| 2023-10-20 00:00:24,420 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,420 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 00:00:24,420 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,420 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 | |
| 2023-10-20 00:00:24,420 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,420 Train: 1166 sentences | |
| 2023-10-20 00:00:24,420 (train_with_dev=False, train_with_test=False) | |
| 2023-10-20 00:00:24,420 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,420 Training Params: | |
| 2023-10-20 00:00:24,420 - learning_rate: "5e-05" | |
| 2023-10-20 00:00:24,420 - mini_batch_size: "8" | |
| 2023-10-20 00:00:24,421 - max_epochs: "10" | |
| 2023-10-20 00:00:24,421 - shuffle: "True" | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 Plugins: | |
| 2023-10-20 00:00:24,421 - TensorboardLogger | |
| 2023-10-20 00:00:24,421 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-20 00:00:24,421 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 Computation: | |
| 2023-10-20 00:00:24,421 - compute on device: cuda:0 | |
| 2023-10-20 00:00:24,421 - embedding storage: none | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 Model training base path: "hmbench-newseye/fi-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5" | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:24,421 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-20 00:00:24,767 epoch 1 - iter 14/146 - loss 3.17723201 - time (sec): 0.35 - samples/sec: 11122.26 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:00:25,120 epoch 1 - iter 28/146 - loss 3.15612779 - time (sec): 0.70 - samples/sec: 10763.94 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:00:25,478 epoch 1 - iter 42/146 - loss 3.09001890 - time (sec): 1.06 - samples/sec: 10740.23 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:00:25,849 epoch 1 - iter 56/146 - loss 3.04725899 - time (sec): 1.43 - samples/sec: 10784.62 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:00:26,225 epoch 1 - iter 70/146 - loss 2.87972314 - time (sec): 1.80 - samples/sec: 11088.76 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:00:26,593 epoch 1 - iter 84/146 - loss 2.72017656 - time (sec): 2.17 - samples/sec: 11030.59 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:00:26,980 epoch 1 - iter 98/146 - loss 2.49850046 - time (sec): 2.56 - samples/sec: 11104.17 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:00:27,515 epoch 1 - iter 112/146 - loss 2.28917480 - time (sec): 3.09 - samples/sec: 10779.71 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:00:27,902 epoch 1 - iter 126/146 - loss 2.12831603 - time (sec): 3.48 - samples/sec: 10785.86 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:00:28,284 epoch 1 - iter 140/146 - loss 1.97863993 - time (sec): 3.86 - samples/sec: 11006.51 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:00:28,429 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:28,429 EPOCH 1 done: loss 1.9241 - lr: 0.000048 | |
| 2023-10-20 00:00:28,690 DEV : loss 0.4782404601573944 - f1-score (micro avg) 0.0 | |
| 2023-10-20 00:00:28,694 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:29,098 epoch 2 - iter 14/146 - loss 1.09887195 - time (sec): 0.40 - samples/sec: 11822.61 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-20 00:00:29,466 epoch 2 - iter 28/146 - loss 0.95519720 - time (sec): 0.77 - samples/sec: 11969.87 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-20 00:00:29,835 epoch 2 - iter 42/146 - loss 0.84853598 - time (sec): 1.14 - samples/sec: 11667.50 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:00:30,190 epoch 2 - iter 56/146 - loss 0.81643196 - time (sec): 1.50 - samples/sec: 11366.36 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:00:30,567 epoch 2 - iter 70/146 - loss 0.77926945 - time (sec): 1.87 - samples/sec: 11260.00 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:00:30,917 epoch 2 - iter 84/146 - loss 0.75411002 - time (sec): 2.22 - samples/sec: 