2023-10-14 08:40:07,077 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 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 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 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 2023-10-14 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 Train: 5777 sentences 2023-10-14 08:40:07,078 (train_with_dev=False, train_with_test=False) 2023-10-14 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 Training Params: 2023-10-14 08:40:07,078 - learning_rate: "5e-05" 2023-10-14 08:40:07,078 - mini_batch_size: "8" 2023-10-14 08:40:07,078 - max_epochs: "10" 2023-10-14 08:40:07,078 - shuffle: "True" 2023-10-14 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 Plugins: 2023-10-14 08:40:07,078 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,078 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 08:40:07,078 - metric: "('micro avg', 'f1-score')" 2023-10-14 08:40:07,078 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,079 Computation: 2023-10-14 08:40:07,079 - compute on device: cuda:0 2023-10-14 08:40:07,079 - embedding storage: none 2023-10-14 08:40:07,079 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,079 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-14 08:40:07,079 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:07,079 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:40:12,831 epoch 1 - iter 72/723 - loss 2.05918268 - time (sec): 5.75 - samples/sec: 2946.51 - lr: 0.000005 - momentum: 0.000000 2023-10-14 08:40:18,424 epoch 1 - iter 144/723 - loss 1.16818121 - time (sec): 11.34 - samples/sec: 2978.79 - lr: 0.000010 - momentum: 0.000000 2023-10-14 08:40:24,479 epoch 1 - iter 216/723 - loss 0.82822698 - time (sec): 17.40 - samples/sec: 3002.20 - lr: 0.000015 - momentum: 0.000000 2023-10-14 08:40:30,235 epoch 1 - iter 288/723 - loss 0.66985125 - time (sec): 23.16 - samples/sec: 3016.41 - lr: 0.000020 - momentum: 0.000000 2023-10-14 08:40:35,853 epoch 1 - iter 360/723 - loss 0.57437342 - time (sec): 28.77 - samples/sec: 3014.67 - lr: 0.000025 - momentum: 0.000000 2023-10-14 08:40:41,454 epoch 1 - iter 432/723 - loss 0.51652129 - time (sec): 34.37 - samples/sec: 2973.95 - lr: 0.000030 - momentum: 0.000000 2023-10-14 08:40:47,515 epoch 1 - iter 504/723 - loss 0.46201015 - time (sec): 40.43 - samples/sec: 2987.94 - lr: 0.000035 - momentum: 0.000000 2023-10-14 08:40:53,810 epoch 1 - iter 576/723 - loss 0.42250578 - time (sec): 46.73 - samples/sec: 2971.37 - lr: 0.000040 - momentum: 0.000000 2023-10-14 08:40:59,867 epoch 1 - iter 648/723 - loss 0.39178574 - time (sec): 52.79 - samples/sec: 2972.34 - lr: 0.000045 - momentum: 0.000000 2023-10-14 08:41:05,871 epoch 1 - iter 720/723 - loss 0.36449097 - time (sec): 58.79 - samples/sec: 2986.16 - lr: 0.000050 - momentum: 0.000000 2023-10-14 08:41:06,127 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:41:06,128 EPOCH 1 done: loss 0.3636 - lr: 0.000050 2023-10-14 08:41:10,093 DEV : loss 0.16045822203159332 - f1-score (micro avg) 0.5348 2023-10-14 08:41:10,116 saving best model 2023-10-14 08:41:10,556 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:41:16,325 epoch 2 - iter 72/723 - loss 0.14172033 - time (sec): 5.77 - samples/sec: 3010.48 - lr: 