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hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
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sequence-tagger-model
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+ 2023-10-25 21:20:06,302 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,303 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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-25 21:20:06,303 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,303 MultiCorpus: 1085 train + 148 dev + 364 test sentences
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+ - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator
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+ 2023-10-25 21:20:06,303 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,303 Train: 1085 sentences
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+ 2023-10-25 21:20:06,303 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 21:20:06,303 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,303 Training Params:
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+ 2023-10-25 21:20:06,303 - learning_rate: "3e-05"
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+ 2023-10-25 21:20:06,303 - mini_batch_size: "8"
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+ 2023-10-25 21:20:06,304 - max_epochs: "10"
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+ 2023-10-25 21:20:06,304 - shuffle: "True"
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 Plugins:
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+ 2023-10-25 21:20:06,304 - TensorboardLogger
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+ 2023-10-25 21:20:06,304 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 21:20:06,304 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 Computation:
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+ 2023-10-25 21:20:06,304 - compute on device: cuda:0
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+ 2023-10-25 21:20:06,304 - embedding storage: none
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 Model training base path: "hmbench-newseye/sv-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:06,304 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 21:20:07,355 epoch 1 - iter 13/136 - loss 3.19537858 - time (sec): 1.05 - samples/sec: 5006.81 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 21:20:08,259 epoch 1 - iter 26/136 - loss 2.76016229 - time (sec): 1.95 - samples/sec: 5426.73 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:20:09,146 epoch 1 - iter 39/136 - loss 2.31898621 - time (sec): 2.84 - samples/sec: 5352.27 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:20:10,159 epoch 1 - iter 52/136 - loss 1.87763258 - time (sec): 3.85 - samples/sec: 5221.03 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:20:11,143 epoch 1 - iter 65/136 - loss 1.62815715 - time (sec): 4.84 - samples/sec: 5007.79 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:20:12,209 epoch 1 - iter 78/136 - loss 1.39999778 - time (sec): 5.90 - samples/sec: 5074.14 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 21:20:13,409 epoch 1 - iter 91/136 - loss 1.24281698 - time (sec): 7.10 - samples/sec: 4954.26 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:20:14,325 epoch 1 - iter 104/136 - loss 1.13361333 - time (sec): 8.02 - samples/sec: 4962.06 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:15,286 epoch 1 - iter 117/136 - loss 1.04110203 - time (sec): 8.98 - samples/sec: 4950.04 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:16,373 epoch 1 - iter 130/136 - loss 0.96009861 - time (sec): 10.07 - samples/sec: 4950.16 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:16,839 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:16,839 EPOCH 1 done: loss 0.9272 - lr: 0.000028
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+ 2023-10-25 21:20:17,482 DEV : loss 0.16158084571361542 - f1-score (micro avg) 0.6429
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+ 2023-10-25 21:20:17,488 saving best model
