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flair-co-funer-gbert_base-bs8-e10-lr5e-05-3

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-lr5e-05-3
File size: 23,770 Bytes 
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2024-03-26 10:12:07,550 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 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 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Train:  758 sentences
2024-03-26 10:12:07,551         (train_with_dev=False, train_with_test=False)
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Training Params:
2024-03-26 10:12:07,551  - learning_rate: "5e-05" 
2024-03-26 10:12:07,551  - mini_batch_size: "8"
2024-03-26 10:12:07,551  - max_epochs: "10"
2024-03-26 10:12:07,551  - shuffle: "True"
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Plugins:
2024-03-26 10:12:07,551  - TensorboardLogger
2024-03-26 10:12:07,551  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 10:12:07,551  - metric: "('micro avg', 'f1-score')"
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Computation:
2024-03-26 10:12:07,551  - compute on device: cuda:0
2024-03-26 10:12:07,551  - embedding storage: none
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Model training base path: "flair-co-funer-gbert_base-bs8-e10-lr5e-05-3"
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:07,551 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 10:12:08,913 epoch 1 - iter 9/95 - loss 3.30878654 - time (sec): 1.36 - samples/sec: 2342.42 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:12:10,727 epoch 1 - iter 18/95 - loss 3.10425395 - time (sec): 3.18 - samples/sec: 1988.82 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:12:12,640 epoch 1 - iter 27/95 - loss 2.80107707 - time (sec): 5.09 - samples/sec: 1941.49 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:12:14,009 epoch 1 - iter 36/95 - loss 2.56832707 - time (sec): 6.46 - samples/sec: 1959.16 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:12:15,906 epoch 1 - iter 45/95 - loss 2.38441941 - time (sec): 8.35 - samples/sec: 1941.76 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:12:17,254 epoch 1 - iter 54/95 - loss 2.23503114 - time (sec): 9.70 - samples/sec: 1967.22 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:12:18,501 epoch 1 - iter 63/95 - loss 2.10127196 - time (sec): 10.95 - samples/sec: 1994.22 - lr: 0.000033 - momentum: 0.000000
2024-03-26 10:12:20,434 epoch 1 - iter 72/95 - loss 1.93262394 - time (sec): 12.88 - samples/sec: 1980.87 - lr: 0.000037 - momentum: 0.000000
2024-03-26 10:12:22,397 epoch 1 - iter 81/95 - loss 1.77367380 - time (sec): 14.85 - samples/sec: 1966.51 - lr: 0.000042 - momentum: 0.000000
2024-03-26 10:12:23,920 epoch 1 - iter 90/95 - loss 1.66263281 - time (sec): 16.37 - samples/sec: 1984.51 - lr: 0.000047 - momentum: 0.000000
2024-03-26 10:12:24,965 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:24,965 EPOCH 1 done: loss 1.5923 - lr: 0.000047
2024-03-26 10:12:25,850 DEV : loss 0.4564521014690399 - f1-score (micro avg)  0.6914
2024-03-26 10:12:25,851 saving best model
2024-03-26 10:12:26,114 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:27,479 epoch 2 - iter 9/95 - loss 0.52426643 - time (sec): 1.36 - samples/sec: 2007.72 - lr: 0.000050 - momentum: 0.000000
2024-03-26 10:12:29,308 epoch 2 - iter 18/95 - loss 0.41660600 - time (sec): 3.19 - samples/sec: 1913.27 - lr: 0.000049 - momentum: 0.000000
2024-03-26 10:12:30,481 epoch 2 - iter 27/95 - loss 0.40217596 - time (sec): 4.37 - samples/sec: 1965.47 - lr: 0.000048 - momentum: 0.000000
2024-03-26 10:12:32,723 epoch 2 - iter 36/95 - loss 0.38722517 - time (sec): 6.61 - samples/sec: 1918.56 - lr: 0.000048 - momentum: 0.000000
2024-03-26 10:12:34,656 epoch 2 - iter 45/95 - loss 0.37676757 - time (sec): 8.54 - samples/sec: 1925.24 - lr: 0.000047 - momentum: 0.000000
2024-03-26 10:12:36,809 epoch 2 - iter 54/95 - loss 0.36567606 - time (sec): 10.69 - samples/sec: 1897.06 - lr: 0.000047 - momentum: 0.000000
2024-03-26 10:12:38,805 epoch 2 - iter 63/95 - loss 0.35177076 - time (sec): 12.69 - samples/sec: 1852.39 - lr: 0.000046 - momentum: 0.000000
2024-03-26 10:12:40,311 epoch 2 - iter 72/95 - loss 0.35231443 - time (sec): 14.20 - samples/sec: 1860.87 - lr: 0.000046 - momentum: 0.000000
