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flair-co-funer-gbert_base-bs8-e10-lr3e-05-4

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
3
flair-co-funer-gbert_base-bs8-e10-lr3e-05-4
File size: 23,814 Bytes 
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2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(31103, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=17, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Train:  758 sentences
2024-03-26 10:24:52,166         (train_with_dev=False, train_with_test=False)
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Training Params:
2024-03-26 10:24:52,166  - learning_rate: "3e-05" 
2024-03-26 10:24:52,166  - mini_batch_size: "8"
2024-03-26 10:24:52,166  - max_epochs: "10"
2024-03-26 10:24:52,166  - shuffle: "True"
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Plugins:
2024-03-26 10:24:52,166  - TensorboardLogger
2024-03-26 10:24:52,166  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,166 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 10:24:52,166  - metric: "('micro avg', 'f1-score')"
2024-03-26 10:24:52,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,167 Computation:
2024-03-26 10:24:52,167  - compute on device: cuda:0
2024-03-26 10:24:52,167  - embedding storage: none
2024-03-26 10:24:52,167 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,167 Model training base path: "flair-co-funer-gbert_base-bs8-e10-lr3e-05-4"
2024-03-26 10:24:52,167 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,167 ----------------------------------------------------------------------------------------------------
2024-03-26 10:24:52,167 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 10:24:53,520 epoch 1 - iter 9/95 - loss 3.31156291 - time (sec): 1.35 - samples/sec: 2144.74 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:24:54,905 epoch 1 - iter 18/95 - loss 3.18392069 - time (sec): 2.74 - samples/sec: 2014.03 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:24:56,522 epoch 1 - iter 27/95 - loss 2.95366883 - time (sec): 4.36 - samples/sec: 1969.00 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:24:58,491 epoch 1 - iter 36/95 - loss 2.72938282 - time (sec): 6.32 - samples/sec: 1887.79 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:25:00,336 epoch 1 - iter 45/95 - loss 2.51840902 - time (sec): 8.17 - samples/sec: 1908.99 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:25:02,554 epoch 1 - iter 54/95 - loss 2.37628283 - time (sec): 10.39 - samples/sec: 1843.30 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:25:04,557 epoch 1 - iter 63/95 - loss 2.24029015 - time (sec): 12.39 - samples/sec: 1824.17 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:25:05,526 epoch 1 - iter 72/95 - loss 2.15411890 - time (sec): 13.36 - samples/sec: 1869.87 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:25:07,799 epoch 1 - iter 81/95 - loss 2.02104777 - time (sec): 15.63 - samples/sec: 1817.14 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:25:09,119 epoch 1 - iter 90/95 - loss 1.88999619 - time (sec): 16.95 - samples/sec: 1885.32 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:25:10,378 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:10,379 EPOCH 1 done: loss 1.8224 - lr: 0.000028
2024-03-26 10:25:11,332 DEV : loss 0.5509695410728455 - f1-score (micro avg)  0.6394
2024-03-26 10:25:11,333 saving best model
2024-03-26 10:25:11,615 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:13,192 epoch 2 - iter 9/95 - loss 0.74076124 - time (sec): 1.58 - samples/sec: 1827.24 - lr: 0.000030 - momentum: 0.000000
