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hmbench-newseye-sv-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
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sequence-tagger-model
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+ 2023-10-25 21:22:31,902 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,903 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 21:22:31,903 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,903 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 21:22:31,903 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,903 Train: 1085 sentences
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+ 2023-10-25 21:22:31,903 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 21:22:31,903 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,903 Training Params:
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+ 2023-10-25 21:22:31,903 - learning_rate: "5e-05"
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+ 2023-10-25 21:22:31,903 - mini_batch_size: "8"
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+ 2023-10-25 21:22:31,903 - max_epochs: "10"
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+ 2023-10-25 21:22:31,903 - shuffle: "True"
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+ 2023-10-25 21:22:31,903 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 Plugins:
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+ 2023-10-25 21:22:31,904 - TensorboardLogger
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+ 2023-10-25 21:22:31,904 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 21:22:31,904 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 21:22:31,904 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 21:22:31,904 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 Computation:
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+ 2023-10-25 21:22:31,904 - compute on device: cuda:0
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+ 2023-10-25 21:22:31,904 - embedding storage: none
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+ 2023-10-25 21:22:31,904 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 Model training base path: "hmbench-newseye/sv-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-25 21:22:31,904 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:31,904 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 21:22:32,963 epoch 1 - iter 13/136 - loss 3.09896681 - time (sec): 1.06 - samples/sec: 4971.59 - lr: 0.000004 - momentum: 0.000000
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+ 2023-10-25 21:22:33,851 epoch 1 - iter 26/136 - loss 2.47193972 - time (sec): 1.95 - samples/sec: 5448.35 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:22:34,739 epoch 1 - iter 39/136 - loss 1.97440985 - time (sec): 2.83 - samples/sec: 5365.69 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:22:35,727 epoch 1 - iter 52/136 - loss 1.60287064 - time (sec): 3.82 - samples/sec: 5263.45 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:22:36,687 epoch 1 - iter 65/136 - loss 1.38982177 - time (sec): 4.78 - samples/sec: 5065.73 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:22:37,736 epoch 1 - iter 78/136 - loss 1.19122238 - time (sec): 5.83 - samples/sec: 5137.90 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:22:38,910 epoch 1 - iter 91/136 - loss 1.05383990 - time (sec): 7.01 - samples/sec: 5024.46 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-25 21:22:39,818 epoch 1 - iter 104/136 - loss 0.96075020 - time (sec): 7.91 - samples/sec: 5029.37 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-25 21:22:40,747 epoch 1 - iter 117/136 - loss 0.88175036 - time (sec): 8.84 - samples/sec: 5027.46 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-25 21:22:41,808 epoch 1 - iter 130/136 - loss 0.81359821 - time (sec): 9.90 - samples/sec: 5032.99 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-25 21:22:42,244 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:42,244 EPOCH 1 done: loss 0.7861 - lr: 0.000047
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+ 2023-10-25 21:22:43,416 DEV : loss 0.1567286252975464 - f1-score (micro avg) 0.6667
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+ 2023-10-25 21:22:43,422 saving best model
