2023-10-19 19:36:24,482 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,482 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() )" 2023-10-19 19:36:24,482 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,482 MultiCorpus: 7142 train + 698 dev + 2570 test sentences - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator 2023-10-19 19:36:24,482 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,482 Train: 7142 sentences 2023-10-19 19:36:24,482 (train_with_dev=False, train_with_test=False) 2023-10-19 19:36:24,482 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,482 Training Params: 2023-10-19 19:36:24,482 - learning_rate: "5e-05" 2023-10-19 19:36:24,482 - mini_batch_size: "4" 2023-10-19 19:36:24,482 - max_epochs: "10" 2023-10-19 19:36:24,482 - shuffle: "True" 2023-10-19 19:36:24,482 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,482 Plugins: 2023-10-19 19:36:24,482 - TensorboardLogger 2023-10-19 19:36:24,483 - LinearScheduler | warmup_fraction: '0.1' 2023-10-19 19:36:24,483 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,483 Final evaluation on model from best epoch (best-model.pt) 2023-10-19 19:36:24,483 - metric: "('micro avg', 'f1-score')" 2023-10-19 19:36:24,483 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,483 Computation: 2023-10-19 19:36:24,483 - compute on device: cuda:0 2023-10-19 19:36:24,483 - embedding storage: none 2023-10-19 19:36:24,483 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,483 Model training base path: "hmbench-newseye/fr-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-19 19:36:24,483 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,483 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:24,483 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-19 19:36:27,605 epoch 1 - iter 178/1786 - loss 3.27396970 - time (sec): 3.12 - samples/sec: 8577.89 - lr: 0.000005 - momentum: 0.000000 2023-10-19 19:36:30,669 epoch 1 - iter 356/1786 - loss 2.78021747 - time (sec): 6.19 - samples/sec: 8319.68 - lr: 0.000010 - momentum: 0.000000 2023-10-19 19:36:33,854 epoch 1 - iter 534/1786 - loss 2.20153535 - time (sec): 9.37 - samples/sec: 8225.33 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:36:36,825 epoch 1 - iter 712/1786 - loss 1.87990522 - time (sec): 12.34 - samples/sec: 8105.74 - lr: 0.000020 - momentum: 0.000000 2023-10-19 19:36:39,787 epoch 1 - iter 890/1786 - loss 1.65052606 - time (sec): 15.30 - samples/sec: 8162.50 - lr: 0.000025 - momentum: 0.000000 2023-10-19 19:36:42,845 epoch 1 - iter 1068/1786 - loss 1.48583748 - time (sec): 18.36 - samples/sec: 8135.08 - lr: 0.000030 - momentum: 0.000000 2023-10-19 19:36:45,942 epoch 1 - iter 1246/1786 - loss 1.35348330 - time (sec): 21.46 - samples/sec: 8120.55 - lr: 0.000035 - momentum: 0.000000 2023-10-19 19:36:48,993 epoch 1 - iter 1424/1786 - loss 1.25058687 - time (sec): 24.51 - samples/sec: 8187.36 - lr: 0.000040 - momentum: 0.000000 2023-10-19 19:36:52,056 epoch 1 - iter 1602/1786 - loss 1.16632694 - time (sec): 27.57 - samples/sec: 8211.05 - lr: 0.000045 - momentum: 0.000000 2023-10-19 19:36:55,021 epoch 1 - iter 1780/1786 - loss 1.10109838 - time (sec): 30.54 - samples/sec: 8128.81 - lr: 0.000050 - momentum: 0.000000 2023-10-19 19:36:55,111 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:55,111 EPOCH 1 done: loss 1.1002 - lr: 0.000050 2023-10-19 