2023-10-14 01:06:03,859 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,860 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (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): 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=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-14 01:06:03,860 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,860 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 2023-10-14 01:06:03,860 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,860 Train: 7936 sentences 2023-10-14 01:06:03,860 (train_with_dev=False, train_with_test=False) 2023-10-14 01:06:03,860 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,860 Training Params: 2023-10-14 01:06:03,860 - learning_rate: "3e-05" 2023-10-14 01:06:03,860 - mini_batch_size: "8" 2023-10-14 01:06:03,860 - max_epochs: "10" 2023-10-14 01:06:03,860 - shuffle: "True" 2023-10-14 01:06:03,860 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,860 Plugins: 2023-10-14 01:06:03,860 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 01:06:03,860 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,861 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 01:06:03,861 - metric: "('micro avg', 'f1-score')" 2023-10-14 01:06:03,861 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,861 Computation: 2023-10-14 01:06:03,861 - compute on device: cuda:0 2023-10-14 01:06:03,861 - embedding storage: none 2023-10-14 01:06:03,861 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,861 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-14 01:06:03,861 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:03,861 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:06:09,367 epoch 1 - iter 99/992 - loss 2.17471007 - time (sec): 5.51 - samples/sec: 2804.90 - lr: 0.000003 - momentum: 0.000000 2023-10-14 01:06:15,065 epoch 1 - iter 198/992 - loss 1.30549124 - time (sec): 11.20 - samples/sec: 2813.64 - lr: 0.000006 - momentum: 0.000000 2023-10-14 01:06:20,977 epoch 1 - iter 297/992 - loss 0.95601849 - time (sec): 17.11 - samples/sec: 2810.76 - lr: 0.000009 - momentum: 0.000000 2023-10-14 01:06:26,541 epoch 1 - iter 396/992 - loss 0.76749053 - time (sec): 22.68 - samples/sec: 2838.05 - lr: 0.000012 - momentum: 0.000000 2023-10-14 01:06:32,406 epoch 1 - iter 495/992 - loss 0.65224931 - time (sec): 28.54 - samples/sec: 2831.49 - lr: 0.000015 - momentum: 0.000000 2023-10-14 01:06:38,300 epoch 1 - iter 594/992 - loss 0.56475688 - time (sec): 34.44 - samples/sec: 2841.74 - lr: 0.000018 - momentum: 0.000000 2023-10-14 01:06:44,094 epoch 1 - iter 693/992 - loss 0.50715690 - time (sec): 40.23 - samples/sec: 2827.09 - lr: 0.000021 - momentum: 0.000000 2023-10-14 01:06:50,014 epoch 1 - iter 792/992 - loss 0.46053485 - time (sec): 46.15 - samples/sec: 2818.19 - lr: 0.000024 - momentum: 0.000000 2023-10-14 01:06:55,862 epoch 1 - iter 891/992 - loss 0.42443987 - time (sec): 52.00 - samples/sec: 2813.04 - lr: 0.000027 - momentum: 0.000000 2023-10-14 01:07:01,950 epoch 1 - iter 990/992 - loss 0.39381793 - time (sec): 58.09 - samples/sec: 2813.05 - lr: 0.000030 - momentum: 0.000000 2023-10-14 01:07:02,157 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:07:02,157 EPOCH 1 done: loss 0.3930 - lr: 0.000030 2023-10-14 01:07:05,244 DEV : loss 0.09621600061655045 - f1-score (micro avg) 0.6851 2023-10-14 01:07:05,264 saving best model 2023-10-14 01:07:05,661 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:07:11,275 epoch 2 - iter 99/992 - loss 0.13490577 - time (sec): 5.61 - samples/sec: 2709.42 - lr: 0.000030 - momentum: 0.000000 2023-10-14 01:07:17,108 epoch 2 - iter 198/992 - loss 0.11610667 - time (sec): 11.45 - samples/sec: 2725.35 - lr: 0.000029 - momentum: 0.000000 2023-10-14 01:07:22,696 epoch 2 - iter 297/992 - loss 0.11416877 - time (sec): 17.03 - samples/sec: 2766.88 - lr: 0.000029 - momentum: 0.000000 2023-10-14 01:07:28,612 epoch 2 - iter 396/992 - loss 0.10769612 - time (sec): 22.95 - samples/sec: 2773.46 - lr: 0.000029 - momentum: 0.000000 2023-10-14 01:07:34,383 epoch 2 - iter 495/992 - loss 0.10656848 - time (sec): 28.72 - samples/sec: 2814.53 - lr: 0.000028 - momentum: 0.000000 2023-10-14 01:07:40,280 epoch 2 - iter 594/992 - loss 0.10563479 - time (sec): 34.62 - samples/sec: 2823.23 - lr: 0.000028 - momentum: 0.000000 2023-10-14 01:07:46,122 epoch 2 - iter 693/992 - loss 0.10497520 - time (sec): 40.46 - samples/sec: 2823.34 - lr: 0.000028 - momentum: 0.000000 2023-10-14 01:07:51,931 epoch 2 - iter 792/992 - loss 0.10311384 - time (sec): 46.27 - samples/sec: 2814.64 - lr: 0.000027 - momentum: 0.000000 2023-10-14 01:07:58,133 epoch 2 - iter 891/992 - loss 0.10194024 - time (sec): 52.47 - samples/sec: 2802.48 - lr: 0.000027 - momentum: 0.000000 2023-10-14 01:08:03,960 epoch 2 - iter 990/992 - loss 0.10262038 - time (sec): 58.30 - samples/sec: 2804.64 - lr: 0.000027 - momentum: 0.000000 2023-10-14 01:08:04,121 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:08:04,121 EPOCH 2 done: loss 0.1025 - lr: 0.000027 2023-10-14 01:08:07,983 DEV : loss 0.08396855741739273 - f1-score (micro avg) 0.7416 2023-10-14 01:08:08,004 saving best model 2023-10-14 01:08:08,517 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:08:14,189 epoch 3 - iter 99/992 - loss 0.06551401 - time (sec): 5.67 - samples/sec: 2662.64 - lr: 0.000026 - momentum: 0.000000 2023-10-14 01:08:20,258 epoch 3 - iter 198/992 - loss 0.06924244 - time (sec): 11.74 - samples/sec: 2763.88 - lr: 0.000026 - momentum: 0.000000 2023-10-14 01:08:25,798 epoch 3 - iter 297/992 - loss 0.07056573 - time (sec): 17.28 - samples/sec: 2771.16 - lr: 0.000026 - momentum: 0.000000 2023-10-14 01:08:31,708 epoch 3 - iter 396/992 - loss 0.07055584 - time (sec): 23.19 - samples/sec: 2748.52 - lr: 0.000025 - momentum: 0.000000 2023-10-14 01:08:37,722 epoch 3 - iter 495/992 - loss 0.06873774 - time (sec): 29.20 - samples/sec: 2788.98 - lr: 0.000025 - momentum: 0.000000 2023-10-14 01:08:43,529 epoch 3 - iter 594/992 - loss 0.07045008 - time (sec): 35.01 - samples/sec: 2794.24 - lr: 0.000025 - momentum: 0.000000 2023-10-14 01:08:49,391 epoch 3 - iter 693/992 - loss 0.07037162 - time (sec): 40.87 - samples/sec: 2803.95 - lr: 0.000024 - momentum: 0.000000 2023-10-14 01:08:55,506 epoch 3 - iter 792/992 - loss 0.07021216 - time (sec): 46.99 - samples/sec: 2796.17 - lr: 0.000024 - momentum: 0.000000 2023-10-14 01:09:01,195 epoch 3 - iter 891/992 - loss 0.06994634 - time (sec): 52.68 - samples/sec: 2790.15 - lr: 0.000024 - momentum: 0.000000 2023-10-14 01:09:06,919 epoch 3 - iter 990/992 - loss 0.06967297 - time (sec): 58.40 - samples/sec: 2801.51 - lr: 