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+ 2023-10-25 21:22:43,922 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:44,828 epoch 2 - iter 13/136 - loss 0.15479314 - time (sec): 0.90 - samples/sec: 5122.73 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-25 21:22:45,884 epoch 2 - iter 26/136 - loss 0.16353171 - time (sec): 1.96 - samples/sec: 5090.38 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-25 21:22:46,917 epoch 2 - iter 39/136 - loss 0.17834152 - time (sec): 2.99 - samples/sec: 4770.56 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-25 21:22:47,974 epoch 2 - iter 52/136 - loss 0.18487150 - time (sec): 4.05 - samples/sec: 4971.38 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-25 21:22:49,007 epoch 2 - iter 65/136 - loss 0.17806542 - time (sec): 5.08 - samples/sec: 5028.22 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-25 21:22:49,975 epoch 2 - iter 78/136 - loss 0.18173881 - time (sec): 6.05 - samples/sec: 4984.22 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-25 21:22:51,088 epoch 2 - iter 91/136 - loss 0.17775518 - time (sec): 7.16 - samples/sec: 5019.83 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-25 21:22:52,125 epoch 2 - iter 104/136 - loss 0.17122320 - time (sec): 8.20 - samples/sec: 5069.36 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-25 21:22:53,011 epoch 2 - iter 117/136 - loss 0.16735079 - time (sec): 9.09 - samples/sec: 5044.82 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-25 21:22:53,975 epoch 2 - iter 130/136 - loss 0.17094941 - time (sec): 10.05 - samples/sec: 5012.09 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-25 21:22:54,364 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:54,365 EPOCH 2 done: loss 0.1728 - lr: 0.000045
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+ 2023-10-25 21:22:55,592 DEV : loss 0.1621260643005371 - f1-score (micro avg) 0.6223
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+ 2023-10-25 21:22:55,599 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:22:56,591 epoch 3 - iter 13/136 - loss 0.20659280 - time (sec): 0.99 - samples/sec: 4997.92 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-25 21:22:57,499 epoch 3 - iter 26/136 - loss 0.16584398 - time (sec): 1.90 - samples/sec: 5368.53 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-25 21:22:58,542 epoch 3 - iter 39/136 - loss 0.15400013 - time (sec): 2.94 - samples/sec: 5170.35 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-25 21:22:59,533 epoch 3 - iter 52/136 - loss 0.13409050 - time (sec): 3.93 - samples/sec: 5240.89 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-25 21:23:00,543 epoch 3 - iter 65/136 - loss 0.12610680 - time (sec): 4.94 - samples/sec: 5222.21 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-25 21:23:01,529 epoch 3 - iter 78/136 - loss 0.11908215 - time (sec): 5.93 - samples/sec: 5167.99 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-25 21:23:02,462 epoch 3 - iter 91/136 - loss 0.11495720 - time (sec): 6.86 - samples/sec: 5073.98 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-25 21:23:03,550 epoch 3 - iter 104/136 - loss 0.11135219 - time (sec): 7.95 - samples/sec: 5030.75 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-25 21:23:04,600 epoch 3 - iter 117/136 - loss 0.11206355 - time (sec): 9.00 - samples/sec: 5011.62 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-25 21:23:05,486 epoch 3 - iter 130/136 - loss 0.10930128 - time (sec): 9.89 - samples/sec: 4995.03 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-25 21:23:05,953 ----------------------------------------------------------------------------------------------------
118
+ 2023-10-25 21:23:05,954 EPOCH 3 done: loss 0.1076 - lr: 0.000039
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+ 2023-10-25 21:23:07,353 DEV : loss 0.09914213418960571 - f1-score (micro avg) 0.7544
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+ 2023-10-25 21:23:07,358 saving best model
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+ 2023-10-25 21:23:08,006 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:23:08,960 epoch 4 - iter 13/136 - loss 0.04799237 - time (sec): 0.95 - samples/sec: 5326.71 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-25 21:23:09,864 epoch 4 - iter 26/136 - loss 0.05744887 - time (sec): 1.86 - samples/sec: 5258.12 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-25 21:23:10,956 epoch 4 - iter 39/136 - loss 0.05011840 - time (sec): 2.95 - samples/sec: 5364.96 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-25 21:23:11,915 epoch 4 - iter 52/136 - loss 0.06335648 - time (sec): 3.91 - samples/sec: 5281.09 