2023-10-25 21:17:46,995 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,996 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(64001, 768) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-25 21:17:46,996 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,996 MultiCorpus: 1166 train + 165 dev + 415 test sentences - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator 2023-10-25 21:17:46,996 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,996 Train: 1166 sentences 2023-10-25 21:17:46,996 (train_with_dev=False, train_with_test=False) 2023-10-25 21:17:46,996 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,996 Training Params: 2023-10-25 21:17:46,996 - learning_rate: "5e-05" 2023-10-25 21:17:46,996 - mini_batch_size: "8" 2023-10-25 21:17:46,996 - max_epochs: "10" 2023-10-25 21:17:46,996 - shuffle: "True" 2023-10-25 21:17:46,996 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,996 Plugins: 2023-10-25 21:17:46,997 - TensorboardLogger 2023-10-25 21:17:46,997 - LinearScheduler | warmup_fraction: '0.1' 2023-10-25 21:17:46,997 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,997 Final evaluation on model from best epoch (best-model.pt) 2023-10-25 21:17:46,997 - metric: "('micro avg', 'f1-score')" 2023-10-25 21:17:46,997 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,997 Computation: 2023-10-25 21:17:46,997 - compute on device: cuda:0 2023-10-25 21:17:46,997 - embedding storage: none 2023-10-25 21:17:46,997 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,997 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-25 21:17:46,997 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,997 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:46,997 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-25 21:17:47,929 epoch 1 - iter 14/146 - loss 3.25226463 - time (sec): 0.93 - samples/sec: 4885.87 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:17:48,770 epoch 1 - iter 28/146 - loss 2.55317170 - time (sec): 1.77 - samples/sec: 4857.98 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:17:49,542 epoch 1 - iter 42/146 - loss 2.05595893 - time (sec): 2.54 - samples/sec: 4866.50 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:17:50,368 epoch 1 - iter 56/146 - loss 1.67247460 - time (sec): 3.37 - samples/sec: 4975.54 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:17:51,219 epoch 1 - iter 70/146 - loss 1.44306959 - time (sec): 4.22 - samples/sec: 4875.31 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:17:52,277 epoch 1 - iter 84/146 - loss 1.24458071 - time (sec): 5.28 - samples/sec: 4795.51 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:17:53,133 epoch 1 - iter 98/146 - loss 1.10964271 - time (sec): 6.14 - samples/sec: 4834.68 - lr: 0.000033 - momentum: 0.000000 2023-10-25 21:17:53,951 epoch 1 - iter 112/146 - loss 1.01272313 - time (sec): 6.95 - samples/sec: 4803.87 - lr: 0.000038 - momentum: 0.000000 2023-10-25 21:17:54,877 epoch 1 - iter 126/146 - loss 0.90550822 - time (sec): 7.88 - samples/sec: 4861.78 - lr: 0.000043 - momentum: 0.000000 2023-10-25 21:17:55,745 epoch 1 - iter 140/146 - loss 0.83647696 - time (sec): 8.75 - samples/sec: 4825.74 - lr: 0.000048 - momentum: 0.000000 2023-10-25 21:17:56,215 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:56,215 EPOCH 1 done: loss 0.8028 - lr: 0.000048 2023-10-25 21:17:56,870 DEV : loss 0.15297161042690277 - f1-score (micro avg) 0.5579 2023-10-25 21:17:56,874 saving best model 2023-10-25 21:17:57,259 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:17:58,168 epoch 2 - iter 14/146 - loss 0.22538995 - time (sec): 0.91 - samples/sec: 4654.15 - lr: 0.000050 - momentum: 0.000000 2023-10-25 21:17:59,136 epoch 2 - iter 28/146 - loss 0.17893266 - time (sec): 1.88 - samples/sec: 4749.32 - lr: 0.000049 - momentum: 0.000000 2023-10-25 21:17:59,957 epoch 2 - iter 42/146 - loss 0.17000545 - time (sec): 2.70 - samples/sec: 4841.19 - lr: 0.000048 - momentum: 0.000000 2023-10-25 