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hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3

Token Classification
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sequence-tagger-model
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hmbench-newseye-fi-hmbert_tiny-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
File size: 23,876 Bytes 
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2023-10-19 23:49:44,654 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,654 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 128)
        (position_embeddings): Embedding(512, 128)
        (token_type_embeddings): Embedding(2, 128)
        (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-1): 2 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=128, out_features=128, bias=True)
                (key): Linear(in_features=128, out_features=128, bias=True)
                (value): Linear(in_features=128, out_features=128, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=128, out_features=128, bias=True)
                (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=128, out_features=512, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=512, out_features=128, bias=True)
              (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=128, out_features=128, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=128, out_features=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-19 23:49:44,654 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,654 MultiCorpus: 1166 train + 165 dev + 415 test sentences
 - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Train:  1166 sentences
2023-10-19 23:49:44,655         (train_with_dev=False, train_with_test=False)
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Training Params:
2023-10-19 23:49:44,655  - learning_rate: "3e-05" 
2023-10-19 23:49:44,655  - mini_batch_size: "4"
2023-10-19 23:49:44,655  - max_epochs: "10"
2023-10-19 23:49:44,655  - shuffle: "True"
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Plugins:
2023-10-19 23:49:44,655  - TensorboardLogger
2023-10-19 23:49:44,655  - LinearScheduler | warmup_fraction: '0.1'
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Final evaluation on model from best epoch (best-model.pt)
2023-10-19 23:49:44,655  - metric: "('micro avg', 'f1-score')"
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Computation:
2023-10-19 23:49:44,655  - compute on device: cuda:0
2023-10-19 23:49:44,655  - embedding storage: none
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Model training base path: "hmbench-newseye/fi-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:44,655 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-19 23:49:45,171 epoch 1 - iter 29/292 - loss 3.23366766 - time (sec): 0.52 - samples/sec: 9308.03 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:49:45,672 epoch 1 - iter 58/292 - loss 3.25275791 - time (sec): 1.02 - samples/sec: 8780.75 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:49:46,209 epoch 1 - iter 87/292 - loss 3.09522052 - time (sec): 1.55 - samples/sec: 8385.87 - lr: 0.000009 - momentum: 0.000000