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-25 21:23:12,949 epoch 4 - iter 65/136 - loss 0.05707390 - time (sec): 4.94 - samples/sec: 5229.29 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-25 21:23:13,947 epoch 4 - iter 78/136 - loss 0.05712659 - time (sec): 5.94 - samples/sec: 5146.40 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-25 21:23:14,885 epoch 4 - iter 91/136 - loss 0.06298637 - time (sec): 6.88 - samples/sec: 5192.18 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-25 21:23:15,832 epoch 4 - iter 104/136 - loss 0.05884088 - time (sec): 7.82 - samples/sec: 5184.34 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-25 21:23:16,753 epoch 4 - iter 117/136 - loss 0.05907822 - time (sec): 8.74 - samples/sec: 5180.77 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-25 21:23:17,753 epoch 4 - iter 130/136 - loss 0.05836541 - time (sec): 9.74 - samples/sec: 5144.96 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-25 21:23:18,168 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-25 21:23:18,169 EPOCH 4 done: loss 0.0574 - lr: 0.000034
134
+ 2023-10-25 21:23:19,439 DEV : loss 0.11081506311893463 - f1-score (micro avg) 0.7933
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+ 2023-10-25 21:23:19,445 saving best model
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+ 2023-10-25 21:23:20,096 ----------------------------------------------------------------------------------------------------
137
+ 2023-10-25 21:23:21,121 epoch 5 - iter 13/136 - loss 0.02247676 - time (sec): 1.02 - samples/sec: 4833.41 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-25 21:23:22,043 epoch 5 - iter 26/136 - loss 0.02563925 - time (sec): 1.95 - samples/sec: 4735.65 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-25 21:23:23,049 epoch 5 - iter 39/136 - loss 0.03505350 - time (sec): 2.95 - samples/sec: 4789.08 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-25 21:23:24,069 epoch 5 - iter 52/136 - loss 0.03318240 - time (sec): 3.97 - samples/sec: 4883.85 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-25 21:23:24,957 epoch 5 - iter 65/136 - loss 0.03056275 - time (sec): 4.86 - samples/sec: 4755.76 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-25 21:23:25,951 epoch 5 - iter 78/136 - loss 0.03285871 - time (sec): 5.85 - samples/sec: 4800.79 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 21:23:27,006 epoch 5 - iter 91/136 - loss 0.03506467 - time (sec): 6.91 - samples/sec: 4895.08 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 21:23:27,942 epoch 5 - iter 104/136 - loss 0.03246624 - time (sec): 7.84 - samples/sec: 4940.57 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:23:28,929 epoch 5 - iter 117/136 - loss 0.03207926 - time (sec): 8.83 - samples/sec: 4980.09 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:23:30,038 epoch 5 - iter 130/136 - loss 0.03457919 - time (sec): 9.94 - samples/sec: 5007.25 - lr: 0.000028 - momentum: 0.000000
147
+ 2023-10-25 21:23:30,479 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-25 21:23:30,479 EPOCH 5 done: loss 0.0341 - lr: 0.000028
149
+ 2023-10-25 21:23:31,951 DEV : loss 0.11888163536787033 - f1-score (micro avg) 0.7949
150
+ 2023-10-25 21:23:31,956 saving best model
151
+ 2023-10-25 21:23:32,607 ----------------------------------------------------------------------------------------------------
152
+ 2023-10-25 21:23:33,712 epoch 6 - iter 13/136 - loss 0.02123620 - time (sec): 1.10 - samples/sec: 4702.84 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:23:34,631 epoch 6 - iter 26/136 - loss 0.01830822 - time (sec): 2.02 - samples/sec: 5115.63 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:23:35,614 epoch 6 - iter 39/136 - loss 0.02156613 - time (sec): 3.00 - samples/sec: 4949.99 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:23:36,746 epoch 6 - iter 52/136 - loss 0.02165435 - time (sec): 4.14 - samples/sec: 4761.05 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:23:37,721 epoch 6 - iter 65/136 - loss 0.01848345 - time (sec): 5.11 - samples/sec: 4801.07 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:23:38,751 epoch 6 - iter 78/136 - loss 0.01947350 - time (sec): 6.14 - samples/sec: 4893.75 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:23:39,686 epoch 6 - iter 91/136 - loss 0.02372842 - time (sec): 7.08 - samples/sec: 4928.04 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:23:40,697 epoch 6 - iter 104/136 - loss 0.02320998 - time (sec): 8.09 - samples/sec: 4912.87 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:23:41,673 epoch 6 - iter 117/136 - loss 0.02372198 - time (sec): 9.06 - samples/sec: 4909.61 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:23:42,638 epoch 6 - iter 130/136 - loss 0.02408145 - time (sec): 10.03 - samples/sec: 4908.33 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:23:43,073 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-25 21:23:43,074 EPOCH 6 done: loss 0.0240 - lr: 0.000023
