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hmbench-topres19th-en-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
PyTorch
TensorBoard
English
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697539618.4c6324b99746.1159.6 +3 -0
  5. test.tsv +0 -0
  6. training.log +237 -0
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 10:48:13 0.0000 0.4094 0.0525 0.7519 0.8186 0.7838 0.6576
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+ 2 10:49:29 0.0000 0.0744 0.0550 0.7645 0.7806 0.7724 0.6514
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+ 3 10:50:45 0.0000 0.0474 0.0784 0.7597 0.8270 0.7919 0.6644
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+ 4 10:52:02 0.0000 0.0322 0.0996 0.7636 0.8312 0.7960 0.6724
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+ 5 10:53:20 0.0000 0.0228 0.0998 0.7470 0.7975 0.7714 0.6585
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+ 6 10:54:39 0.0000 0.0152 0.1078 0.7729 0.8186 0.7951 0.6713
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+ 7 10:55:53 0.0000 0.0106 0.1188 0.8261 0.8017 0.8137 0.6985
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+ 8 10:57:11 0.0000 0.0079 0.1125 0.7795 0.8354 0.8065 0.6899
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+ 9 10:58:26 0.0000 0.0049 0.1250 0.7717 0.8270 0.7984 0.6806
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+ 10 10:59:40 0.0000 0.0029 0.1306 0.7863 0.8228 0.8041 0.6890
runs/events.out.tfevents.1697539618.4c6324b99746.1159.6 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 434848
test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 10:46:58,799 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,800 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 10:46:58,801 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,801 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
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+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
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+ 2023-10-17 10:46:58,801 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,801 Train: 6183 sentences
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+ 2023-10-17 10:46:58,801 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 10:46:58,801 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,801 Training Params:
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+ 2023-10-17 10:46:58,801 - learning_rate: "3e-05"
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+ 2023-10-17 10:46:58,801 - mini_batch_size: "8"
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+ 2023-10-17 10:46:58,801 - max_epochs: "10"
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+ 2023-10-17 10:46:58,802 - shuffle: "True"
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+ 2023-10-17 10:46:58,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,802 Plugins:
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+ 2023-10-17 10:46:58,802 - TensorboardLogger
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+ 2023-10-17 10:46:58,802 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 10:46:58,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,802 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 10:46:58,802 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 10:46:58,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,802 Computation:
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+ 2023-10-17 10:46:58,802 - compute on device: cuda:0
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+ 2023-10-17 10:46:58,802 - embedding storage: none
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+ 2023-10-17 10:46:58,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,802 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2"
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+ 2023-10-17 10:46:58,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,803 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:46:58,803 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 10:47:05,789 epoch 1 - iter 77/773 - loss 2.84092854 - time (sec): 6.98 - samples/sec: 1617.95 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 10:47:13,250 epoch 1 - iter 154/773 - loss 1.53424492 - time (sec): 14.45 - samples/sec: 1717.08 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:47:20,771 epoch 1 - iter 231/773 - loss 1.08862813 - time (sec): 21.97 - samples/sec: 1719.41 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:47:28,354 epoch 1 - iter 308/773 - loss 0.85171098 - time (sec): 29.55 - samples/sec: 1710.60 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:47:35,897 epoch 1 - iter 385/773 - loss 0.70544828 - time (sec): 37.09 - samples/sec: 1707.17 