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

Token Classification
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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.1697535447.4c6324b99746.1159.2 +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 09:38:42 0.0000 0.3842 0.0575 0.8038 0.7089 0.7534 0.6109
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+ 2 09:39:56 0.0000 0.0772 0.0471 0.7729 0.7468 0.7597 0.6254
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+ 3 09:41:14 0.0000 0.0479 0.0488 0.7713 0.8397 0.8040 0.6862
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+ 4 09:42:36 0.0000 0.0311 0.0733 0.7305 0.8692 0.7938 0.6710
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+ 5 09:43:54 0.0000 0.0212 0.0960 0.7570 0.8017 0.7787 0.6690
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+ 6 09:45:12 0.0000 0.0161 0.1021 0.7738 0.8228 0.7975 0.6747
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+ 7 09:46:24 0.0000 0.0105 0.1029 0.7717 0.8270 0.7984 0.6782
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+ 8 09:47:36 0.0000 0.0069 0.1238 0.7549 0.8186 0.7854 0.6667
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+ 9 09:48:51 0.0000 0.0046 0.1187 0.7804 0.8397 0.8089 0.6934
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+ 10 09:50:05 0.0000 0.0031 0.1231 0.7686 0.8270 0.7967 0.6806
runs/events.out.tfevents.1697535447.4c6324b99746.1159.2 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 09:37:27,236 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,238 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 09:37:27,238 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,238 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 09:37:27,238 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,239 Train: 6183 sentences
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+ 2023-10-17 09:37:27,239 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 09:37:27,239 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,239 Training Params:
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+ 2023-10-17 09:37:27,239 - learning_rate: "3e-05"
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+ 2023-10-17 09:37:27,239 - mini_batch_size: "8"
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+ 2023-10-17 09:37:27,239 - max_epochs: "10"
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+ 2023-10-17 09:37:27,239 - shuffle: "True"
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+ 2023-10-17 09:37:27,239 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,239 Plugins:
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+ 2023-10-17 09:37:27,239 - TensorboardLogger
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+ 2023-10-17 09:37:27,239 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 09:37:27,239 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,239 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 09:37:27,240 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 09:37:27,240 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,240 Computation:
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+ 2023-10-17 09:37:27,240 - compute on device: cuda:0
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+ 2023-10-17 09:37:27,240 - embedding storage: none
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+ 2023-10-17 09:37:27,240 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,240 Model training base path: "hmbench-topres19th/en-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-17 09:37:27,240 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,240 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:37:27,240 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 09:37:34,576 epoch 1 - iter 77/773 - loss 2.33879566 - time (sec): 7.33 - samples/sec: 1752.72 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 09:37:41,584 epoch 1 - iter 154/773 - loss 1.42646834 - time (sec): 14.34 - samples/sec: 1749.19 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 09:37:48,222 epoch 1 - iter 231/773 - loss 1.01107736 - time (sec): 20.98 - samples/sec: 1783.57 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 09:37:55,237 epoch 1 - iter 308/773 - loss 0.78648939 - time (sec): 28.00 - samples/sec: 1800.87 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 09:38:02,692 epoch 1 - iter 385/773 - loss 0.65487679 - time (sec): 35.45 - samples/sec: 1767.05 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:38:09,721 epoch 1 - iter 462/773 - loss 0.56517862 - time (sec): 42.48 - samples/sec: 1763.85 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:38:17,445 epoch 1 - iter 539/773 - loss 0.50859951 - time (sec): 50.20 - samples/sec: 1727.34 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:38:24,994 epoch 1 - iter 616/773 - loss 0.46183259 - time (sec): 57.75 - samples/sec: 1710.88 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:38:32,538 epoch 1 - iter 693/773 - loss 0.41835378 - time (sec): 65.30 - samples/sec: 1708.79 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:38:39,593 epoch 1 - iter 770/773 - loss 0.38499874 - time (sec): 72.35 - samples/sec: 1713.83 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 09:38:39,844 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:38:39,844 EPOCH 1 done: loss 0.3842 - lr: 0.000030