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+ 2023-10-25 21:20:17,990 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:18,887 epoch 2 - iter 13/136 - loss 0.14478029 - time (sec): 0.90 - samples/sec: 5170.78 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 21:20:20,273 epoch 2 - iter 26/136 - loss 0.13903227 - time (sec): 2.28 - samples/sec: 4372.66 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:21,307 epoch 2 - iter 39/136 - loss 0.14383107 - time (sec): 3.32 - samples/sec: 4307.00 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:22,354 epoch 2 - iter 52/136 - loss 0.14741814 - time (sec): 4.36 - samples/sec: 4615.83 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:23,357 epoch 2 - iter 65/136 - loss 0.14244389 - time (sec): 5.37 - samples/sec: 4763.95 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:24,327 epoch 2 - iter 78/136 - loss 0.14786401 - time (sec): 6.34 - samples/sec: 4760.82 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:25,424 epoch 2 - iter 91/136 - loss 0.14575958 - time (sec): 7.43 - samples/sec: 4838.69 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:26,475 epoch 2 - iter 104/136 - loss 0.14340023 - time (sec): 8.48 - samples/sec: 4900.45 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:27,349 epoch 2 - iter 117/136 - loss 0.14202521 - time (sec): 9.36 - samples/sec: 4899.16 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:28,296 epoch 2 - iter 130/136 - loss 0.14295048 - time (sec): 10.31 - samples/sec: 4888.77 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:28,683 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:28,683 EPOCH 2 done: loss 0.1434 - lr: 0.000027
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+ 2023-10-25 21:20:29,924 DEV : loss 0.09990036487579346 - f1-score (micro avg) 0.7468
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+ 2023-10-25 21:20:29,930 saving best model
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+ 2023-10-25 21:20:30,644 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:31,648 epoch 3 - iter 13/136 - loss 0.11960243 - time (sec): 1.00 - samples/sec: 4942.09 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:32,586 epoch 3 - iter 26/136 - loss 0.09759495 - time (sec): 1.94 - samples/sec: 5254.25 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:33,650 epoch 3 - iter 39/136 - loss 0.08584252 - time (sec): 3.00 - samples/sec: 5065.02 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:34,640 epoch 3 - iter 52/136 - loss 0.08045591 - time (sec): 3.99 - samples/sec: 5160.60 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:35,645 epoch 3 - iter 65/136 - loss 0.07965884 - time (sec): 5.00 - samples/sec: 5163.72 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:36,647 epoch 3 - iter 78/136 - loss 0.07654363 - time (sec): 6.00 - samples/sec: 5105.24 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:37,586 epoch 3 - iter 91/136 - loss 0.07736097 - time (sec): 6.94 - samples/sec: 5017.45 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:38,660 epoch 3 - iter 104/136 - loss 0.07706710 - time (sec): 8.01 - samples/sec: 4990.12 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:39,700 epoch 3 - iter 117/136 - loss 0.07804906 - time (sec): 9.05 - samples/sec: 4981.63 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:40,611 epoch 3 - iter 130/136 - loss 0.07904440 - time (sec): 9.97 - samples/sec: 4955.02 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:41,066 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-25 21:20:41,066 EPOCH 3 done: loss 0.0780 - lr: 0.000024
120
+ 2023-10-25 21:20:42,197 DEV : loss 0.10422874242067337 - f1-score (micro avg) 0.7585
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+ 2023-10-25 21:20:42,203 saving best model
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+ 2023-10-25 21:20:42,876 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:44,128 epoch 4 - iter 13/136 - loss 0.04067450 - time (sec): 1.25 - samples/sec: 4058.14 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:45,027 epoch 4 - iter 26/136 - loss 0.05076903 - time (sec): 2.15 - samples/sec: 4540.53 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:46,128 epoch 4 - iter 39/136 - loss 0.04630433 - time (sec): 3.25 - samples/sec: 4867.37 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:47,087 epoch 4 - iter 52/136 - loss 0.04968443 - time (sec): 4.21 - samples/sec: 4902.47 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:48,116 epoch 4 - iter 65/136 - loss 0.04696549 - time (sec): 5.24 - samples/sec: 4932.60 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:49,083 