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hmbench-topres19th-en-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
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sequence-tagger-model
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+ 2023-10-25 12:55:17,372 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,373 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(64001, 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): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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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): BertSelfOutput(
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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): BertIntermediate(
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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): BertOutput(
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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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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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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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+ 2023-10-25 12:55:17,373 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,373 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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+ 2023-10-25 12:55:17,373 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,373 Train: 6183 sentences
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+ 2023-10-25 12:55:17,373 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Training Params:
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+ 2023-10-25 12:55:17,374 - learning_rate: "3e-05"
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+ 2023-10-25 12:55:17,374 - mini_batch_size: "8"
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+ 2023-10-25 12:55:17,374 - max_epochs: "10"
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+ 2023-10-25 12:55:17,374 - shuffle: "True"
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Plugins:
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+ 2023-10-25 12:55:17,374 - TensorboardLogger
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+ 2023-10-25 12:55:17,374 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 12:55:17,374 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Computation:
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+ 2023-10-25 12:55:17,374 - compute on device: cuda:0
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+ 2023-10-25 12:55:17,374 - embedding storage: none
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Model training base path: "hmbench-topres19th/en-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:55:17,374 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 12:55:22,325 epoch 1 - iter 77/773 - loss 1.93757916 - time (sec): 4.95 - samples/sec: 2633.95 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 12:55:27,147 epoch 1 - iter 154/773 - loss 1.11916091 - time (sec): 9.77 - samples/sec: 2623.41 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 12:55:31,976 epoch 1 - iter 231/773 - loss 0.82756714 - time (sec): 14.60 - samples/sec: 2575.99 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 12:55:36,821 epoch 1 - iter 308/773 - loss 0.66515291 - time (sec): 19.45 - samples/sec: 2548.10 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 12:55:41,560 epoch 1 - iter 385/773 - loss 0.56018412 - time (sec): 24.18 - samples/sec: 2536.59 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 12:55:46,415 epoch 1 - iter 462/773 - loss 0.48843856 - time (sec): 29.04 - samples/sec: 2539.21 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 12:55:51,206 epoch 1 - iter 539/773 - loss 0.43466720 - time (sec): 33.83 - samples/sec: 2528.25 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 12:55:56,079 epoch 1 - iter 616/773 - loss 0.39209967 - time (sec): 38.70 - samples/sec: 2530.25 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 12:56:00,886 epoch 1 - iter 693/773 - loss 0.35744237 - time (sec): 43.51 - samples/sec: 2545.35 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 12:56:05,722 epoch 1 - iter 770/773 - loss 0.32866683 - time (sec): 48.35 - samples/sec: 2563.64 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 12:56:05,903 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:56:05,903 EPOCH 1 done: loss 0.3279 - lr: 0.000030