11220.62 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:00:31,280 epoch 2 - iter 98/146 - loss 0.73229273 - time (sec): 2.59 - samples/sec: 11100.80 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:00:31,664 epoch 2 - iter 112/146 - loss 0.70389120 - time (sec): 2.97 - samples/sec: 11371.26 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:00:32,049 epoch 2 - iter 126/146 - loss 0.67720702 - time (sec): 3.36 - samples/sec: 11665.57 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:00:32,398 epoch 2 - iter 140/146 - loss 0.68241789 - time (sec): 3.70 - samples/sec: 11569.27 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:00:32,541 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:32,541 EPOCH 2 done: loss 0.6755 - lr: 0.000045 | |
| 2023-10-20 00:00:33,184 DEV : loss 0.3925292193889618 - f1-score (micro avg) 0.0 | |
| 2023-10-20 00:00:33,189 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:33,561 epoch 3 - iter 14/146 - loss 0.45005562 - time (sec): 0.37 - samples/sec: 10815.64 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-20 00:00:33,943 epoch 3 - iter 28/146 - loss 0.47075459 - time (sec): 0.75 - samples/sec: 11363.18 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:00:34,329 epoch 3 - iter 42/146 - loss 0.50697891 - time (sec): 1.14 - samples/sec: 11363.12 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:00:34,723 epoch 3 - iter 56/146 - loss 0.55832426 - time (sec): 1.53 - samples/sec: 11356.86 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:00:35,241 epoch 3 - iter 70/146 - loss 0.55201558 - time (sec): 2.05 - samples/sec: 10326.26 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:00:35,622 epoch 3 - iter 84/146 - loss 0.54282315 - time (sec): 2.43 - samples/sec: 10823.47 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:00:35,961 epoch 3 - iter 98/146 - loss 0.54065809 - time (sec): 2.77 - samples/sec: 10884.07 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:00:36,326 epoch 3 - iter 112/146 - loss 0.52730552 - time (sec): 3.14 - samples/sec: 10993.96 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:00:36,680 epoch 3 - iter 126/146 - loss 0.52185578 - time (sec): 3.49 - samples/sec: 10896.07 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:00:37,046 epoch 3 - iter 140/146 - loss 0.51713714 - time (sec): 3.86 - samples/sec: 11080.53 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-20 00:00:37,194 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:37,194 EPOCH 3 done: loss 0.5156 - lr: 0.000039 | |
| 2023-10-20 00:00:37,825 DEV : loss 0.36764416098594666 - f1-score (micro avg) 0.0082 | |
| 2023-10-20 00:00:37,829 saving best model | |
| 2023-10-20 00:00:37,856 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:38,213 epoch 4 - iter 14/146 - loss 0.42998140 - time (sec): 0.36 - samples/sec: 10386.28 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:00:38,576 epoch 4 - iter 28/146 - loss 0.44282841 - time (sec): 0.72 - samples/sec: 10623.64 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:00:38,971 epoch 4 - iter 42/146 - loss 0.42573623 - time (sec): 1.11 - samples/sec: 11103.16 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:00:39,329 epoch 4 - iter 56/146 - loss 0.43880130 - time (sec): 1.47 - samples/sec: 11205.95 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:00:39,684 epoch 4 - iter 70/146 - loss 0.43866120 - time (sec): 1.83 - samples/sec: 11241.65 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:00:40,035 epoch 4 - iter 84/146 - loss 0.44082921 - time (sec): 2.18 - samples/sec: 11247.76 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:00:40,399 epoch 4 - iter 98/146 - loss 0.47160037 - time (sec): 2.54 - samples/sec: 11494.57 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:00:40,756 epoch 4 - iter 112/146 - loss 0.45736533 - time (sec): 2.90 - samples/sec: 11635.12 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:00:41,115 epoch 4 - iter 126/146 - loss 0.45742994 - time (sec): 3.26 - samples/sec: 11572.35 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:00:41,489 epoch 4 - iter 140/146 - loss 0.45572260 - time (sec): 3.63 - samples/sec: 11683.19 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:00:41,652 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:41,652 EPOCH 4 done: loss 0.4557 - lr: 0.000034 | |