2024-03-26 10:25:14,827 epoch 2 - iter 18/95 - loss 0.64312417 - time (sec): 3.21 - samples/sec: 1924.49 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:25:16,603 epoch 2 - iter 27/95 - loss 0.59053178 - time (sec): 4.99 - samples/sec: 1895.84 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:25:18,977 epoch 2 - iter 36/95 - loss 0.52064870 - time (sec): 7.36 - samples/sec: 1772.23 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:25:20,940 epoch 2 - iter 45/95 - loss 0.49481256 - time (sec): 9.32 - samples/sec: 1769.45 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:25:22,693 epoch 2 - iter 54/95 - loss 0.49494445 - time (sec): 11.08 - samples/sec: 1790.39 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:25:25,097 epoch 2 - iter 63/95 - loss 0.46932616 - time (sec): 13.48 - samples/sec: 1773.50 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:25:26,915 epoch 2 - iter 72/95 - loss 0.46316340 - time (sec): 15.30 - samples/sec: 1767.50 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:25:29,086 epoch 2 - iter 81/95 - loss 0.45228059 - time (sec): 17.47 - samples/sec: 1751.09 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:25:30,368 epoch 2 - iter 90/95 - loss 0.44481235 - time (sec): 18.75 - samples/sec: 1776.61 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:25:30,810 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:30,810 EPOCH 2 done: loss 0.4387 - lr: 0.000027
2024-03-26 10:25:31,701 DEV : loss 0.27566346526145935 - f1-score (micro avg)  0.8453
2024-03-26 10:25:31,704 saving best model
2024-03-26 10:25:32,186 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:33,679 epoch 3 - iter 9/95 - loss 0.28125940 - time (sec): 1.49 - samples/sec: 1776.87 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:25:35,396 epoch 3 - iter 18/95 - loss 0.24015894 - time (sec): 3.21 - samples/sec: 1742.05 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:25:37,224 epoch 3 - iter 27/95 - loss 0.23445075 - time (sec): 5.04 - samples/sec: 1768.16 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:25:38,998 epoch 3 - iter 36/95 - loss 0.23589931 - time (sec): 6.81 - samples/sec: 1772.37 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:25:40,991 epoch 3 - iter 45/95 - loss 0.23131598 - time (sec): 8.80 - samples/sec: 1793.43 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:25:43,209 epoch 3 - iter 54/95 - loss 0.22372789 - time (sec): 11.02 - samples/sec: 1757.31 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:25:44,900 epoch 3 - iter 63/95 - loss 0.21970639 - time (sec): 12.71 - samples/sec: 1759.73 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:25:46,852 epoch 3 - iter 72/95 - loss 0.21660214 - time (sec): 14.66 - samples/sec: 1765.29 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:25:48,807 epoch 3 - iter 81/95 - loss 0.22201067 - time (sec): 16.62 - samples/sec: 1782.08 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:25:51,025 epoch 3 - iter 90/95 - loss 0.21545506 - time (sec): 18.84 - samples/sec: 1760.47 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:25:51,637 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:51,638 EPOCH 3 done: loss 0.2183 - lr: 0.000024
2024-03-26 10:25:52,544 DEV : loss 0.20986029505729675 - f1-score (micro avg)  0.8793
2024-03-26 10:25:52,546 saving best model
2024-03-26 10:25:52,986 ----------------------------------------------------------------------------------------------------
2024-03-26 10:25:55,360 epoch 4 - iter 9/95 - loss 0.10059051 - time (sec): 2.37 - samples/sec: 1667.65 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:25:56,506 epoch 4 - iter 18/95 - loss 0.12160418 - time (sec): 3.52 - samples/sec: 1819.41 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:25:58,610 epoch 4 - iter 27/95 - loss 0.13599375 - time (sec): 5.62 - samples/sec: 1849.65 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:26:00,074 epoch 4 - iter 36/95 - loss 0.13862392 - time (sec): 7.09 - samples/sec: 1882.76 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:26:01,369 epoch 4 - iter 45/95 - loss 0.13926567 - time (sec): 8.38 - samples/sec: 1914.79 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:26:03,402 epoch 4 - iter 54/95 - loss 0.13469600 - time (sec): 10.41 - samples/sec: 1858.54 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:26:05,642 epoch 4 - iter 63/95 - loss 0.14272038 - time (sec): 12.65 - samples/sec: 1829.66 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:26:07,074 epoch 4 - iter 72/95 - loss 0.14102890 - time (sec): 14.09 - samples/sec: 1865.50 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:26:08,646 epoch 4 - iter 81/95 - loss 0.13827607 - time (sec): 15.66 - samples/sec: 1896.84 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:26:10,235 epoch 4 - iter 90/95 - loss 0.13680899 - time (sec): 17.25 - samples/sec: 1925.34 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:26:10,854 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:10,854 EPOCH 4 done: loss 0.1367 - lr: 0.000020