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+ 2023-10-25 12:56:09,118 DEV : loss 0.04656985402107239 - f1-score (micro avg) 0.7598
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+ 2023-10-25 12:56:09,136 saving best model
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+ 2023-10-25 12:56:09,633 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:56:14,324 epoch 2 - iter 77/773 - loss 0.08563895 - time (sec): 4.69 - samples/sec: 2443.89 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 12:56:19,149 epoch 2 - iter 154/773 - loss 0.07537749 - time (sec): 9.51 - samples/sec: 2436.76 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 12:56:24,014 epoch 2 - iter 231/773 - loss 0.07532105 - time (sec): 14.38 - samples/sec: 2440.29 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 12:56:28,747 epoch 2 - iter 308/773 - loss 0.07738630 - time (sec): 19.11 - samples/sec: 2509.80 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 12:56:33,915 epoch 2 - iter 385/773 - loss 0.07528257 - time (sec): 24.28 - samples/sec: 2523.33 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 12:56:38,709 epoch 2 - iter 462/773 - loss 0.07420890 - time (sec): 29.07 - samples/sec: 2528.91 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 12:56:43,381 epoch 2 - iter 539/773 - loss 0.07258413 - time (sec): 33.75 - samples/sec: 2581.48 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 12:56:48,044 epoch 2 - iter 616/773 - loss 0.07168966 - time (sec): 38.41 - samples/sec: 2585.65 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 12:56:52,727 epoch 2 - iter 693/773 - loss 0.07240517 - time (sec): 43.09 - samples/sec: 2583.59 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 12:56:57,388 epoch 2 - iter 770/773 - loss 0.07049818 - time (sec): 47.75 - samples/sec: 2593.56 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 12:56:57,548 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:56:57,548 EPOCH 2 done: loss 0.0705 - lr: 0.000027
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+ 2023-10-25 12:57:00,100 DEV : loss 0.05241599678993225 - f1-score (micro avg) 0.766
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+ 2023-10-25 12:57:00,121 saving best model
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+ 2023-10-25 12:57:00,787 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:57:05,053 epoch 3 - iter 77/773 - loss 0.03937428 - time (sec): 4.26 - samples/sec: 2830.87 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 12:57:10,111 epoch 3 - iter 154/773 - loss 0.03820952 - time (sec): 9.32 - samples/sec: 2597.71 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 12:57:14,565 epoch 3 - iter 231/773 - loss 0.04003269 - time (sec): 13.77 - samples/sec: 2757.09 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 12:57:18,915 epoch 3 - iter 308/773 - loss 0.04207363 - time (sec): 18.12 - samples/sec: 2736.31 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 12:57:23,359 epoch 3 - iter 385/773 - loss 0.04520442 - time (sec): 22.57 - samples/sec: 2747.11 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 12:57:27,600 epoch 3 - iter 462/773 - loss 0.04505890 - time (sec): 26.81 - samples/sec: 2750.23 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 12:57:31,826 epoch 3 - iter 539/773 - loss 0.04438068 - time (sec): 31.04 - samples/sec: 2778.92 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 12:57:36,080 epoch 3 - iter 616/773 - loss 0.04412004 - time (sec): 35.29 - samples/sec: 2803.10 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 12:57:40,438 epoch 3 - iter 693/773 - loss 0.04308429 - time (sec): 39.65 - samples/sec: 2803.93 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 12:57:44,861 epoch 3 - iter 770/773 - loss 0.04355937 - time (sec): 44.07 - samples/sec: 2811.96 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 12:57:45,041 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-25 12:57:45,042 EPOCH 3 done: loss 0.0435 - lr: 0.000023
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+ 2023-10-25 12:57:47,526 DEV : loss 0.07553908228874207 - f1-score (micro avg) 0.7581