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 10:47:43,030 epoch 1 - iter 462/773 - loss 0.60370873 - time (sec): 44.23 - samples/sec: 1722.03 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 10:47:49,959 epoch 1 - iter 539/773 - loss 0.53873054 - time (sec): 51.15 - samples/sec: 1710.83 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:47:56,937 epoch 1 - iter 616/773 - loss 0.48527896 - time (sec): 58.13 - samples/sec: 1716.28 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:48:04,037 epoch 1 - iter 693/773 - loss 0.44261791 - time (sec): 65.23 - samples/sec: 1722.47 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:48:11,055 epoch 1 - iter 770/773 - loss 0.41025787 - time (sec): 72.25 - samples/sec: 1715.14 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 10:48:11,320 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:48:11,320 EPOCH 1 done: loss 0.4094 - lr: 0.000030
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+ 2023-10-17 10:48:13,960 DEV : loss 0.05252358317375183 - f1-score (micro avg) 0.7838
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+ 2023-10-17 10:48:13,989 saving best model
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+ 2023-10-17 10:48:14,524 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:48:21,570 epoch 2 - iter 77/773 - loss 0.09652664 - time (sec): 7.04 - samples/sec: 1728.04 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 10:48:28,751 epoch 2 - iter 154/773 - loss 0.08979408 - time (sec): 14.22 - samples/sec: 1771.30 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:48:35,823 epoch 2 - iter 231/773 - loss 0.08806850 - time (sec): 21.30 - samples/sec: 1734.24 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:48:43,040 epoch 2 - iter 308/773 - loss 0.08255607 - time (sec): 28.51 - samples/sec: 1729.09 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:48:50,429 epoch 2 - iter 385/773 - loss 0.08195398 - time (sec): 35.90 - samples/sec: 1707.30 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:48:57,538 epoch 2 - iter 462/773 - loss 0.07976764 - time (sec): 43.01 - samples/sec: 1704.63 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:49:04,676 epoch 2 - iter 539/773 - loss 0.07710304 - time (sec): 50.15 - samples/sec: 1715.42 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:49:11,854 epoch 2 - iter 616/773 - loss 0.07732638 - time (sec): 57.33 - samples/sec: 1717.14 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:49:18,955 epoch 2 - iter 693/773 - loss 0.07637746 - time (sec): 64.43 - samples/sec: 1724.33 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:49:26,241 epoch 2 - iter 770/773 - loss 0.07445810 - time (sec): 71.71 - samples/sec: 1728.57 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:49:26,504 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:49:26,504 EPOCH 2 done: loss 0.0744 - lr: 0.000027
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+ 2023-10-17 10:49:29,437 DEV : loss 0.055021319538354874 - f1-score (micro avg) 0.7724
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+ 2023-10-17 10:49:29,470 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:49:36,699 epoch 3 - iter 77/773 - loss 0.06692996 - time (sec): 7.23 - samples/sec: 1625.39 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:49:44,491 epoch 3 - iter 154/773 - loss 0.05595855 - time (sec): 15.02 - samples/sec: 1705.92 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:49:52,092 epoch 3 - iter 231/773 - loss 0.05237529 - time (sec): 22.62 - samples/sec: 1713.92 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:49:58,924 epoch 3 - iter 308/773 - loss 0.05167815 - time (sec): 29.45 - samples/sec: 1708.51 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:50:05,851 epoch 3 - iter 385/773 - loss 0.05019417 - time (sec): 36.38 - samples/sec: 1712.12 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:50:13,249 epoch 3 - iter 462/773 - loss 0.05060451 - time (sec): 43.78 - samples/sec: 1713.86 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:50:20,307 epoch 3 - iter 539/773 - loss 0.04890835 - time (sec): 50.84 - samples/sec: 1713.08 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:50:27,552 epoch 3 - iter 616/773 - loss 0.04926124 - time (sec): 58.08 - samples/sec: 1711.91 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:50:34,925 epoch 3 - iter 693/773 - loss 0.04820747 - time (sec): 65.45 - samples/sec: 1715.27 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:50:42,182 epoch 3 - iter 770/773 - loss 0.04749550 - time (sec): 72.71 - samples/sec: 1704.16 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:50:42,457 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:50:42,457 EPOCH 3 done: loss 0.0474 - lr: 0.000023