epoch 4 - iter 78/136 - loss 0.04539194 - time (sec): 6.21 - samples/sec: 4925.62 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:50,011 epoch 4 - iter 91/136 - loss 0.04798111 - time (sec): 7.13 - samples/sec: 5005.83 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:50,929 epoch 4 - iter 104/136 - loss 0.04633553 - time (sec): 8.05 - samples/sec: 5037.74 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:51,856 epoch 4 - iter 117/136 - loss 0.04647949 - time (sec): 8.98 - samples/sec: 5046.46 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:52,836 epoch 4 - iter 130/136 - loss 0.04697246 - time (sec): 9.96 - samples/sec: 5034.81 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:20:53,211 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-25 21:20:53,211 EPOCH 4 done: loss 0.0462 - lr: 0.000020
135
+ 2023-10-25 21:20:54,399 DEV : loss 0.10547740757465363 - f1-score (micro avg) 0.7802
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+ 2023-10-25 21:20:54,405 saving best model
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+ 2023-10-25 21:20:55,135 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:56,129 epoch 5 - iter 13/136 - loss 0.01663346 - time (sec): 0.99 - samples/sec: 5001.88 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:20:57,031 epoch 5 - iter 26/136 - loss 0.02629321 - time (sec): 1.89 - samples/sec: 4870.26 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:20:58,002 epoch 5 - iter 39/136 - loss 0.02794944 - time (sec): 2.86 - samples/sec: 4938.10 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:20:58,982 epoch 5 - iter 52/136 - loss 0.02661567 - time (sec): 3.84 - samples/sec: 5046.85 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:20:59,832 epoch 5 - iter 65/136 - loss 0.02489471 - time (sec): 4.69 - samples/sec: 4924.08 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:00,804 epoch 5 - iter 78/136 - loss 0.02726968 - time (sec): 5.66 - samples/sec: 4960.06 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:01,828 epoch 5 - iter 91/136 - loss 0.02863633 - time (sec): 6.69 - samples/sec: 5056.21 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:02,735 epoch 5 - iter 104/136 - loss 0.02720610 - time (sec): 7.60 - samples/sec: 5102.12 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:03,693 epoch 5 - iter 117/136 - loss 0.02758476 - time (sec): 8.55 - samples/sec: 5141.63 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 21:21:04,781 epoch 5 - iter 130/136 - loss 0.02952936 - time (sec): 9.64 - samples/sec: 5162.52 - lr: 0.000017 - momentum: 0.000000
148
+ 2023-10-25 21:21:05,187 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-25 21:21:05,187 EPOCH 5 done: loss 0.0292 - lr: 0.000017
150
+ 2023-10-25 21:21:06,376 DEV : loss 0.12244772911071777 - f1-score (micro avg) 0.7927
151
+ 2023-10-25 21:21:06,382 saving best model
152
+ 2023-10-25 21:21:07,096 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-25 21:21:08,438 epoch 6 - iter 13/136 - loss 0.01683556 - time (sec): 1.34 - samples/sec: 3876.93 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:09,366 epoch 6 - iter 26/136 - loss 0.01690694 - time (sec): 2.27 - samples/sec: 4564.47 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:10,317 epoch 6 - iter 39/136 - loss 0.01687007 - time (sec): 3.22 - samples/sec: 4624.06 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:11,401 epoch 6 - iter 52/136 - loss 0.01889602 - time (sec): 4.30 - samples/sec: 4578.36 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:12,369 epoch 6 - iter 65/136 - loss 0.01614339 - time (sec): 5.27 - samples/sec: 4657.62 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:13,359 epoch 6 - iter 78/136 - loss 0.01655885 - time (sec): 6.26 - samples/sec: 4801.57 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:14,288 epoch 6 - iter 91/136 - loss 0.01946751 - time (sec): 7.19 - samples/sec: 4851.68 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:15,295 epoch 6 - iter 104/136 - loss 0.01892046 - time (sec): 8.19 - samples/sec: 4848.33 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:16,230 epoch 6 - iter 117/136 - loss 0.01916421 - time (sec): 9.13 - samples/sec: 4873.62 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:17,157 epoch 6 - iter 130/136 - loss 0.01967563 - time (sec): 10.06 - samples/sec: 4894.41 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:17,580 ----------------------------------------------------------------------------------------------------
