| 2023-10-20 00:15:41,859 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,859 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (word_embeddings): Embedding(32001, 128) | |
| (position_embeddings): Embedding(512, 128) | |
| (token_type_embeddings): Embedding(2, 128) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): BertEncoder( | |
| (layer): ModuleList( | |
| (0-1): 2 x BertLayer( | |
| (attention): BertAttention( | |
| (self): BertSelfAttention( | |
| (query): Linear(in_features=128, out_features=128, bias=True) | |
| (key): Linear(in_features=128, out_features=128, bias=True) | |
| (value): Linear(in_features=128, out_features=128, bias=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (output): BertSelfOutput( | |
| (dense): Linear(in_features=128, out_features=128, bias=True) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): BertIntermediate( | |
| (dense): Linear(in_features=128, out_features=512, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): BertOutput( | |
| (dense): Linear(in_features=512, out_features=128, bias=True) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): BertPooler( | |
| (dense): Linear(in_features=128, out_features=128, bias=True) | |
| (activation): Tanh() | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=128, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-20 00:15:41,859 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,859 MultiCorpus: 1085 train + 148 dev + 364 test sentences | |
| - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator | |
| 2023-10-20 00:15:41,859 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,859 Train: 1085 sentences | |
| 2023-10-20 00:15:41,859 (train_with_dev=False, train_with_test=False) | |
| 2023-10-20 00:15:41,859 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,859 Training Params: | |
| 2023-10-20 00:15:41,859 - learning_rate: "5e-05" | |
| 2023-10-20 00:15:41,860 - mini_batch_size: "8" | |
| 2023-10-20 00:15:41,860 - max_epochs: "10" | |
| 2023-10-20 00:15:41,860 - shuffle: "True" | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 Plugins: | |
| 2023-10-20 00:15:41,860 - TensorboardLogger | |
| 2023-10-20 00:15:41,860 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-20 00:15:41,860 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 Computation: | |
| 2023-10-20 00:15:41,860 - compute on device: cuda:0 | |
| 2023-10-20 00:15:41,860 - embedding storage: none | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 Model training base path: "hmbench-newseye/sv-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:41,860 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-20 00:15:42,195 epoch 1 - iter 13/136 - loss 2.71580342 - time (sec): 0.33 - samples/sec: 14153.70 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:15:42,545 epoch 1 - iter 26/136 - loss 2.75774241 - time (sec): 0.68 - samples/sec: 15045.68 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:15:42,867 epoch 1 - iter 39/136 - loss 2.72096802 - time (sec): 1.01 - samples/sec: 13856.46 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:15:43,191 epoch 1 - iter 52/136 - loss 2.61686082 - time (sec): 1.33 - samples/sec: 13726.73 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:15:43,554 epoch 1 - iter 65/136 - loss 2.46982325 - time (sec): 1.69 - samples/sec: 13890.17 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:15:43,910 epoch 1 - iter 78/136 - loss 2.33117949 - time (sec): 2.05 - samples/sec: 13978.28 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:15:44,261 epoch 1 - iter 91/136 - loss 2.15087490 - time (sec): 2.40 - samples/sec: 14298.59 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:15:44,613 epoch 1 - iter 104/136 - loss 2.00246764 - time (sec): 2.75 - samples/sec: 14355.78 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:15:44,981 epoch 1 - iter 117/136 - loss 1.82360781 - time (sec): 3.12 - samples/sec: 14661.62 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:15:45,335 epoch 1 - iter 130/136 - loss 1.72004646 - time (sec): 3.47 - samples/sec: 14430.62 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:15:45,481 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:45,481 EPOCH 1 done: loss 1.6896 - lr: 0.000047 | |
