lr: 0.000045 - momentum: 0.000000 2023-10-17 11:22:38,704 epoch 1 - iter 990/992 - loss 0.36782772 - time (sec): 58.65 - samples/sec: 2790.32 - lr: 0.000050 - momentum: 0.000000 2023-10-17 11:22:38,823 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:22:38,823 EPOCH 1 done: loss 0.3674 - lr: 0.000050 2023-10-17 11:22:41,920 DEV : loss 0.09267440438270569 - f1-score (micro avg) 0.7287 2023-10-17 11:22:41,943 saving best model 2023-10-17 11:22:42,310 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:22:48,213 epoch 2 - iter 99/992 - loss 0.11596098 - time (sec): 5.90 - samples/sec: 2864.40 - lr: 0.000049 - momentum: 0.000000 2023-10-17 11:22:54,188 epoch 2 - iter 198/992 - loss 0.11376424 - time (sec): 11.88 - samples/sec: 2788.50 - lr: 0.000049 - momentum: 0.000000 2023-10-17 11:23:00,295 epoch 2 - iter 297/992 - loss 0.11209663 - time (sec): 17.98 - samples/sec: 2759.41 - lr: 0.000048 - momentum: 0.000000 2023-10-17 11:23:06,316 epoch 2 - iter 396/992 - loss 0.11161254 - time (sec): 24.00 - samples/sec: 2767.95 - lr: 0.000048 - momentum: 0.000000 2023-10-17 11:23:12,200 epoch 2 - iter 495/992 - loss 0.10962236 - time (sec): 29.89 - samples/sec: 2762.55 - lr: 0.000047 - momentum: 0.000000 2023-10-17 11:23:18,445 epoch 2 - iter 594/992 - loss 0.10658082 - time (sec): 36.13 - samples/sec: 2749.28 - lr: 0.000047 - momentum: 0.000000 2023-10-17 11:23:24,452 epoch 2 - iter 693/992 - loss 0.10590489 - time (sec): 42.14 - samples/sec: 2752.00 - lr: 0.000046 - momentum: 0.000000 2023-10-17 11:23:30,157 epoch 2 - iter 792/992 - loss 0.10554854 - time (sec): 47.85 - samples/sec: 2752.16 - lr: 0.000046 - momentum: 0.000000 2023-10-17 11:23:35,744 epoch 2 - iter 891/992 - loss 0.10485385 - time (sec): 53.43 - samples/sec: 2756.15 - lr: 0.000045 - momentum: 0.000000 2023-10-17 11:23:41,651 epoch 2 - iter 990/992 - loss 0.10541228 - time (sec): 59.34 - samples/sec: 2757.92 - lr: 0.000044 - momentum: 0.000000 2023-10-17 11:23:41,762 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:23:41,763 EPOCH 2 done: loss 0.1053 - lr: 0.000044 2023-10-17 11:23:45,546 DEV : loss 0.07957779616117477 - f1-score (micro avg) 0.7488 2023-10-17 11:23:45,567 saving best model 2023-10-17 11:23:46,062 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:23:51,998 epoch 3 - iter 99/992 - loss 0.07399883 - time (sec): 5.93 - samples/sec: 2792.44 - lr: 0.000044 - momentum: 0.000000 2023-10-17 11:23:57,894 epoch 3 - iter 198/992 - loss 0.07782338 - time (sec): 11.83 - samples/sec: 2777.78 - lr: 0.000043 - momentum: 0.000000 2023-10-17 11:24:03,807 epoch 3 - iter 297/992 - loss 0.07965927 - time (sec): 17.74 - samples/sec: 2795.99 - lr: 0.000043 - momentum: 0.000000 2023-10-17 11:24:09,711 epoch 3 - iter 396/992 - loss 0.07595699 - time (sec): 23.65 - samples/sec: 2808.81 - lr: 0.000042 - momentum: 0.000000 2023-10-17 11:24:15,396 epoch 3 - iter 495/992 - loss 0.07484677 - time (sec): 29.33 - samples/sec: 2819.60 - lr: 0.000042 - momentum: 0.000000 2023-10-17 11:24:21,231 epoch 3 - iter 594/992 - loss 0.07449912 - time (sec): 35.17 - samples/sec: 2800.02 - lr: 0.000041 - momentum: 0.000000 2023-10-17 11:24:27,126 epoch 3 - iter 693/992 - loss 0.07526423 - time (sec): 41.06 - samples/sec: 2795.76 - lr: 0.000041 - momentum: 0.000000 2023-10-17 11:24:33,067 epoch 3 - iter 792/992 - loss 0.07578093 - time (sec): 47.00 - samples/sec: 