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hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
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German
sequence-tagger-model
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hmbench-hipe2020-de-hmbert_tiny-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
File size: 24,031 Bytes 
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2023-10-18 18:19:01,645 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,645 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=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 18:19:01,645 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,645 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-18 18:19:01,645 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,645 Train:  3575 sentences
2023-10-18 18:19:01,646         (train_with_dev=False, train_with_test=False)
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Training Params:
2023-10-18 18:19:01,646  - learning_rate: "3e-05" 
2023-10-18 18:19:01,646  - mini_batch_size: "8"
2023-10-18 18:19:01,646  - max_epochs: "10"
2023-10-18 18:19:01,646  - shuffle: "True"
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Plugins:
2023-10-18 18:19:01,646  - TensorboardLogger
2023-10-18 18:19:01,646  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 18:19:01,646  - metric: "('micro avg', 'f1-score')"
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Computation:
2023-10-18 18:19:01,646  - compute on device: cuda:0
2023-10-18 18:19:01,646  - embedding storage: none
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:01,646 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 18:19:02,711 epoch 1 - iter 44/447 - loss 4.26136576 - time (sec): 1.06 - samples/sec: 7683.39 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:19:03,723 epoch 1 - iter 88/447 - loss 4.19678316 - time (sec): 2.08 - samples/sec: 8059.03 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:19:04,806 epoch 1 - iter 132/447 - loss 3.92643463 - time (sec): 3.16 - samples/sec: 8288.46 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:19:05,849 epoch 1 - iter 176/447 - loss 3.69414494 - time (sec): 4.20 - samples/sec: 8386.38 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:19:07,106 epoch 1 - iter 220/447 - loss 3.39968934 - time (sec): 5.46 - samples/sec: 8144.20 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:19:08,094 epoch 1 - iter 264/447 - loss 3.08646118 - time (sec): 6.45 - samples/sec: 8230.31 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:19:09,072 epoch 1 - iter 308/447 - loss 2.80295080 - time (sec): 7.43 - samples/sec: 8211.99 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:19:10,022 epoch 1 - iter 352/447 - loss 2.55929558 - time (sec): 8.38 - samples/sec: 8228.60 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:19:11,033 epoch 1 - iter 396/447 - loss 2.36406683 - time (sec): 9.39 - samples/sec: 8184.90 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:19:12,060 epoch 1 - iter 440/447 - loss 2.19005741 - time (sec): 10.41 - samples/sec: 8187.98 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:19:12,216 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:12,217 EPOCH 1 done: loss 2.1657 - lr: 0.000029
2023-10-18 18:19:14,163 DEV : loss 0.48721176385879517 - f1-score (micro avg)  0.0
2023-10-18 18:19:14,189 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:15,119 epoch 2 - iter 44/447 - loss 0.65125143 - time (sec): 0.93 - samples/sec: 9426.59 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:19:15,993 epoch 2 - iter 88/447 - loss 0.61628616 - time (sec): 1.80 - samples/sec: 9461.75 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:19:16,940 epoch 2 - iter 132/447 - loss 0.61557362 - time (sec): 2.75 - samples/sec: 9161.17 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:19:17,946 epoch 2 - iter 176/447 - loss 0.61002674 - time (sec): 3.76 - samples/sec: 8949.74 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:19:18,961 epoch 2 - iter 220/447 - loss 0.60306428 - time (sec): 4.77 - samples/sec: 8804.65 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:19:19,982 epoch 2 - iter 264/447 - loss 0.59448973 - time (sec): 5.79 - samples/sec: 8553.15 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:19:20,985 epoch 2 - iter 308/447 - loss 0.58231488 - time (sec): 6.80 - samples/sec: 8557.62 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:19:22,356 epoch 2 - iter 352/447 - loss 0.56475657 - time (sec): 8.17 - samples/sec: 8324.13 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:19:23,378 epoch 2 - iter 396/447 - loss 0.55753500 - time (sec): 9.19 - samples/sec: 8386.51 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:19:24,394 epoch 2 - iter 440/447 - loss 0.55052779 - time (sec): 10.20 - samples/sec: 8365.55 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:19:24,546 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:24,546 EPOCH 2 done: loss 0.5507 - lr: 0.000027
2023-10-18 18:19:29,410 DEV : loss 0.3801082968711853 - f1-score (micro avg)  0.0