19:36:56,586 DEV : loss 0.2988516390323639 - f1-score (micro avg) 0.2148 2023-10-19 19:36:56,601 saving best model 2023-10-19 19:36:56,632 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:36:59,872 epoch 2 - iter 178/1786 - loss 0.45233865 - time (sec): 3.24 - samples/sec: 7524.97 - lr: 0.000049 - momentum: 0.000000 2023-10-19 19:37:02,958 epoch 2 - iter 356/1786 - loss 0.41531477 - time (sec): 6.33 - samples/sec: 7897.94 - lr: 0.000049 - momentum: 0.000000 2023-10-19 19:37:06,164 epoch 2 - iter 534/1786 - loss 0.42140018 - time (sec): 9.53 - samples/sec: 7904.32 - lr: 0.000048 - momentum: 0.000000 2023-10-19 19:37:09,284 epoch 2 - iter 712/1786 - loss 0.41082544 - time (sec): 12.65 - samples/sec: 7981.69 - lr: 0.000048 - momentum: 0.000000 2023-10-19 19:37:12,285 epoch 2 - iter 890/1786 - loss 0.40282999 - time (sec): 15.65 - samples/sec: 7882.50 - lr: 0.000047 - momentum: 0.000000 2023-10-19 19:37:15,326 epoch 2 - iter 1068/1786 - loss 0.39333372 - time (sec): 18.69 - samples/sec: 7950.62 - lr: 0.000047 - momentum: 0.000000 2023-10-19 19:37:18,468 epoch 2 - iter 1246/1786 - loss 0.39031933 - time (sec): 21.84 - samples/sec: 7946.19 - lr: 0.000046 - momentum: 0.000000 2023-10-19 19:37:21,578 epoch 2 - iter 1424/1786 - loss 0.39082695 - time (sec): 24.95 - samples/sec: 8003.47 - lr: 0.000046 - momentum: 0.000000 2023-10-19 19:37:24,609 epoch 2 - iter 1602/1786 - loss 0.38591567 - time (sec): 27.98 - samples/sec: 7988.62 - lr: 0.000045 - momentum: 0.000000 2023-10-19 19:37:27,778 epoch 2 - iter 1780/1786 - loss 0.38165514 - time (sec): 31.15 - samples/sec: 7964.48 - lr: 0.000044 - momentum: 0.000000 2023-10-19 19:37:27,871 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:37:27,871 EPOCH 2 done: loss 0.3817 - lr: 0.000044 2023-10-19 19:37:30,634 DEV : loss 0.2359563112258911 - f1-score (micro avg) 0.4146 2023-10-19 19:37:30,648 saving best model 2023-10-19 19:37:30,680 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:37:33,740 epoch 3 - iter 178/1786 - loss 0.29124349 - time (sec): 3.06 - samples/sec: 7695.75 - lr: 0.000044 - momentum: 0.000000 2023-10-19 19:37:36,723 epoch 3 - iter 356/1786 - loss 0.30538165 - time (sec): 6.04 - samples/sec: 7877.90 - lr: 0.000043 - momentum: 0.000000 2023-10-19 19:37:39,776 epoch 3 - iter 534/1786 - loss 0.31807769 - time (sec): 9.09 - samples/sec: 7900.96 - lr: 0.000043 - momentum: 0.000000 2023-10-19 19:37:42,785 epoch 3 - iter 712/1786 - loss 0.31141018 - time (sec): 12.10 - samples/sec: 7965.42 - lr: 0.000042 - momentum: 0.000000 2023-10-19 19:37:45,725 epoch 3 - iter 890/1786 - loss 0.31100537 - time (sec): 15.04 - samples/sec: 8024.61 - lr: 0.000042 - momentum: 0.000000 2023-10-19 19:37:48,854 epoch 3 - iter 1068/1786 - loss 0.30996002 - time (sec): 18.17 - samples/sec: 8102.43 - lr: 0.000041 - momentum: 0.000000 2023-10-19 19:37:51,863 epoch 3 - iter 1246/1786 - loss 0.30713704 - time (sec): 21.18 - samples/sec: 8081.12 - lr: 0.000041 - momentum: 0.000000 2023-10-19 19:37:55,026 epoch 3 - iter 1424/1786 - loss 0.30756342 - time (sec): 24.35 - samples/sec: 8084.50 - lr: 0.000040 - momentum: 0.000000 2023-10-19 19:37:58,152 epoch 3 - iter 1602/1786 - loss 0.30206285 - time (sec): 27.47 - samples/sec: 8134.80 - lr: 0.000039 - momentum: 0.000000 2023-10-19 19:38:01,178 epoch 3 - iter 1780/1786 - loss 0.30084992 - time (sec): 30.50 - samples/sec: 8129.64 - lr: 0.000039 - momentum: 0.000000 2023-10-19 19:38:01,277 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:38:01,277 EPOCH 3 done: loss 0.3013 - lr: 0.000039 2023-10-19 19:38:03,640 DEV : loss 0.21294961869716644 - f1-score (micro avg) 0.4955 2023-10-19 19:38:03,654 saving best model 2023-10-19 19:38:03,687 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:38:06,205 epoch 4 - iter 178/1786 - loss 0.28951262 - time (sec): 2.52 - samples/sec: 9326.20 - lr: 0.000038 - momentum: 0.000000 2023-10-19 19:38:08,836 epoch 4 - iter 356/1786 - loss 0.27653901 - time (sec): 5.15 - samples/sec: 9375.61 - lr: 0.000038 - momentum: 0.000000 2023-10-19 19:38:11,931 epoch 4 - iter 534/1786 - loss 0.27776588 - time (sec): 8.24 - samples/sec: 9028.31 - lr: 0.000037 - momentum: 0.000000 2023-10-19 19:38:14,936 epoch 4 - iter 712/1786 - loss 0.27643033 - time (sec): 11.25 - samples/sec: 8604.89 - lr: 0.000037 - momentum: 0.000000 2023-10-19 19:38:17,926 epoch 4 - iter 890/1786 - loss 0.27157129 - time (sec): 14.24 - samples/sec: 8501.64 - lr: 0.000036 - momentum: 0.000000 2023-10-19 19:38:20,907 epoch 4 - iter 1068/1786 - loss 0.27366911 - time (sec): 17.22 - samples/sec: 8487.46 - lr: 0.000036 - momentum: 0.000000 2023-10-19 19:38:23,995 epoch 4 - iter 1246/1786 - loss 0.26878768 - time (sec): 20.31 - samples/sec: 8465.61 - lr: 0.000035 - momentum: 0.000000 2023-10-19 19:38:27,060 epoch 4 - iter 1424/1786 - loss 0.26860832 - time (sec): 23.37 - samples/sec: 8435.48 - lr: 0.000034 - momentum: 0.000000 2023-10-19 19:38:30,106 epoch 4 - iter 1602/1786 - loss 0.26521033 - time (sec): 26.42 - samples/sec: 8428.78 - lr: 0.000034 - momentum: 0.000000 2023-10-19 19:38:33,181 epoch 4 - iter 1780/1786 - loss 0.26377235 - time (sec): 29.49 - samples/sec: 8412.91 - lr: 0.000033 - momentum: 0.000000 2023-10-19 19:38:33,280 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:38:33,280 EPOCH 4 done: loss 0.2639 - lr: 0.000033 2023-10-19 19:38:36,104 DEV : loss 0.1954185664653778 - f1-score (micro avg) 0.5386 2023-10-19 19:38:36,118 saving best model 2023-10-19 19:38:36,150 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:38:39,326 epoch 5 - iter 178/1786 - loss 0.22782338 - time (sec): 3.17 - samples/sec: 8017.78 - lr: 0.000033 - momentum: 0.000000 2023-10-19 19:38:42,405 epoch 5 - iter 356/1786 - loss 0.24493235 - time (sec): 6.25 - samples/sec: 8072.13 - lr: 0.000032 - momentum: 0.000000 2023-10-19 19:38:45,548 epoch 5 - iter 534/1786 - loss 0.24576962 - time (sec): 9.40 - samples/sec: 8134.03 - lr: 0.000032 - momentum: 0.000000 2023-10-19 19:38:48,433 epoch 5 - iter 712/1786 - loss 0.25073368 - time (sec): 12.28 - samples/sec: 8034.20 - lr: 0.000031 - momentum: 0.000000 2023-10-19 19:38:51,659 epoch 5 - iter 890/1786 - loss 0.24792561 - time (sec): 15.51 - samples/sec: 7972.54 - lr: 0.000031 - momentum: 0.000000 2023-10-19 19:38:54,683 epoch 5 - iter 1068/1786 - loss 0.24376175 - time (sec): 18.53 - samples/sec: 8021.19 - lr: 0.000030 - momentum: 0.000000 2023-10-19 19:38:57,803 epoch 5 - iter 1246/1786 - loss 0.24271881 - time (sec): 21.65 - samples/sec: 8010.20 - lr: 0.000029 - momentum: 0.000000 2023-10-19 19:39:00,899 epoch 5 - iter 1424/1786 - loss 0.23911368 - time (sec): 24.75 - samples/sec: 8072.76 - lr: 0.000029 - momentum: 0.000000 2023-10-19 19:39:03,941 epoch 5 - iter 