2023-10-25 21:25:37,600 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,601 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 21:25:37,601 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,601 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 21:25:37,601 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,601 Train: 1166 sentences 2023-10-25 21:25:37,601 (train_with_dev=False, train_with_test=False) 2023-10-25 21:25:37,601 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,601 Training Params: 2023-10-25 21:25:37,601 - learning_rate: "3e-05" 2023-10-25 21:25:37,601 - mini_batch_size: "8" 2023-10-25 21:25:37,601 - max_epochs: "10" 2023-10-25 21:25:37,601 - shuffle: "True" 2023-10-25 21:25:37,601 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,601 Plugins: 2023-10-25 21:25:37,601 - TensorboardLogger 2023-10-25 21:25:37,601 - LinearScheduler | warmup_fraction: '0.1' 2023-10-25 21:25:37,601 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,602 Final evaluation on model from best epoch (best-model.pt) 2023-10-25 21:25:37,602 - metric: "('micro avg', 'f1-score')" 2023-10-25 21:25:37,602 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,602 Computation: 2023-10-25 21:25:37,602 - compute on device: cuda:0 2023-10-25 21:25:37,602 - embedding storage: none 2023-10-25 21:25:37,602 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,602 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5" 2023-10-25 21:25:37,602 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,602 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:37,602 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-25 21:25:38,456 epoch 1 - iter 14/146 - loss 2.60294278 - time (sec): 0.85 - samples/sec: 4554.56 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:25:39,251 epoch 1 - iter 28/146 - loss 2.23371535 - time (sec): 1.65 - samples/sec: 4624.06 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:25:40,234 epoch 1 - iter 42/146 - loss 1.80138209 - time (sec): 2.63 - samples/sec: 4632.61 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:25:41,163 epoch 1 - iter 56/146 - loss 1.53922194 - time (sec): 3.56 - samples/sec: 4611.81 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:25:42,048 epoch 1 - iter 70/146 - loss 1.31289657 - time (sec): 4.44 - samples/sec: 4710.80 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:25:42,819 epoch 1 - iter 84/146 - loss 1.19311406 - time (sec): 5.22 - samples/sec: 4682.69 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:25:43,898 epoch 1 - iter 98/146 - loss 1.06894666 - time (sec): 6.30 - samples/sec: 4636.30 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:25:44,851 epoch 1 - iter 112/146 - loss 0.96000516 - time (sec): 7.25 - samples/sec: 4688.87 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:25:45,925 epoch 1 - iter 126/146 - loss 0.87262223 - time (sec): 8.32 - samples/sec: 4714.46 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:25:46,732 epoch 1 - iter 140/146 - loss 0.82531461 - time (sec): 9.13 - samples/sec: 4677.43 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:25:47,124 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:47,125 EPOCH 1 done: loss 0.8031 - lr: 0.000029 2023-10-25 21:25:47,636 DEV : loss 0.17106929421424866 - f1-score (micro avg) 0.5556 2023-10-25 21:25:47,641 saving best model 2023-10-25 21:25:48,125 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:49,058 epoch 2 - iter 14/146 - loss 0.21725174 - time (sec): 0.93 - samples/sec: 4670.16 - lr: 0.000030 - momentum: 0.000000 2023-10-25 21:25:49,982 epoch 2 - iter 28/146 - loss 0.26028088 - time (sec): 1.86 - samples/sec: 4639.64 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:25:50,829 epoch 2 - iter 42/146 - loss 0.23972876 - time (sec): 2.70 - samples/sec: 4534.87 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:25:51,852 epoch 2 - iter 56/146 - loss 0.21456830 - time (sec): 3.73 - samples/sec: 