0.000049 - momentum: 0.000000 2023-10-14 08:41:22,109 epoch 2 - iter 144/723 - loss 0.12806058 - time (sec): 11.55 - samples/sec: 2980.57 - lr: 0.000049 - momentum: 0.000000 2023-10-14 08:41:28,315 epoch 2 - iter 216/723 - loss 0.12803539 - time (sec): 17.76 - samples/sec: 2933.19 - lr: 0.000048 - momentum: 0.000000 2023-10-14 08:41:33,862 epoch 2 - iter 288/723 - loss 0.12202263 - time (sec): 23.31 - samples/sec: 2973.59 - lr: 0.000048 - momentum: 0.000000 2023-10-14 08:41:40,189 epoch 2 - iter 360/723 - loss 0.12080964 - time (sec): 29.63 - samples/sec: 2958.52 - lr: 0.000047 - momentum: 0.000000 2023-10-14 08:41:45,874 epoch 2 - iter 432/723 - loss 0.11694382 - time (sec): 35.32 - samples/sec: 2965.78 - lr: 0.000047 - momentum: 0.000000 2023-10-14 08:41:51,983 epoch 2 - iter 504/723 - loss 0.11733998 - time (sec): 41.43 - samples/sec: 2962.82 - lr: 0.000046 - momentum: 0.000000 2023-10-14 08:41:57,323 epoch 2 - iter 576/723 - loss 0.11441675 - time (sec): 46.77 - samples/sec: 2964.30 - lr: 0.000046 - momentum: 0.000000 2023-10-14 08:42:03,643 epoch 2 - iter 648/723 - loss 0.11224219 - time (sec): 53.09 - samples/sec: 2972.82 - lr: 0.000045 - momentum: 0.000000 2023-10-14 08:42:09,681 epoch 2 - iter 720/723 - loss 0.11078356 - time (sec): 59.12 - samples/sec: 2970.99 - lr: 0.000044 - momentum: 0.000000 2023-10-14 08:42:09,901 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:42:09,902 EPOCH 2 done: loss 0.1107 - lr: 0.000044 2023-10-14 08:42:13,440 DEV : loss 0.10363695025444031 - f1-score (micro avg) 0.7041 2023-10-14 08:42:13,459 saving best model 2023-10-14 08:42:13,965 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:42:20,064 epoch 3 - iter 72/723 - loss 0.07046006 - time (sec): 6.09 - samples/sec: 2907.60 - lr: 0.000044 - momentum: 0.000000 2023-10-14 08:42:26,285 epoch 3 - iter 144/723 - loss 0.06632641 - time (sec): 12.31 - samples/sec: 2888.10 - lr: 0.000043 - momentum: 0.000000 2023-10-14 08:42:32,534 epoch 3 - iter 216/723 - loss 0.06851821 - time (sec): 18.56 - samples/sec: 2865.87 - lr: 0.000043 - momentum: 0.000000 2023-10-14 08:42:38,432 epoch 3 - iter 288/723 - loss 0.06615459 - time (sec): 24.46 - samples/sec: 2895.34 - lr: 0.000042 - momentum: 0.000000 2023-10-14 08:42:44,260 epoch 3 - iter 360/723 - loss 0.06572883 - time (sec): 30.29 - samples/sec: 2923.03 - lr: 0.000042 - momentum: 0.000000 2023-10-14 08:42:49,881 epoch 3 - iter 432/723 - loss 0.06538057 - time (sec): 35.91 - samples/sec: 2954.69 - lr: 0.000041 - momentum: 0.000000 2023-10-14 08:42:56,024 epoch 3 - iter 504/723 - loss 0.06700951 - time (sec): 42.05 - samples/sec: 2925.32 - lr: 0.000041 - momentum: 0.000000 2023-10-14 08:43:02,061 epoch 3 - iter 576/723 - loss 0.06591234 - time (sec): 48.09 - samples/sec: 2936.96 - lr: 0.000040 - momentum: 0.000000 2023-10-14 08:43:07,986 epoch 3 - iter 648/723 - loss 0.06585569 - time (sec): 54.01 - samples/sec: 2941.72 - lr: 0.000039 - momentum: 0.000000 2023-10-14 08:43:14,208 epoch 3 - iter 720/723 - loss 0.06567386 - time (sec): 60.24 - samples/sec: 2918.27 - lr: 0.000039 - momentum: 0.000000 2023-10-14 08:43:14,384 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:43:14,384 EPOCH 3 done: loss 0.0657 - lr: 0.000039 2023-10-14 08:43:18,341 DEV : loss 0.09261729568243027 - f1-score (micro avg) 0.7609 2023-10-14 08:43:18,361 saving best model 2023-10-14 08:43:18,852 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:43:25,236 epoch 4 - iter 72/723 - loss 0.03634882 - time (sec): 6.38 - samples/sec: 2825.65 - lr: 0.000038 - momentum: 0.000000 2023-10-14 08:43:31,297 epoch 4 - iter 144/723 - loss 0.03662606 - time (sec): 12.44 - samples/sec: 2942.03 - lr: 0.000038 - momentum: 0.000000 2023-10-14 08:43:37,068 epoch 4 - iter 216/723 - loss 0.04038379 - time (sec): 18.21 - samples/sec: 2920.40 - lr: 0.000037 - momentum: 0.000000 2023-10-14 08:43:43,362 epoch 4 - iter 288/723 - loss 0.04257444 - time (sec): 24.51 - samples/sec: 2908.61 - lr: 0.000037 - momentum: 0.000000 2023-10-14 08:43:49,090 epoch 4 - iter 360/723 - loss 0.04462673 - time (sec): 30.24 - samples/sec: 2930.97 - lr: 0.000036 - momentum: 0.000000 2023-10-14 08:43:54,761 epoch 4 - iter 432/723 - loss 0.04482846 - time (sec): 35.91 - samples/sec: 2930.90 - lr: 0.000036 - momentum: 0.000000 2023-10-14 08:44:00,366 epoch 4 - iter 504/723 - loss 0.04395167 - time (sec): 41.51 - samples/sec: 2937.71 - lr: 0.000035 - momentum: 0.000000 2023-10-14 08:44:06,685 epoch 4 - iter 576/723 - loss 0.04429902 - time (sec): 47.83 - samples/sec: 2937.07 - lr: 0.000034 - momentum: 0.000000 2023-10-14 08:44:12,568 epoch 4 - iter 648/723 - loss 0.04540314 - time (sec): 53.72 - samples/sec: 2927.97 - lr: 0.000034 - momentum: 0.000000 2023-10-14 08:44:18,568 epoch 4 - iter 720/723 - loss 0.04549307 - time (sec): 59.71 - samples/sec: 2942.83 - lr: 0.000033 - momentum: 0.000000 2023-10-14 08:44:18,769 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:44:18,769 EPOCH 4 done: loss 0.0459 - lr: 0.000033 2023-10-14 08:44:22,278 DEV : loss 0.0912981629371643 - f1-score (micro avg) 0.8023 2023-10-14 08:44:22,302 saving best model 2023-10-14 08:44:22,801 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:44:29,581 epoch 5 - iter 72/723 - loss 0.02994168 - time (sec): 6.78 - samples/sec: 2619.32 - lr: 0.000033 - momentum: 0.000000 2023-10-14 08:44:35,538 epoch 5 - iter 144/723 - loss 0.03265802 - time (sec): 12.73 - samples/sec: 2796.63 - lr: 0.000032 - momentum: 0.000000 2023-10-14 08:44:41,207 epoch 5 - iter 216/723 - loss 0.03167073 - time (sec): 18.40 - samples/sec: 2852.04 - lr: 0.000032 - momentum: 0.000000 2023-10-14 08:44:46,988 epoch 5 - iter 288/723 - loss 0.03339260 - time (sec): 24.18 - samples/sec: 2891.82 - lr: 0.000031 - momentum: 0.000000 2023-10-14 08:44:52,816 epoch 5 - iter 360/723 - loss 0.03182288 - time (sec): 30.01 - samples/sec: 2921.00 - lr: 0.000031 - momentum: 0.000000 2023-10-14 08:44:59,507 epoch 5 - iter 432/723 - loss 0.03101441 - time (sec): 36.70 - samples/sec: 2884.85 - lr: 0.000030 - momentum: 0.000000 2023-10-14 08:45:05,301 epoch 5 - iter 504/723 - loss 0.03178002 - time (sec): 42.50 - samples/sec: 2887.99 - lr: 0.000029 - momentum: 0.000000 2023-10-14 08:45:11,413 epoch 5 - iter 576/723 - loss 0.03195886 - time (sec): 48.61 - samples/sec: 2892.36 - lr: 0.000029 - momentum: 0.000000 2023-10-14 08:45:17,766 epoch 5 - iter 648/723 - loss 0.03371410 - time (sec): 54.96 - samples/sec: 2883.93 - lr: 0.000028 - momentum: 0.000000 2023-10-14 08:45:23,372 epoch 5 - iter 720/723 - loss 0.03353303 - time (sec): 60.57 - samples/sec: 2897.64 - lr: 0.000028 - momentum: 0.000000 2023-10-14 08:45:23,707 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:45:23,707 EPOCH 5 done: loss 0.0334 - lr: 0.000028 2023-10-14 08:45:27,341 DEV : loss 0.15092381834983826 - f1-score (micro avg) 0.7634 2023-10-14 08:45:27,363 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:45:33,495 epoch 6 - iter 72/723 - loss 0.02383597 - time (sec): 6.13 - samples/sec: 2932.56 - lr: 0.000027 - momentum: 0.000000 2023-10-14 08:45:39,895 epoch 6 - iter 144/723 - loss 0.02275131 - time (sec): 12.53 - samples/sec: 2885.29 - lr: 0.000027 - momentum: 0.000000 2023-10-14 08:45:45,901 epoch 6 - iter 216/723 - loss 0.02559205 - time (sec): 18.54 - samples/sec: 2912.43 - lr: 0.000026 - momentum: 0.000000 2023-10-14 08:45:52,387 epoch 6 - iter 288/723 - loss 0.02648482 - time (sec): 25.02 - samples/sec: 2872.80 - lr: 0.000026 - momentum: 0.000000 2023-10-14 08:45:57,924 epoch 6 - iter 360/723 - loss 0.02538316 - time (sec): 30.56 - samples/sec: 2900.53 - lr: 0.000025 - momentum: 0.000000 2023-10-14 08:46:03,765 epoch 6 - iter 432/723 - loss 0.02448723 - time (sec): 36.40 - samples/sec: 2901.17 - lr: 0.000024 - momentum: 0.000000 2023-10-14 08:46:09,811 epoch 6 - iter 504/723 - loss 0.02458171 - time (sec): 42.45 - samples/sec: 2906.03 - lr: 0.000024 - momentum: 0.000000 2023-10-14 08:46:16,026 epoch 6 - iter 576/723 - loss 0.02508343 - time (sec): 48.66 - samples/sec: 2911.09 - lr: 0.000023 - momentum: 0.000000 2023-10-14 08:46:21,790 epoch 6 - iter 648/723 - loss 0.02441954 - time (sec): 54.43 - samples/sec: 2909.44 - lr: 0.000023 - momentum: 0.000000 2023-10-14 08:46:27,487 epoch 6 - iter 720/723 - loss 0.02461414 - time (sec): 60.12 - samples/sec: 2921.93 - lr: 0.000022 - momentum: 0.000000 2023-10-14 08:46:27,706 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:46:27,706 EPOCH 6 done: loss 0.0246 - lr: 0.000022 2023-10-14 08:46:31,624 DEV : loss 0.13394637405872345 - f1-score (micro avg) 0.8007 2023-10-14 08:46:31,640 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:46:37,789 epoch 7 - iter 72/723 - loss 0.01052973 - time (sec): 6.15 - samples/sec: 2831.45 - lr: 0.000022 - momentum: 0.000000 2023-10-14 08:46:43,883 epoch 7 - iter 144/723 - loss 0.01534049 - time (sec): 12.24 - samples/sec: 2788.45 - lr: 0.000021 - momentum: 0.000000 2023-10-14 08:46:50,266 epoch 7 - iter 216/723 - loss 0.01640192 - time (sec): 18.62 - samples/sec: 2816.89 - lr: 0.000021 - momentum: 0.000000 2023-10-14 08:46:56,437 epoch 7 - iter 288/723 - loss 0.01582983 - time (sec): 24.79 - samples/sec: 2843.68 - lr: 0.000020 - momentum: 0.000000 2023-10-14 08:47:02,511 epoch 7 - iter 360/723 - loss 0.01684897 - time (sec): 30.87 - samples/sec: 2851.30 - lr: 0.000019 - momentum: 0.000000 2023-10-14 08:47:08,860 epoch 7 - iter 432/723 - loss 0.01771060 - time (sec): 37.22 - samples/sec: 2861.05 - lr: 0.000019 - momentum: 0.000000 2023-10-14 08:47:14,726 epoch 7 - iter 504/723 - loss 0.01806623 - time (sec): 43.08 - samples/sec: 2860.00 - lr: 0.000018 - momentum: 0.000000 2023-10-14 08:47:20,914 epoch 7 - iter 576/723 - loss 0.01763909 - time (sec): 49.27 - samples/sec: 2870.54 - lr: 0.000018 - momentum: 