164
+ 2023-10-25 21:21:17,580 EPOCH 6 done: loss 0.0194 - lr: 0.000014
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+ 2023-10-25 21:21:18,716 DEV : loss 0.1296090930700302 - f1-score (micro avg) 0.7934
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+ 2023-10-25 21:21:18,722 saving best model
167
+ 2023-10-25 21:21:19,434 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-25 21:21:20,348 epoch 7 - iter 13/136 - loss 0.01080744 - time (sec): 0.91 - samples/sec: 5668.49 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:21:21,361 epoch 7 - iter 26/136 - loss 0.01106692 - time (sec): 1.92 - samples/sec: 5326.04 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:21:22,288 epoch 7 - iter 39/136 - loss 0.01073739 - time (sec): 2.85 - samples/sec: 5389.66 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:23,287 epoch 7 - iter 52/136 - loss 0.01029492 - time (sec): 3.85 - samples/sec: 5354.39 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:24,361 epoch 7 - iter 65/136 - loss 0.01122951 - time (sec): 4.92 - samples/sec: 5259.47 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:25,294 epoch 7 - iter 78/136 - loss 0.01059452 - time (sec): 5.86 - samples/sec: 5231.06 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:26,352 epoch 7 - iter 91/136 - loss 0.01186817 - time (sec): 6.92 - samples/sec: 5155.37 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:27,230 epoch 7 - iter 104/136 - loss 0.01209798 - time (sec): 7.79 - samples/sec: 5208.33 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:28,112 epoch 7 - iter 117/136 - loss 0.01394009 - time (sec): 8.68 - samples/sec: 5185.34 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:29,033 epoch 7 - iter 130/136 - loss 0.01372476 - time (sec): 9.60 - samples/sec: 5200.08 - lr: 0.000010 - momentum: 0.000000
178
+ 2023-10-25 21:21:29,485 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-25 21:21:29,486 EPOCH 7 done: loss 0.0136 - lr: 0.000010
180
+ 2023-10-25 21:21:30,725 DEV : loss 0.1454724222421646 - f1-score (micro avg) 0.792
181
+ 2023-10-25 21:21:30,732 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-25 21:21:32,074 epoch 8 - iter 13/136 - loss 0.01218010 - time (sec): 1.34 - samples/sec: 4384.56 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 21:21:33,025 epoch 8 - iter 26/136 - loss 0.01693779 - time (sec): 2.29 - samples/sec: 4772.46 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:21:34,093 epoch 8 - iter 39/136 - loss 0.01703926 - time (sec): 3.36 - samples/sec: 4856.76 - lr: 0.000009 - momentum: 0.000000
185
+ 2023-10-25 21:21:34,973 epoch 8 - iter 52/136 - loss 0.01542773 - time (sec): 4.24 - samples/sec: 4941.74 - lr: 0.000009 - momentum: 0.000000
186
+ 2023-10-25 21:21:35,998 epoch 8 - iter 65/136 - loss 0.01460635 - time (sec): 5.26 - samples/sec: 4954.47 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:21:36,935 epoch 8 - iter 78/136 - loss 0.01371676 - time (sec): 6.20 - samples/sec: 5011.86 - lr: 0.000008 - momentum: 0.000000
188
+ 2023-10-25 21:21:37,953 epoch 8 - iter 91/136 - loss 0.01319613 - time (sec): 7.22 - samples/sec: 4994.88 - lr: 0.000008 - momentum: 0.000000
189
+ 2023-10-25 21:21:38,901 epoch 8 - iter 104/136 - loss 0.01203454 - time (sec): 8.17 - samples/sec: 5027.05 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:21:39,909 epoch 8 - iter 117/136 - loss 0.01120187 - time (sec): 9.18 - samples/sec: 4985.48 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:21:40,947 epoch 8 - iter 130/136 - loss 0.01051438 - time (sec): 10.21 - samples/sec: 4914.41 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:21:41,391 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:21:41,392 EPOCH 8 done: loss 0.0106 - lr: 0.000007
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+ 2023-10-25 21:21:42,626 DEV : loss 0.1543840914964676 - f1-score (micro avg) 0.8059
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+ 2023-10-25 21:21:42,632 saving best model