4529.17 - lr: 0.000029 - momentum: 0.000000 2023-10-25 21:25:52,760 epoch 2 - iter 70/146 - loss 0.20980275 - time (sec): 4.63 - samples/sec: 4586.61 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:25:53,548 epoch 2 - iter 84/146 - loss 0.20500047 - time (sec): 5.42 - samples/sec: 4663.26 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:25:54,385 epoch 2 - iter 98/146 - loss 0.20211037 - time (sec): 6.26 - samples/sec: 4703.40 - lr: 0.000028 - momentum: 0.000000 2023-10-25 21:25:55,213 epoch 2 - iter 112/146 - loss 0.19844997 - time (sec): 7.09 - samples/sec: 4734.97 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:25:56,153 epoch 2 - iter 126/146 - loss 0.19974107 - time (sec): 8.03 - samples/sec: 4721.44 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:25:57,151 epoch 2 - iter 140/146 - loss 0.19108156 - time (sec): 9.03 - samples/sec: 4716.34 - lr: 0.000027 - momentum: 0.000000 2023-10-25 21:25:57,479 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:25:57,479 EPOCH 2 done: loss 0.1879 - lr: 0.000027 2023-10-25 21:25:58,559 DEV : loss 0.11886167526245117 - f1-score (micro avg) 0.628 2023-10-25 21:25:58,564 saving best model 2023-10-25 21:25:59,181 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:00,384 epoch 3 - iter 14/146 - loss 0.09759941 - time (sec): 1.20 - samples/sec: 4773.89 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:26:01,355 epoch 3 - iter 28/146 - loss 0.10716313 - time (sec): 2.17 - samples/sec: 4677.63 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:26:02,184 epoch 3 - iter 42/146 - loss 0.10722639 - time (sec): 3.00 - samples/sec: 4725.88 - lr: 0.000026 - momentum: 0.000000 2023-10-25 21:26:03,077 epoch 3 - iter 56/146 - loss 0.10523426 - time (sec): 3.89 - samples/sec: 4598.51 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:26:04,013 epoch 3 - iter 70/146 - loss 0.10392697 - time (sec): 4.83 - samples/sec: 4631.03 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:26:04,838 epoch 3 - iter 84/146 - loss 0.10454309 - time (sec): 5.65 - samples/sec: 4614.89 - lr: 0.000025 - momentum: 0.000000 2023-10-25 21:26:05,722 epoch 3 - iter 98/146 - loss 0.09893681 - time (sec): 6.54 - samples/sec: 4658.43 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:26:06,558 epoch 3 - iter 112/146 - loss 0.09875386 - time (sec): 7.38 - samples/sec: 4621.33 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:26:07,479 epoch 3 - iter 126/146 - loss 0.09913673 - time (sec): 8.30 - samples/sec: 4648.08 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:26:08,263 epoch 3 - iter 140/146 - loss 0.09719812 - time (sec): 9.08 - samples/sec: 4722.37 - lr: 0.000024 - momentum: 0.000000 2023-10-25 21:26:08,631 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:08,631 EPOCH 3 done: loss 0.0987 - lr: 0.000024 2023-10-25 21:26:09,551 DEV : loss 0.10990928113460541 - f1-score (micro avg) 0.7281 2023-10-25 21:26:09,556 saving best model 2023-10-25 21:26:10,167 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:10,976 epoch 4 - iter 14/146 - loss 0.08318771 - time (sec): 0.81 - samples/sec: 4774.15 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:26:11,965 epoch 4 - iter 28/146 - loss 0.06355907 - time (sec): 1.80 - samples/sec: 4570.83 - lr: 0.000023 - momentum: 0.000000 2023-10-25 21:26:12,846 epoch 4 - iter 42/146 - loss 0.05940453 - time (sec): 2.68 - samples/sec: 4712.30 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:26:13,683 epoch 4 - iter 56/146 - loss 0.05624618 - time (sec): 3.51 - samples/sec: 4578.48 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:26:14,614 epoch 4 - iter 70/146 - loss 0.05687332 - time (sec): 4.44 - samples/sec: 4793.07 - lr: 0.000022 - momentum: 0.000000 2023-10-25 21:26:15,618 epoch 4 - iter 84/146 - loss 0.05866038 - time (sec): 5.45 - samples/sec: 4832.66 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:26:16,470 epoch 4 - iter 98/146 - loss 0.05691631 - time (sec): 6.30 - samples/sec: 4897.68 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:26:17,311 epoch 4 - iter 112/146 - loss 0.06125083 - time (sec): 7.14 - samples/sec: 4852.90 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:26:18,415 epoch 4 - iter 126/146 - loss 0.06362815 - time (sec): 8.25 - samples/sec: 4759.95 - lr: 0.000021 - momentum: 0.000000 2023-10-25 21:26:19,217 epoch 4 - iter 140/146 - loss 0.06182318 - time (sec): 9.05 - samples/sec: 4740.79 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:26:19,541 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:19,541 EPOCH 4 done: loss 0.0618 - lr: 0.000020 2023-10-25 21:26:20,462 DEV : loss 0.0920729711651802 - f1-score (micro avg) 0.7702 2023-10-25 21:26:20,467 saving best model 2023-10-25 21:26:20,948 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:21,871 epoch 5 - iter 14/146 - loss 0.03669754 - time (sec): 0.92 - samples/sec: 4823.31 - lr: 0.000020 - momentum: 0.000000 2023-10-25 21:26:22,778 epoch 5 - iter 28/146 - loss 0.03920318 - time (sec): 1.83 - samples/sec: 4663.07 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:26:23,664 epoch 5 - iter 42/146 - loss 0.03549894 - time (sec): 2.71 - samples/sec: 4675.88 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:26:24,500 epoch 5 - iter 56/146 - loss 0.03524770 - time (sec): 3.55 - samples/sec: 4765.02 - lr: 0.000019 - momentum: 0.000000 2023-10-25 21:26:25,348 epoch 5 - iter 70/146 - loss 0.03474532 - time (sec): 4.40 - samples/sec: 4816.39 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:26:26,121 epoch 5 - iter 84/146 - loss 0.03887679 - time (sec): 5.17 - samples/sec: 4820.26 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:26:27,253 epoch 5 - iter 98/146 - loss 0.04147485 - time (sec): 6.30 - samples/sec: 4715.08 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:26:28,113 epoch 5 - iter 112/146 - loss 0.04145546 - time (sec): 7.16 - samples/sec: 4758.84 - lr: 0.000018 - momentum: 0.000000 2023-10-25 21:26:29,050 epoch 5 - iter 126/146 - loss 0.04076736 - time (sec): 8.10 - samples/sec: 4785.00 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:26:29,887 epoch 5 - iter 140/146 - loss 0.03908635 - time (sec): 8.94 - samples/sec: 4809.16 - lr: 0.000017 - momentum: 0.000000 2023-10-25 21:26:30,239 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:30,239 EPOCH 5 done: loss 0.0394 - lr: 0.000017 2023-10-25 21:26:31,319 DEV : loss 0.11569201946258545 - f1-score (micro avg) 0.7261 2023-10-25 21:26:31,324 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:32,244 epoch 6 - iter 14/146 - loss 0.03038153 - time (sec): 0.92 - samples/sec: 5188.57 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:26:33,136 epoch 6 - iter 28/146 - loss 0.03130799 - time (sec): 1.81 - samples/sec: 4906.54 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:26:33,995 epoch 6 - iter 42/146 - loss 0.02641538 - time (sec): 2.67 - samples/sec: 4942.07 - lr: 0.000016 - momentum: 0.000000 2023-10-25 21:26:34,916 epoch 6 - iter 56/146 - loss 0.03146130 - time (sec): 3.59 - samples/sec: 4891.39 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:26:35,779 epoch 6 - iter 70/146 - loss 0.03030920 - time (sec): 4.45 - samples/sec: 4938.51 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:26:36,627 epoch 6 - iter 84/146 - loss 0.02957139 - time (sec): 5.30 - samples/sec: 4865.32 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:26:37,593 epoch 6 - iter 98/146 - loss 0.02770553 - time (sec): 6.27 - samples/sec: 4793.74 - lr: 0.000015 - momentum: 0.000000 2023-10-25 21:26:38,516 epoch 6 - iter 112/146 - loss 0.02699072 - time (sec): 7.19 - samples/sec: 4747.91 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:26:39,578 epoch 6 - iter 126/146 - loss 0.02595757 - time (sec): 8.25 - samples/sec: 4701.74 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:26:40,381 epoch 6 - iter 140/146 - loss 0.02607979 - time (sec): 9.06 - samples/sec: 4699.00 - lr: 0.000014 - momentum: 0.000000 2023-10-25 21:26:40,763 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:40,764 EPOCH 6 done: loss 0.0259 - lr: 0.000014 2023-10-25 21:26:41,690 DEV : loss 0.12295451760292053 - f1-score (micro avg) 0.7401 2023-10-25 21:26:41,696 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:42,577 epoch 7 - iter 14/146 - loss 0.02415144 - time (sec): 0.88 - samples/sec: 5234.42 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:26:43,586 epoch 7 - iter 28/146 - loss 0.03236498 - time (sec): 1.89 - samples/sec: 4997.27 - lr: 0.000013 - momentum: 0.000000 2023-10-25 21:26:44,412 epoch 7 - iter 42/146 - loss 0.03067054 - time (sec): 2.71 - samples/sec: 4934.20 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:26:45,263 epoch 7 - iter 56/146 - loss 0.02611424 - time (sec): 3.57 - samples/sec: 4843.92 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:26:46,053 epoch 7 - iter 70/146 - loss 0.02398715 - time (sec): 4.36 - samples/sec: 4818.12 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:26:47,091 epoch 7 - iter 84/146 - loss 0.02253424 - time (sec): 5.39 - samples/sec: 4774.42 - lr: 0.000012 - momentum: 0.000000 2023-10-25 21:26:47,983 epoch 7 - iter 98/146 - loss 0.02258653 - time (sec): 6.29 - samples/sec: 4831.43 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:26:48,818 epoch 7 - iter 112/146 - loss 0.02125092 - time (sec): 7.12 - samples/sec: 4805.93 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:26:49,735 epoch 7 - iter 126/146 - loss 0.02038749 - time (sec): 8.04 - samples/sec: 4787.52 - lr: 0.000011 - momentum: 0.000000 2023-10-25 21:26:50,593 epoch 7 - iter 140/146 - loss 0.02016293 - time (sec): 8.90 - samples/sec: 4762.46 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:26:51,047 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:51,048 EPOCH 7 done: loss 0.0195 - lr: 0.000010 2023-10-25 21:26:51,973 DEV : loss 0.1449124813079834 - f1-score (micro avg) 0.7158 2023-10-25 21:26:51,978 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:26:52,924 epoch 8 - iter 14/146 - loss 0.01568499 - time (sec): 0.95 - samples/sec: 4492.68 - lr: 0.000010 - momentum: 0.000000 2023-10-25 21:26:53,864 epoch 8 - iter 28/146 - loss 0.01851063 - time (sec): 1.88 - samples/sec: 4485.90 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:26:54,698 epoch 8 - iter 42/146 - loss 0.01478075 - time (sec): 2.72 - samples/sec: 4596.52 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:26:55,645 epoch 8 - iter 56/146 - loss 0.01476943 - time (sec): 3.67 - samples/sec: 4680.34 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:26:56,459 epoch 8 - iter 70/146 - loss 0.01459552 - time (sec): 4.48 - samples/sec: 4686.56 - lr: 0.000009 - momentum: 0.000000 2023-10-25 21:26:57,274 epoch 8 - iter 84/146 - loss 0.01588161 - time (sec): 5.30 - samples/sec: 4758.94 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:26:58,098 epoch 8 - iter 98/146 - loss 0.01515460 - time (sec): 6.12 - samples/sec: 4735.78 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:26:59,012 epoch 8 - iter 112/146 - loss 0.01564759 - time (sec): 7.03 - samples/sec: 4711.07 - lr: 0.000008 - momentum: 0.000000 2023-10-25 21:26:59,942 epoch 8 - iter 126/146 - loss 0.01592133 - time (sec): 7.96 - samples/sec: 4714.23 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:27:00,945 epoch 8 - iter 140/146 - loss 0.01519304 - time (sec): 8.97 - samples/sec: 4745.85 - lr: 0.000007 - momentum: 0.000000 2023-10-25 21:27:01,340 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:01,340 EPOCH 8 done: loss 0.0159 - lr: 0.000007 2023-10-25 21:27:02,424 DEV : loss 0.14217789471149445 - f1-score (micro avg) 0.755 2023-10-25 21:27:02,430 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:03,382 epoch 9 - iter 14/146 - loss 0.00940644 - time (sec): 0.95 - samples/sec: 5197.42 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:27:04,214 epoch 9 - iter 28/146 - loss 0.01224290 - time (sec): 1.78 - samples/sec: 5124.09 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:27:05,021 epoch 9 - iter 42/146 - loss 0.01134241 - time (sec): 2.59 - samples/sec: 5030.86 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:27:05,958 epoch 9 - iter 56/146 - loss 0.01127674 - time (sec): 3.53 - samples/sec: 5002.42 - lr: 0.000006 - momentum: 0.000000 2023-10-25 21:27:06,926 epoch 9 - iter 70/146 - loss 0.01508017 - time (sec): 4.50 - samples/sec: 4872.49 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:27:07,920 epoch 9 - iter 84/146 - loss 0.01540634 - time (sec): 5.49 - samples/sec: 4773.73 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:27:08,821 epoch 9 - iter 98/146 - loss 0.01387577 - time (sec): 6.39 - samples/sec: 4775.83 - lr: 0.000005 - momentum: 0.000000 2023-10-25 21:27:09,723 epoch 9 - iter 112/146 - loss 0.01346312 - time (sec): 7.29 - samples/sec: 4762.16 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:27:10,619 epoch 9 - iter 126/146 - loss 0.01415713 - time (sec): 8.19 - samples/sec: 4712.60 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:27:11,529 epoch 9 - iter 140/146 - loss 0.01466539 - time (sec): 9.10 - samples/sec: 4690.74 - lr: 0.000004 - momentum: 0.000000 2023-10-25 21:27:11,876 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:11,877 EPOCH 9 done: loss 0.0142 - lr: 0.000004 2023-10-25 21:27:12,796 DEV : loss 0.14243099093437195 - f1-score (micro avg) 0.7387 2023-10-25 21:27:12,801 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:13,674 epoch 10 - iter 14/146 - loss 0.00899830 - time (sec): 0.87 - samples/sec: 4927.08 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:27:14,530 epoch 10 - iter 28/146 - loss 0.00557953 - time (sec): 1.73 - samples/sec: 4608.78 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:27:15,417 epoch 10 - iter 42/146 - loss 0.01050729 - time (sec): 2.61 - samples/sec: 4620.84 - lr: 0.000003 - momentum: 0.000000 2023-10-25 21:27:16,202 epoch 10 - iter 56/146 - loss 0.01109768 - time (sec): 3.40 - samples/sec: 4647.40 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:27:17,172 epoch 10 - iter 70/146 - loss 0.01198941 - time (sec): 4.37 - samples/sec: 4708.51 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:27:18,082 epoch 10 - iter 84/146 - loss 0.01146295 - time (sec): 5.28 - samples/sec: 4686.69 - lr: 0.000002 - momentum: 0.000000 2023-10-25 21:27:18,904 epoch 10 - iter 98/146 - loss 0.01083949 - time (sec): 6.10 - samples/sec: 4773.97 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:27:19,896 epoch 10 - iter 112/146 - loss 0.01100900 - time (sec): 7.09 - samples/sec: 4798.30 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:27:20,832 epoch 10 - iter 126/146 - loss 0.01016506 - time (sec): 8.03 - samples/sec: 4761.08 - lr: 0.000001 - momentum: 0.000000 2023-10-25 21:27:21,781 epoch 10 - iter 140/146 - loss 0.01013197 - time (sec): 8.98 - samples/sec: 4791.19 - lr: 0.000000 - momentum: 0.000000 2023-10-25 21:27:22,091 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:22,091 EPOCH 10 done: loss 0.0099 - lr: 0.000000 2023-10-25 21:27:23,012 DEV : loss 0.14977367222309113 - f1-score (micro avg) 0.7419 2023-10-25 21:27:23,485 ---------------------------------------------------------------------------------------------------- 2023-10-25 21:27:23,486 Loading model from best epoch ... 2023-10-25 21:27:25,065 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 21:27:26,606 Results: - F-score (micro) 0.7631 - F-score (macro) 0.6653 - Accuracy 0.6408 By class: precision recall f1-score support PER 0.7919 0.8420 0.8162 348 LOC 0.7026 0.8238 0.7584 261 ORG 0.5111 0.4423 0.4742 52 HumanProd 0.5556 0.6818 0.6122 22 micro avg 0.7299 0.7994 0.7631 683 macro avg 0.6403 0.6975 0.6653 683 weighted avg 0.7288 0.7994 0.7615 683 2023-10-25 21:27:26,606 ----------------------------------------------------------------------------------------------------