2024-03-26 10:26:11,748 DEV : loss 0.20099645853042603 - f1-score (micro avg)  0.8814
2024-03-26 10:26:11,749 saving best model
2024-03-26 10:26:12,202 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:13,391 epoch 5 - iter 9/95 - loss 0.14854127 - time (sec): 1.19 - samples/sec: 2490.69 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:26:14,805 epoch 5 - iter 18/95 - loss 0.13874171 - time (sec): 2.60 - samples/sec: 2240.53 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:26:16,759 epoch 5 - iter 27/95 - loss 0.12505013 - time (sec): 4.56 - samples/sec: 2018.87 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:26:19,148 epoch 5 - iter 36/95 - loss 0.11891017 - time (sec): 6.94 - samples/sec: 1828.85 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:26:20,348 epoch 5 - iter 45/95 - loss 0.12166274 - time (sec): 8.14 - samples/sec: 1875.26 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:26:22,184 epoch 5 - iter 54/95 - loss 0.11393605 - time (sec): 9.98 - samples/sec: 1917.94 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:26:24,209 epoch 5 - iter 63/95 - loss 0.10482095 - time (sec): 12.01 - samples/sec: 1903.57 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:26:25,458 epoch 5 - iter 72/95 - loss 0.10370554 - time (sec): 13.25 - samples/sec: 1931.40 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:26:27,975 epoch 5 - iter 81/95 - loss 0.09741425 - time (sec): 15.77 - samples/sec: 1862.26 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:26:29,980 epoch 5 - iter 90/95 - loss 0.09710361 - time (sec): 17.78 - samples/sec: 1841.82 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:26:30,838 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:30,838 EPOCH 5 done: loss 0.0989 - lr: 0.000017
2024-03-26 10:26:31,822 DEV : loss 0.1644967943429947 - f1-score (micro avg)  0.9117
2024-03-26 10:26:31,823 saving best model
2024-03-26 10:26:32,270 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:33,920 epoch 6 - iter 9/95 - loss 0.10413120 - time (sec): 1.65 - samples/sec: 2011.85 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:26:35,965 epoch 6 - iter 18/95 - loss 0.08089059 - time (sec): 3.69 - samples/sec: 1835.17 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:26:37,392 epoch 6 - iter 27/95 - loss 0.08805997 - time (sec): 5.12 - samples/sec: 1866.44 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:26:39,706 epoch 6 - iter 36/95 - loss 0.07261456 - time (sec): 7.43 - samples/sec: 1727.43 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:26:41,473 epoch 6 - iter 45/95 - loss 0.06884988 - time (sec): 9.20 - samples/sec: 1748.39 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:26:43,942 epoch 6 - iter 54/95 - loss 0.07558854 - time (sec): 11.67 - samples/sec: 1725.16 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:26:45,446 epoch 6 - iter 63/95 - loss 0.07659241 - time (sec): 13.17 - samples/sec: 1742.49 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:26:46,973 epoch 6 - iter 72/95 - loss 0.07788665 - time (sec): 14.70 - samples/sec: 1764.59 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:26:49,046 epoch 6 - iter 81/95 - loss 0.07804266 - time (sec): 16.77 - samples/sec: 1757.52 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:26:50,216 epoch 6 - iter 90/95 - loss 0.08145631 - time (sec): 17.94 - samples/sec: 1802.53 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:26:51,543 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:51,543 EPOCH 6 done: loss 0.0800 - lr: 0.000014
2024-03-26 10:26:52,437 DEV : loss 0.16239121556282043 - f1-score (micro avg)  0.9151
2024-03-26 10:26:52,438 saving best model