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+ 2023-10-17 09:38:42,623 DEV : loss 0.05754069611430168 - f1-score (micro avg) 0.7534
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+ 2023-10-17 09:38:42,650 saving best model
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+ 2023-10-17 09:38:43,192 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:38:49,786 epoch 2 - iter 77/773 - loss 0.10261422 - time (sec): 6.59 - samples/sec: 1792.64 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 09:38:56,506 epoch 2 - iter 154/773 - loss 0.08620224 - time (sec): 13.31 - samples/sec: 1814.77 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 09:39:03,586 epoch 2 - iter 231/773 - loss 0.08217454 - time (sec): 20.39 - samples/sec: 1848.76 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 09:39:10,410 epoch 2 - iter 308/773 - loss 0.08124803 - time (sec): 27.22 - samples/sec: 1841.98 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 09:39:17,499 epoch 2 - iter 385/773 - loss 0.07988568 - time (sec): 34.31 - samples/sec: 1829.13 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 09:39:25,060 epoch 2 - iter 462/773 - loss 0.07997375 - time (sec): 41.87 - samples/sec: 1789.65 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 09:39:32,052 epoch 2 - iter 539/773 - loss 0.07763047 - time (sec): 48.86 - samples/sec: 1790.10 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 09:39:39,204 epoch 2 - iter 616/773 - loss 0.07703563 - time (sec): 56.01 - samples/sec: 1794.93 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:39:46,211 epoch 2 - iter 693/773 - loss 0.07619810 - time (sec): 63.02 - samples/sec: 1780.52 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:39:53,478 epoch 2 - iter 770/773 - loss 0.07711628 - time (sec): 70.28 - samples/sec: 1764.60 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 09:39:53,755 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:39:53,755 EPOCH 2 done: loss 0.0772 - lr: 0.000027
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+ 2023-10-17 09:39:56,582 DEV : loss 0.047075219452381134 - f1-score (micro avg) 0.7597
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+ 2023-10-17 09:39:56,610 saving best model
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+ 2023-10-17 09:39:57,996 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:40:05,357 epoch 3 - iter 77/773 - loss 0.04621094 - time (sec): 7.36 - samples/sec: 1588.98 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 09:40:13,046 epoch 3 - iter 154/773 - loss 0.04711698 - time (sec): 15.05 - samples/sec: 1650.32 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 09:40:20,395 epoch 3 - iter 231/773 - loss 0.04627990 - time (sec): 22.39 - samples/sec: 1705.13 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 09:40:27,418 epoch 3 - iter 308/773 - loss 0.04368407 - time (sec): 29.42 - samples/sec: 1719.86 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 09:40:34,373 epoch 3 - iter 385/773 - loss 0.04513632 - time (sec): 36.37 - samples/sec: 1716.82 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 09:40:41,366 epoch 3 - iter 462/773 - loss 0.04669092 - time (sec): 43.37 - samples/sec: 1731.18 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 09:40:48,481 epoch 3 - iter 539/773 - loss 0.04627462 - time (sec): 50.48 - samples/sec: 1726.60 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:40:55,508 epoch 3 - iter 616/773 - loss 0.04565734 - time (sec): 57.51 - samples/sec: 1730.70 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:41:02,972 epoch 3 - iter 693/773 - loss 0.04735239 - time (sec): 64.97 - samples/sec: 1698.13 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 09:41:10,809 epoch 3 - iter 770/773 - loss 0.04789770 - time (sec): 72.81 - samples/sec: 1701.70 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 09:41:11,097 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:41:11,097 EPOCH 3 done: loss 0.0479 - lr: 0.000023