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+ 2023-10-25 12:57:47,545 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 12:57:52,456 epoch 4 - iter 77/773 - loss 0.02787168 - time (sec): 4.91 - samples/sec: 2464.84 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 12:57:57,409 epoch 4 - iter 154/773 - loss 0.02466669 - time (sec): 9.86 - samples/sec: 2463.70 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 12:58:01,920 epoch 4 - iter 231/773 - loss 0.02719373 - time (sec): 14.37 - samples/sec: 2567.69 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 12:58:06,111 epoch 4 - iter 308/773 - loss 0.02590039 - time (sec): 18.56 - samples/sec: 2641.33 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 12:58:10,335 epoch 4 - iter 385/773 - loss 0.02730470 - time (sec): 22.79 - samples/sec: 2649.13 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 12:58:14,488 epoch 4 - iter 462/773 - loss 0.02758635 - time (sec): 26.94 - samples/sec: 2667.62 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 12:58:19,096 epoch 4 - iter 539/773 - loss 0.02908079 - time (sec): 31.55 - samples/sec: 2686.53 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 12:58:23,572 epoch 4 - iter 616/773 - loss 0.02974028 - time (sec): 36.03 - samples/sec: 2711.79 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 12:58:27,965 epoch 4 - iter 693/773 - loss 0.02923596 - time (sec): 40.42 - samples/sec: 2751.85 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 12:58:32,489 epoch 4 - iter 770/773 - loss 0.02839604 - time (sec): 44.94 - samples/sec: 2757.19 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 12:58:32,667 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-25 12:58:32,667 EPOCH 4 done: loss 0.0285 - lr: 0.000020
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+ 2023-10-25 12:58:35,305 DEV : loss 0.10123448073863983 - f1-score (micro avg) 0.7588
135
+ 2023-10-25 12:58:35,322 ----------------------------------------------------------------------------------------------------
136
+ 2023-10-25 12:58:40,130 epoch 5 - iter 77/773 - loss 0.01818218 - time (sec): 4.81 - samples/sec: 2724.06 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 12:58:44,779 epoch 5 - iter 154/773 - loss 0.01934967 - time (sec): 9.45 - samples/sec: 2642.61 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 12:58:49,349 epoch 5 - iter 231/773 - loss 0.01860970 - time (sec): 14.02 - samples/sec: 2622.37 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 12:58:53,878 epoch 5 - iter 308/773 - loss 0.01852623 - time (sec): 18.55 - samples/sec: 2604.00 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 12:58:58,223 epoch 5 - iter 385/773 - loss 0.02029059 - time (sec): 22.90 - samples/sec: 2677.70 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 12:59:02,519 epoch 5 - iter 462/773 - loss 0.01982896 - time (sec): 27.19 - samples/sec: 2678.43 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 12:59:06,948 epoch 5 - iter 539/773 - loss 0.01903206 - time (sec): 31.62 - samples/sec: 2682.60 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 12:59:11,469 epoch 5 - iter 616/773 - loss 0.01983065 - time (sec): 36.14 - samples/sec: 2715.33 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 12:59:15,959 epoch 5 - iter 693/773 - loss 0.01906685 - time (sec): 40.63 - samples/sec: 2734.27 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 12:59:20,380 epoch 5 - iter 770/773 - loss 0.01928792 - time (sec): 45.06 - samples/sec: 2749.87 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 12:59:20,549 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-25 12:59:20,549 EPOCH 5 done: loss 0.0193 - lr: 0.000017
148
+ 2023-10-25 12:59:23,132 DEV : loss 0.09339083731174469 - f1-score (micro avg) 0.7702
149
+ 2023-10-25 12:59:23,152 saving best model
150
+ 2023-10-25 12:59:23,843 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-25 12:59:28,319 epoch 6 - iter 77/773 - loss 0.01168986 - time (sec): 4.47 - samples/sec: 2758.77 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 12:59:32,805 epoch 6 - iter 154/773 - loss 0.01402440 - time (sec): 8.96 - samples/sec: 2807.18 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 12:59:37,192 epoch 6 - iter 231/773 - loss 0.01359986 - time (sec): 13.35 - samples/sec: 2773.09 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 12:59:41,538 epoch 6 - iter 308/773 - loss 0.01576820 - time (sec): 17.69 - samples/sec: 2806.65 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 12:59:45,952 epoch 6 - iter 385/773 - loss 0.01553924 - time (sec): 22.11 - samples/sec: 2811.99 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 12:59:50,240 epoch 6 - iter 462/773 - loss 0.01476289 - time (sec): 26.40 - samples/sec: 2838.14 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 12:59:54,601 epoch 6 - iter 539/773 - loss 0.01422328 - time (sec): 30.76 - samples/sec: 2834.90 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 12:59:58,868 epoch 6 - iter 616/773 - loss 0.01396188 - time (sec): 35.02 - samples/sec: 2835.21 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 13:00:04,003 epoch 6 - iter 693/773 - loss 0.01434232 - time (sec): 40.16 - samples/sec: 2781.23 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 13:00:08,377 epoch 6 - iter 770/773 - loss 0.01390229 - time (sec): 44.53 - samples/sec: 2782.03 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 13:00:08,548 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-25 13:00:08,548 EPOCH 6 done: loss 0.0139 - lr: 0.000013