| 2023-10-20 00:15:45,742 DEV : loss 0.5129944086074829 - f1-score (micro avg) 0.0 | |
| 2023-10-20 00:15:45,746 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:46,093 epoch 2 - iter 13/136 - loss 0.62034732 - time (sec): 0.35 - samples/sec: 13208.37 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-20 00:15:46,463 epoch 2 - iter 26/136 - loss 0.57324884 - time (sec): 0.72 - samples/sec: 13480.12 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-20 00:15:46,840 epoch 2 - iter 39/136 - loss 0.58051663 - time (sec): 1.09 - samples/sec: 14015.23 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:15:47,193 epoch 2 - iter 52/136 - loss 0.59433839 - time (sec): 1.45 - samples/sec: 13933.89 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:15:47,548 epoch 2 - iter 65/136 - loss 0.58965203 - time (sec): 1.80 - samples/sec: 14035.15 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:15:47,920 epoch 2 - iter 78/136 - loss 0.57088650 - time (sec): 2.17 - samples/sec: 13849.79 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:15:48,258 epoch 2 - iter 91/136 - loss 0.56744435 - time (sec): 2.51 - samples/sec: 13913.96 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:15:48,628 epoch 2 - iter 104/136 - loss 0.57337062 - time (sec): 2.88 - samples/sec: 14004.86 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:15:48,981 epoch 2 - iter 117/136 - loss 0.57817989 - time (sec): 3.23 - samples/sec: 13742.13 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:15:49,331 epoch 2 - iter 130/136 - loss 0.56972448 - time (sec): 3.58 - samples/sec: 13867.30 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:15:49,487 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:49,487 EPOCH 2 done: loss 0.5703 - lr: 0.000045 | |
| 2023-10-20 00:15:50,240 DEV : loss 0.40280210971832275 - f1-score (micro avg) 0.0 | |
| 2023-10-20 00:15:50,245 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:50,751 epoch 3 - iter 13/136 - loss 0.47143283 - time (sec): 0.51 - samples/sec: 9211.51 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-20 00:15:51,094 epoch 3 - iter 26/136 - loss 0.51209350 - time (sec): 0.85 - samples/sec: 10166.85 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:15:51,459 epoch 3 - iter 39/136 - loss 0.52369734 - time (sec): 1.21 - samples/sec: 10933.95 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:15:51,820 epoch 3 - iter 52/136 - loss 0.49813373 - time (sec): 1.57 - samples/sec: 11544.73 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:15:52,178 epoch 3 - iter 65/136 - loss 0.49048450 - time (sec): 1.93 - samples/sec: 12257.12 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:15:52,542 epoch 3 - iter 78/136 - loss 0.50132646 - time (sec): 2.30 - samples/sec: 12446.64 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:15:52,900 epoch 3 - iter 91/136 - loss 0.49578107 - time (sec): 2.65 - samples/sec: 12747.96 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:15:53,277 epoch 3 - iter 104/136 - loss 0.49932953 - time (sec): 3.03 - samples/sec: 13238.80 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:15:53,637 epoch 3 - iter 117/136 - loss 0.48966975 - time (sec): 3.39 - samples/sec: 13250.97 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:15:54,003 epoch 3 - iter 130/136 - loss 0.48182243 - time (sec): 3.76 - samples/sec: 13366.32 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-20 00:15:54,151 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:54,151 EPOCH 3 done: loss 0.4773 - lr: 0.000039 | |
| 2023-10-20 00:15:54,913 DEV : loss 0.3341137170791626 - f1-score (micro avg) 0.0449 | |
| 2023-10-20 00:15:54,917 saving best model | |