2023-10-19 23:49:46,752 epoch 1 - iter 116/292 - loss 2.96850491 - time (sec): 2.10 - samples/sec: 8369.71 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:49:47,307 epoch 1 - iter 145/292 - loss 2.77725528 - time (sec): 2.65 - samples/sec: 8488.65 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:49:47,849 epoch 1 - iter 174/292 - loss 2.58029420 - time (sec): 3.19 - samples/sec: 8553.64 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:49:48,361 epoch 1 - iter 203/292 - loss 2.39676813 - time (sec): 3.70 - samples/sec: 8574.16 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:49:48,846 epoch 1 - iter 232/292 - loss 2.23157178 - time (sec): 4.19 - samples/sec: 8552.98 - lr: 0.000024 - momentum: 0.000000
2023-10-19 23:49:49,383 epoch 1 - iter 261/292 - loss 2.09117800 - time (sec): 4.73 - samples/sec: 8454.45 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:49:49,865 epoch 1 - iter 290/292 - loss 1.99194641 - time (sec): 5.21 - samples/sec: 8489.62 - lr: 0.000030 - momentum: 0.000000
2023-10-19 23:49:49,893 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:49,893 EPOCH 1 done: loss 1.9855 - lr: 0.000030
2023-10-19 23:49:50,154 DEV : loss 0.45264244079589844 - f1-score (micro avg)  0.0
2023-10-19 23:49:50,158 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:50,701 epoch 2 - iter 29/292 - loss 0.92771541 - time (sec): 0.54 - samples/sec: 9419.62 - lr: 0.000030 - momentum: 0.000000
2023-10-19 23:49:51,190 epoch 2 - iter 58/292 - loss 0.79135239 - time (sec): 1.03 - samples/sec: 8802.41 - lr: 0.000029 - momentum: 0.000000
2023-10-19 23:49:51,708 epoch 2 - iter 87/292 - loss 0.76416654 - time (sec): 1.55 - samples/sec: 8865.95 - lr: 0.000029 - momentum: 0.000000
2023-10-19 23:49:52,232 epoch 2 - iter 116/292 - loss 0.75800533 - time (sec): 2.07 - samples/sec: 8888.41 - lr: 0.000029 - momentum: 0.000000
2023-10-19 23:49:52,749 epoch 2 - iter 145/292 - loss 0.74324513 - time (sec): 2.59 - samples/sec: 8725.33 - lr: 0.000028 - momentum: 0.000000
2023-10-19 23:49:53,239 epoch 2 - iter 174/292 - loss 0.72235129 - time (sec): 3.08 - samples/sec: 8666.19 - lr: 0.000028 - momentum: 0.000000
2023-10-19 23:49:53,750 epoch 2 - iter 203/292 - loss 0.68411345 - time (sec): 3.59 - samples/sec: 8684.86 - lr: 0.000028 - momentum: 0.000000
2023-10-19 23:49:54,296 epoch 2 - iter 232/292 - loss 0.66363694 - time (sec): 4.14 - samples/sec: 8702.86 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:49:54,805 epoch 2 - iter 261/292 - loss 0.65749594 - time (sec): 4.65 - samples/sec: 8595.78 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:49:55,316 epoch 2 - iter 290/292 - loss 0.64381828 - time (sec): 5.16 - samples/sec: 8537.64 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:49:55,353 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:55,353 EPOCH 2 done: loss 0.6399 - lr: 0.000027
2023-10-19 23:49:55,987 DEV : loss 0.4109416902065277 - f1-score (micro avg)  0.0
2023-10-19 23:49:55,991 ----------------------------------------------------------------------------------------------------
2023-10-19 23:49:56,511 epoch 3 - iter 29/292 - loss 0.56836488 - time (sec): 0.52 - samples/sec: 8126.62 - lr: 0.000026 - momentum: 0.000000
2023-10-19 23:49:57,065 epoch 3 - iter 58/292 - loss 0.52701593 - time (sec): 1.07 - samples/sec: 8165.60 - lr: 0.000026 - momentum: 0.000000
2023-10-19 23:49:57,621 epoch 3 - iter 87/292 - loss 0.53413707 - time (sec): 1.63 - samples/sec: 8445.86 - lr: 0.000026 - momentum: 0.000000
2023-10-19 23:49:58,141 epoch 3 - iter 116/292 - loss 0.53704706 - time (sec): 2.15 - samples/sec: 8606.37 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:49:58,652 epoch 3 - iter 145/292 - loss 0.53492671 - time (sec): 2.66 - samples/sec: 8476.20 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:49:59,173 epoch 3 - iter 174/292 - loss 0.52807808 - time (sec): 3.18 - samples/sec: 8499.75 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:49:59,678 epoch 3 - iter 203/292 - loss 0.52293822 - time (sec): 3.69 - samples/sec: 8483.35 - lr: 0.000024 - momentum: 0.000000