2023-10-18 18:19:29,436 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:30,447 epoch 3 - iter 44/447 - loss 0.46421760 - time (sec): 1.01 - samples/sec: 8751.88 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:19:31,438 epoch 3 - iter 88/447 - loss 0.45746619 - time (sec): 2.00 - samples/sec: 8519.88 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:19:32,423 epoch 3 - iter 132/447 - loss 0.47573888 - time (sec): 2.99 - samples/sec: 8600.46 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:19:33,391 epoch 3 - iter 176/447 - loss 0.48006629 - time (sec): 3.95 - samples/sec: 8515.13 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:19:34,417 epoch 3 - iter 220/447 - loss 0.46868404 - time (sec): 4.98 - samples/sec: 8503.02 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:19:35,477 epoch 3 - iter 264/447 - loss 0.46315261 - time (sec): 6.04 - samples/sec: 8639.24 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:19:36,483 epoch 3 - iter 308/447 - loss 0.46299866 - time (sec): 7.05 - samples/sec: 8645.31 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:19:37,476 epoch 3 - iter 352/447 - loss 0.46009593 - time (sec): 8.04 - samples/sec: 8590.88 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:19:38,514 epoch 3 - iter 396/447 - loss 0.45871433 - time (sec): 9.08 - samples/sec: 8478.04 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:19:39,492 epoch 3 - iter 440/447 - loss 0.45734948 - time (sec): 10.06 - samples/sec: 8474.22 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:19:39,655 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:39,655 EPOCH 3 done: loss 0.4553 - lr: 0.000023
2023-10-18 18:19:44,879 DEV : loss 0.33706629276275635 - f1-score (micro avg)  0.1432
2023-10-18 18:19:44,905 saving best model
2023-10-18 18:19:44,941 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:46,013 epoch 4 - iter 44/447 - loss 0.37224151 - time (sec): 1.07 - samples/sec: 7205.04 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:19:46,914 epoch 4 - iter 88/447 - loss 0.40787854 - time (sec): 1.97 - samples/sec: 8002.55 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:19:47,750 epoch 4 - iter 132/447 - loss 0.43149977 - time (sec): 2.81 - samples/sec: 8545.35 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:19:48,736 epoch 4 - iter 176/447 - loss 0.43216701 - time (sec): 3.80 - samples/sec: 8482.73 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:19:49,840 epoch 4 - iter 220/447 - loss 0.42198826 - time (sec): 4.90 - samples/sec: 8519.71 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:19:50,895 epoch 4 - iter 264/447 - loss 0.41309935 - time (sec): 5.95 - samples/sec: 8480.78 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:19:51,944 epoch 4 - iter 308/447 - loss 0.41310603 - time (sec): 7.00 - samples/sec: 8576.86 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:19:52,954 epoch 4 - iter 352/447 - loss 0.41193824 - time (sec): 8.01 - samples/sec: 8604.67 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:19:53,957 epoch 4 - iter 396/447 - loss 0.40773735 - time (sec): 9.02 - samples/sec: 8545.41 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:19:54,939 epoch 4 - iter 440/447 - loss 0.41410780 - time (sec): 10.00 - samples/sec: 8526.83 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:19:55,101 ----------------------------------------------------------------------------------------------------
2023-10-18 18:19:55,102 EPOCH 4 done: loss 0.4145 - lr: 0.000020
2023-10-18 18:20:00,414 DEV : loss 0.32761430740356445 - f1-score (micro avg)  0.2488
2023-10-18 18:20:00,442 saving best model
2023-10-18 18:20:00,474 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:01,352 epoch 5 - iter 44/447 - loss 0.38465646 - time (sec): 0.88 - samples/sec: 10054.45 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:20:02,193 epoch 5 - iter 88/447 - loss 0.37699137 - time (sec): 1.72 - samples/sec: 9710.21 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:20:03,252 epoch 5 - iter 132/447 - loss 0.38217974 - time (sec): 2.78 - samples/sec: 9148.86 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:20:04,346 epoch 5 - iter 176/447 - loss 0.38342169 - time (sec): 3.87 - samples/sec: 8917.58 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:20:05,413 epoch 5 - iter 220/447 - loss 0.37709541 - time (sec): 4.94 - samples/sec: 8744.40 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:20:06,453 epoch 5 - iter 264/447 - loss 0.38233170 - time (sec): 5.98 - samples/sec: 8634.14 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:20:07,441 epoch 5 - iter 308/447 - loss 0.38767324 - time (sec): 6.97 - samples/sec: 8503.73 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:20:08,412 epoch 5 - iter 352/447 - loss 0.38682295 - time (sec): 7.94 - samples/sec: 8474.06 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:20:09,405 epoch 5 - iter 396/447 - loss 0.38506534 - time (sec): 8.93 - samples/sec: 8468.99 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:20:10,485 epoch 5 - iter 440/447 - loss 0.38807466 - time (sec): 10.01 - samples/sec: 8530.12 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:20:10,646 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:10,646 EPOCH 5 done: loss 0.3866 - lr: 0.000017