| 2023-10-20 00:15:54,944 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:55,298 epoch 4 - iter 13/136 - loss 0.43682280 - time (sec): 0.35 - samples/sec: 14480.67 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:15:55,664 epoch 4 - iter 26/136 - loss 0.43805806 - time (sec): 0.72 - samples/sec: 14581.64 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:15:56,021 epoch 4 - iter 39/136 - loss 0.42569577 - time (sec): 1.08 - samples/sec: 14557.46 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:15:56,366 epoch 4 - iter 52/136 - loss 0.43024297 - time (sec): 1.42 - samples/sec: 14120.12 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:15:56,709 epoch 4 - iter 65/136 - loss 0.43240328 - time (sec): 1.76 - samples/sec: 13951.12 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:15:57,056 epoch 4 - iter 78/136 - loss 0.43473880 - time (sec): 2.11 - samples/sec: 13893.05 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:15:57,424 epoch 4 - iter 91/136 - loss 0.43156765 - time (sec): 2.48 - samples/sec: 14057.94 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:15:57,800 epoch 4 - iter 104/136 - loss 0.41744288 - time (sec): 2.85 - samples/sec: 14170.57 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:15:58,153 epoch 4 - iter 117/136 - loss 0.42078699 - time (sec): 3.21 - samples/sec: 14016.33 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:15:58,517 epoch 4 - iter 130/136 - loss 0.42729131 - time (sec): 3.57 - samples/sec: 14153.82 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:15:58,665 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:58,665 EPOCH 4 done: loss 0.4280 - lr: 0.000034 | |
| 2023-10-20 00:15:59,439 DEV : loss 0.3015088438987732 - f1-score (micro avg) 0.1556 | |
| 2023-10-20 00:15:59,443 saving best model | |
| 2023-10-20 00:15:59,475 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:15:59,818 epoch 5 - iter 13/136 - loss 0.40628820 - time (sec): 0.34 - samples/sec: 11995.51 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:16:00,142 epoch 5 - iter 26/136 - loss 0.40792736 - time (sec): 0.67 - samples/sec: 13550.05 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:16:00,489 epoch 5 - iter 39/136 - loss 0.39472020 - time (sec): 1.01 - samples/sec: 13879.54 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:16:00,852 epoch 5 - iter 52/136 - loss 0.38363745 - time (sec): 1.38 - samples/sec: 13812.24 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:16:01,220 epoch 5 - iter 65/136 - loss 0.39560354 - time (sec): 1.74 - samples/sec: 14214.90 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:16:01,560 epoch 5 - iter 78/136 - loss 0.40319316 - time (sec): 2.08 - samples/sec: 13951.22 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:16:01,906 epoch 5 - iter 91/136 - loss 0.40240499 - time (sec): 2.43 - samples/sec: 13899.76 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:16:02,283 epoch 5 - iter 104/136 - loss 0.40383466 - time (sec): 2.81 - samples/sec: 14115.06 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:16:02,783 epoch 5 - iter 117/136 - loss 0.39938236 - time (sec): 3.31 - samples/sec: 13521.46 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:16:03,127 epoch 5 - iter 130/136 - loss 0.39994900 - time (sec): 3.65 - samples/sec: 13480.71 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:16:03,293 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:03,294 EPOCH 5 done: loss 0.3989 - lr: 0.000028 | |
| 2023-10-20 00:16:04,057 DEV : loss 0.2784372568130493 - f1-score (micro avg) 0.2439 | |
| 2023-10-20 00:16:04,061 saving best model | |
| 2023-10-20 00:16:04,091 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:04,438 epoch 6 - iter 13/136 - loss 0.35323433 - time (sec): 0.35 - samples/sec: 14856.59 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:16:04,787 epoch 6 - iter 26/136 - loss 0.33177870 - time (sec): 0.70 - samples/sec: 14858.13 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:16:05,146 epoch 6 - iter 39/136 - loss 0.32621623 - time (sec): 1.05 - samples/sec: 14212.70 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:16:05,502 epoch 6 - iter 52/136 - loss 0.34518206 - time (sec): 1.41 - samples/sec: 14138.18 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:16:05,881 epoch 6 - iter 65/136 - loss 0.36510659 - time (sec): 1.79 - samples/sec: 14228.33 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:16:06,219 epoch 6 - iter 78/136 - loss 0.37685407 - time (sec): 2.13 - samples/sec: 14271.61 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:16:06,558 epoch 6 - iter 91/136 - loss 0.37415174 - time (sec): 2.47 - samples/sec: 14208.04 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:16:06,906 epoch 6 - iter 104/136 - loss 0.37156632 - time (sec): 2.81 - samples/sec: 14184.70 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:16:07,250 epoch 6 - iter 117/136 - loss 0.37053677 - time (sec): 3.16 - samples/sec: 14284.81 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:16:07,585 epoch 6 - iter 130/136 - loss 0.37004141 - time (sec): 3.49 - samples/sec: 14164.21 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:16:07,756 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:07,757 EPOCH 6 done: loss 0.3718 - lr: 0.000023 | |