| 2023-10-20 00:00:42,292 DEV : loss 0.3331185281276703 - f1-score (micro avg) 0.0491 | |
| 2023-10-20 00:00:42,296 saving best model | |
| 2023-10-20 00:00:42,336 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:42,741 epoch 5 - iter 14/146 - loss 0.41530283 - time (sec): 0.40 - samples/sec: 12759.36 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:00:43,124 epoch 5 - iter 28/146 - loss 0.48554595 - time (sec): 0.79 - samples/sec: 12148.86 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:00:43,492 epoch 5 - iter 42/146 - loss 0.45840546 - time (sec): 1.16 - samples/sec: 11590.19 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:00:43,866 epoch 5 - iter 56/146 - loss 0.45258262 - time (sec): 1.53 - samples/sec: 11242.28 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:00:44,256 epoch 5 - iter 70/146 - loss 0.44561905 - time (sec): 1.92 - samples/sec: 11460.68 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:00:44,623 epoch 5 - iter 84/146 - loss 0.43129736 - time (sec): 2.29 - samples/sec: 11495.36 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:00:44,976 epoch 5 - iter 98/146 - loss 0.42846010 - time (sec): 2.64 - samples/sec: 11316.63 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:00:45,358 epoch 5 - iter 112/146 - loss 0.42713898 - time (sec): 3.02 - samples/sec: 11406.47 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:00:45,717 epoch 5 - iter 126/146 - loss 0.43711608 - time (sec): 3.38 - samples/sec: 11265.73 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:00:46,111 epoch 5 - iter 140/146 - loss 0.42715479 - time (sec): 3.77 - samples/sec: 11395.93 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:00:46,261 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:46,261 EPOCH 5 done: loss 0.4227 - lr: 0.000028 | |
| 2023-10-20 00:00:46,901 DEV : loss 0.32058432698249817 - f1-score (micro avg) 0.1371 | |
| 2023-10-20 00:00:46,905 saving best model | |
| 2023-10-20 00:00:46,939 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:47,319 epoch 6 - iter 14/146 - loss 0.43001814 - time (sec): 0.38 - samples/sec: 11530.08 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:00:47,649 epoch 6 - iter 28/146 - loss 0.39433315 - time (sec): 0.71 - samples/sec: 11589.67 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:00:47,963 epoch 6 - iter 42/146 - loss 0.40571073 - time (sec): 1.02 - samples/sec: 11968.50 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:00:48,306 epoch 6 - iter 56/146 - loss 0.40796916 - time (sec): 1.37 - samples/sec: 12155.22 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:00:48,675 epoch 6 - iter 70/146 - loss 0.39108897 - time (sec): 1.74 - samples/sec: 12294.12 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:00:49,047 epoch 6 - iter 84/146 - loss 0.38197870 - time (sec): 2.11 - samples/sec: 12125.77 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:00:49,433 epoch 6 - iter 98/146 - loss 0.37692165 - time (sec): 2.49 - samples/sec: 12136.60 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:00:49,796 epoch 6 - iter 112/146 - loss 0.37784940 - time (sec): 2.86 - samples/sec: 12169.87 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:00:50,147 epoch 6 - iter 126/146 - loss 0.37856587 - time (sec): 3.21 - samples/sec: 12056.87 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:00:50,536 epoch 6 - iter 140/146 - loss 0.38924008 - time (sec): 3.60 - samples/sec: 11965.06 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:00:50,687 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:50,687 EPOCH 6 done: loss 0.3894 - lr: 0.000023 | |
| 2023-10-20 00:00:51,326 DEV : loss 0.3271176218986511 - f1-score (micro avg) 0.1433 | |