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+ 2023-10-17 10:50:45,693 DEV : loss 0.07840536534786224 - f1-score (micro avg) 0.7919
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+ 2023-10-17 10:50:45,730 saving best model
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+ 2023-10-17 10:50:47,205 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:50:54,491 epoch 4 - iter 77/773 - loss 0.03791889 - time (sec): 7.28 - samples/sec: 1781.26 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:51:01,614 epoch 4 - iter 154/773 - loss 0.03523555 - time (sec): 14.41 - samples/sec: 1757.71 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:51:08,817 epoch 4 - iter 231/773 - loss 0.03503291 - time (sec): 21.61 - samples/sec: 1723.30 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:51:16,053 epoch 4 - iter 308/773 - loss 0.03382046 - time (sec): 28.84 - samples/sec: 1708.12 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:51:23,255 epoch 4 - iter 385/773 - loss 0.03384325 - time (sec): 36.05 - samples/sec: 1723.94 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:51:30,499 epoch 4 - iter 462/773 - loss 0.03365221 - time (sec): 43.29 - samples/sec: 1718.87 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:51:38,090 epoch 4 - iter 539/773 - loss 0.03394297 - time (sec): 50.88 - samples/sec: 1702.50 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:51:45,197 epoch 4 - iter 616/773 - loss 0.03375060 - time (sec): 57.99 - samples/sec: 1717.16 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:51:52,365 epoch 4 - iter 693/773 - loss 0.03369243 - time (sec): 65.16 - samples/sec: 1718.67 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 10:51:59,480 epoch 4 - iter 770/773 - loss 0.03218794 - time (sec): 72.27 - samples/sec: 1713.19 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 10:51:59,764 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 10:51:59,765 EPOCH 4 done: loss 0.0322 - lr: 0.000020
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+ 2023-10-17 10:52:02,651 DEV : loss 0.09961310774087906 - f1-score (micro avg) 0.796
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+ 2023-10-17 10:52:02,682 saving best model
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+ 2023-10-17 10:52:04,149 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:52:11,494 epoch 5 - iter 77/773 - loss 0.01433858 - time (sec): 7.34 - samples/sec: 1665.90 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 10:52:18,610 epoch 5 - iter 154/773 - loss 0.01668492 - time (sec): 14.45 - samples/sec: 1654.98 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 10:52:25,909 epoch 5 - iter 231/773 - loss 0.01582806 - time (sec): 21.75 - samples/sec: 1643.04 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 10:52:33,185 epoch 5 - iter 308/773 - loss 0.01747638 - time (sec): 29.03 - samples/sec: 1657.76 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 10:52:40,748 epoch 5 - iter 385/773 - loss 0.01907711 - time (sec): 36.59 - samples/sec: 1652.85 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 10:52:48,237 epoch 5 - iter 462/773 - loss 0.01962981 - time (sec): 44.08 - samples/sec: 1653.28 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 10:52:55,906 epoch 5 - iter 539/773 - loss 0.02022564 - time (sec): 51.75 - samples/sec: 1674.60 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 10:53:03,049 epoch 5 - iter 616/773 - loss 0.02094257 - time (sec): 58.89 - samples/sec: 1683.39 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 10:53:10,152 epoch 5 - iter 693/773 - loss 0.02132988 - time (sec): 66.00 - samples/sec: 1682.38 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 10:53:17,390 epoch 5 - iter 770/773 - loss 0.02276432 - time (sec): 73.23 - samples/sec: 1691.35 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 10:53:17,652 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 10:53:17,653 EPOCH 5 done: loss 0.0228 - lr: 0.000017
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+ 2023-10-17 10:53:20,568 DEV : loss 0.09981973469257355 - f1-score (micro avg) 0.7714