1602/1786 - loss 0.23851321 - time (sec): 27.79 - samples/sec: 8075.32 - lr: 0.000028 - momentum: 0.000000 2023-10-19 19:39:07,099 epoch 5 - iter 1780/1786 - loss 0.23554334 - time (sec): 30.95 - samples/sec: 8021.42 - lr: 0.000028 - momentum: 0.000000 2023-10-19 19:39:07,184 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:39:07,184 EPOCH 5 done: loss 0.2359 - lr: 0.000028 2023-10-19 19:39:09,528 DEV : loss 0.19769060611724854 - f1-score (micro avg) 0.5429 2023-10-19 19:39:09,544 saving best model 2023-10-19 19:39:09,578 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:39:12,652 epoch 6 - iter 178/1786 - loss 0.22045055 - time (sec): 3.07 - samples/sec: 8026.99 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:39:15,743 epoch 6 - iter 356/1786 - loss 0.21078132 - time (sec): 6.16 - samples/sec: 8203.99 - lr: 0.000027 - momentum: 0.000000 2023-10-19 19:39:18,777 epoch 6 - iter 534/1786 - loss 0.21210754 - time (sec): 9.20 - samples/sec: 8270.68 - lr: 0.000026 - momentum: 0.000000 2023-10-19 19:39:21,787 epoch 6 - iter 712/1786 - loss 0.21530597 - time (sec): 12.21 - samples/sec: 8235.79 - lr: 0.000026 - momentum: 0.000000 2023-10-19 19:39:24,845 epoch 6 - iter 890/1786 - loss 0.21713503 - time (sec): 15.27 - samples/sec: 8286.95 - lr: 0.000025 - momentum: 0.000000 2023-10-19 19:39:27,887 epoch 6 - iter 1068/1786 - loss 0.21502049 - time (sec): 18.31 - samples/sec: 8238.44 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:39:30,932 epoch 6 - iter 1246/1786 - loss 0.21781126 - time (sec): 21.35 - samples/sec: 8159.07 - lr: 0.000024 - momentum: 0.000000 2023-10-19 19:39:33,995 epoch 6 - iter 1424/1786 - loss 0.21736778 - time (sec): 24.42 - samples/sec: 8139.12 - lr: 0.000023 - momentum: 0.000000 2023-10-19 19:39:36,992 epoch 6 - iter 1602/1786 - loss 0.21517526 - time (sec): 27.41 - samples/sec: 8142.54 - lr: 0.000023 - momentum: 0.000000 2023-10-19 19:39:40,261 epoch 6 - iter 1780/1786 - loss 0.21641064 - time (sec): 30.68 - samples/sec: 8090.65 - lr: 0.000022 - momentum: 0.000000 2023-10-19 19:39:40,351 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:39:40,351 EPOCH 6 done: loss 0.2166 - lr: 0.000022 2023-10-19 19:39:43,159 DEV : loss 0.19191302359104156 - f1-score (micro avg) 0.5607 2023-10-19 19:39:43,174 saving best model 2023-10-19 19:39:43,211 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:39:46,173 epoch 7 - iter 178/1786 - loss 0.20637887 - time (sec): 2.96 - samples/sec: 7808.24 - lr: 0.000022 - momentum: 0.000000 2023-10-19 19:39:49,227 epoch 7 - iter 356/1786 - loss 0.20872566 - time (sec): 6.02 - samples/sec: 7998.94 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:39:52,303 epoch 7 - iter 534/1786 - loss 0.20004438 - time (sec): 9.09 - samples/sec: 8075.75 - lr: 0.000021 - momentum: 0.000000 2023-10-19 19:39:55,213 epoch 7 - iter 712/1786 - loss 0.20338323 - time (sec): 12.00 - samples/sec: 8150.91 - lr: 0.000020 - momentum: 0.000000 2023-10-19 19:39:58,054 epoch 7 - iter 890/1786 - loss 0.20370059 - time (sec): 14.84 - samples/sec: 8295.45 - lr: 0.000019 - momentum: 0.000000 2023-10-19 19:40:01,168 epoch 7 - iter 1068/1786 - loss 0.19908535 - time (sec): 17.96 - samples/sec: 8292.89 - lr: 0.000019 - momentum: 0.000000 2023-10-19 19:40:04,243 epoch 7 - iter 1246/1786 - loss 0.20158080 - time (sec): 21.03 - samples/sec: 8271.76 