2790.69 - lr: 0.000040 - momentum: 0.000000 2023-10-17 11:24:39,214 epoch 3 - iter 891/992 - loss 0.07554286 - time (sec): 53.15 - samples/sec: 2782.60 - lr: 0.000039 - momentum: 0.000000 2023-10-17 11:24:45,213 epoch 3 - iter 990/992 - loss 0.07580919 - time (sec): 59.15 - samples/sec: 2766.04 - lr: 0.000039 - momentum: 0.000000 2023-10-17 11:24:45,340 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:24:45,340 EPOCH 3 done: loss 0.0761 - lr: 0.000039 2023-10-17 11:24:48,746 DEV : loss 0.09182097762823105 - f1-score (micro avg) 0.7489 2023-10-17 11:24:48,769 saving best model 2023-10-17 11:24:49,263 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:24:55,170 epoch 4 - iter 99/992 - loss 0.05468852 - time (sec): 5.90 - samples/sec: 2780.10 - lr: 0.000038 - momentum: 0.000000 2023-10-17 11:25:01,487 epoch 4 - iter 198/992 - loss 0.05293064 - time (sec): 12.22 - samples/sec: 2794.12 - lr: 0.000038 - momentum: 0.000000 2023-10-17 11:25:07,607 epoch 4 - iter 297/992 - loss 0.05364317 - time (sec): 18.34 - samples/sec: 2776.94 - lr: 0.000037 - momentum: 0.000000 2023-10-17 11:25:13,550 epoch 4 - iter 396/992 - loss 0.05402008 - time (sec): 24.28 - samples/sec: 2777.04 - lr: 0.000037 - momentum: 0.000000 2023-10-17 11:25:19,296 epoch 4 - iter 495/992 - loss 0.05392577 - time (sec): 30.03 - samples/sec: 2786.92 - lr: 0.000036 - momentum: 0.000000 2023-10-17 11:25:24,951 epoch 4 - iter 594/992 - loss 0.05443890 - time (sec): 35.69 - samples/sec: 2782.91 - lr: 0.000036 - momentum: 0.000000 2023-10-17 11:25:30,557 epoch 4 - iter 693/992 - loss 0.05350202 - time (sec): 41.29 - samples/sec: 2783.23 - lr: 0.000035 - momentum: 0.000000 2023-10-17 11:25:36,431 epoch 4 - iter 792/992 - loss 0.05357310 - time (sec): 47.17 - samples/sec: 2777.82 - lr: 0.000034 - momentum: 0.000000 2023-10-17 11:25:42,388 epoch 4 - iter 891/992 - loss 0.05445889 - time (sec): 53.12 - samples/sec: 2780.60 - lr: 0.000034 - momentum: 0.000000 2023-10-17 11:25:48,076 epoch 4 - iter 990/992 - loss 0.05431231 - time (sec): 58.81 - samples/sec: 2784.57 - lr: 0.000033 - momentum: 0.000000 2023-10-17 11:25:48,189 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:25:48,190 EPOCH 4 done: loss 0.0544 - lr: 0.000033 2023-10-17 11:25:51,644 DEV : loss 0.1407020390033722 - f1-score (micro avg) 0.7598 2023-10-17 11:25:51,666 saving best model 2023-10-17 11:25:52,143 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:25:58,088 epoch 5 - iter 99/992 - loss 0.04345102 - time (sec): 5.94 - samples/sec: 2737.20 - lr: 0.000033 - momentum: 0.000000 2023-10-17 11:26:04,229 epoch 5 - iter 198/992 - loss 0.04013191 - time (sec): 12.08 - samples/sec: 2773.50 - lr: 0.000032 - momentum: 0.000000 2023-10-17 11:26:10,153 epoch 5 - iter 297/992 - loss 0.04013654 - time (sec): 18.01 - samples/sec: 2788.59 - lr: 0.000032 - momentum: 0.000000 2023-10-17 11:26:16,359 epoch 5 - iter 396/992 - loss 0.04153776 - time (sec): 24.21 - samples/sec: 2797.16 - lr: 0.000031 - momentum: 0.000000 2023-10-17 11:26:22,273 epoch 5 - iter 495/992 - loss 0.04178549 - time (sec): 30.13 - samples/sec: 2793.12 - lr: 0.000031 - momentum: 0.000000 2023-10-17 11:26:27,904 epoch 5 - iter 594/992 - loss 0.04313324 - time (sec): 35.76 - samples/sec: 2796.33 - lr: 0.000030 - momentum: 0.000000 2023-10-17 11:26:34,115 epoch 5 - iter 693/992 - loss 0.04394145 - time (sec): 41.97 - samples/sec: 