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+ 2023-10-17 10:53:20,598 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 10:53:27,937 epoch 6 - iter 77/773 - loss 0.01178514 - time (sec): 7.34 - samples/sec: 1692.35 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 10:53:35,153 epoch 6 - iter 154/773 - loss 0.01440421 - time (sec): 14.55 - samples/sec: 1715.19 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 10:53:42,304 epoch 6 - iter 231/773 - loss 0.01414618 - time (sec): 21.70 - samples/sec: 1732.08 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 10:53:49,943 epoch 6 - iter 308/773 - loss 0.01396628 - time (sec): 29.34 - samples/sec: 1709.14 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 10:53:57,579 epoch 6 - iter 385/773 - loss 0.01453643 - time (sec): 36.98 - samples/sec: 1681.34 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 10:54:06,017 epoch 6 - iter 462/773 - loss 0.01514750 - time (sec): 45.42 - samples/sec: 1641.00 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 10:54:13,503 epoch 6 - iter 539/773 - loss 0.01444656 - time (sec): 52.90 - samples/sec: 1660.58 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 10:54:20,944 epoch 6 - iter 616/773 - loss 0.01500934 - time (sec): 60.34 - samples/sec: 1648.86 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 10:54:28,692 epoch 6 - iter 693/773 - loss 0.01459877 - time (sec): 68.09 - samples/sec: 1638.37 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 10:54:35,865 epoch 6 - iter 770/773 - loss 0.01529271 - time (sec): 75.27 - samples/sec: 1645.13 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 10:54:36,121 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:54:36,121 EPOCH 6 done: loss 0.0152 - lr: 0.000013
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+ 2023-10-17 10:54:39,064 DEV : loss 0.10781947523355484 - f1-score (micro avg) 0.7951
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+ 2023-10-17 10:54:39,093 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-17 10:54:46,077 epoch 7 - iter 77/773 - loss 0.00893014 - time (sec): 6.98 - samples/sec: 1798.13 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 10:54:53,444 epoch 7 - iter 154/773 - loss 0.00871907 - time (sec): 14.35 - samples/sec: 1821.79 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 10:55:00,489 epoch 7 - iter 231/773 - loss 0.00923813 - time (sec): 21.39 - samples/sec: 1793.59 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:55:08,020 epoch 7 - iter 308/773 - loss 0.00995155 - time (sec): 28.93 - samples/sec: 1745.50 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:55:14,908 epoch 7 - iter 385/773 - loss 0.01128060 - time (sec): 35.81 - samples/sec: 1749.45 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:55:21,855 epoch 7 - iter 462/773 - loss 0.01127523 - time (sec): 42.76 - samples/sec: 1734.82 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:55:28,920 epoch 7 - iter 539/773 - loss 0.01198194 - time (sec): 49.83 - samples/sec: 1727.98 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:55:35,930 epoch 7 - iter 616/773 - loss 0.01116719 - time (sec): 56.84 - samples/sec: 1740.19 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:55:43,129 epoch 7 - iter 693/773 - loss 0.01065408 - time (sec): 64.03 - samples/sec: 1745.57 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:55:50,299 epoch 7 - iter 770/773 - loss 0.01059549 - time (sec): 71.20 - samples/sec: 1740.53 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:55:50,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:55:50,572 EPOCH 7 done: loss 0.0106 - lr: 0.000010
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+ 2023-10-17 10:55:53,470 DEV : loss 0.11879457533359528 - f1-score (micro avg) 0.8137
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+ 2023-10-17 10:55:53,500 saving best model
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+ 2023-10-17 10:55:54,945 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 10:56:02,442 epoch 8 - iter 77/773 - loss 0.00849530 - time (sec): 7.49 - samples/sec: 1650.42 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:56:09,696 epoch 8 - iter 154/773 - loss 0.00668992 - time (sec): 14.75 - samples/sec: 1715.41 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:56:16,634 epoch 8 - iter 231/773 - loss 0.00632277 - time (sec): 21.68 - samples/sec: 1711.90 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:56:24,003 epoch 8 - iter 308/773 - loss 0.00710856 - time (sec): 29.05 - samples/sec: 1703.90 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:56:31,183 epoch 8 - iter 385/773 - loss 0.00694469 - time (sec): 36.23 - samples/sec: 1720.14 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 10:56:38,232 epoch 8 - iter 462/773 - loss 0.00715802 - time (sec): 43.28 - samples/sec: 1733.62 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 10:56:45,558 epoch 8 - iter 539/773 - loss 0.00732958 - time (sec): 50.61 - samples/sec: 1721.98 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 10:56:52,703 epoch 8 - iter 616/773 - loss 0.00761398 - time (sec): 57.75 - samples/sec: 1717.23 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 10:57:00,365 epoch 8 - iter 693/773 - loss 0.00808954 - time (sec): 65.42 - samples/sec: 1707.19 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 10:57:07,847 epoch 8 - iter 770/773 - loss 0.00789069 - time (sec): 72.90 - samples/sec: 1697.25 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 10:57:08,147 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:57:08,147 EPOCH 8 done: loss 0.0079 - lr: 0.000007