0.000023 - momentum: 0.000000 2023-10-14 01:09:07,048 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:09:07,048 EPOCH 3 done: loss 0.0696 - lr: 0.000023 2023-10-14 01:09:10,503 DEV : loss 0.11555210500955582 - f1-score (micro avg) 0.7446 2023-10-14 01:09:10,523 saving best model 2023-10-14 01:09:11,025 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:09:16,953 epoch 4 - iter 99/992 - loss 0.03972797 - time (sec): 5.93 - samples/sec: 2955.76 - lr: 0.000023 - momentum: 0.000000 2023-10-14 01:09:22,805 epoch 4 - iter 198/992 - loss 0.04570840 - time (sec): 11.78 - samples/sec: 2867.88 - lr: 0.000023 - momentum: 0.000000 2023-10-14 01:09:28,524 epoch 4 - iter 297/992 - loss 0.04904627 - time (sec): 17.50 - samples/sec: 2862.88 - lr: 0.000022 - momentum: 0.000000 2023-10-14 01:09:34,474 epoch 4 - iter 396/992 - loss 0.04945405 - time (sec): 23.45 - samples/sec: 2817.49 - lr: 0.000022 - momentum: 0.000000 2023-10-14 01:09:40,476 epoch 4 - iter 495/992 - loss 0.04831346 - time (sec): 29.45 - samples/sec: 2809.35 - lr: 0.000022 - momentum: 0.000000 2023-10-14 01:09:46,365 epoch 4 - iter 594/992 - loss 0.04825902 - time (sec): 35.34 - samples/sec: 2794.59 - lr: 0.000021 - momentum: 0.000000 2023-10-14 01:09:52,103 epoch 4 - iter 693/992 - loss 0.04856953 - time (sec): 41.08 - samples/sec: 2782.77 - lr: 0.000021 - momentum: 0.000000 2023-10-14 01:09:57,796 epoch 4 - iter 792/992 - loss 0.04919667 - time (sec): 46.77 - samples/sec: 2790.01 - lr: 0.000021 - momentum: 0.000000 2023-10-14 01:10:03,524 epoch 4 - iter 891/992 - loss 0.04895947 - time (sec): 52.50 - samples/sec: 2784.48 - lr: 0.000020 - momentum: 0.000000 2023-10-14 01:10:09,641 epoch 4 - iter 990/992 - loss 0.05170296 - time (sec): 58.62 - samples/sec: 2792.06 - lr: 0.000020 - momentum: 0.000000 2023-10-14 01:10:09,813 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:10:09,813 EPOCH 4 done: loss 0.0517 - lr: 0.000020 2023-10-14 01:10:13,733 DEV : loss 0.11588922142982483 - f1-score (micro avg) 0.7508 2023-10-14 01:10:13,754 saving best model 2023-10-14 01:10:14,263 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:10:20,108 epoch 5 - iter 99/992 - loss 0.03385090 - time (sec): 5.84 - samples/sec: 2833.18 - lr: 0.000020 - momentum: 0.000000 2023-10-14 01:10:25,922 epoch 5 - iter 198/992 - loss 0.03715409 - time (sec): 11.66 - samples/sec: 2861.38 - lr: 0.000019 - momentum: 0.000000 2023-10-14 01:10:31,745 epoch 5 - iter 297/992 - loss 0.03954664 - time (sec): 17.48 - samples/sec: 2820.44 - lr: 0.000019 - momentum: 0.000000 2023-10-14 01:10:37,565 epoch 5 - iter 396/992 - loss 0.03785856 - time (sec): 23.30 - samples/sec: 2828.39 - lr: 0.000019 - momentum: 0.000000 2023-10-14 01:10:43,326 epoch 5 - iter 495/992 - loss 0.03770491 - time (sec): 29.06 - samples/sec: 2832.89 - lr: 0.000018 - momentum: 0.000000 2023-10-14 01:10:49,025 epoch 5 - iter 594/992 - loss 0.03881175 - time (sec): 34.76 - samples/sec: 2844.93 - lr: 0.000018 - momentum: 0.000000 2023-10-14 01:10:54,554 epoch 5 - iter 693/992 - loss 0.04006727 - time (sec): 40.29 - samples/sec: 2834.78 - lr: 0.000018 - momentum: 0.000000 2023-10-14 01:11:00,484 epoch 5 - iter 792/992 - loss 0.04022835 - time (sec): 46.22 - samples/sec: 2839.18 - lr: 0.000017 - momentum: 0.000000 2023-10-14 01:11:06,349 epoch 5 - iter 