2773.35 - lr: 0.000029 - momentum: 0.000000 2023-10-17 11:26:40,034 epoch 5 - iter 792/992 - loss 0.04428695 - time (sec): 47.89 - samples/sec: 2761.16 - lr: 0.000029 - momentum: 0.000000 2023-10-17 11:26:45,880 epoch 5 - iter 891/992 - loss 0.04347387 - time (sec): 53.73 - samples/sec: 2758.23 - lr: 0.000028 - momentum: 0.000000 2023-10-17 11:26:51,571 epoch 5 - iter 990/992 - loss 0.04302101 - time (sec): 59.42 - samples/sec: 2753.59 - lr: 0.000028 - momentum: 0.000000 2023-10-17 11:26:51,696 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:26:51,696 EPOCH 5 done: loss 0.0429 - lr: 0.000028 2023-10-17 11:26:55,092 DEV : loss 0.1663055419921875 - f1-score (micro avg) 0.7778 2023-10-17 11:26:55,113 saving best model 2023-10-17 11:26:55,582 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:27:01,664 epoch 6 - iter 99/992 - loss 0.03345702 - time (sec): 6.08 - samples/sec: 2705.79 - lr: 0.000027 - momentum: 0.000000 2023-10-17 11:27:07,592 epoch 6 - iter 198/992 - loss 0.03258281 - time (sec): 12.01 - samples/sec: 2720.35 - lr: 0.000027 - momentum: 0.000000 2023-10-17 11:27:13,856 epoch 6 - iter 297/992 - loss 0.03031919 - time (sec): 18.27 - samples/sec: 2750.49 - lr: 0.000026 - momentum: 0.000000 2023-10-17 11:27:19,774 epoch 6 - iter 396/992 - loss 0.03089649 - time (sec): 24.19 - samples/sec: 2754.91 - lr: 0.000026 - momentum: 0.000000 2023-10-17 11:27:25,776 epoch 6 - iter 495/992 - loss 0.02999409 - time (sec): 30.19 - samples/sec: 2762.61 - lr: 0.000025 - momentum: 0.000000 2023-10-17 11:27:31,638 epoch 6 - iter 594/992 - loss 0.03070964 - time (sec): 36.05 - samples/sec: 2765.56 - lr: 0.000024 - momentum: 0.000000 2023-10-17 11:27:37,231 epoch 6 - iter 693/992 - loss 0.03109293 - time (sec): 41.65 - samples/sec: 2769.11 - lr: 0.000024 - momentum: 0.000000 2023-10-17 11:27:42,876 epoch 6 - iter 792/992 - loss 0.03056361 - time (sec): 47.29 - samples/sec: 2773.64 - lr: 0.000023 - momentum: 0.000000 2023-10-17 11:27:48,608 epoch 6 - iter 891/992 - loss 0.03117794 - time (sec): 53.02 - samples/sec: 2778.13 - lr: 0.000023 - momentum: 0.000000 2023-10-17 11:27:54,391 epoch 6 - iter 990/992 - loss 0.03126223 - time (sec): 58.80 - samples/sec: 2781.63 - lr: 0.000022 - momentum: 0.000000 2023-10-17 11:27:54,511 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:27:54,511 EPOCH 6 done: loss 0.0312 - lr: 0.000022 2023-10-17 11:27:59,227 DEV : loss 0.1633480340242386 - f1-score (micro avg) 0.7584 2023-10-17 11:27:59,263 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:28:05,215 epoch 7 - iter 99/992 - loss 0.01789778 - time (sec): 5.95 - samples/sec: 2739.15 - lr: 0.000022 - momentum: 0.000000 2023-10-17 11:28:11,218 epoch 7 - iter 198/992 - loss 0.01724841 - time (sec): 11.95 - samples/sec: 2745.60 - lr: 0.000021 - momentum: 0.000000 2023-10-17 11:28:17,621 epoch 7 - iter 297/992 - loss 0.01961745 - time (sec): 18.36 - samples/sec: 2700.02 - lr: 0.000021 - momentum: 0.000000 2023-10-17 11:28:23,473 epoch 7 - iter 396/992 - loss 0.01992616 - time (sec): 24.21 - samples/sec: 2712.23 - lr: 0.000020 - momentum: 0.000000 2023-10-17 11:28:29,388 epoch 7 - iter 495/992 - loss 0.02094923 - time (sec): 30.12 - samples/sec: 2720.79 - lr: 0.000019 - momentum: 0.000000 2023-10-17 11:28:35,340 epoch 7 - iter 594/992 - loss 0.01985550 - time (sec): 36.08 - samples/sec: 2728.57 - lr: 0.000019 - momentum: 