2023-10-18 18:20:15,898 DEV : loss 0.31816405057907104 - f1-score (micro avg)  0.2825
2023-10-18 18:20:15,924 saving best model
2023-10-18 18:20:15,958 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:16,989 epoch 6 - iter 44/447 - loss 0.38056401 - time (sec): 1.03 - samples/sec: 7596.10 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:20:18,046 epoch 6 - iter 88/447 - loss 0.34090423 - time (sec): 2.09 - samples/sec: 8278.24 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:20:19,076 epoch 6 - iter 132/447 - loss 0.33389594 - time (sec): 3.12 - samples/sec: 8022.86 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:20:20,140 epoch 6 - iter 176/447 - loss 0.35003072 - time (sec): 4.18 - samples/sec: 8204.12 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:20:21,194 epoch 6 - iter 220/447 - loss 0.34996780 - time (sec): 5.24 - samples/sec: 8283.50 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:20:22,184 epoch 6 - iter 264/447 - loss 0.35206872 - time (sec): 6.23 - samples/sec: 8267.82 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:20:23,188 epoch 6 - iter 308/447 - loss 0.35224360 - time (sec): 7.23 - samples/sec: 8221.03 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:20:24,248 epoch 6 - iter 352/447 - loss 0.35331911 - time (sec): 8.29 - samples/sec: 8293.17 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:20:25,269 epoch 6 - iter 396/447 - loss 0.35145673 - time (sec): 9.31 - samples/sec: 8286.32 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:20:26,245 epoch 6 - iter 440/447 - loss 0.36336901 - time (sec): 10.29 - samples/sec: 8314.03 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:20:26,396 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:26,396 EPOCH 6 done: loss 0.3645 - lr: 0.000013
2023-10-18 18:20:31,361 DEV : loss 0.3086220622062683 - f1-score (micro avg)  0.3146
2023-10-18 18:20:31,387 saving best model
2023-10-18 18:20:31,418 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:32,436 epoch 7 - iter 44/447 - loss 0.29886952 - time (sec): 1.02 - samples/sec: 9097.13 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:20:33,435 epoch 7 - iter 88/447 - loss 0.33142182 - time (sec): 2.02 - samples/sec: 8643.37 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:20:34,837 epoch 7 - iter 132/447 - loss 0.35366934 - time (sec): 3.42 - samples/sec: 7931.27 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:20:35,896 epoch 7 - iter 176/447 - loss 0.35387378 - time (sec): 4.48 - samples/sec: 7998.56 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:20:36,938 epoch 7 - iter 220/447 - loss 0.35892506 - time (sec): 5.52 - samples/sec: 7967.79 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:20:37,967 epoch 7 - iter 264/447 - loss 0.36143849 - time (sec): 6.55 - samples/sec: 7955.37 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:20:39,030 epoch 7 - iter 308/447 - loss 0.35509108 - time (sec): 7.61 - samples/sec: 7920.36 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:20:40,089 epoch 7 - iter 352/447 - loss 0.35704565 - time (sec): 8.67 - samples/sec: 7925.82 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:20:41,129 epoch 7 - iter 396/447 - loss 0.35473783 - time (sec): 9.71 - samples/sec: 7933.59 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:20:42,141 epoch 7 - iter 440/447 - loss 0.35569652 - time (sec): 10.72 - samples/sec: 7944.80 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:20:42,290 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:42,291 EPOCH 7 done: loss 0.3560 - lr: 0.000010
2023-10-18 18:20:47,332 DEV : loss 0.31271788477897644 - f1-score (micro avg)  0.3084
2023-10-18 18:20:47,359 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:48,418 epoch 8 - iter 44/447 - loss 0.35611823 - time (sec): 1.06 - samples/sec: 8936.31 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:20:49,444 epoch 8 - iter 88/447 - loss 0.33913763 - time (sec): 2.08 - samples/sec: 8439.88 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:20:50,449 epoch 8 - iter 132/447 - loss 0.35433170 - time (sec): 3.09 - samples/sec: 8459.77 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:20:51,446 epoch 8 - iter 176/447 - loss 0.36068417 - time (sec): 4.09 - samples/sec: 8370.91 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:20:52,446 epoch 8 - iter 220/447 - loss 0.36434763 - time (sec): 5.09 - samples/sec: 8367.59 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:20:53,489 epoch 8 - iter 264/447 - loss 0.35829319 - time (sec): 6.13 - samples/sec: 8302.49 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:20:54,488 epoch 8 - iter 308/447 - loss 0.35481202 - time (sec): 7.13 - samples/sec: 8227.89 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:20:55,533 epoch 8 - iter 352/447 - loss 0.35284971 - time (sec): 8.17 - samples/sec: 8234.63 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:20:56,603 epoch 8 - iter 396/447 - loss 0.34912209 - time (sec): 9.24 - samples/sec: 8207.41 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:20:57,689 epoch 8 - iter 440/447 - loss 0.35044302 - time (sec): 10.33 - samples/sec: 8232.50 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:20:57,851 ----------------------------------------------------------------------------------------------------