2023-10-19 23:50:00,319 epoch 3 - iter 232/292 - loss 0.53248911 - time (sec): 4.33 - samples/sec: 8196.29 - lr: 0.000024 - momentum: 0.000000
2023-10-19 23:50:00,857 epoch 3 - iter 261/292 - loss 0.55584902 - time (sec): 4.87 - samples/sec: 8346.87 - lr: 0.000024 - momentum: 0.000000
2023-10-19 23:50:01,356 epoch 3 - iter 290/292 - loss 0.54349142 - time (sec): 5.36 - samples/sec: 8243.27 - lr: 0.000023 - momentum: 0.000000
2023-10-19 23:50:01,385 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:01,386 EPOCH 3 done: loss 0.5425 - lr: 0.000023
2023-10-19 23:50:02,033 DEV : loss 0.3629387617111206 - f1-score (micro avg)  0.0
2023-10-19 23:50:02,037 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:02,583 epoch 4 - iter 29/292 - loss 0.44234018 - time (sec): 0.55 - samples/sec: 8145.45 - lr: 0.000023 - momentum: 0.000000
2023-10-19 23:50:03,112 epoch 4 - iter 58/292 - loss 0.47095915 - time (sec): 1.08 - samples/sec: 8028.67 - lr: 0.000023 - momentum: 0.000000
2023-10-19 23:50:03,606 epoch 4 - iter 87/292 - loss 0.48713525 - time (sec): 1.57 - samples/sec: 8091.97 - lr: 0.000022 - momentum: 0.000000
2023-10-19 23:50:04,084 epoch 4 - iter 116/292 - loss 0.54057413 - time (sec): 2.05 - samples/sec: 8646.72 - lr: 0.000022 - momentum: 0.000000
2023-10-19 23:50:04,597 epoch 4 - iter 145/292 - loss 0.54157699 - time (sec): 2.56 - samples/sec: 8471.16 - lr: 0.000022 - momentum: 0.000000
2023-10-19 23:50:05,106 epoch 4 - iter 174/292 - loss 0.52419907 - time (sec): 3.07 - samples/sec: 8467.18 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:50:05,603 epoch 4 - iter 203/292 - loss 0.50823803 - time (sec): 3.57 - samples/sec: 8393.88 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:50:06,145 epoch 4 - iter 232/292 - loss 0.49738370 - time (sec): 4.11 - samples/sec: 8590.14 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:50:06,650 epoch 4 - iter 261/292 - loss 0.48959920 - time (sec): 4.61 - samples/sec: 8553.94 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:50:07,166 epoch 4 - iter 290/292 - loss 0.48932254 - time (sec): 5.13 - samples/sec: 8567.77 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:50:07,205 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:07,205 EPOCH 4 done: loss 0.4862 - lr: 0.000020
2023-10-19 23:50:07,841 DEV : loss 0.3369694650173187 - f1-score (micro avg)  0.0522
2023-10-19 23:50:07,845 saving best model
2023-10-19 23:50:07,873 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:08,389 epoch 5 - iter 29/292 - loss 0.59986532 - time (sec): 0.52 - samples/sec: 9669.90 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:50:08,890 epoch 5 - iter 58/292 - loss 0.54446958 - time (sec): 1.02 - samples/sec: 8851.61 - lr: 0.000019 - momentum: 0.000000
2023-10-19 23:50:09,415 epoch 5 - iter 87/292 - loss 0.52382245 - time (sec): 1.54 - samples/sec: 8780.25 - lr: 0.000019 - momentum: 0.000000
2023-10-19 23:50:09,958 epoch 5 - iter 116/292 - loss 0.49345204 - time (sec): 2.08 - samples/sec: 8805.88 - lr: 0.000019 - momentum: 0.000000