891/992 - loss 0.03964020 - time (sec): 52.08 - samples/sec: 2827.37 - lr: 0.000017 - momentum: 0.000000 2023-10-14 01:11:12,062 epoch 5 - iter 990/992 - loss 0.03963685 - time (sec): 57.80 - samples/sec: 2832.44 - lr: 0.000017 - momentum: 0.000000 2023-10-14 01:11:12,174 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:11:12,174 EPOCH 5 done: loss 0.0396 - lr: 0.000017 2023-10-14 01:11:15,549 DEV : loss 0.149429589509964 - f1-score (micro avg) 0.7512 2023-10-14 01:11:15,571 saving best model 2023-10-14 01:11:16,077 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:11:22,503 epoch 6 - iter 99/992 - loss 0.03207299 - time (sec): 6.42 - samples/sec: 2699.08 - lr: 0.000016 - momentum: 0.000000 2023-10-14 01:11:28,059 epoch 6 - iter 198/992 - loss 0.03319848 - time (sec): 11.98 - samples/sec: 2789.57 - lr: 0.000016 - momentum: 0.000000 2023-10-14 01:11:33,724 epoch 6 - iter 297/992 - loss 0.03023090 - time (sec): 17.64 - samples/sec: 2790.07 - lr: 0.000016 - momentum: 0.000000 2023-10-14 01:11:39,537 epoch 6 - iter 396/992 - loss 0.03113076 - time (sec): 23.45 - samples/sec: 2808.14 - lr: 0.000015 - momentum: 0.000000 2023-10-14 01:11:45,406 epoch 6 - iter 495/992 - loss 0.03125986 - time (sec): 29.32 - samples/sec: 2812.25 - lr: 0.000015 - momentum: 0.000000 2023-10-14 01:11:51,038 epoch 6 - iter 594/992 - loss 0.03070883 - time (sec): 34.95 - samples/sec: 2815.40 - lr: 0.000015 - momentum: 0.000000 2023-10-14 01:11:56,908 epoch 6 - iter 693/992 - loss 0.03147804 - time (sec): 40.82 - samples/sec: 2809.72 - lr: 0.000014 - momentum: 0.000000 2023-10-14 01:12:02,821 epoch 6 - iter 792/992 - loss 0.03114004 - time (sec): 46.74 - samples/sec: 2805.07 - lr: 0.000014 - momentum: 0.000000 2023-10-14 01:12:08,954 epoch 6 - iter 891/992 - loss 0.03098304 - time (sec): 52.87 - samples/sec: 2800.80 - lr: 0.000014 - momentum: 0.000000 2023-10-14 01:12:14,742 epoch 6 - iter 990/992 - loss 0.03115872 - time (sec): 58.66 - samples/sec: 2790.74 - lr: 0.000013 - momentum: 0.000000 2023-10-14 01:12:14,852 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:12:14,852 EPOCH 6 done: loss 0.0311 - lr: 0.000013 2023-10-14 01:12:18,275 DEV : loss 0.1660223752260208 - f1-score (micro avg) 0.7549 2023-10-14 01:12:18,296 saving best model 2023-10-14 01:12:18,723 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:12:24,554 epoch 7 - iter 99/992 - loss 0.02003484 - time (sec): 5.83 - samples/sec: 2773.72 - lr: 0.000013 - momentum: 0.000000 2023-10-14 01:12:30,403 epoch 7 - iter 198/992 - loss 0.02219555 - time (sec): 11.68 - samples/sec: 2749.50 - lr: 0.000013 - momentum: 0.000000 2023-10-14 01:12:36,359 epoch 7 - iter 297/992 - loss 0.02044054 - time (sec): 17.63 - samples/sec: 2783.24 - lr: 0.000012 - momentum: 0.000000 2023-10-14 01:12:42,275 epoch 7 - iter 396/992 - loss 0.02115285 - time (sec): 23.55 - samples/sec: 2787.47 - lr: 0.000012 - momentum: 0.000000 2023-10-14 01:12:47,979 epoch 7 - iter 495/992 - loss 0.02111835 - time (sec): 29.25 - samples/sec: 2790.21 - lr: 0.000012 - momentum: 0.000000 2023-10-14 01:12:53,906 epoch 7 - iter 594/992 - loss 0.02231115 - time (sec): 35.18 - samples/sec: 2794.36 - lr: 0.000011 - momentum: 0.000000 2023-10-14 01:12:59,944 epoch 7 - iter 693/992 - loss 0.02257417 - time (sec): 41.22 - samples/sec: 2790.76 - lr: 