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+ 2023-10-17 09:41:14,193 DEV : loss 0.04879758879542351 - f1-score (micro avg) 0.804
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+ 2023-10-17 09:41:14,221 saving best model
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+ 2023-10-17 09:41:15,684 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:41:23,490 epoch 4 - iter 77/773 - loss 0.02803532 - time (sec): 7.80 - samples/sec: 1647.61 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 09:41:31,172 epoch 4 - iter 154/773 - loss 0.02614174 - time (sec): 15.49 - samples/sec: 1583.78 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 09:41:38,912 epoch 4 - iter 231/773 - loss 0.02740037 - time (sec): 23.22 - samples/sec: 1615.22 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 09:41:46,418 epoch 4 - iter 308/773 - loss 0.02810327 - time (sec): 30.73 - samples/sec: 1627.11 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 09:41:54,017 epoch 4 - iter 385/773 - loss 0.02856760 - time (sec): 38.33 - samples/sec: 1628.38 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 09:42:02,005 epoch 4 - iter 462/773 - loss 0.03151706 - time (sec): 46.32 - samples/sec: 1625.94 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:42:09,711 epoch 4 - iter 539/773 - loss 0.03118289 - time (sec): 54.02 - samples/sec: 1629.16 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:42:17,213 epoch 4 - iter 616/773 - loss 0.03069938 - time (sec): 61.53 - samples/sec: 1620.50 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 09:42:24,916 epoch 4 - iter 693/773 - loss 0.03102882 - time (sec): 69.23 - samples/sec: 1610.69 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 09:42:32,858 epoch 4 - iter 770/773 - loss 0.03091728 - time (sec): 77.17 - samples/sec: 1606.19 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 09:42:33,116 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:42:33,116 EPOCH 4 done: loss 0.0311 - lr: 0.000020
131
+ 2023-10-17 09:42:36,004 DEV : loss 0.0732564851641655 - f1-score (micro avg) 0.7938
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+ 2023-10-17 09:42:36,033 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:42:43,080 epoch 5 - iter 77/773 - loss 0.02562462 - time (sec): 7.04 - samples/sec: 1684.07 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 09:42:50,338 epoch 5 - iter 154/773 - loss 0.02186778 - time (sec): 14.30 - samples/sec: 1698.22 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 09:42:57,692 epoch 5 - iter 231/773 - loss 0.02197283 - time (sec): 21.66 - samples/sec: 1670.07 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 09:43:05,104 epoch 5 - iter 308/773 - loss 0.02085800 - time (sec): 29.07 - samples/sec: 1665.35 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 09:43:12,720 epoch 5 - iter 385/773 - loss 0.02066640 - time (sec): 36.68 - samples/sec: 1673.37 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:43:19,967 epoch 5 - iter 462/773 - loss 0.02032896 - time (sec): 43.93 - samples/sec: 1684.46 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:43:27,490 epoch 5 - iter 539/773 - loss 0.01942875 - time (sec): 51.45 - samples/sec: 1681.14 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 09:43:35,178 epoch 5 - iter 616/773 - loss 0.02036285 - time (sec): 59.14 - samples/sec: 1668.33 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 09:43:42,777 epoch 5 - iter 693/773 - loss 0.02050199 - time (sec): 66.74 - samples/sec: 1676.73 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 09:43:50,579 epoch 5 - iter 770/773 - loss 0.02101369 - time (sec): 74.54 - samples/sec: 1660.09 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 09:43:50,903 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 09:43:50,904 EPOCH 5 done: loss 0.0212 - lr: 0.000017
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+ 2023-10-17 09:43:54,291 DEV : loss 0.09599114209413528 - f1-score (micro avg) 0.7787
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+ 2023-10-17 09:43:54,321 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:44:02,198 epoch 6 - iter 77/773 - loss 0.01113512 - time (sec): 7.87 - samples/sec: 1629.16 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 09:44:09,915 epoch 6 - iter 154/773 - loss 0.01041077 - time (sec): 15.59 - samples/sec: 1650.97 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 09:44:17,666 epoch 6 - iter 231/773 - loss 0.01184309 - time (sec): 23.34 - samples/sec: 1629.14 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 09:44:25,575 epoch 6 - iter 308/773 - loss 0.01381827 - time (sec): 31.25 - samples/sec: 1617.95 