| 2023-10-20 00:00:51,330 saving best model | |
| 2023-10-20 00:00:51,363 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:51,750 epoch 7 - iter 14/146 - loss 0.31479723 - time (sec): 0.39 - samples/sec: 13842.19 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-20 00:00:52,094 epoch 7 - iter 28/146 - loss 0.37446333 - time (sec): 0.73 - samples/sec: 12377.93 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:00:52,444 epoch 7 - iter 42/146 - loss 0.38718033 - time (sec): 1.08 - samples/sec: 11771.73 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:00:52,802 epoch 7 - iter 56/146 - loss 0.36802516 - time (sec): 1.44 - samples/sec: 12119.42 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:00:53,167 epoch 7 - iter 70/146 - loss 0.37056283 - time (sec): 1.80 - samples/sec: 11682.57 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:00:53,522 epoch 7 - iter 84/146 - loss 0.36763286 - time (sec): 2.16 - samples/sec: 11496.29 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:00:53,944 epoch 7 - iter 98/146 - loss 0.37991055 - time (sec): 2.58 - samples/sec: 11791.09 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:00:54,307 epoch 7 - iter 112/146 - loss 0.38168082 - time (sec): 2.94 - samples/sec: 11725.81 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:00:54,667 epoch 7 - iter 126/146 - loss 0.38505741 - time (sec): 3.30 - samples/sec: 11786.73 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:00:55,014 epoch 7 - iter 140/146 - loss 0.38238690 - time (sec): 3.65 - samples/sec: 11643.60 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-20 00:00:55,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:55,168 EPOCH 7 done: loss 0.3772 - lr: 0.000017 | |
| 2023-10-20 00:00:55,969 DEV : loss 0.30373382568359375 - f1-score (micro avg) 0.2063 | |
| 2023-10-20 00:00:55,973 saving best model | |
| 2023-10-20 00:00:56,008 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:56,366 epoch 8 - iter 14/146 - loss 0.33318853 - time (sec): 0.36 - samples/sec: 11928.89 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:00:56,737 epoch 8 - iter 28/146 - loss 0.34962528 - time (sec): 0.73 - samples/sec: 11985.49 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:00:57,126 epoch 8 - iter 42/146 - loss 0.31483010 - time (sec): 1.12 - samples/sec: 12767.48 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:00:57,469 epoch 8 - iter 56/146 - loss 0.34048941 - time (sec): 1.46 - samples/sec: 12385.45 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:00:57,798 epoch 8 - iter 70/146 - loss 0.34548513 - time (sec): 1.79 - samples/sec: 11953.56 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:00:58,153 epoch 8 - iter 84/146 - loss 0.35334959 - time (sec): 2.14 - samples/sec: 11784.25 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:00:58,518 epoch 8 - iter 98/146 - loss 0.35855547 - time (sec): 2.51 - samples/sec: 11605.02 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:00:58,892 epoch 8 - iter 112/146 - loss 0.35659111 - time (sec): 2.88 - samples/sec: 11459.13 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:00:59,262 epoch 8 - iter 126/146 - loss 0.35769873 - time (sec): 3.25 - samples/sec: 11455.20 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:00:59,666 epoch 8 - iter 140/146 - loss 0.36940728 - time (sec): 3.66 - samples/sec: 11741.94 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:00:59,815 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:00:59,815 EPOCH 8 done: loss 0.3708 - lr: 0.000012 | |
| 2023-10-20 00:01:00,455 DEV : loss 0.30823713541030884 - f1-score (micro avg) 0.2164 | |
| 2023-10-20 00:01:00,459 saving best model | |