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:40:07,312 epoch 7 - iter 1424/1786 - loss 0.20291764 - time (sec): 24.10 - samples/sec: 8212.95 - lr: 0.000018 - momentum: 0.000000 2023-10-19 19:40:10,437 epoch 7 - iter 1602/1786 - loss 0.20298768 - time (sec): 27.22 - samples/sec: 8170.91 - lr: 0.000017 - momentum: 0.000000 2023-10-19 19:40:13,465 epoch 7 - iter 1780/1786 - loss 0.20413992 - time (sec): 30.25 - samples/sec: 8202.27 - lr: 0.000017 - momentum: 0.000000 2023-10-19 19:40:13,563 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:40:13,563 EPOCH 7 done: loss 0.2037 - lr: 0.000017 2023-10-19 19:40:15,934 DEV : loss 0.19076649844646454 - f1-score (micro avg) 0.5546 2023-10-19 19:40:15,948 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:40:19,099 epoch 8 - iter 178/1786 - loss 0.20287556 - time (sec): 3.15 - samples/sec: 8037.68 - lr: 0.000016 - momentum: 0.000000 2023-10-19 19:40:22,162 epoch 8 - iter 356/1786 - loss 0.20229720 - time (sec): 6.21 - samples/sec: 8081.69 - lr: 0.000016 - momentum: 0.000000 2023-10-19 19:40:25,163 epoch 8 - iter 534/1786 - loss 0.19898193 - time (sec): 9.21 - samples/sec: 7978.92 - lr: 0.000015 - momentum: 0.000000 2023-10-19 19:40:28,253 epoch 8 - iter 712/1786 - loss 0.19369768 - time (sec): 12.30 - samples/sec: 8044.90 - lr: 0.000014 - momentum: 0.000000 2023-10-19 19:40:31,293 epoch 8 - iter 890/1786 - loss 0.19392117 - time (sec): 15.34 - samples/sec: 8021.72 - lr: 0.000014 - momentum: 0.000000 2023-10-19 19:40:34,380 epoch 8 - iter 1068/1786 - loss 0.19084573 - time (sec): 18.43 - samples/sec: 8149.87 - lr: 0.000013 - momentum: 0.000000 2023-10-19 19:40:37,408 epoch 8 - iter 1246/1786 - loss 0.19257386 - time (sec): 21.46 - samples/sec: 8073.89 - lr: 0.000013 - momentum: 0.000000 2023-10-19 19:40:40,545 epoch 8 - iter 1424/1786 - loss 0.19073891 - time (sec): 24.60 - samples/sec: 8070.27 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:40:43,699 epoch 8 - iter 1602/1786 - loss 0.19251240 - time (sec): 27.75 - samples/sec: 8085.32 - lr: 0.000012 - momentum: 0.000000 2023-10-19 19:40:46,825 epoch 8 - iter 1780/1786 - loss 0.19379490 - time (sec): 30.88 - samples/sec: 8023.17 - lr: 0.000011 - momentum: 0.000000 2023-10-19 19:40:46,927 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:40:46,927 EPOCH 8 done: loss 0.1933 - lr: 0.000011 2023-10-19 19:40:49,730 DEV : loss 0.19240671396255493 - f1-score (micro avg) 0.5782 2023-10-19 19:40:49,743 saving best model 2023-10-19 19:40:49,776 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:40:52,886 epoch 9 - iter 178/1786 - loss 0.18684976 - time (sec): 3.11 - samples/sec: 7832.90 - lr: 0.000011 - momentum: 0.000000 2023-10-19 19:40:55,903 epoch 9 - iter 356/1786 - loss 0.19878074 - time (sec): 6.13 - samples/sec: 7964.77 - lr: 0.000010 - momentum: 0.000000 2023-10-19 19:40:58,822 epoch 9 - iter 534/1786 - loss 0.18798472 - time (sec): 9.04 - samples/sec: 8049.85 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:41:01,517 epoch 9 - iter 712/1786 - loss 0.18858620 - time (sec): 11.74 - samples/sec: 8388.76 - lr: 0.000009 - momentum: 0.000000 2023-10-19 19:41:04,448 epoch 9 - iter 890/1786 - loss 0.18757950 - time (sec): 14.67 - samples/sec: 8347.49 - lr: 0.000008 - momentum: 0.000000 2023-10-19 19:41:07,616 epoch 9 - iter 1068/1786 - loss 0.18804694 - time (sec): 17.84 - samples/sec: 