0.000000 2023-10-17 11:28:41,393 epoch 7 - iter 693/992 - loss 0.02067558 - time (sec): 42.13 - samples/sec: 2724.08 - lr: 0.000018 - momentum: 0.000000 2023-10-17 11:28:47,206 epoch 7 - iter 792/992 - loss 0.02093216 - time (sec): 47.94 - samples/sec: 2718.22 - lr: 0.000018 - momentum: 0.000000 2023-10-17 11:28:53,073 epoch 7 - iter 891/992 - loss 0.02197206 - time (sec): 53.81 - samples/sec: 2739.75 - lr: 0.000017 - momentum: 0.000000 2023-10-17 11:28:58,840 epoch 7 - iter 990/992 - loss 0.02197388 - time (sec): 59.58 - samples/sec: 2747.71 - lr: 0.000017 - momentum: 0.000000 2023-10-17 11:28:58,961 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:28:58,961 EPOCH 7 done: loss 0.0219 - lr: 0.000017 2023-10-17 11:29:02,566 DEV : loss 0.1898750215768814 - f1-score (micro avg) 0.7665 2023-10-17 11:29:02,594 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:29:08,347 epoch 8 - iter 99/992 - loss 0.00858606 - time (sec): 5.75 - samples/sec: 2846.11 - lr: 0.000016 - momentum: 0.000000 2023-10-17 11:29:14,064 epoch 8 - iter 198/992 - loss 0.01171469 - time (sec): 11.47 - samples/sec: 2824.80 - lr: 0.000016 - momentum: 0.000000 2023-10-17 11:29:20,281 epoch 8 - iter 297/992 - loss 0.01211733 - time (sec): 17.69 - samples/sec: 2828.38 - lr: 0.000015 - momentum: 0.000000 2023-10-17 11:29:26,152 epoch 8 - iter 396/992 - loss 0.01169790 - time (sec): 23.56 - samples/sec: 2805.84 - lr: 0.000014 - momentum: 0.000000 2023-10-17 11:29:32,084 epoch 8 - iter 495/992 - loss 0.01211186 - time (sec): 29.49 - samples/sec: 2821.28 - lr: 0.000014 - momentum: 0.000000 2023-10-17 11:29:38,180 epoch 8 - iter 594/992 - loss 0.01413340 - time (sec): 35.58 - samples/sec: 2807.68 - lr: 0.000013 - momentum: 0.000000 2023-10-17 11:29:43,898 epoch 8 - iter 693/992 - loss 0.01436134 - time (sec): 41.30 - samples/sec: 2798.96 - lr: 0.000013 - momentum: 0.000000 2023-10-17 11:29:49,496 epoch 8 - iter 792/992 - loss 0.01496934 - time (sec): 46.90 - samples/sec: 2784.54 - lr: 0.000012 - momentum: 0.000000 2023-10-17 11:29:55,299 epoch 8 - iter 891/992 - loss 0.01507510 - time (sec): 52.70 - samples/sec: 2793.17 - lr: 0.000012 - momentum: 0.000000 2023-10-17 11:30:01,345 epoch 8 - iter 990/992 - loss 0.01559016 - time (sec): 58.75 - samples/sec: 2785.33 - lr: 0.000011 - momentum: 0.000000 2023-10-17 11:30:01,478 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:30:01,478 EPOCH 8 done: loss 0.0156 - lr: 0.000011 2023-10-17 11:30:05,096 DEV : loss 0.21736501157283783 - f1-score (micro avg) 0.7736 2023-10-17 11:30:05,127 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:30:11,217 epoch 9 - iter 99/992 - loss 0.01189736 - time (sec): 6.09 - samples/sec: 2601.72 - lr: 0.000011 - momentum: 0.000000 2023-10-17 11:30:17,689 epoch 9 - iter 198/992 - loss 0.01120784 - time (sec): 12.56 - samples/sec: 2643.77 - lr: 0.000010 - momentum: 0.000000 2023-10-17 11:30:23,899 epoch 9 - iter 297/992 - loss 0.01112974 - time (sec): 18.77 - samples/sec: 2685.89 - lr: 0.000009 - momentum: 0.000000 2023-10-17 11:30:29,897 epoch 9 - iter 396/992 - loss 0.00969053 - time (sec): 24.77 - samples/sec: 2692.32 - lr: 0.000009 - momentum: 0.000000 2023-10-17 11:30:35,679 epoch 9 - iter 495/992 - loss 0.00960030 - time (sec): 30.55 - samples/sec: 2702.51 - lr: 0.000008 - momentum: 0.000000 2023-10-17 11:30:41,692 epoch 9 - iter 594/992 - loss 0.00957094 - time (sec): 36.56 - samples/sec: 2700.06 - lr: 0.000008 - momentum: 0.000000 2023-10-17 11:30:47,567 epoch 9 - iter 693/992 - loss 0.00941102 - time (sec): 42.44 - samples/sec: 2704.48 - lr: 0.000007 - momentum: 0.000000 2023-10-17 11:30:53,670 epoch 9 - iter 792/992 - loss 0.01010825 - time (sec): 48.54 - samples/sec: 2709.90 - lr: 0.000007 - momentum: 0.000000 2023-10-17 11:30:59,631 epoch 9 - iter 891/992 - loss 0.01035941 - time (sec): 54.50 - samples/sec: 2710.26 - lr: 0.000006 - momentum: 0.000000 2023-10-17 11:31:05,562 epoch 9 - iter 990/992 - loss 0.01067263 - time (sec): 60.43 - samples/sec: 2708.56 - lr: 0.000006 - momentum: 0.000000 2023-10-17 11:31:05,685 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:31:05,685 EPOCH 9 done: loss 0.0107 - lr: 0.000006 2023-10-17 11:31:09,296 DEV : loss 0.2289990335702896 - f1-score (micro avg) 0.7598 2023-10-17 11:31:09,320 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:31:15,610 epoch 10 - iter 99/992 - loss 0.00433310 - time (sec): 6.29 - samples/sec: 2625.70 - lr: 0.000005 - momentum: 0.000000 2023-10-17 11:31:21,873 epoch 10 - iter 198/992 - loss 0.00533994 - time (sec): 12.55 - samples/sec: 2561.60 - lr: 0.000004 - momentum: 0.000000 2023-10-17 11:31:28,202 epoch 10 - iter 297/992 - loss 0.00637622 - time (sec): 18.88 - samples/sec: 2575.30 - lr: 0.000004 - momentum: 0.000000 2023-10-17 11:31:34,426 epoch 10 - iter 396/992 - loss 0.00717457 - time (sec): 25.10 - samples/sec: 2581.66 - lr: 0.000003 - momentum: 0.000000 2023-10-17 11:31:40,384 epoch 10 - iter 495/992 - loss 0.00716627 - time (sec): 31.06 - samples/sec: 2593.97 - lr: 0.000003 - momentum: 0.000000 2023-10-17 11:31:46,392 epoch 10 - iter 594/992 - loss 0.00678007 - time (sec): 37.07 - samples/sec: 2628.76 - lr: 0.000002 - momentum: 0.000000 2023-10-17 11:31:52,420 epoch 10 - iter 693/992 - loss 0.00705269 - time (sec): 43.10 - samples/sec: 2659.76 - lr: 0.000002 - momentum: 0.000000 2023-10-17 11:31:58,173 epoch 10 - iter 792/992 - loss 0.00695957 - time (sec): 48.85 - samples/sec: 2683.39 - lr: 0.000001 - momentum: 0.000000 2023-10-17 11:32:04,222 epoch 10 - iter 891/992 - loss 0.00746367 - time (sec): 54.90 - samples/sec: 2676.79 - lr: 0.000001 - momentum: 0.000000 2023-10-17 11:32:10,346 epoch 10 - iter 990/992 - loss 0.00797105 - time (sec): 61.02 - samples/sec: 2682.68 - lr: 0.000000 - momentum: 0.000000 2023-10-17 11:32:10,450 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:32:10,450 EPOCH 10 done: loss 0.0080 - lr: 0.000000 2023-10-17 11:32:15,038 DEV : loss 0.23297961056232452 - f1-score (micro avg) 0.7648 2023-10-17 11:32:15,550 ---------------------------------------------------------------------------------------------------- 2023-10-17 11:32:15,552 Loading model from best epoch ... 2023-10-17 11:32:17,148 SequenceTagger predicts: Dictionary with 13 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 2023-10-17 11:32:21,019 Results: - F-score (micro) 0.7587 - F-score (macro) 0.6925 - Accuracy 0.6447 By class: precision recall f1-score support LOC 0.8560 0.7893 0.8213 655 PER 0.6335 0.7982 0.7063 223 ORG 0.5565 0.5433 0.5498 127 micro avg 0.7572 0.7602 0.7587 1005 macro avg 0.6820 0.7103 0.6925 1005 weighted avg 0.7687 0.7602 0.7615 1005 2023-10-17 11:32:21,020 ----------------------------------------------------------------------------------------------------