2023-10-19 23:50:10,521 epoch 5 - iter 145/292 - loss 0.46446324 - time (sec): 2.65 - samples/sec: 8647.69 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:50:10,998 epoch 5 - iter 174/292 - loss 0.45495738 - time (sec): 3.12 - samples/sec: 8585.51 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:50:11,506 epoch 5 - iter 203/292 - loss 0.44868989 - time (sec): 3.63 - samples/sec: 8651.19 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:50:12,002 epoch 5 - iter 232/292 - loss 0.45470579 - time (sec): 4.13 - samples/sec: 8429.60 - lr: 0.000017 - momentum: 0.000000
2023-10-19 23:50:12,488 epoch 5 - iter 261/292 - loss 0.45728633 - time (sec): 4.61 - samples/sec: 8503.52 - lr: 0.000017 - momentum: 0.000000
2023-10-19 23:50:12,998 epoch 5 - iter 290/292 - loss 0.45813778 - time (sec): 5.12 - samples/sec: 8626.62 - lr: 0.000017 - momentum: 0.000000
2023-10-19 23:50:13,028 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:13,029 EPOCH 5 done: loss 0.4589 - lr: 0.000017
2023-10-19 23:50:13,664 DEV : loss 0.3155231177806854 - f1-score (micro avg)  0.2
2023-10-19 23:50:13,668 saving best model
2023-10-19 23:50:13,701 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:14,204 epoch 6 - iter 29/292 - loss 0.53462839 - time (sec): 0.50 - samples/sec: 8790.02 - lr: 0.000016 - momentum: 0.000000
2023-10-19 23:50:14,710 epoch 6 - iter 58/292 - loss 0.45387607 - time (sec): 1.01 - samples/sec: 8763.56 - lr: 0.000016 - momentum: 0.000000
2023-10-19 23:50:15,202 epoch 6 - iter 87/292 - loss 0.43858593 - time (sec): 1.50 - samples/sec: 8859.98 - lr: 0.000016 - momentum: 0.000000
2023-10-19 23:50:15,682 epoch 6 - iter 116/292 - loss 0.44130740 - time (sec): 1.98 - samples/sec: 8591.37 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:50:16,206 epoch 6 - iter 145/292 - loss 0.43534614 - time (sec): 2.50 - samples/sec: 8722.92 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:50:16,721 epoch 6 - iter 174/292 - loss 0.45146060 - time (sec): 3.02 - samples/sec: 8892.81 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:50:17,227 epoch 6 - iter 203/292 - loss 0.43994146 - time (sec): 3.53 - samples/sec: 8858.23 - lr: 0.000014 - momentum: 0.000000
2023-10-19 23:50:17,738 epoch 6 - iter 232/292 - loss 0.43069230 - time (sec): 4.04 - samples/sec: 8776.63 - lr: 0.000014 - momentum: 0.000000
2023-10-19 23:50:18,259 epoch 6 - iter 261/292 - loss 0.42140208 - time (sec): 4.56 - samples/sec: 8859.96 - lr: 0.000014 - momentum: 0.000000
2023-10-19 23:50:18,773 epoch 6 - iter 290/292 - loss 0.42268096 - time (sec): 5.07 - samples/sec: 8732.95 - lr: 0.000013 - momentum: 0.000000
2023-10-19 23:50:18,802 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:18,802 EPOCH 6 done: loss 0.4225 - lr: 0.000013
2023-10-19 23:50:19,452 DEV : loss 0.3091878592967987 - f1-score (micro avg)  0.2337
2023-10-19 23:50:19,457 saving best model
2023-10-19 23:50:19,488 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:19,962 epoch 7 - iter 29/292 - loss 0.36939978 - time (sec): 0.47 - samples/sec: 8508.54 - lr: 0.000013 - momentum: 0.000000
2023-10-19 23:50:20,469 epoch 7 - iter 58/292 - loss 0.34863665 - time (sec): 0.98 - samples/sec: 8253.29 - lr: 0.000013 - momentum: 0.000000
2023-10-19 23:50:20,967 epoch 7 - iter 87/292 - loss 0.37735444 - time (sec): 1.48 - samples/sec: 8488.20 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:50:21,461 epoch 7 - iter 116/292 - loss 0.42584412 - time (sec): 1.97 - samples/sec: 8521.83 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:50:21,956 epoch 7 - iter 145/292 - loss 0.41926330 - time (sec): 2.47 - samples/sec: 8485.20 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:50:22,465 epoch 7 - iter 174/292 - loss 0.41470611 - time (sec): 2.98 - samples/sec: 8410.67 - lr: 0.000011 - momentum: 0.000000