0.000011 - momentum: 0.000000 2023-10-14 01:13:05,688 epoch 7 - iter 792/992 - loss 0.02390230 - time (sec): 46.96 - samples/sec: 2792.08 - lr: 0.000011 - momentum: 0.000000 2023-10-14 01:13:11,978 epoch 7 - iter 891/992 - loss 0.02331735 - time (sec): 53.25 - samples/sec: 2769.06 - lr: 0.000010 - momentum: 0.000000 2023-10-14 01:13:17,717 epoch 7 - iter 990/992 - loss 0.02276912 - time (sec): 58.99 - samples/sec: 2774.18 - lr: 0.000010 - momentum: 0.000000 2023-10-14 01:13:17,823 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:13:17,823 EPOCH 7 done: loss 0.0228 - lr: 0.000010 2023-10-14 01:13:21,216 DEV : loss 0.19811701774597168 - f1-score (micro avg) 0.7519 2023-10-14 01:13:21,240 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:13:26,990 epoch 8 - iter 99/992 - loss 0.01904585 - time (sec): 5.75 - samples/sec: 2988.62 - lr: 0.000010 - momentum: 0.000000 2023-10-14 01:13:32,631 epoch 8 - iter 198/992 - loss 0.01503292 - time (sec): 11.39 - samples/sec: 2919.52 - lr: 0.000009 - momentum: 0.000000 2023-10-14 01:13:38,169 epoch 8 - iter 297/992 - loss 0.01613248 - time (sec): 16.93 - samples/sec: 2884.32 - lr: 0.000009 - momentum: 0.000000 2023-10-14 01:13:44,251 epoch 8 - iter 396/992 - loss 0.01565979 - time (sec): 23.01 - samples/sec: 2873.38 - lr: 0.000009 - momentum: 0.000000 2023-10-14 01:13:50,113 epoch 8 - iter 495/992 - loss 0.01515949 - time (sec): 28.87 - samples/sec: 2875.24 - lr: 0.000008 - momentum: 0.000000 2023-10-14 01:13:56,010 epoch 8 - iter 594/992 - loss 0.01545050 - time (sec): 34.77 - samples/sec: 2873.67 - lr: 0.000008 - momentum: 0.000000 2023-10-14 01:14:01,470 epoch 8 - iter 693/992 - loss 0.01547792 - time (sec): 40.23 - samples/sec: 2886.91 - lr: 0.000008 - momentum: 0.000000 2023-10-14 01:14:07,035 epoch 8 - iter 792/992 - loss 0.01620138 - time (sec): 45.79 - samples/sec: 2880.33 - lr: 0.000007 - momentum: 0.000000 2023-10-14 01:14:12,743 epoch 8 - iter 891/992 - loss 0.01643518 - time (sec): 51.50 - samples/sec: 2871.73 - lr: 0.000007 - momentum: 0.000000 2023-10-14 01:14:18,495 epoch 8 - iter 990/992 - loss 0.01691683 - time (sec): 57.25 - samples/sec: 2860.38 - lr: 0.000007 - momentum: 0.000000 2023-10-14 01:14:18,596 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:14:18,596 EPOCH 8 done: loss 0.0169 - lr: 0.000007 2023-10-14 01:14:22,033 DEV : loss 0.2040073573589325 - f1-score (micro avg) 0.7532 2023-10-14 01:14:22,053 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:14:27,795 epoch 9 - iter 99/992 - loss 0.01058698 - time (sec): 5.74 - samples/sec: 2818.02 - lr: 0.000006 - momentum: 0.000000 2023-10-14 01:14:33,771 epoch 9 - iter 198/992 - loss 0.01076997 - time (sec): 11.72 - samples/sec: 2835.15 - lr: 0.000006 - momentum: 0.000000 2023-10-14 01:14:39,862 epoch 9 - iter 297/992 - loss 0.01232413 - time (sec): 17.81 - samples/sec: 2809.95 - lr: 0.000006 - momentum: 0.000000 2023-10-14 01:14:45,579 epoch 9 - iter 396/992 - loss 0.01181418 - time (sec): 23.52 - samples/sec: 2791.33 - lr: 0.000005 - momentum: 0.000000 2023-10-14 01:14:51,506 epoch 9 - iter 495/992 - loss 0.01146147 - time (sec): 29.45 - samples/sec: 2793.73 - lr: 0.000005 - momentum: 0.000000 2023-10-14 01:14:57,263 epoch 9 - iter 594/992 - loss 0.01205104 - time (sec): 35.21 - samples/sec: 2802.96 - lr: 0.000005 - momentum: 