| 2023-10-20 00:16:08,520 DEV : loss 0.26717308163642883 - f1-score (micro avg) 0.3377 | |
| 2023-10-20 00:16:08,525 saving best model | |
| 2023-10-20 00:16:08,559 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:08,894 epoch 7 - iter 13/136 - loss 0.40120032 - time (sec): 0.33 - samples/sec: 15234.80 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-20 00:16:09,240 epoch 7 - iter 26/136 - loss 0.39554959 - time (sec): 0.68 - samples/sec: 15805.35 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:16:09,585 epoch 7 - iter 39/136 - loss 0.39304469 - time (sec): 1.03 - samples/sec: 14369.40 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:16:09,936 epoch 7 - iter 52/136 - loss 0.38423271 - time (sec): 1.38 - samples/sec: 14393.90 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:16:10,304 epoch 7 - iter 65/136 - loss 0.36837505 - time (sec): 1.74 - samples/sec: 14450.82 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:16:10,639 epoch 7 - iter 78/136 - loss 0.35903008 - time (sec): 2.08 - samples/sec: 14344.88 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:16:11,016 epoch 7 - iter 91/136 - loss 0.35381799 - time (sec): 2.46 - samples/sec: 14332.63 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:16:11,379 epoch 7 - iter 104/136 - loss 0.35303774 - time (sec): 2.82 - samples/sec: 14264.87 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:16:11,722 epoch 7 - iter 117/136 - loss 0.35243192 - time (sec): 3.16 - samples/sec: 14170.51 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:16:12,058 epoch 7 - iter 130/136 - loss 0.35436940 - time (sec): 3.50 - samples/sec: 14177.06 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-20 00:16:12,214 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:12,214 EPOCH 7 done: loss 0.3525 - lr: 0.000017 | |
| 2023-10-20 00:16:12,988 DEV : loss 0.26430419087409973 - f1-score (micro avg) 0.3866 | |
| 2023-10-20 00:16:12,991 saving best model | |
| 2023-10-20 00:16:13,028 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:13,367 epoch 8 - iter 13/136 - loss 0.29813625 - time (sec): 0.34 - samples/sec: 17220.95 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:16:13,673 epoch 8 - iter 26/136 - loss 0.33255910 - time (sec): 0.64 - samples/sec: 16743.16 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:16:13,978 epoch 8 - iter 39/136 - loss 0.34317481 - time (sec): 0.95 - samples/sec: 15949.58 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:16:14,490 epoch 8 - iter 52/136 - loss 0.32334500 - time (sec): 1.46 - samples/sec: 14093.23 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:16:14,843 epoch 8 - iter 65/136 - loss 0.33185318 - time (sec): 1.82 - samples/sec: 14103.91 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:16:15,198 epoch 8 - iter 78/136 - loss 0.32576278 - time (sec): 2.17 - samples/sec: 13984.00 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:16:15,575 epoch 8 - iter 91/136 - loss 0.33042413 - time (sec): 2.55 - samples/sec: 13702.12 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:16:15,960 epoch 8 - iter 104/136 - loss 0.33147055 - time (sec): 2.93 - samples/sec: 13641.42 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:16:16,334 epoch 8 - iter 117/136 - loss 0.33563937 - time (sec): 3.31 - samples/sec: 13536.66 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:16:16,693 epoch 8 - iter 130/136 - loss 0.33446959 - time (sec): 3.66 - samples/sec: 13400.45 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:16:16,873 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:16,873 EPOCH 8 done: loss 0.3366 - lr: 0.000012 | |
| 2023-10-20 00:16:17,644 DEV : loss 0.2584059238433838 - f1-score (micro avg) 0.4194 | |
| 2023-10-20 00:16:17,647 saving best model | |