2023-10-19 23:50:22,985 epoch 7 - iter 203/292 - loss 0.41112570 - time (sec): 3.50 - samples/sec: 8532.31 - lr: 0.000011 - momentum: 0.000000
2023-10-19 23:50:23,524 epoch 7 - iter 232/292 - loss 0.41695875 - time (sec): 4.04 - samples/sec: 8494.03 - lr: 0.000011 - momentum: 0.000000
2023-10-19 23:50:24,066 epoch 7 - iter 261/292 - loss 0.41595517 - time (sec): 4.58 - samples/sec: 8616.91 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:50:24,614 epoch 7 - iter 290/292 - loss 0.40657648 - time (sec): 5.13 - samples/sec: 8630.87 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:50:24,651 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:24,651 EPOCH 7 done: loss 0.4063 - lr: 0.000010
2023-10-19 23:50:25,299 DEV : loss 0.30544406175613403 - f1-score (micro avg)  0.2442
2023-10-19 23:50:25,304 saving best model
2023-10-19 23:50:25,335 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:25,862 epoch 8 - iter 29/292 - loss 0.46693410 - time (sec): 0.53 - samples/sec: 9430.55 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:50:26,374 epoch 8 - iter 58/292 - loss 0.45223574 - time (sec): 1.04 - samples/sec: 8889.98 - lr: 0.000009 - momentum: 0.000000
2023-10-19 23:50:26,919 epoch 8 - iter 87/292 - loss 0.41985147 - time (sec): 1.58 - samples/sec: 8638.02 - lr: 0.000009 - momentum: 0.000000
2023-10-19 23:50:27,500 epoch 8 - iter 116/292 - loss 0.39639438 - time (sec): 2.16 - samples/sec: 8397.44 - lr: 0.000009 - momentum: 0.000000
2023-10-19 23:50:27,950 epoch 8 - iter 145/292 - loss 0.38044177 - time (sec): 2.61 - samples/sec: 8685.66 - lr: 0.000008 - momentum: 0.000000
2023-10-19 23:50:28,444 epoch 8 - iter 174/292 - loss 0.39141118 - time (sec): 3.11 - samples/sec: 8534.62 - lr: 0.000008 - momentum: 0.000000
2023-10-19 23:50:28,981 epoch 8 - iter 203/292 - loss 0.38177708 - time (sec): 3.65 - samples/sec: 8433.50 - lr: 0.000008 - momentum: 0.000000
2023-10-19 23:50:29,486 epoch 8 - iter 232/292 - loss 0.39149494 - time (sec): 4.15 - samples/sec: 8434.27 - lr: 0.000007 - momentum: 0.000000
2023-10-19 23:50:30,009 epoch 8 - iter 261/292 - loss 0.38697211 - time (sec): 4.67 - samples/sec: 8473.81 - lr: 0.000007 - momentum: 0.000000
2023-10-19 23:50:30,523 epoch 8 - iter 290/292 - loss 0.38508142 - time (sec): 5.19 - samples/sec: 8518.94 - lr: 0.000007 - momentum: 0.000000
2023-10-19 23:50:30,554 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:30,554 EPOCH 8 done: loss 0.3861 - lr: 0.000007
2023-10-19 23:50:31,188 DEV : loss 0.306864857673645 - f1-score (micro avg)  0.2274
2023-10-19 23:50:31,192 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:31,687 epoch 9 - iter 29/292 - loss 0.34080573 - time (sec): 0.49 - samples/sec: 8069.80 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:50:32,174 epoch 9 - iter 58/292 - loss 0.39740699 - time (sec): 0.98 - samples/sec: 8275.65 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:50:32,672 epoch 9 - iter 87/292 - loss 0.38941554 - time (sec): 1.48 - samples/sec: 8329.76 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:50:33,175 epoch 9 - iter 116/292 - loss 0.37411188 - time (sec): 1.98 - samples/sec: 8364.62 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:50:33,678 epoch 9 - iter 145/292 - loss 0.37705396 - time (sec): 2.49 - samples/sec: 8584.38 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:50:34,207 epoch 9 - iter 174/292 - loss 0.37556133 - time (sec): 3.01 - samples/sec: 8673.00 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:50:34,741 epoch 9 - iter 203/292 - loss 0.38081345 - time (sec): 3.55 - samples/sec: 8845.59 - lr: 0.000004 - momentum: 0.000000