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+ 2023-10-25 21:21:43,341 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:21:44,407 epoch 9 - iter 13/136 - loss 0.00445394 - time (sec): 1.06 - samples/sec: 4658.17 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:21:45,462 epoch 9 - iter 26/136 - loss 0.00733912 - time (sec): 2.12 - samples/sec: 5035.08 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:21:46,388 epoch 9 - iter 39/136 - loss 0.00679164 - time (sec): 3.05 - samples/sec: 5053.37 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:21:47,268 epoch 9 - iter 52/136 - loss 0.00759414 - time (sec): 3.93 - samples/sec: 4980.00 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:21:48,269 epoch 9 - iter 65/136 - loss 0.00752308 - time (sec): 4.93 - samples/sec: 5085.53 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 21:21:49,176 epoch 9 - iter 78/136 - loss 0.00768783 - time (sec): 5.83 - samples/sec: 5083.57 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 21:21:50,246 epoch 9 - iter 91/136 - loss 0.00732468 - time (sec): 6.90 - samples/sec: 5165.41 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 21:21:51,141 epoch 9 - iter 104/136 - loss 0.00684016 - time (sec): 7.80 - samples/sec: 5111.40 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 21:21:52,117 epoch 9 - iter 117/136 - loss 0.00700631 - time (sec): 8.77 - samples/sec: 5067.64 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 21:21:53,131 epoch 9 - iter 130/136 - loss 0.00730047 - time (sec): 9.79 - samples/sec: 5056.15 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 21:21:53,632 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:21:53,632 EPOCH 9 done: loss 0.0075 - lr: 0.000004
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+ 2023-10-25 21:21:54,814 DEV : loss 0.16468970477581024 - f1-score (micro avg) 0.8059
210
+ 2023-10-25 21:21:54,821 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-25 21:21:56,267 epoch 10 - iter 13/136 - loss 0.01036517 - time (sec): 1.44 - samples/sec: 3369.07 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 21:21:57,242 epoch 10 - iter 26/136 - loss 0.01231050 - time (sec): 2.42 - samples/sec: 4055.70 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 21:21:58,278 epoch 10 - iter 39/136 - loss 0.00954086 - time (sec): 3.45 - samples/sec: 4327.08 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 21:21:59,305 epoch 10 - iter 52/136 - loss 0.00847034 - time (sec): 4.48 - samples/sec: 4621.28 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-10-25 21:22:00,240 epoch 10 - iter 65/136 - loss 0.00800394 - time (sec): 5.42 - samples/sec: 4682.11 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-10-25 21:22:01,177 epoch 10 - iter 78/136 - loss 0.00757495 - time (sec): 6.35 - samples/sec: 4711.89 - lr: 0.000002 - momentum: 0.000000
217
+ 2023-10-25 21:22:02,192 epoch 10 - iter 91/136 - loss 0.00773310 - time (sec): 7.37 - samples/sec: 4804.46 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-10-25 21:22:03,206 epoch 10 - iter 104/136 - loss 0.00705573 - time (sec): 8.38 - samples/sec: 4813.82 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-10-25 21:22:04,042 epoch 10 - iter 117/136 - loss 0.00676122 - time (sec): 9.22 - samples/sec: 4834.45 - lr: 0.000001 - momentum: 0.000000
220
+ 2023-10-25 21:22:05,026 epoch 10 - iter 130/136 - loss 0.00658402 - time (sec): 10.20 - samples/sec: 4874.95 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-10-25 21:22:05,439 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-25 21:22:05,439 EPOCH 10 done: loss 0.0069 - lr: 0.000000
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+ 2023-10-25 21:22:06,638 DEV : loss 0.16538743674755096 - f1-score (micro avg) 0.7877
224
+ 2023-10-25 21:22:07,172 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-25 21:22:07,174 Loading model from best epoch ...
226
+ 2023-10-25 21:22:09,130 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG
227
+ 2023-10-25 21:22:11,237
228
+ Results:
229
+ - F-score (micro) 0.7892
230
+ - F-score (macro) 0.7524
231
+ - Accuracy 0.664
232
+
233
+ By class:
234
+ precision recall f1-score support
235
+
236
+ LOC 0.8201 0.8622 0.8406 312
237
+ PER 0.6973 0.8750 0.7761 208
238
+ ORG 0.5000 0.4545 0.4762 55
239
+ HumanProd 0.8462 1.0000 0.9167 22
240
+
241
+ micro avg 0.7489 0.8342 0.7892 597
242
+ macro avg 0.7159 0.7979 0.7524 597
243
+ weighted avg 0.7488 0.8342 0.7874 597
244
+
245
+ 2023-10-25 21:22:11,237 ----------------------------------------------------------------------------------------------------