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:44:32,527 epoch 6 - iter 385/773 - loss 0.01530116 - time (sec): 38.20 - samples/sec: 1663.06 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:44:39,617 epoch 6 - iter 462/773 - loss 0.01664026 - time (sec): 45.29 - samples/sec: 1658.96 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 09:44:46,872 epoch 6 - iter 539/773 - loss 0.01619538 - time (sec): 52.55 - samples/sec: 1653.81 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 09:44:54,077 epoch 6 - iter 616/773 - loss 0.01625589 - time (sec): 59.75 - samples/sec: 1652.08 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 09:45:01,550 epoch 6 - iter 693/773 - loss 0.01623770 - time (sec): 67.22 - samples/sec: 1656.58 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 09:45:09,080 epoch 6 - iter 770/773 - loss 0.01610707 - time (sec): 74.75 - samples/sec: 1657.18 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 09:45:09,364 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 09:45:09,364 EPOCH 6 done: loss 0.0161 - lr: 0.000013
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+ 2023-10-17 09:45:12,313 DEV : loss 0.10213357210159302 - f1-score (micro avg) 0.7975
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+ 2023-10-17 09:45:12,341 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 09:45:19,108 epoch 7 - iter 77/773 - loss 0.00659626 - time (sec): 6.77 - samples/sec: 1731.83 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 09:45:25,993 epoch 7 - iter 154/773 - loss 0.01050483 - time (sec): 13.65 - samples/sec: 1738.79 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 09:45:32,858 epoch 7 - iter 231/773 - loss 0.01031709 - time (sec): 20.52 - samples/sec: 1766.06 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 09:45:39,920 epoch 7 - iter 308/773 - loss 0.01094286 - time (sec): 27.58 - samples/sec: 1774.72 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 09:45:46,969 epoch 7 - iter 385/773 - loss 0.01060798 - time (sec): 34.63 - samples/sec: 1778.49 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 09:45:53,878 epoch 7 - iter 462/773 - loss 0.00964801 - time (sec): 41.53 - samples/sec: 1779.93 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 09:46:00,494 epoch 7 - iter 539/773 - loss 0.00899137 - time (sec): 48.15 - samples/sec: 1785.57 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 09:46:07,442 epoch 7 - iter 616/773 - loss 0.00899506 - time (sec): 55.10 - samples/sec: 1798.25 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 09:46:14,354 epoch 7 - iter 693/773 - loss 0.00947777 - time (sec): 62.01 - samples/sec: 1804.02 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 09:46:21,321 epoch 7 - iter 770/773 - loss 0.01042790 - time (sec): 68.98 - samples/sec: 1793.18 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 09:46:21,605 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-17 09:46:21,605 EPOCH 7 done: loss 0.0105 - lr: 0.000010
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+ 2023-10-17 09:46:24,548 DEV : loss 0.10294033586978912 - f1-score (micro avg) 0.7984
174
+ 2023-10-17 09:46:24,575 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-17 09:46:31,444 epoch 8 - iter 77/773 - loss 0.01082701 - time (sec): 6.87 - samples/sec: 1802.66 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 09:46:38,217 epoch 8 - iter 154/773 - loss 0.00937227 - time (sec): 13.64 - samples/sec: 1852.12 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 09:46:44,941 epoch 8 - iter 231/773 - loss 0.01054096 - time (sec): 20.36 - samples/sec: 1835.19 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 09:46:51,562 epoch 8 - iter 308/773 - loss 0.00981916 - time (sec): 26.99 - samples/sec: 1833.97 - lr: 0.000009 - momentum: 0.000000
179
+ 2023-10-17 09:46:58,389 epoch 8 - iter 385/773 - loss 0.00891815 - time (sec): 33.81 - samples/sec: 1819.33 - lr: 0.000008 - momentum: 0.000000
180
+ 2023-10-17 09:47:05,424 epoch 8 - iter 462/773 - loss 0.00820688 - time (sec): 40.85 - samples/sec: 1827.76 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-17 09:47:12,186 epoch 8 - iter 539/773 - loss 0.00762464 - time (sec): 47.61 - samples/sec: 1840.44 - lr: 0.000008 - momentum: 0.000000
182
+ 2023-10-17 09:47:18,915 epoch 8 - iter 616/773 - loss 0.00722670 - time (sec): 54.34 - samples/sec: 1830.88 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 09:47:26,084 epoch 8 - iter 693/773 - loss 0.00708273 - time (sec): 61.51 - samples/sec: 1804.20 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 09:47:33,076 epoch 8 - iter 770/773 - loss 0.00691715 - time (sec): 68.50 - samples/sec: 1809.37 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-17 09:47:33,365 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-17 09:47:33,366 EPOCH 8 done: loss 0.0069 - lr: 0.000007