21:18:00,877 epoch 2 - iter 56/146 - loss 0.17256647 - time (sec): 3.62 - samples/sec: 4838.73 - lr: 0.000048 - momentum: 0.000000 2023-10-25 21:18:01,792 epoch 2 - iter 70/146 - loss 0.16856484 - time (sec): 4.53 - samples/sec: 4708.65 - lr: 0.000047 - momentum: 0.000000 2023-10-25 21:18:02,569 epoch 2 - iter 84/146 - loss 0.16763435 - time (sec): 5.31 - samples/sec: 4689.76 - lr: 0.000047 - momentum: 0.000000 2023-10-25 21:18:03,413 epoch 2 - iter 98/146 - loss 0.16570481 - time (sec): 6.15 - samples/sec: 4716.09 - lr: 0.000046 - momentum: 0.000000 2023-10-25 21:18:04,405 epoch 2 - iter 112/146 - loss 0.16574954 - time (sec): 7.14 - samples/sec: 4690.41 - lr: 0.000046 - momentum: 0.000000 2023-10-25 21:18:05,376 epoch 2 - iter 126/146 - loss 0.16070878 - time (sec): 8.12 - samples/sec: 4703.69 - lr: 0.000045 - momentum: 0.000000 2023-10-25 21:18:06,290 epoch 2 - iter 140/146 - loss 0.15828404 - time (sec): 9.03 - samples/sec: 4735.04 - lr: 0.000045 - momentum: 0.000000 2023-10-25 21:18:06,674 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:06,674 EPOCH 2 done: loss 0.1575 - lr: 0.000045 2023-10-25 21:18:07,586 DEV : loss 0.10719096660614014 - f1-score (micro avg) 0.689 2023-10-25 21:18:07,590 saving best model 2023-10-25 21:18:08,264 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:09,307 epoch 3 - iter 14/146 - loss 0.08892807 - time (sec): 1.04 - samples/sec: 4531.22 - lr: 0.000044 - momentum: 0.000000 2023-10-25 21:18:10,135 epoch 3 - iter 28/146 - loss 0.09012832 - time (sec): 1.87 - samples/sec: 4706.43 - lr: 0.000043 - momentum: 0.000000 2023-10-25 21:18:10,916 epoch 3 - iter 42/146 - loss 0.09342662 - time (sec): 2.65 - samples/sec: 4776.18 - lr: 0.000043 - momentum: 0.000000 2023-10-25 21:18:11,924 epoch 3 - iter 56/146 - loss 0.10492414 - time (sec): 3.66 - samples/sec: 4678.82 - lr: 0.000042 - momentum: 0.000000 2023-10-25 21:18:12,834 epoch 3 - iter 70/146 - loss 0.11480081 - time (sec): 4.57 - samples/sec: 4716.90 - lr: 0.000042 - momentum: 0.000000 2023-10-25 21:18:13,629 epoch 3 - iter 84/146 - loss 0.11121453 - time (sec): 5.36 - samples/sec: 4662.18 - lr: 0.000041 - momentum: 0.000000 2023-10-25 21:18:14,461 epoch 3 - iter 98/146 - loss 0.10452791 - time (sec): 6.19 - samples/sec: 4690.07 - lr: 0.000041 - momentum: 0.000000 2023-10-25 21:18:15,365 epoch 3 - iter 112/146 - loss 0.10131230 - time (sec): 7.10 - samples/sec: 4669.60 - lr: 0.000040 - momentum: 0.000000 2023-10-25 21:18:16,388 epoch 3 - iter 126/146 - loss 0.10122777 - time (sec): 8.12 - samples/sec: 4643.65 - lr: 0.000040 - momentum: 0.000000 2023-10-25 21:18:17,337 epoch 3 - iter 140/146 - loss 0.09817162 - time (sec): 9.07 - samples/sec: 4660.96 - lr: 0.000039 - momentum: 0.000000 2023-10-25 21:18:17,762 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:17,762 EPOCH 3 done: loss 0.0959 - lr: 0.000039 2023-10-25 21:18:18,668 DEV : loss 0.1064475029706955 - f1-score (micro avg) 0.7389 2023-10-25 21:18:18,673 saving best model 2023-10-25 21:18:19,350 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:20,237 epoch 4 - iter 14/146 - loss 0.04861052 - time (sec): 0.89 - samples/sec: 4719.25 - lr: 0.000038 - momentum: 0.000000 2023-10-25 21:18:21,215 epoch 4 - iter 28/146 - loss 0.05191836 - time (sec): 1.86 - samples/sec: 4877.09 - lr: 0.000038 - momentum: 0.000000 2023-10-25 21:18:22,236 epoch 4 - iter 42/146 - loss 0.06395996 - time (sec): 2.88 - samples/sec: 4809.19 - lr: 0.000037 - momentum: 0.000000 2023-10-25 21:18:23,076 epoch 4 - iter 56/146 - loss 0.06048888 - time (sec): 3.72 - samples/sec: 4874.77 - lr: 0.000037 - momentum: 0.000000 2023-10-25 21:18:23,950 epoch 4 - iter 70/146 - loss 0.06286319 - time (sec): 4.60 - samples/sec: 4835.86 - lr: 0.000036 - momentum: 0.000000 2023-10-25 21:18:24,738 epoch 4 - iter 84/146 - loss 0.06582380 - time (sec): 5.39 - samples/sec: 4807.83 - lr: 0.000036 - momentum: 0.000000 2023-10-25 21:18:25,554 epoch 4 - iter 98/146 - loss 0.06500042 - time (sec): 6.20 - samples/sec: 4816.03 - lr: 0.000035 - momentum: 0.000000 2023-10-25 21:18:26,501 epoch 4 - iter 112/146 - loss 0.06090963 - time (sec): 7.15 - samples/sec: 4799.24 - lr: 0.000035 - momentum: 0.000000 2023-10-25 21:18:27,383 epoch 4 - iter 126/146 - loss 0.05983610 - time (sec): 8.03 - samples/sec: 4786.06 - lr: 0.000034 - momentum: 0.000000 2023-10-25 21:18:28,318 epoch 4 - iter 140/146 - loss 0.05759203 - time (sec): 8.97 - samples/sec: 4790.33 - lr: 0.000034 - momentum: 0.000000 2023-10-25 21:18:28,721 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:28,721 EPOCH 4 done: loss 0.0581 - lr: 0.000034 2023-10-25 21:18:29,633 DEV : loss 0.11335166543722153 - f1-score (micro avg) 0.7289 2023-10-25 21:18:29,637 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:30,635 epoch 5 - iter 14/146 - loss 0.02310730 - time (sec): 1.00 - samples/sec: 4565.19 - lr: 0.000033 - momentum: 0.000000 2023-10-25 21:18:31,564 epoch 5 - iter 28/146 - loss 0.03667454 - time (sec): 1.93 - samples/sec: 4622.32 - lr: 0.000032 - momentum: 0.000000 2023-10-25 21:18:32,482 epoch 5 - iter 42/146 - loss 0.03684771 - time (sec): 2.84 - samples/sec: 4751.29 - lr: 0.000032 - momentum: 0.000000 2023-10-25 21:18:33,524 epoch 5 - iter 56/146 - loss 0.03238162 - time (sec): 3.89 - samples/sec: 4664.15 - lr: 0.000031 - momentum: 0.000000 2023-10-25 21:18:34,483 epoch 5 - iter 70/146 - loss 0.03312718 - time (sec): 4.84 - samples/sec: 4705.98 - lr: 0.000031 - momentum: 0.000000 2023-10-25 21:18:35,374 epoch 5 - iter 84/146 - loss 0.03488909 - time (sec): 5.74 - samples/sec: 4658.50 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:18:36,254 epoch 5 - iter 98/146 - loss 0.03511040 - time (sec): 6.62 - samples/sec: 4588.03 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:18:37,094 epoch 5 - iter 112/146 - loss 0.03459069 - time (sec): 7.46 - samples/sec: 4634.93 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:18:37,946 epoch 5 - iter 126/146 - loss 0.03501141 - time (sec): 8.31 - samples/sec: 4628.01 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:18:38,791 epoch 5 - iter 140/146 - loss 0.03582364 - time (sec): 9.15 - samples/sec: 4661.80 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:18:39,129 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:39,129 EPOCH 5 done: loss 0.0355 - lr: 0.000028 2023-10-25 21:18:40,050 DEV : loss 0.1298971325159073 - f1-score (micro avg) 0.7406 2023-10-25 21:18:40,054 saving best model 2023-10-25 21:18:40,729 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:41,777 epoch 6 - iter 14/146 - loss 0.03101011 - time (sec): 1.04 - samples/sec: 3989.79 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:18:42,674 epoch 6 - iter 28/146 - loss 0.02855869 - time (sec): 1.94 - samples/sec: 4176.16 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:18:43,620 epoch 6 - iter 42/146 - loss 0.02671193 - time (sec): 2.88 - samples/sec: 4207.24 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:18:44,432 epoch 6 - iter 56/146 - loss 0.02864686 - time (sec): 3.70 - samples/sec: 4361.20 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:18:45,347 epoch 6 - iter 70/146 - loss 0.02700276 - time (sec): 4.61 - samples/sec: 4449.25 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:18:46,316 epoch 6 - iter 84/146 - loss 0.02771092 - time (sec): 5.58 - samples/sec: 4453.29 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:18:47,196 epoch 6 - iter 98/146 - loss 0.02716645 - time (sec): 6.46 - samples/sec: 4495.58 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:18:48,272 epoch 6 - iter 112/146 - loss 0.02790357 - time (sec): 7.54 - samples/sec: 4587.95 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:18:49,070 epoch 6 - iter 126/146 - loss 0.02763179 - time (sec): 8.33 - samples/sec: 4624.59 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:18:50,011 epoch 6 - iter 140/146 - loss 0.02596328 - time (sec): 9.28 - samples/sec: 4628.42 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:18:50,348 