2024-03-26 10:26:52,911 ----------------------------------------------------------------------------------------------------
2024-03-26 10:26:54,266 epoch 7 - iter 9/95 - loss 0.05622469 - time (sec): 1.35 - samples/sec: 2343.97 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:26:56,378 epoch 7 - iter 18/95 - loss 0.05406749 - time (sec): 3.47 - samples/sec: 1945.67 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:26:58,265 epoch 7 - iter 27/95 - loss 0.06078903 - time (sec): 5.35 - samples/sec: 1826.18 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:26:59,548 epoch 7 - iter 36/95 - loss 0.05888607 - time (sec): 6.64 - samples/sec: 1885.06 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:27:01,230 epoch 7 - iter 45/95 - loss 0.05971791 - time (sec): 8.32 - samples/sec: 1891.63 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:27:03,423 epoch 7 - iter 54/95 - loss 0.05716422 - time (sec): 10.51 - samples/sec: 1864.14 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:27:05,458 epoch 7 - iter 63/95 - loss 0.05760654 - time (sec): 12.55 - samples/sec: 1814.82 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:27:07,579 epoch 7 - iter 72/95 - loss 0.05620365 - time (sec): 14.67 - samples/sec: 1789.21 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:27:09,084 epoch 7 - iter 81/95 - loss 0.06064974 - time (sec): 16.17 - samples/sec: 1795.78 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:27:10,952 epoch 7 - iter 90/95 - loss 0.06445434 - time (sec): 18.04 - samples/sec: 1823.26 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:27:11,633 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:11,633 EPOCH 7 done: loss 0.0640 - lr: 0.000010
2024-03-26 10:27:12,537 DEV : loss 0.15409202873706818 - f1-score (micro avg)  0.9208
2024-03-26 10:27:12,538 saving best model
2024-03-26 10:27:12,998 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:14,640 epoch 8 - iter 9/95 - loss 0.02608247 - time (sec): 1.64 - samples/sec: 1793.80 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:27:16,763 epoch 8 - iter 18/95 - loss 0.03144674 - time (sec): 3.76 - samples/sec: 1761.66 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:27:18,610 epoch 8 - iter 27/95 - loss 0.04433786 - time (sec): 5.61 - samples/sec: 1729.74 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:27:20,578 epoch 8 - iter 36/95 - loss 0.04263690 - time (sec): 7.58 - samples/sec: 1738.07 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:27:21,601 epoch 8 - iter 45/95 - loss 0.04899586 - time (sec): 8.60 - samples/sec: 1823.91 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:27:23,528 epoch 8 - iter 54/95 - loss 0.04889158 - time (sec): 10.53 - samples/sec: 1814.37 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:27:25,748 epoch 8 - iter 63/95 - loss 0.05325093 - time (sec): 12.75 - samples/sec: 1797.74 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:27:27,949 epoch 8 - iter 72/95 - loss 0.05321026 - time (sec): 14.95 - samples/sec: 1788.16 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:27:29,646 epoch 8 - iter 81/95 - loss 0.05236218 - time (sec): 16.65 - samples/sec: 1792.92 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:27:31,568 epoch 8 - iter 90/95 - loss 0.04940129 - time (sec): 18.57 - samples/sec: 1788.03 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:27:32,166 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:32,166 EPOCH 8 done: loss 0.0496 - lr: 0.000007
2024-03-26 10:27:33,063 DEV : loss 0.16207414865493774 - f1-score (micro avg)  0.9223
2024-03-26 10:27:33,064 saving best model