0.000000 2023-10-14 08:47:26,694 epoch 7 - iter 648/723 - loss 0.01807010 - time (sec): 55.05 - samples/sec: 2870.39 - lr: 0.000017 - momentum: 0.000000 2023-10-14 08:47:32,584 epoch 7 - iter 720/723 - loss 0.01793967 - time (sec): 60.94 - samples/sec: 2882.95 - lr: 0.000017 - momentum: 0.000000 2023-10-14 08:47:32,768 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:47:32,768 EPOCH 7 done: loss 0.0180 - lr: 0.000017 2023-10-14 08:47:36,309 DEV : loss 0.15739385783672333 - f1-score (micro avg) 0.8091 2023-10-14 08:47:36,330 saving best model 2023-10-14 08:47:36,972 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:47:42,658 epoch 8 - iter 72/723 - loss 0.00886684 - time (sec): 5.68 - samples/sec: 3068.63 - lr: 0.000016 - momentum: 0.000000 2023-10-14 08:47:49,383 epoch 8 - iter 144/723 - loss 0.00889193 - time (sec): 12.41 - samples/sec: 2843.55 - lr: 0.000016 - momentum: 0.000000 2023-10-14 08:47:55,741 epoch 8 - iter 216/723 - loss 0.01045971 - time (sec): 18.77 - samples/sec: 2819.84 - lr: 0.000015 - momentum: 0.000000 2023-10-14 08:48:01,528 epoch 8 - iter 288/723 - loss 0.01209747 - time (sec): 24.55 - samples/sec: 2858.09 - lr: 0.000014 - momentum: 0.000000 2023-10-14 08:48:07,734 epoch 8 - iter 360/723 - loss 0.01220357 - time (sec): 30.76 - samples/sec: 2896.09 - lr: 0.000014 - momentum: 0.000000 2023-10-14 08:48:13,536 epoch 8 - iter 432/723 - loss 0.01156847 - time (sec): 36.56 - samples/sec: 2896.66 - lr: 0.000013 - momentum: 0.000000 2023-10-14 08:48:19,226 epoch 8 - iter 504/723 - loss 0.01200927 - time (sec): 42.25 - samples/sec: 2924.08 - lr: 0.000013 - momentum: 0.000000 2023-10-14 08:48:24,682 epoch 8 - iter 576/723 - loss 0.01257051 - time (sec): 47.71 - samples/sec: 2936.85 - lr: 0.000012 - momentum: 0.000000 2023-10-14 08:48:31,135 epoch 8 - iter 648/723 - loss 0.01301488 - time (sec): 54.16 - samples/sec: 2925.42 - lr: 0.000012 - momentum: 0.000000 2023-10-14 08:48:37,310 epoch 8 - iter 720/723 - loss 0.01265192 - time (sec): 60.34 - samples/sec: 2914.65 - lr: 0.000011 - momentum: 0.000000 2023-10-14 08:48:37,477 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:48:37,477 EPOCH 8 done: loss 0.0126 - lr: 0.000011 2023-10-14 08:48:41,031 DEV : loss 0.16435782611370087 - f1-score (micro avg) 0.8191 2023-10-14 08:48:41,057 saving best model 2023-10-14 08:48:41,555 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:48:47,795 epoch 9 - iter 72/723 - loss 0.01070952 - time (sec): 6.24 - samples/sec: 2923.12 - lr: 0.000011 - momentum: 0.000000 2023-10-14 08:48:54,609 epoch 9 - iter 144/723 - loss 0.01060093 - time (sec): 13.05 - samples/sec: 2817.51 - lr: 0.000010 - momentum: 0.000000 2023-10-14 08:49:00,950 epoch 9 - iter 216/723 - loss 0.01086091 - time (sec): 19.39 - samples/sec: 2884.36 - lr: 0.000009 - momentum: 0.000000 2023-10-14 08:49:06,724 epoch 9 - iter 288/723 - loss 0.01001855 - time (sec): 25.17 - samples/sec: 2866.40 - lr: 0.000009 - momentum: 0.000000 2023-10-14 08:49:13,184 epoch 9 - iter 360/723 - loss 0.00908806 - time (sec): 31.63 - samples/sec: 2871.35 - lr: 0.000008 - momentum: 0.000000 2023-10-14 08:49:18,726 epoch 9 - iter 432/723 - loss 0.00884104 - time (sec): 37.17 - samples/sec: 2881.99 - lr: 0.000008 - momentum: 0.000000 