164
+ 2023-10-25 21:23:44,386 DEV : loss 0.13894939422607422 - f1-score (micro avg) 0.792
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+ 2023-10-25 21:23:44,392 ----------------------------------------------------------------------------------------------------
166
+ 2023-10-25 21:23:45,351 epoch 7 - iter 13/136 - loss 0.01023683 - time (sec): 0.96 - samples/sec: 5393.57 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:23:46,420 epoch 7 - iter 26/136 - loss 0.00988703 - time (sec): 2.03 - samples/sec: 5056.61 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:23:47,430 epoch 7 - iter 39/136 - loss 0.00855606 - time (sec): 3.04 - samples/sec: 5059.97 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:23:48,440 epoch 7 - iter 52/136 - loss 0.00902452 - time (sec): 4.05 - samples/sec: 5093.50 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:23:49,587 epoch 7 - iter 65/136 - loss 0.01254680 - time (sec): 5.19 - samples/sec: 4986.34 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:23:50,586 epoch 7 - iter 78/136 - loss 0.01308189 - time (sec): 6.19 - samples/sec: 4947.44 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:23:51,687 epoch 7 - iter 91/136 - loss 0.01516775 - time (sec): 7.29 - samples/sec: 4887.76 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:23:52,629 epoch 7 - iter 104/136 - loss 0.01675394 - time (sec): 8.24 - samples/sec: 4928.48 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:23:53,581 epoch 7 - iter 117/136 - loss 0.01755872 - time (sec): 9.19 - samples/sec: 4896.27 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:23:54,563 epoch 7 - iter 130/136 - loss 0.01838104 - time (sec): 10.17 - samples/sec: 4906.70 - lr: 0.000017 - momentum: 0.000000
176
+ 2023-10-25 21:23:55,045 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-25 21:23:55,046 EPOCH 7 done: loss 0.0180 - lr: 0.000017
178
+ 2023-10-25 21:23:56,352 DEV : loss 0.14390961825847626 - f1-score (micro avg) 0.8007
179
+ 2023-10-25 21:23:56,359 saving best model
180
+ 2023-10-25 21:23:57,482 ----------------------------------------------------------------------------------------------------
181
+ 2023-10-25 21:23:58,480 epoch 8 - iter 13/136 - loss 0.01707130 - time (sec): 1.00 - samples/sec: 5902.42 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:23:59,424 epoch 8 - iter 26/136 - loss 0.01715658 - time (sec): 1.94 - samples/sec: 5637.99 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:24:00,502 epoch 8 - iter 39/136 - loss 0.01607778 - time (sec): 3.02 - samples/sec: 5407.40 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:24:01,409 epoch 8 - iter 52/136 - loss 0.01464815 - time (sec): 3.92 - samples/sec: 5338.78 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:24:02,478 epoch 8 - iter 65/136 - loss 0.01481962 - time (sec): 4.99 - samples/sec: 5222.95 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:24:03,465 epoch 8 - iter 78/136 - loss 0.01416660 - time (sec): 5.98 - samples/sec: 5196.59 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:24:04,492 epoch 8 - iter 91/136 - loss 0.01363303 - time (sec): 7.01 - samples/sec: 5146.20 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:24:05,474 epoch 8 - iter 104/136 - loss 0.01327422 - time (sec): 7.99 - samples/sec: 5138.33 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:24:06,500 epoch 8 - iter 117/136 - loss 0.01229904 - time (sec): 9.02 - samples/sec: 5073.80 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:24:07,552 epoch 8 - iter 130/136 - loss 0.01172602 - time (sec): 10.07 - samples/sec: 4985.49 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:24:07,981 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:24:07,981 EPOCH 8 done: loss 0.0120 - lr: 0.000012
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+ 2023-10-25 21:24:09,205 DEV : loss 0.14648671448230743 - f1-score (micro avg) 0.8059
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+ 2023-10-25 21:24:09,212 saving best model