2023-10-19 23:50:35,256 epoch 9 - iter 232/292 - loss 0.38515839 - time (sec): 4.06 - samples/sec: 8916.11 - lr: 0.000004 - momentum: 0.000000
2023-10-19 23:50:35,754 epoch 9 - iter 261/292 - loss 0.38318305 - time (sec): 4.56 - samples/sec: 8878.53 - lr: 0.000004 - momentum: 0.000000
2023-10-19 23:50:36,245 epoch 9 - iter 290/292 - loss 0.38427222 - time (sec): 5.05 - samples/sec: 8745.25 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:50:36,279 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:36,279 EPOCH 9 done: loss 0.3833 - lr: 0.000003
2023-10-19 23:50:37,074 DEV : loss 0.306318461894989 - f1-score (micro avg)  0.2234
2023-10-19 23:50:37,078 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:37,653 epoch 10 - iter 29/292 - loss 0.30956639 - time (sec): 0.58 - samples/sec: 8756.50 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:50:38,195 epoch 10 - iter 58/292 - loss 0.35087803 - time (sec): 1.12 - samples/sec: 8239.31 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:50:38,681 epoch 10 - iter 87/292 - loss 0.35546959 - time (sec): 1.60 - samples/sec: 8456.65 - lr: 0.000002 - momentum: 0.000000
2023-10-19 23:50:39,183 epoch 10 - iter 116/292 - loss 0.34859194 - time (sec): 2.10 - samples/sec: 8631.38 - lr: 0.000002 - momentum: 0.000000
2023-10-19 23:50:39,649 epoch 10 - iter 145/292 - loss 0.37253886 - time (sec): 2.57 - samples/sec: 8795.86 - lr: 0.000002 - momentum: 0.000000
2023-10-19 23:50:40,139 epoch 10 - iter 174/292 - loss 0.37750606 - time (sec): 3.06 - samples/sec: 8752.59 - lr: 0.000001 - momentum: 0.000000
2023-10-19 23:50:40,599 epoch 10 - iter 203/292 - loss 0.38037581 - time (sec): 3.52 - samples/sec: 8598.64 - lr: 0.000001 - momentum: 0.000000
2023-10-19 23:50:41,069 epoch 10 - iter 232/292 - loss 0.37226905 - time (sec): 3.99 - samples/sec: 8751.82 - lr: 0.000001 - momentum: 0.000000
2023-10-19 23:50:41,578 epoch 10 - iter 261/292 - loss 0.37712323 - time (sec): 4.50 - samples/sec: 8867.59 - lr: 0.000000 - momentum: 0.000000
2023-10-19 23:50:42,056 epoch 10 - iter 290/292 - loss 0.37760770 - time (sec): 4.98 - samples/sec: 8879.88 - lr: 0.000000 - momentum: 0.000000
2023-10-19 23:50:42,079 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:42,080 EPOCH 10 done: loss 0.3783 - lr: 0.000000
2023-10-19 23:50:42,723 DEV : loss 0.308378130197525 - f1-score (micro avg)  0.2222
2023-10-19 23:50:42,755 ----------------------------------------------------------------------------------------------------
2023-10-19 23:50:42,756 Loading model from best epoch ...
2023-10-19 23:50:42,833 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
2023-10-19 23:50:43,726 
Results:
- F-score (micro) 0.2325
- F-score (macro) 0.1217
- Accuracy 0.137

By class:
              precision    recall  f1-score   support

         PER     0.2661    0.2615    0.2638       348
         LOC     0.2537    0.1992    0.2232       261
         ORG     0.0000    0.0000    0.0000        52
   HumanProd     0.0000    0.0000    0.0000        22

   micro avg     0.2614    0.2094    0.2325       683
   macro avg     0.1299    0.1152    0.1217       683
weighted avg     0.2325    0.2094    0.2197       683

2023-10-19 23:50:43,726 ----------------------------------------------------------------------------------------------------