188
+ 2023-10-17 10:57:11,061 DEV : loss 0.11245165020227432 - f1-score (micro avg) 0.8065
189
+ 2023-10-17 10:57:11,090 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 10:57:17,927 epoch 9 - iter 77/773 - loss 0.00678704 - time (sec): 6.83 - samples/sec: 1792.37 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:57:25,076 epoch 9 - iter 154/773 - loss 0.00603493 - time (sec): 13.98 - samples/sec: 1728.89 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:57:32,293 epoch 9 - iter 231/773 - loss 0.00487800 - time (sec): 21.20 - samples/sec: 1781.00 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:57:40,077 epoch 9 - iter 308/773 - loss 0.00497749 - time (sec): 28.98 - samples/sec: 1707.92 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 10:57:47,281 epoch 9 - iter 385/773 - loss 0.00481061 - time (sec): 36.19 - samples/sec: 1699.34 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 10:57:54,356 epoch 9 - iter 462/773 - loss 0.00526176 - time (sec): 43.26 - samples/sec: 1696.55 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 10:58:01,333 epoch 9 - iter 539/773 - loss 0.00508493 - time (sec): 50.24 - samples/sec: 1702.12 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 10:58:08,962 epoch 9 - iter 616/773 - loss 0.00501247 - time (sec): 57.87 - samples/sec: 1715.29 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 10:58:15,614 epoch 9 - iter 693/773 - loss 0.00488110 - time (sec): 64.52 - samples/sec: 1722.18 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-17 10:58:22,667 epoch 9 - iter 770/773 - loss 0.00486167 - time (sec): 71.58 - samples/sec: 1732.27 - lr: 0.000003 - momentum: 0.000000
200
+ 2023-10-17 10:58:22,949 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 10:58:22,950 EPOCH 9 done: loss 0.0049 - lr: 0.000003
202
+ 2023-10-17 10:58:26,006 DEV : loss 0.1249605268239975 - f1-score (micro avg) 0.7984
203
+ 2023-10-17 10:58:26,042 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 10:58:33,332 epoch 10 - iter 77/773 - loss 0.00242796 - time (sec): 7.29 - samples/sec: 1792.63 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 10:58:40,248 epoch 10 - iter 154/773 - loss 0.00282294 - time (sec): 14.20 - samples/sec: 1729.26 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-17 10:58:47,465 epoch 10 - iter 231/773 - loss 0.00368862 - time (sec): 21.42 - samples/sec: 1696.13 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 10:58:54,604 epoch 10 - iter 308/773 - loss 0.00321279 - time (sec): 28.56 - samples/sec: 1716.83 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 10:59:01,489 epoch 10 - iter 385/773 - loss 0.00288216 - time (sec): 35.44 - samples/sec: 1725.88 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 10:59:08,465 epoch 10 - iter 462/773 - loss 0.00268120 - time (sec): 42.42 - samples/sec: 1726.17 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 10:59:15,643 epoch 10 - iter 539/773 - loss 0.00271947 - time (sec): 49.60 - samples/sec: 1736.60 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 10:59:22,844 epoch 10 - iter 616/773 - loss 0.00268237 - time (sec): 56.80 - samples/sec: 1764.79 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 10:59:29,999 epoch 10 - iter 693/773 - loss 0.00287023 - time (sec): 63.95 - samples/sec: 1747.91 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 10:59:37,487 epoch 10 - iter 770/773 - loss 0.00290057 - time (sec): 71.44 - samples/sec: 1734.34 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 10:59:37,759 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 10:59:37,759 EPOCH 10 done: loss 0.0029 - lr: 0.000000
216
+ 2023-10-17 10:59:40,948 DEV : loss 0.13062351942062378 - f1-score (micro avg) 0.8041
217
+ 2023-10-17 10:59:41,616 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 10:59:41,618 Loading model from best epoch ...
219
+ 2023-10-17 10:59:44,193 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
220
+ 2023-10-17 10:59:53,111
221
+ Results:
222
+ - F-score (micro) 0.8174
223
+ - F-score (macro) 0.7401
224
+ - Accuracy 0.7088
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.8804 0.8404 0.8599 946
230
+ BUILDING 0.6604 0.5676 0.6105 185
231
+ STREET 0.7500 0.7500 0.7500 56
232
+
233
+ micro avg 0.8426 0.7936 0.8174 1187
234
+ macro avg 0.7636 0.7193 0.7401 1187
235
+ weighted avg 0.8400 0.7936 0.8159 1187
236
+
237
+ 2023-10-17 10:59:53,111 ----------------------------------------------------------------------------------------------------