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+ 2023-10-25 21:24:09,882 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:24:10,933 epoch 9 - iter 13/136 - loss 0.00211235 - time (sec): 1.05 - samples/sec: 4725.27 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:24:11,954 epoch 9 - iter 26/136 - loss 0.00666773 - time (sec): 2.07 - samples/sec: 5156.50 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 21:24:12,849 epoch 9 - iter 39/136 - loss 0.00638834 - time (sec): 2.96 - samples/sec: 5192.18 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 21:24:13,723 epoch 9 - iter 52/136 - loss 0.00686059 - time (sec): 3.84 - samples/sec: 5093.48 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:24:14,702 epoch 9 - iter 65/136 - loss 0.00731458 - time (sec): 4.82 - samples/sec: 5200.74 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:24:15,617 epoch 9 - iter 78/136 - loss 0.00764700 - time (sec): 5.73 - samples/sec: 5173.20 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:24:16,706 epoch 9 - iter 91/136 - loss 0.00708190 - time (sec): 6.82 - samples/sec: 5227.13 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:24:17,600 epoch 9 - iter 104/136 - loss 0.00681427 - time (sec): 7.71 - samples/sec: 5166.31 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:24:18,567 epoch 9 - iter 117/136 - loss 0.00709413 - time (sec): 8.68 - samples/sec: 5121.21 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:24:19,601 epoch 9 - iter 130/136 - loss 0.00726556 - time (sec): 9.72 - samples/sec: 5093.54 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:24:20,105 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:24:20,105 EPOCH 9 done: loss 0.0075 - lr: 0.000006
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+ 2023-10-25 21:24:21,277 DEV : loss 0.14979466795921326 - f1-score (micro avg) 0.8222
209
+ 2023-10-25 21:24:21,284 saving best model
210
+ 2023-10-25 21:24:21,932 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-25 21:24:22,991 epoch 10 - iter 13/136 - loss 0.00573592 - time (sec): 1.06 - samples/sec: 4604.37 - lr: 0.000005 - momentum: 0.000000
212
+ 2023-10-25 21:24:24,354 epoch 10 - iter 26/136 - loss 0.00771814 - time (sec): 2.42 - samples/sec: 4054.98 - lr: 0.000005 - momentum: 0.000000
213
+ 2023-10-25 21:24:25,410 epoch 10 - iter 39/136 - loss 0.00673013 - time (sec): 3.48 - samples/sec: 4301.76 - lr: 0.000004 - momentum: 0.000000
214
+ 2023-10-25 21:24:26,449 epoch 10 - iter 52/136 - loss 0.00561897 - time (sec): 4.51 - samples/sec: 4588.30 - lr: 0.000004 - momentum: 0.000000
215
+ 2023-10-25 21:24:27,420 epoch 10 - iter 65/136 - loss 0.00554251 - time (sec): 5.49 - samples/sec: 4623.66 - lr: 0.000003 - momentum: 0.000000
216
+ 2023-10-25 21:24:28,326 epoch 10 - iter 78/136 - loss 0.00502923 - time (sec): 6.39 - samples/sec: 4684.23 - lr: 0.000003 - momentum: 0.000000
217
+ 2023-10-25 21:24:29,339 epoch 10 - iter 91/136 - loss 0.00506657 - time (sec): 7.40 - samples/sec: 4781.52 - lr: 0.000002 - momentum: 0.000000
218
+ 2023-10-25 21:24:30,377 epoch 10 - iter 104/136 - loss 0.00484601 - time (sec): 8.44 - samples/sec: 4779.86 - lr: 0.000002 - momentum: 0.000000
219
+ 2023-10-25 21:24:31,227 epoch 10 - iter 117/136 - loss 0.00515945 - time (sec): 9.29 - samples/sec: 4795.92 - lr: 0.000001 - momentum: 0.000000
220
+ 2023-10-25 21:24:32,261 epoch 10 - iter 130/136 - loss 0.00489036 - time (sec): 10.33 - samples/sec: 4816.89 - lr: 0.000000 - momentum: 0.000000
221
+ 2023-10-25 21:24:32,674 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-25 21:24:32,674 EPOCH 10 done: loss 0.0052 - lr: 0.000000
223
+ 2023-10-25 21:24:33,806 DEV : loss 0.15492050349712372 - f1-score (micro avg) 0.8177
224
+ 2023-10-25 21:24:34,274 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-25 21:24:34,276 Loading model from best epoch ...
226
+ 2023-10-25 21:24:36,160 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 21:24:38,129
228
+ Results:
229
+ - F-score (micro) 0.7872
230
+ - F-score (macro) 0.739
231
+ - Accuracy 0.6627
232
+
233
+ By class:
234
+ precision recall f1-score support
235
+
236
+ LOC 0.8081 0.8910 0.8476 312
237
+ PER 0.6716 0.8654 0.7563 208
238
+ ORG 0.5714 0.4364 0.4948 55
239
+ HumanProd 0.7778 0.9545 0.8571 22
240
+
241
+ micro avg 0.7386 0.8425 0.7872 597
242
+ macro avg 0.7072 0.7868 0.7390 597
243
+ weighted avg 0.7377 0.8425 0.7836 597
244
+
245
+ 2023-10-25 21:24:38,129 ----------------------------------------------------------------------------------------------------