| 2023-10-20 00:01:00,492 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:01:00,845 epoch 9 - iter 14/146 - loss 0.37337414 - time (sec): 0.35 - samples/sec: 11208.87 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-20 00:01:01,215 epoch 9 - iter 28/146 - loss 0.34486223 - time (sec): 0.72 - samples/sec: 11175.85 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:01:01,583 epoch 9 - iter 42/146 - loss 0.34714814 - time (sec): 1.09 - samples/sec: 11513.31 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:01:01,964 epoch 9 - iter 56/146 - loss 0.33548603 - time (sec): 1.47 - samples/sec: 11196.69 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:01:02,313 epoch 9 - iter 70/146 - loss 0.34515425 - time (sec): 1.82 - samples/sec: 11158.20 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:01:02,677 epoch 9 - iter 84/146 - loss 0.35039747 - time (sec): 2.18 - samples/sec: 11232.36 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:01:03,030 epoch 9 - iter 98/146 - loss 0.34912574 - time (sec): 2.54 - samples/sec: 11341.25 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:01:03,430 epoch 9 - iter 112/146 - loss 0.34284354 - time (sec): 2.94 - samples/sec: 11437.46 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:01:03,805 epoch 9 - iter 126/146 - loss 0.35573528 - time (sec): 3.31 - samples/sec: 11600.31 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:01:04,162 epoch 9 - iter 140/146 - loss 0.35953636 - time (sec): 3.67 - samples/sec: 11628.28 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-20 00:01:04,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:01:04,307 EPOCH 9 done: loss 0.3588 - lr: 0.000006 | |
| 2023-10-20 00:01:04,967 DEV : loss 0.30730772018432617 - f1-score (micro avg) 0.2005 | |
| 2023-10-20 00:01:04,971 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:01:05,337 epoch 10 - iter 14/146 - loss 0.32241848 - time (sec): 0.37 - samples/sec: 13356.22 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:01:05,746 epoch 10 - iter 28/146 - loss 0.34594006 - time (sec): 0.77 - samples/sec: 13440.68 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:01:06,077 epoch 10 - iter 42/146 - loss 0.36323167 - time (sec): 1.11 - samples/sec: 12461.47 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:01:06,449 epoch 10 - iter 56/146 - loss 0.35186528 - time (sec): 1.48 - samples/sec: 12336.13 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:01:06,816 epoch 10 - iter 70/146 - loss 0.35596110 - time (sec): 1.84 - samples/sec: 11822.64 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:01:07,203 epoch 10 - iter 84/146 - loss 0.34984725 - time (sec): 2.23 - samples/sec: 11545.29 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:01:07,577 epoch 10 - iter 98/146 - loss 0.35158385 - time (sec): 2.61 - samples/sec: 11423.74 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:01:07,952 epoch 10 - iter 112/146 - loss 0.35863875 - time (sec): 2.98 - samples/sec: 11356.89 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:01:08,342 epoch 10 - iter 126/146 - loss 0.36121485 - time (sec): 3.37 - samples/sec: 11193.43 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-20 00:01:08,742 epoch 10 - iter 140/146 - loss 0.35905657 - time (sec): 3.77 - samples/sec: 11105.24 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-20 00:01:08,922 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:01:08,922 EPOCH 10 done: loss 0.3633 - lr: 0.000000 | |
| 2023-10-20 00:01:09,559 DEV : loss 0.30496951937675476 - f1-score (micro avg) 0.2047 | |
| 2023-10-20 00:01:09,591 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:01:09,591 Loading model from best epoch ... | |
| 2023-10-20 00:01:09,665 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 | |
| 2023-10-20 00:01:10,560 | |
| Results: | |
| - F-score (micro) 0.3197 | |
| - F-score (macro) 0.1637 | |
| - Accuracy 0.1977 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.3933 0.4023 0.3977 348 | |
| LOC 0.2700 0.2452 0.2570 261 | |
| ORG 0.0000 0.0000 0.0000 52 | |
| HumanProd 0.0000 0.0000 0.0000 22 | |
| micro avg 0.3440 0.2987 0.3197 683 | |
| macro avg 0.1658 0.1619 0.1637 683 | |
| weighted avg 0.3036 0.2987 0.3009 683 | |
| 2023-10-20 00:01:10,560 ---------------------------------------------------------------------------------------------------- | |