2023-10-14 08:49:24,886 epoch 9 - iter 504/723 - loss 0.00923270 - time (sec): 43.33 - samples/sec: 2865.04 - lr: 0.000007 - momentum: 0.000000 2023-10-14 08:49:30,450 epoch 9 - iter 576/723 - loss 0.00934944 - time (sec): 48.89 - samples/sec: 2866.48 - lr: 0.000007 - momentum: 0.000000 2023-10-14 08:49:36,426 epoch 9 - iter 648/723 - loss 0.00957257 - time (sec): 54.87 - samples/sec: 2870.69 - lr: 0.000006 - momentum: 0.000000 2023-10-14 08:49:42,770 epoch 9 - iter 720/723 - loss 0.00929602 - time (sec): 61.21 - samples/sec: 2869.96 - lr: 0.000006 - momentum: 0.000000 2023-10-14 08:49:42,966 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:49:42,966 EPOCH 9 done: loss 0.0093 - lr: 0.000006 2023-10-14 08:49:47,108 DEV : loss 0.19722115993499756 - f1-score (micro avg) 0.7954 2023-10-14 08:49:47,126 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:49:53,385 epoch 10 - iter 72/723 - loss 0.00259989 - time (sec): 6.26 - samples/sec: 2936.21 - lr: 0.000005 - momentum: 0.000000 2023-10-14 08:49:59,031 epoch 10 - iter 144/723 - loss 0.00417789 - time (sec): 11.90 - samples/sec: 2913.46 - lr: 0.000004 - momentum: 0.000000 2023-10-14 08:50:05,446 epoch 10 - iter 216/723 - loss 0.00571543 - time (sec): 18.32 - samples/sec: 2873.95 - lr: 0.000004 - momentum: 0.000000 2023-10-14 08:50:11,774 epoch 10 - iter 288/723 - loss 0.00605918 - time (sec): 24.65 - samples/sec: 2877.11 - lr: 0.000003 - momentum: 0.000000 2023-10-14 08:50:17,626 epoch 10 - iter 360/723 - loss 0.00556952 - time (sec): 30.50 - samples/sec: 2880.45 - lr: 0.000003 - momentum: 0.000000 2023-10-14 08:50:24,462 epoch 10 - iter 432/723 - loss 0.00569447 - time (sec): 37.33 - samples/sec: 2868.44 - lr: 0.000002 - momentum: 0.000000 2023-10-14 08:50:30,331 epoch 10 - iter 504/723 - loss 0.00564912 - time (sec): 43.20 - samples/sec: 2872.78 - lr: 0.000002 - momentum: 0.000000 2023-10-14 08:50:36,276 epoch 10 - iter 576/723 - loss 0.00551211 - time (sec): 49.15 - samples/sec: 2871.73 - lr: 0.000001 - momentum: 0.000000 2023-10-14 08:50:42,081 epoch 10 - iter 648/723 - loss 0.00538978 - time (sec): 54.95 - samples/sec: 2871.64 - lr: 0.000001 - momentum: 0.000000 2023-10-14 08:50:48,575 epoch 10 - iter 720/723 - loss 0.00516187 - time (sec): 61.45 - samples/sec: 2862.03 - lr: 0.000000 - momentum: 0.000000 2023-10-14 08:50:48,765 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:50:48,765 EPOCH 10 done: loss 0.0052 - lr: 0.000000 2023-10-14 08:50:52,276 DEV : loss 0.1937766820192337 - f1-score (micro avg) 0.8068 2023-10-14 08:50:52,725 ---------------------------------------------------------------------------------------------------- 2023-10-14 08:50:52,727 Loading model from best epoch ... 2023-10-14 08:50:55,066 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 2023-10-14 08:50:57,817 Results: - F-score (micro) 0.8168 - F-score (macro) 0.7131 - Accuracy 0.7047 By class: precision recall f1-score support PER 0.8333 0.8195 0.8264 482 LOC 0.8821 0.8493 0.8654 458 ORG 0.4324 0.4638 0.4476 69 micro avg 0.8251 0.8087 0.8168 1009 macro avg 0.7160 0.7109 0.7131 1009 weighted avg 0.8280 0.8087 0.8182 1009 2023-10-14 08:50:57,817 ----------------------------------------------------------------------------------------------------