2023-10-18 18:20:57,852 EPOCH 8 done: loss 0.3489 - lr: 0.000007
2023-10-18 18:21:03,152 DEV : loss 0.30949148535728455 - f1-score (micro avg)  0.3076
2023-10-18 18:21:03,178 ----------------------------------------------------------------------------------------------------
2023-10-18 18:21:04,185 epoch 9 - iter 44/447 - loss 0.32539682 - time (sec): 1.01 - samples/sec: 8095.26 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:21:05,147 epoch 9 - iter 88/447 - loss 0.33926847 - time (sec): 1.97 - samples/sec: 7891.42 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:21:06,141 epoch 9 - iter 132/447 - loss 0.32815419 - time (sec): 2.96 - samples/sec: 8170.80 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:21:07,120 epoch 9 - iter 176/447 - loss 0.33881194 - time (sec): 3.94 - samples/sec: 8211.72 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:21:08,159 epoch 9 - iter 220/447 - loss 0.33813350 - time (sec): 4.98 - samples/sec: 8335.27 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:21:09,196 epoch 9 - iter 264/447 - loss 0.33632820 - time (sec): 6.02 - samples/sec: 8422.72 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:21:10,211 epoch 9 - iter 308/447 - loss 0.33126545 - time (sec): 7.03 - samples/sec: 8414.31 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:21:11,191 epoch 9 - iter 352/447 - loss 0.32912919 - time (sec): 8.01 - samples/sec: 8409.18 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:21:12,247 epoch 9 - iter 396/447 - loss 0.33415306 - time (sec): 9.07 - samples/sec: 8461.75 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:21:13,232 epoch 9 - iter 440/447 - loss 0.33729193 - time (sec): 10.05 - samples/sec: 8513.26 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:21:13,386 ----------------------------------------------------------------------------------------------------
2023-10-18 18:21:13,386 EPOCH 9 done: loss 0.3373 - lr: 0.000003
2023-10-18 18:21:18,663 DEV : loss 0.30950450897216797 - f1-score (micro avg)  0.3172
2023-10-18 18:21:18,689 saving best model
2023-10-18 18:21:18,727 ----------------------------------------------------------------------------------------------------
2023-10-18 18:21:19,781 epoch 10 - iter 44/447 - loss 0.38408660 - time (sec): 1.05 - samples/sec: 7711.43 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:21:20,865 epoch 10 - iter 88/447 - loss 0.36018147 - time (sec): 2.14 - samples/sec: 7669.34 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:21:21,926 epoch 10 - iter 132/447 - loss 0.33677093 - time (sec): 3.20 - samples/sec: 7694.37 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:21:22,942 epoch 10 - iter 176/447 - loss 0.33542182 - time (sec): 4.21 - samples/sec: 7781.88 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:21:23,921 epoch 10 - iter 220/447 - loss 0.33895587 - time (sec): 5.19 - samples/sec: 7785.80 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:21:24,933 epoch 10 - iter 264/447 - loss 0.33158441 - time (sec): 6.21 - samples/sec: 7904.22 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:21:25,897 epoch 10 - iter 308/447 - loss 0.33611357 - time (sec): 7.17 - samples/sec: 7965.44 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:21:26,978 epoch 10 - iter 352/447 - loss 0.33490933 - time (sec): 8.25 - samples/sec: 8196.71 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:21:27,987 epoch 10 - iter 396/447 - loss 0.33935900 - time (sec): 9.26 - samples/sec: 8215.23 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:21:28,964 epoch 10 - iter 440/447 - loss 0.33923817 - time (sec): 10.24 - samples/sec: 8278.81 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:21:29,144 ----------------------------------------------------------------------------------------------------
2023-10-18 18:21:29,144 EPOCH 10 done: loss 0.3389 - lr: 0.000000
2023-10-18 18:21:34,419 DEV : loss 0.30806833505630493 - f1-score (micro avg)  0.3166
2023-10-18 18:21:34,476 ----------------------------------------------------------------------------------------------------
2023-10-18 18:21:34,477 Loading model from best epoch ...
2023-10-18 18:21:34,556 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-18 18:21:36,489 
Results:
- F-score (micro) 0.3113
- F-score (macro) 0.1212
- Accuracy 0.1921

By class:
              precision    recall  f1-score   support

         loc     0.5018    0.4799    0.4906       596
        pers     0.1140    0.1171    0.1156       333
         org     0.0000    0.0000    0.0000       132
        prod     0.0000    0.0000    0.0000        66
        time     0.0000    0.0000    0.0000        49

   micro avg     0.3564    0.2764    0.3113      1176
   macro avg     0.1232    0.1194    0.1212      1176
weighted avg     0.2866    0.2764    0.2813      1176

2023-10-18 18:21:36,489 ----------------------------------------------------------------------------------------------------