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+ 2023-10-25 13:00:11,523 DEV : loss 0.10927439481019974 - f1-score (micro avg) 0.7676
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+ 2023-10-25 13:00:11,545 ----------------------------------------------------------------------------------------------------
165
+ 2023-10-25 13:00:16,317 epoch 7 - iter 77/773 - loss 0.00916644 - time (sec): 4.77 - samples/sec: 2721.74 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 13:00:21,088 epoch 7 - iter 154/773 - loss 0.00780367 - time (sec): 9.54 - samples/sec: 2623.17 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 13:00:26,000 epoch 7 - iter 231/773 - loss 0.00824754 - time (sec): 14.45 - samples/sec: 2616.04 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 13:00:30,653 epoch 7 - iter 308/773 - loss 0.00848303 - time (sec): 19.11 - samples/sec: 2639.15 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 13:00:35,271 epoch 7 - iter 385/773 - loss 0.00832052 - time (sec): 23.72 - samples/sec: 2617.13 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 13:00:39,832 epoch 7 - iter 462/773 - loss 0.00885479 - time (sec): 28.29 - samples/sec: 2634.34 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 13:00:44,324 epoch 7 - iter 539/773 - loss 0.00964508 - time (sec): 32.78 - samples/sec: 2650.42 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 13:00:49,078 epoch 7 - iter 616/773 - loss 0.00959482 - time (sec): 37.53 - samples/sec: 2638.35 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 13:00:53,873 epoch 7 - iter 693/773 - loss 0.00983066 - time (sec): 42.33 - samples/sec: 2657.55 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 13:00:58,356 epoch 7 - iter 770/773 - loss 0.00990440 - time (sec): 46.81 - samples/sec: 2648.46 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 13:00:58,529 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-25 13:00:58,529 EPOCH 7 done: loss 0.0099 - lr: 0.000010
177
+ 2023-10-25 13:01:01,014 DEV : loss 0.11133752763271332 - f1-score (micro avg) 0.7647
178
+ 2023-10-25 13:01:01,031 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-25 13:01:05,488 epoch 8 - iter 77/773 - loss 0.00796583 - time (sec): 4.46 - samples/sec: 2791.97 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 13:01:09,862 epoch 8 - iter 154/773 - loss 0.00803247 - time (sec): 8.83 - samples/sec: 2753.71 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 13:01:14,395 epoch 8 - iter 231/773 - loss 0.00591916 - time (sec): 13.36 - samples/sec: 2744.01 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 13:01:18,853 epoch 8 - iter 308/773 - loss 0.00604710 - time (sec): 17.82 - samples/sec: 2757.60 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 13:01:23,366 epoch 8 - iter 385/773 - loss 0.00748894 - time (sec): 22.33 - samples/sec: 2758.83 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 13:01:27,828 epoch 8 - iter 462/773 - loss 0.00792268 - time (sec): 26.80 - samples/sec: 2747.09 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 13:01:32,471 epoch 8 - iter 539/773 - loss 0.00729680 - time (sec): 31.44 - samples/sec: 2787.77 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 13:01:37,083 epoch 8 - iter 616/773 - loss 0.00697704 - time (sec): 36.05 - samples/sec: 2774.52 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 13:01:41,417 epoch 8 - iter 693/773 - loss 0.00685826 - time (sec): 40.38 - samples/sec: 2764.31 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 13:01:45,884 epoch 8 - iter 770/773 - loss 0.00645588 - time (sec): 44.85 - samples/sec: 2759.56 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 13:01:46,056 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 13:01:46,056 EPOCH 8 done: loss 0.0065 - lr: 0.000007
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+ 2023-10-25 13:01:48,732 DEV : loss 0.11849800497293472 - f1-score (micro avg) 0.7757