2024-03-26 10:27:33,538 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:35,061 epoch 9 - iter 9/95 - loss 0.04824836 - time (sec): 1.52 - samples/sec: 2091.15 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:27:37,352 epoch 9 - iter 18/95 - loss 0.04164786 - time (sec): 3.81 - samples/sec: 1786.39 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:27:38,944 epoch 9 - iter 27/95 - loss 0.03450957 - time (sec): 5.40 - samples/sec: 1803.48 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:27:41,246 epoch 9 - iter 36/95 - loss 0.03847090 - time (sec): 7.71 - samples/sec: 1760.67 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:27:43,135 epoch 9 - iter 45/95 - loss 0.03682640 - time (sec): 9.59 - samples/sec: 1734.04 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:27:44,497 epoch 9 - iter 54/95 - loss 0.04046342 - time (sec): 10.96 - samples/sec: 1780.92 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:27:46,572 epoch 9 - iter 63/95 - loss 0.03871736 - time (sec): 13.03 - samples/sec: 1759.33 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:27:47,792 epoch 9 - iter 72/95 - loss 0.04374224 - time (sec): 14.25 - samples/sec: 1793.60 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:27:50,554 epoch 9 - iter 81/95 - loss 0.04236482 - time (sec): 17.01 - samples/sec: 1745.39 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:27:52,199 epoch 9 - iter 90/95 - loss 0.04069339 - time (sec): 18.66 - samples/sec: 1765.59 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:27:52,862 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:52,862 EPOCH 9 done: loss 0.0423 - lr: 0.000004
2024-03-26 10:27:53,757 DEV : loss 0.1653764247894287 - f1-score (micro avg)  0.9211
2024-03-26 10:27:53,759 ----------------------------------------------------------------------------------------------------
2024-03-26 10:27:55,594 epoch 10 - iter 9/95 - loss 0.04135826 - time (sec): 1.84 - samples/sec: 1689.72 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:27:57,850 epoch 10 - iter 18/95 - loss 0.04412331 - time (sec): 4.09 - samples/sec: 1629.02 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:27:59,259 epoch 10 - iter 27/95 - loss 0.03803873 - time (sec): 5.50 - samples/sec: 1783.48 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:28:00,977 epoch 10 - iter 36/95 - loss 0.03579299 - time (sec): 7.22 - samples/sec: 1828.24 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:28:02,390 epoch 10 - iter 45/95 - loss 0.03456453 - time (sec): 8.63 - samples/sec: 1860.61 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:28:03,413 epoch 10 - iter 54/95 - loss 0.03349836 - time (sec): 9.65 - samples/sec: 1932.99 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:28:05,218 epoch 10 - iter 63/95 - loss 0.03079665 - time (sec): 11.46 - samples/sec: 1907.53 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:28:07,472 epoch 10 - iter 72/95 - loss 0.03522809 - time (sec): 13.71 - samples/sec: 1859.15 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:28:09,104 epoch 10 - iter 81/95 - loss 0.03835410 - time (sec): 15.35 - samples/sec: 1851.59 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:28:11,405 epoch 10 - iter 90/95 - loss 0.03677696 - time (sec): 17.65 - samples/sec: 1841.74 - lr: 0.000000 - momentum: 0.000000
2024-03-26 10:28:12,644 ----------------------------------------------------------------------------------------------------
2024-03-26 10:28:12,644 EPOCH 10 done: loss 0.0370 - lr: 0.000000
2024-03-26 10:28:13,542 DEV : loss 0.16609874367713928 - f1-score (micro avg)  0.9295
2024-03-26 10:28:13,543 saving best model
2024-03-26 10:28:14,275 ----------------------------------------------------------------------------------------------------
2024-03-26 10:28:14,275 Loading model from best epoch ...
2024-03-26 10:28:15,185 SequenceTagger predicts: Dictionary with 17 tags: O, S-Unternehmen, B-Unternehmen, E-Unternehmen, I-Unternehmen, S-Auslagerung, B-Auslagerung, E-Auslagerung, I-Auslagerung, S-Ort, B-Ort, E-Ort, I-Ort, S-Software, B-Software, E-Software, I-Software
2024-03-26 10:28:15,938 
Results:
- F-score (micro) 0.9085
- F-score (macro) 0.6896
- Accuracy 0.8371

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9360    0.8797    0.9070       266
 Auslagerung     0.8561    0.9076    0.8811       249
         Ort     0.9632    0.9776    0.9704       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9064    0.9106    0.9085       649
   macro avg     0.6888    0.6912    0.6896       649
weighted avg     0.9110    0.9106    0.9101       649

2024-03-26 10:28:15,938 ----------------------------------------------------------------------------------------------------