0.000000 2023-10-14 01:15:03,302 epoch 9 - iter 693/992 - loss 0.01212528 - time (sec): 41.25 - samples/sec: 2782.13 - lr: 0.000004 - momentum: 0.000000 2023-10-14 01:15:09,297 epoch 9 - iter 792/992 - loss 0.01203877 - time (sec): 47.24 - samples/sec: 2788.87 - lr: 0.000004 - momentum: 0.000000 2023-10-14 01:15:14,909 epoch 9 - iter 891/992 - loss 0.01226298 - time (sec): 52.85 - samples/sec: 2793.28 - lr: 0.000004 - momentum: 0.000000 2023-10-14 01:15:20,595 epoch 9 - iter 990/992 - loss 0.01257149 - time (sec): 58.54 - samples/sec: 2793.66 - lr: 0.000003 - momentum: 0.000000 2023-10-14 01:15:20,749 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:15:20,750 EPOCH 9 done: loss 0.0125 - lr: 0.000003 2023-10-14 01:15:24,734 DEV : loss 0.20826229453086853 - f1-score (micro avg) 0.7574 2023-10-14 01:15:24,754 saving best model 2023-10-14 01:15:25,268 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:15:31,404 epoch 10 - iter 99/992 - loss 0.00769297 - time (sec): 6.13 - samples/sec: 2865.70 - lr: 0.000003 - momentum: 0.000000 2023-10-14 01:15:37,404 epoch 10 - iter 198/992 - loss 0.00697383 - time (sec): 12.13 - samples/sec: 2809.85 - lr: 0.000003 - momentum: 0.000000 2023-10-14 01:15:42,992 epoch 10 - iter 297/992 - loss 0.00764415 - time (sec): 17.72 - samples/sec: 2775.21 - lr: 0.000002 - momentum: 0.000000 2023-10-14 01:15:48,942 epoch 10 - iter 396/992 - loss 0.00827819 - time (sec): 23.67 - samples/sec: 2778.87 - lr: 0.000002 - momentum: 0.000000 2023-10-14 01:15:54,828 epoch 10 - iter 495/992 - loss 0.00802192 - time (sec): 29.56 - samples/sec: 2786.17 - lr: 0.000002 - momentum: 0.000000 2023-10-14 01:16:00,688 epoch 10 - iter 594/992 - loss 0.00768562 - time (sec): 35.42 - samples/sec: 2778.12 - lr: 0.000001 - momentum: 0.000000 2023-10-14 01:16:06,479 epoch 10 - iter 693/992 - loss 0.00852248 - time (sec): 41.21 - samples/sec: 2781.76 - lr: 0.000001 - momentum: 0.000000 2023-10-14 01:16:12,477 epoch 10 - iter 792/992 - loss 0.00836690 - time (sec): 47.20 - samples/sec: 2781.41 - lr: 0.000001 - momentum: 0.000000 2023-10-14 01:16:18,131 epoch 10 - iter 891/992 - loss 0.00856003 - time (sec): 52.86 - samples/sec: 2797.47 - lr: 0.000000 - momentum: 0.000000 2023-10-14 01:16:23,891 epoch 10 - iter 990/992 - loss 0.00877170 - time (sec): 58.62 - samples/sec: 2792.44 - lr: 0.000000 - momentum: 0.000000 2023-10-14 01:16:23,999 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:16:24,000 EPOCH 10 done: loss 0.0088 - lr: 0.000000 2023-10-14 01:16:27,448 DEV : loss 0.21987785398960114 - f1-score (micro avg) 0.753 2023-10-14 01:16:27,906 ---------------------------------------------------------------------------------------------------- 2023-10-14 01:16:27,907 Loading model from best epoch ... 2023-10-14 01:16:29,242 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 2023-10-14 01:16:32,497 Results: - F-score (micro) 0.7723 - F-score (macro) 0.6898 - Accuracy 0.6513 By class: precision recall f1-score support LOC 0.8118 0.8427 0.8270 655 PER 0.7379 0.8206 0.7771 223 ORG 0.4831 0.4488 0.4653 127 micro avg 0.7572 0.7881 0.7723 1005 macro avg 0.6776 0.7041 0.6898 1005 weighted avg 0.7538 0.7881 0.7702 1005 2023-10-14 01:16:32,497 ----------------------------------------------------------------------------------------------------