8332.91 - lr: 0.000008 - momentum: 0.000000 2023-10-19 19:41:10,612 epoch 9 - iter 1246/1786 - loss 0.18839630 - time (sec): 20.83 - samples/sec: 8262.66 - lr: 0.000007 - momentum: 0.000000 2023-10-19 19:41:13,620 epoch 9 - iter 1424/1786 - loss 0.18752199 - time (sec): 23.84 - samples/sec: 8305.41 - lr: 0.000007 - momentum: 0.000000 2023-10-19 19:41:16,675 epoch 9 - iter 1602/1786 - loss 0.18922005 - time (sec): 26.90 - samples/sec: 8279.74 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:41:19,763 epoch 9 - iter 1780/1786 - loss 0.18631733 - time (sec): 29.99 - samples/sec: 8266.86 - lr: 0.000006 - momentum: 0.000000 2023-10-19 19:41:19,855 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:41:19,856 EPOCH 9 done: loss 0.1861 - lr: 0.000006 2023-10-19 19:41:22,241 DEV : loss 0.19148223102092743 - f1-score (micro avg) 0.5702 2023-10-19 19:41:22,255 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:41:25,303 epoch 10 - iter 178/1786 - loss 0.19431345 - time (sec): 3.05 - samples/sec: 8189.90 - lr: 0.000005 - momentum: 0.000000 2023-10-19 19:41:28,461 epoch 10 - iter 356/1786 - loss 0.18949315 - time (sec): 6.21 - samples/sec: 8109.99 - lr: 0.000004 - momentum: 0.000000 2023-10-19 19:41:31,510 epoch 10 - iter 534/1786 - loss 0.18810189 - time (sec): 9.25 - samples/sec: 8210.97 - lr: 0.000004 - momentum: 0.000000 2023-10-19 19:41:34,529 epoch 10 - iter 712/1786 - loss 0.18254160 - time (sec): 12.27 - samples/sec: 8095.09 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:41:37,560 epoch 10 - iter 890/1786 - loss 0.18671388 - time (sec): 15.30 - samples/sec: 8049.64 - lr: 0.000003 - momentum: 0.000000 2023-10-19 19:41:40,612 epoch 10 - iter 1068/1786 - loss 0.18696598 - time (sec): 18.36 - samples/sec: 8059.17 - lr: 0.000002 - momentum: 0.000000 2023-10-19 19:41:43,694 epoch 10 - iter 1246/1786 - loss 0.18673026 - time (sec): 21.44 - samples/sec: 8064.43 - lr: 0.000002 - momentum: 0.000000 2023-10-19 19:41:46,697 epoch 10 - iter 1424/1786 - loss 0.18361987 - time (sec): 24.44 - samples/sec: 8108.91 - lr: 0.000001 - momentum: 0.000000 2023-10-19 19:41:49,821 epoch 10 - iter 1602/1786 - loss 0.18247854 - time (sec): 27.57 - samples/sec: 8062.99 - lr: 0.000001 - momentum: 0.000000 2023-10-19 19:41:52,926 epoch 10 - iter 1780/1786 - loss 0.18118154 - time (sec): 30.67 - samples/sec: 8093.04 - lr: 0.000000 - momentum: 0.000000 2023-10-19 19:41:53,020 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:41:53,020 EPOCH 10 done: loss 0.1813 - lr: 0.000000 2023-10-19 19:41:55,835 DEV : loss 0.19351665675640106 - f1-score (micro avg) 0.5679 2023-10-19 19:41:55,877 ---------------------------------------------------------------------------------------------------- 2023-10-19 19:41:55,877 Loading model from best epoch ... 2023-10-19 19:41:55,952 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-19 19:42:00,540 Results: - F-score (micro) 0.457 - F-score (macro) 0.297 - Accuracy 0.3051 By class: precision recall f1-score support LOC 0.4775 0.5333 0.5039 1095 PER 0.4620 0.5227 0.4905 1012 ORG 0.2234 0.1709 0.1937 357 HumanProd 0.0000 0.0000 0.0000 33 micro avg 0.4445 0.4702 0.4570 2497 macro avg 0.2907 0.3067 0.2970 2497 weighted avg 0.4286 0.4702 0.4474 2497 2023-10-19 19:42:00,540 ----------------------------------------------------------------------------------------------------