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:50,348 EPOCH 6 done: loss 0.0260 - lr: 0.000023 2023-10-25 21:18:51,258 DEV : loss 0.13975274562835693 - f1-score (micro avg) 0.7446 2023-10-25 21:18:51,263 saving best model 2023-10-25 21:18:51,943 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:18:52,792 epoch 7 - iter 14/146 - loss 0.01511146 - time (sec): 0.84 - samples/sec: 4339.48 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:18:53,708 epoch 7 - iter 28/146 - loss 0.03038031 - time (sec): 1.76 - samples/sec: 4499.40 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:18:54,773 epoch 7 - iter 42/146 - loss 0.02451599 - time (sec): 2.82 - samples/sec: 4488.20 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:18:55,594 epoch 7 - iter 56/146 - loss 0.02420964 - time (sec): 3.65 - samples/sec: 4517.24 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:18:56,404 epoch 7 - iter 70/146 - loss 0.02264035 - time (sec): 4.46 - samples/sec: 4517.72 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:18:57,429 epoch 7 - iter 84/146 - loss 0.01956782 - time (sec): 5.48 - samples/sec: 4525.12 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:18:58,408 epoch 7 - iter 98/146 - loss 0.01927721 - time (sec): 6.46 - samples/sec: 4651.79 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:18:59,240 epoch 7 - iter 112/146 - loss 0.01849363 - time (sec): 7.29 - samples/sec: 4668.93 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:19:00,152 epoch 7 - iter 126/146 - loss 0.01922696 - time (sec): 8.20 - samples/sec: 4654.81 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:19:01,102 epoch 7 - iter 140/146 - loss 0.01984644 - time (sec): 9.15 - samples/sec: 4657.12 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:19:01,430 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:01,431 EPOCH 7 done: loss 0.0197 - lr: 0.000017 2023-10-25 21:19:02,383 DEV : loss 0.1919669657945633 - f1-score (micro avg) 0.7032 2023-10-25 21:19:02,388 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:03,290 epoch 8 - iter 14/146 - loss 0.00645562 - time (sec): 0.90 - samples/sec: 4461.30 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:19:04,288 epoch 8 - iter 28/146 - loss 0.01024029 - time (sec): 1.90 - samples/sec: 4851.43 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:19:05,111 epoch 8 - iter 42/146 - loss 0.01291880 - time (sec): 2.72 - samples/sec: 4749.49 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:19:05,943 epoch 8 - iter 56/146 - loss 0.01125943 - time (sec): 3.55 - samples/sec: 4866.42 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:19:06,777 epoch 8 - iter 70/146 - loss 0.01175429 - time (sec): 4.39 - samples/sec: 4870.43 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:19:07,788 epoch 8 - iter 84/146 - loss 0.01352109 - time (sec): 5.40 - samples/sec: 4832.43 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:19:08,711 epoch 8 - iter 98/146 - loss 0.01341075 - time (sec): 6.32 - samples/sec: 4766.57 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:19:09,567 epoch 8 - iter 112/146 - loss 0.01412573 - time (sec): 7.18 - samples/sec: 4730.64 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:19:10,473 epoch 8 - iter 126/146 - loss 0.01398333 - time (sec): 8.08 - samples/sec: 4746.89 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:19:11,543 epoch 8 - iter 140/146 - loss 0.01416295 - time (sec): 9.15 - samples/sec: 4722.43 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:19:11,859 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:11,859 EPOCH 8 done: loss 0.0141 - lr: 0.000012 2023-10-25 21:19:12,769 DEV : loss 0.1696723997592926 - f1-score (micro avg) 0.7516 2023-10-25 21:19:12,773 saving best model 2023-10-25 21:19:13,447 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:14,627 epoch 9 - iter 14/146 - loss 0.00262122 - time (sec): 1.18 - samples/sec: 3820.50 