187
+ 2023-10-17 09:47:36,625 DEV : loss 0.12377041578292847 - f1-score (micro avg) 0.7854
188
+ 2023-10-17 09:47:36,665 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 09:47:43,762 epoch 9 - iter 77/773 - loss 0.00448652 - time (sec): 7.09 - samples/sec: 1776.99 - lr: 0.000006 - momentum: 0.000000
190
+ 2023-10-17 09:47:50,939 epoch 9 - iter 154/773 - loss 0.00413824 - time (sec): 14.27 - samples/sec: 1718.66 - lr: 0.000006 - momentum: 0.000000
191
+ 2023-10-17 09:47:58,038 epoch 9 - iter 231/773 - loss 0.00450347 - time (sec): 21.37 - samples/sec: 1750.13 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 09:48:04,825 epoch 9 - iter 308/773 - loss 0.00438937 - time (sec): 28.16 - samples/sec: 1744.81 - lr: 0.000005 - momentum: 0.000000
193
+ 2023-10-17 09:48:11,751 epoch 9 - iter 385/773 - loss 0.00389918 - time (sec): 35.08 - samples/sec: 1764.04 - lr: 0.000005 - momentum: 0.000000
194
+ 2023-10-17 09:48:18,655 epoch 9 - iter 462/773 - loss 0.00409811 - time (sec): 41.99 - samples/sec: 1762.60 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 09:48:25,972 epoch 9 - iter 539/773 - loss 0.00408809 - time (sec): 49.30 - samples/sec: 1762.28 - lr: 0.000004 - momentum: 0.000000
196
+ 2023-10-17 09:48:33,178 epoch 9 - iter 616/773 - loss 0.00422096 - time (sec): 56.51 - samples/sec: 1752.15 - lr: 0.000004 - momentum: 0.000000
197
+ 2023-10-17 09:48:40,631 epoch 9 - iter 693/773 - loss 0.00424621 - time (sec): 63.96 - samples/sec: 1756.00 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 09:48:47,886 epoch 9 - iter 770/773 - loss 0.00460245 - time (sec): 71.22 - samples/sec: 1738.54 - lr: 0.000003 - momentum: 0.000000
199
+ 2023-10-17 09:48:48,169 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 09:48:48,169 EPOCH 9 done: loss 0.0046 - lr: 0.000003
201
+ 2023-10-17 09:48:51,211 DEV : loss 0.11872641742229462 - f1-score (micro avg) 0.8089
202
+ 2023-10-17 09:48:51,241 saving best model
203
+ 2023-10-17 09:48:51,815 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 09:48:58,687 epoch 10 - iter 77/773 - loss 0.00222354 - time (sec): 6.87 - samples/sec: 1822.68 - lr: 0.000003 - momentum: 0.000000
205
+ 2023-10-17 09:49:05,736 epoch 10 - iter 154/773 - loss 0.00255318 - time (sec): 13.92 - samples/sec: 1781.09 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-17 09:49:12,757 epoch 10 - iter 231/773 - loss 0.00220584 - time (sec): 20.94 - samples/sec: 1807.28 - lr: 0.000002 - momentum: 0.000000
207
+ 2023-10-17 09:49:19,469 epoch 10 - iter 308/773 - loss 0.00272066 - time (sec): 27.65 - samples/sec: 1817.54 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-17 09:49:26,263 epoch 10 - iter 385/773 - loss 0.00296102 - time (sec): 34.45 - samples/sec: 1814.48 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 09:49:32,975 epoch 10 - iter 462/773 - loss 0.00304331 - time (sec): 41.16 - samples/sec: 1803.57 - lr: 0.000001 - momentum: 0.000000
210
+ 2023-10-17 09:49:40,073 epoch 10 - iter 539/773 - loss 0.00318944 - time (sec): 48.26 - samples/sec: 1803.54 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 09:49:47,138 epoch 10 - iter 616/773 - loss 0.00312936 - time (sec): 55.32 - samples/sec: 1786.57 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 09:49:54,283 epoch 10 - iter 693/773 - loss 0.00302278 - time (sec): 62.47 - samples/sec: 1784.02 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 09:50:01,888 epoch 10 - iter 770/773 - loss 0.00307193 - time (sec): 70.07 - samples/sec: 1767.38 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 09:50:02,150 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 09:50:02,150 EPOCH 10 done: loss 0.0031 - lr: 0.000000
216
+ 2023-10-17 09:50:05,066 DEV : loss 0.12313356250524521 - f1-score (micro avg) 0.7967
217
+ 2023-10-17 09:50:05,705 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 09:50:05,708 Loading model from best epoch ...
219
+ 2023-10-17 09:50:08,303 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 09:50:17,304
221
+ Results:
222
+ - F-score (micro) 0.8152
223
+ - F-score (macro) 0.7257
224
+ - Accuracy 0.7076
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.8495 0.8710 0.8601 946
230
+ BUILDING 0.6301 0.5892 0.6089 185
231
+ STREET 0.7018 0.7143 0.7080 56
232
+
233
+ micro avg 0.8108 0.8197 0.8152 1187
234
+ macro avg 0.7271 0.7248 0.7257 1187
235
+ weighted avg 0.8083 0.8197 0.8138 1187
236
+
237
+ 2023-10-17 09:50:17,304 ----------------------------------------------------------------------------------------------------