| 2023-10-20 00:16:17,678 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:18,033 epoch 9 - iter 13/136 - loss 0.30167206 - time (sec): 0.35 - samples/sec: 14174.49 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-20 00:16:18,401 epoch 9 - iter 26/136 - loss 0.34416720 - time (sec): 0.72 - samples/sec: 14024.17 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:16:18,758 epoch 9 - iter 39/136 - loss 0.36372136 - time (sec): 1.08 - samples/sec: 13615.57 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:16:19,167 epoch 9 - iter 52/136 - loss 0.34342203 - time (sec): 1.49 - samples/sec: 13961.96 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:16:19,530 epoch 9 - iter 65/136 - loss 0.33424330 - time (sec): 1.85 - samples/sec: 14116.64 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:16:19,889 epoch 9 - iter 78/136 - loss 0.33573336 - time (sec): 2.21 - samples/sec: 14013.44 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:16:20,253 epoch 9 - iter 91/136 - loss 0.33573487 - time (sec): 2.58 - samples/sec: 13753.97 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:16:20,607 epoch 9 - iter 104/136 - loss 0.33651787 - time (sec): 2.93 - samples/sec: 13738.42 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:16:20,953 epoch 9 - iter 117/136 - loss 0.33685306 - time (sec): 3.27 - samples/sec: 13701.75 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:16:21,269 epoch 9 - iter 130/136 - loss 0.33220709 - time (sec): 3.59 - samples/sec: 13754.95 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-20 00:16:21,432 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:21,432 EPOCH 9 done: loss 0.3339 - lr: 0.000006 | |
| 2023-10-20 00:16:22,195 DEV : loss 0.2596355974674225 - f1-score (micro avg) 0.4057 | |
| 2023-10-20 00:16:22,199 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:22,529 epoch 10 - iter 13/136 - loss 0.38669202 - time (sec): 0.33 - samples/sec: 14013.53 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:16:22,883 epoch 10 - iter 26/136 - loss 0.32356639 - time (sec): 0.68 - samples/sec: 14649.18 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:16:23,221 epoch 10 - iter 39/136 - loss 0.30991686 - time (sec): 1.02 - samples/sec: 14213.28 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:16:23,594 epoch 10 - iter 52/136 - loss 0.32955241 - time (sec): 1.39 - samples/sec: 14244.46 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:16:23,947 epoch 10 - iter 65/136 - loss 0.31295652 - time (sec): 1.75 - samples/sec: 14096.10 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:16:24,285 epoch 10 - iter 78/136 - loss 0.32045448 - time (sec): 2.09 - samples/sec: 14141.19 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:16:24,642 epoch 10 - iter 91/136 - loss 0.32789206 - time (sec): 2.44 - samples/sec: 14172.52 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:16:24,980 epoch 10 - iter 104/136 - loss 0.33709961 - time (sec): 2.78 - samples/sec: 13970.42 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:16:25,320 epoch 10 - iter 117/136 - loss 0.32235981 - time (sec): 3.12 - samples/sec: 14493.77 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-20 00:16:25,634 epoch 10 - iter 130/136 - loss 0.32526182 - time (sec): 3.43 - samples/sec: 14489.48 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-20 00:16:25,946 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:25,946 EPOCH 10 done: loss 0.3257 - lr: 0.000000 | |
| 2023-10-20 00:16:26,717 DEV : loss 0.25762829184532166 - f1-score (micro avg) 0.4177 | |
| 2023-10-20 00:16:26,746 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:16:26,747 Loading model from best epoch ... | |
| 2023-10-20 00:16:26,821 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG | |
| 2023-10-20 00:16:27,643 | |
| Results: | |
| - F-score (micro) 0.3368 | |
| - F-score (macro) 0.1713 | |
| - Accuracy 0.2119 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.4799 0.4583 0.4689 312 | |
| PER 0.2000 0.2356 0.2163 208 | |
| ORG 0.0000 0.0000 0.0000 55 | |
| HumanProd 0.0000 0.0000 0.0000 22 | |
| micro avg 0.3536 0.3216 0.3368 597 | |
| macro avg 0.1700 0.1735 0.1713 597 | |
| weighted avg 0.3205 0.3216 0.3204 597 | |
| 2023-10-20 00:16:27,643 ---------------------------------------------------------------------------------------------------- | |