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:19:15,556 epoch 9 - iter 28/146 - loss 0.00845440 - time (sec): 2.11 - samples/sec: 4034.86 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:19:16,437 epoch 9 - iter 42/146 - loss 0.00754201 - time (sec): 2.99 - samples/sec: 4232.15 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:19:17,421 epoch 9 - iter 56/146 - loss 0.00744900 - time (sec): 3.97 - samples/sec: 4383.29 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:19:18,283 epoch 9 - iter 70/146 - loss 0.00890328 - time (sec): 4.83 - samples/sec: 4481.93 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:19:19,192 epoch 9 - iter 84/146 - loss 0.00998982 - time (sec): 5.74 - samples/sec: 4490.71 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:19:20,017 epoch 9 - iter 98/146 - loss 0.00904694 - time (sec): 6.57 - samples/sec: 4555.33 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:19:20,783 epoch 9 - iter 112/146 - loss 0.00888017 - time (sec): 7.33 - samples/sec: 4505.62 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:19:21,773 epoch 9 - iter 126/146 - loss 0.00832595 - time (sec): 8.32 - samples/sec: 4546.73 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:19:22,838 epoch 9 - iter 140/146 - loss 0.00937880 - time (sec): 9.39 - samples/sec: 4534.27 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:19:23,210 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:23,210 EPOCH 9 done: loss 0.0091 - lr: 0.000006 2023-10-25 21:19:24,121 DEV : loss 0.19342197477817535 - f1-score (micro avg) 0.7395 2023-10-25 21:19:24,126 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:25,053 epoch 10 - iter 14/146 - loss 0.00885036 - time (sec): 0.93 - samples/sec: 4744.47 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:19:25,916 epoch 10 - iter 28/146 - loss 0.00830142 - time (sec): 1.79 - samples/sec: 4598.25 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:19:26,734 epoch 10 - iter 42/146 - loss 0.00659488 - time (sec): 2.61 - samples/sec: 4611.18 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:19:27,676 epoch 10 - iter 56/146 - loss 0.00754351 - time (sec): 3.55 - samples/sec: 4657.30 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:19:28,550 epoch 10 - iter 70/146 - loss 0.00629635 - time (sec): 4.42 - samples/sec: 4767.11 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:19:29,452 epoch 10 - iter 84/146 - loss 0.00537279 - time (sec): 5.33 - samples/sec: 4796.56 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:19:30,589 epoch 10 - iter 98/146 - loss 0.00525309 - time (sec): 6.46 - samples/sec: 4767.88 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:19:31,437 epoch 10 - iter 112/146 - loss 0.00525225 - time (sec): 7.31 - samples/sec: 4708.13 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:19:32,326 epoch 10 - iter 126/146 - loss 0.00605529 - time (sec): 8.20 - samples/sec: 4673.20 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:19:33,253 epoch 10 - iter 140/146 - loss 0.00601103 - time (sec): 9.13 - samples/sec: 4665.94 - lr: 0.000000 - momentum: 0.000000 2023-10-25 21:19:33,591 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:33,591 EPOCH 10 done: loss 0.0058 - lr: 0.000000 2023-10-25 21:19:34,501 DEV : loss 0.19659662246704102 - f1-score (micro avg) 0.742 2023-10-25 21:19:34,900 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:19:34,901 Loading model from best epoch ... 2023-10-25 21:19:36,575 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-25 21:19:38,113 Results: - F-score (micro) 0.7569 - F-score (macro) 0.6826 - Accuracy 0.6335 By class: precision recall f1-score support PER 0.7879 0.8218 0.8045 348 LOC 0.6769 0.8429 0.7509 261 ORG 0.5102 0.4808 0.4950 52 HumanProd 0.6071 0.7727 0.6800 22 micro avg 0.7163 0.8023 0.7569 683 macro avg 0.6455 0.7296 0.6826 683 weighted avg 0.7185 0.8023 0.7564 683 2023-10-25 21:19:38,113 ----------------------------------------------------------------------------------------------------