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+ 2023-10-25 13:01:48,749 saving best model
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+ 2023-10-25 13:01:49,470 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-25 13:01:53,926 epoch 9 - iter 77/773 - loss 0.00385767 - time (sec): 4.45 - samples/sec: 2952.48 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 13:01:58,418 epoch 9 - iter 154/773 - loss 0.00336774 - time (sec): 8.95 - samples/sec: 2784.84 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 13:02:02,980 epoch 9 - iter 231/773 - loss 0.00334359 - time (sec): 13.51 - samples/sec: 2796.57 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 13:02:07,317 epoch 9 - iter 308/773 - loss 0.00425217 - time (sec): 17.84 - samples/sec: 2778.14 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 13:02:11,731 epoch 9 - iter 385/773 - loss 0.00409554 - time (sec): 22.26 - samples/sec: 2785.82 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 13:02:16,194 epoch 9 - iter 462/773 - loss 0.00412034 - time (sec): 26.72 - samples/sec: 2794.35 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-25 13:02:20,762 epoch 9 - iter 539/773 - loss 0.00422574 - time (sec): 31.29 - samples/sec: 2794.67 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 13:02:25,322 epoch 9 - iter 616/773 - loss 0.00422062 - time (sec): 35.85 - samples/sec: 2797.81 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 13:02:29,701 epoch 9 - iter 693/773 - loss 0.00418772 - time (sec): 40.23 - samples/sec: 2793.58 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 13:02:33,944 epoch 9 - iter 770/773 - loss 0.00459974 - time (sec): 44.47 - samples/sec: 2787.88 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 13:02:34,101 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-25 13:02:34,101 EPOCH 9 done: loss 0.0046 - lr: 0.000003
206
+ 2023-10-25 13:02:36,714 DEV : loss 0.12025374174118042 - f1-score (micro avg) 0.7708
207
+ 2023-10-25 13:02:36,733 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-25 13:02:41,339 epoch 10 - iter 77/773 - loss 0.00335894 - time (sec): 4.60 - samples/sec: 2609.85 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-25 13:02:46,249 epoch 10 - iter 154/773 - loss 0.00503572 - time (sec): 9.51 - samples/sec: 2606.07 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-25 13:02:50,911 epoch 10 - iter 231/773 - loss 0.00379034 - time (sec): 14.18 - samples/sec: 2543.26 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-25 13:02:55,569 epoch 10 - iter 308/773 - loss 0.00329299 - time (sec): 18.83 - samples/sec: 2543.49 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-25 13:03:00,225 epoch 10 - iter 385/773 - loss 0.00325492 - time (sec): 23.49 - samples/sec: 2568.46 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-25 13:03:04,754 epoch 10 - iter 462/773 - loss 0.00345791 - time (sec): 28.02 - samples/sec: 2594.25 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-25 13:03:09,480 epoch 10 - iter 539/773 - loss 0.00316405 - time (sec): 32.75 - samples/sec: 2628.26 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-25 13:03:14,068 epoch 10 - iter 616/773 - loss 0.00348066 - time (sec): 37.33 - samples/sec: 2653.42 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-25 13:03:18,559 epoch 10 - iter 693/773 - loss 0.00321585 - time (sec): 41.83 - samples/sec: 2669.67 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-25 13:03:22,933 epoch 10 - iter 770/773 - loss 0.00290477 - time (sec): 46.20 - samples/sec: 2682.83 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-25 13:03:23,088 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-25 13:03:23,089 EPOCH 10 done: loss 0.0029 - lr: 0.000000
220
+ 2023-10-25 13:03:26,447 DEV : loss 0.12326761335134506 - f1-score (micro avg) 0.7702
221
+ 2023-10-25 13:03:26,922 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-25 13:03:26,923 Loading model from best epoch ...
223
+ 2023-10-25 13:03:28,638 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
224
+ 2023-10-25 13:03:37,408
225
+ Results:
226
+ - F-score (micro) 0.7792
227
+ - F-score (macro) 0.6761
228
+ - Accuracy 0.6601
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ LOC 0.8471 0.8256 0.8362 946
234
+ BUILDING 0.5414 0.4595 0.4971 185
235
+ STREET 0.6613 0.7321 0.6949 56
236
+
237
+ micro avg 0.7949 0.7641 0.7792 1187
238
+ macro avg 0.6833 0.6724 0.6761 1187
239
+ weighted avg 0.7907 0.7641 0.7767 1187
240
+
241
+ 2023-10-25 13:03:37,408 ----------------------------------------------------------------------------------------------------