2024-03-26 10:12:41,751 epoch 2 - iter 81/95 - loss 0.35619563 - time (sec): 15.64 - samples/sec: 1883.95 - lr: 0.000045 - momentum: 0.000000
2024-03-26 10:12:43,960 epoch 2 - iter 90/95 - loss 0.34268426 - time (sec): 17.85 - samples/sec: 1858.76 - lr: 0.000045 - momentum: 0.000000
2024-03-26 10:12:44,600 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:44,600 EPOCH 2 done: loss 0.3389 - lr: 0.000045
2024-03-26 10:12:45,487 DEV : loss 0.2517702877521515 - f1-score (micro avg)  0.8598
2024-03-26 10:12:45,488 saving best model
2024-03-26 10:12:45,916 ----------------------------------------------------------------------------------------------------
2024-03-26 10:12:47,544 epoch 3 - iter 9/95 - loss 0.18626350 - time (sec): 1.63 - samples/sec: 1836.16 - lr: 0.000044 - momentum: 0.000000
2024-03-26 10:12:49,324 epoch 3 - iter 18/95 - loss 0.16963537 - time (sec): 3.41 - samples/sec: 1858.50 - lr: 0.000043 - momentum: 0.000000
2024-03-26 10:12:50,515 epoch 3 - iter 27/95 - loss 0.18166954 - time (sec): 4.60 - samples/sec: 2032.19 - lr: 0.000043 - momentum: 0.000000
2024-03-26 10:12:52,065 epoch 3 - iter 36/95 - loss 0.17639533 - time (sec): 6.15 - samples/sec: 2020.76 - lr: 0.000042 - momentum: 0.000000
2024-03-26 10:12:53,470 epoch 3 - iter 45/95 - loss 0.17991451 - time (sec): 7.55 - samples/sec: 2029.39 - lr: 0.000042 - momentum: 0.000000
2024-03-26 10:12:55,457 epoch 3 - iter 54/95 - loss 0.17836259 - time (sec): 9.54 - samples/sec: 1981.19 - lr: 0.000041 - momentum: 0.000000
2024-03-26 10:12:57,451 epoch 3 - iter 63/95 - loss 0.17801210 - time (sec): 11.53 - samples/sec: 1930.20 - lr: 0.000041 - momentum: 0.000000
2024-03-26 10:12:59,290 epoch 3 - iter 72/95 - loss 0.17908359 - time (sec): 13.37 - samples/sec: 1912.74 - lr: 0.000040 - momentum: 0.000000
2024-03-26 10:13:01,293 epoch 3 - iter 81/95 - loss 0.17473687 - time (sec): 15.38 - samples/sec: 1885.59 - lr: 0.000040 - momentum: 0.000000
2024-03-26 10:13:03,243 epoch 3 - iter 90/95 - loss 0.18331440 - time (sec): 17.33 - samples/sec: 1887.20 - lr: 0.000039 - momentum: 0.000000
2024-03-26 10:13:04,335 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:04,336 EPOCH 3 done: loss 0.1780 - lr: 0.000039
2024-03-26 10:13:05,235 DEV : loss 0.21343179047107697 - f1-score (micro avg)  0.8682
2024-03-26 10:13:05,236 saving best model
2024-03-26 10:13:05,669 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:06,950 epoch 4 - iter 9/95 - loss 0.12934404 - time (sec): 1.28 - samples/sec: 2171.15 - lr: 0.000039 - momentum: 0.000000
2024-03-26 10:13:08,782 epoch 4 - iter 18/95 - loss 0.11598588 - time (sec): 3.11 - samples/sec: 1975.63 - lr: 0.000038 - momentum: 0.000000
2024-03-26 10:13:10,695 epoch 4 - iter 27/95 - loss 0.11184561 - time (sec): 5.02 - samples/sec: 1922.31 - lr: 0.000037 - momentum: 0.000000
2024-03-26 10:13:12,169 epoch 4 - iter 36/95 - loss 0.10779297 - time (sec): 6.50 - samples/sec: 1930.71 - lr: 0.000037 - momentum: 0.000000
2024-03-26 10:13:14,583 epoch 4 - iter 45/95 - loss 0.10822640 - time (sec): 8.91 - samples/sec: 1854.16 - lr: 0.000036 - momentum: 0.000000
2024-03-26 10:13:16,423 epoch 4 - iter 54/95 - loss 0.10627905 - time (sec): 10.75 - samples/sec: 1835.61 - lr: 0.000036 - momentum: 0.000000
2024-03-26 10:13:18,347 epoch 4 - iter 63/95 - loss 0.10563130 - time (sec): 12.68 - samples/sec: 1814.36 - lr: 0.000035 - momentum: 0.000000
2024-03-26 10:13:20,216 epoch 4 - iter 72/95 - loss 0.11061803 - time (sec): 14.54 - samples/sec: 1829.28 - lr: 0.000035 - momentum: 0.000000
2024-03-26 10:13:22,230 epoch 4 - iter 81/95 - loss 0.11629637 - time (sec): 16.56 - samples/sec: 1825.85 - lr: 0.000034 - momentum: 0.000000
2024-03-26 10:13:23,199 epoch 4 - iter 90/95 - loss 0.11624132 - time (sec): 17.53 - samples/sec: 1865.91 - lr: 0.000034 - momentum: 0.000000
2024-03-26 10:13:24,226 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:24,226 EPOCH 4 done: loss 0.1162 - lr: 0.000034
2024-03-26 10:13:25,126 DEV : loss 0.17539489269256592 - f1-score (micro avg)  0.9069
2024-03-26 10:13:25,128 saving best model
2024-03-26 10:13:25,559 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:27,443 epoch 5 - iter 9/95 - loss 0.07744264 - time (sec): 1.88 - samples/sec: 1825.68 - lr: 0.000033 - momentum: 0.000000
2024-03-26 10:13:28,876 epoch 5 - iter 18/95 - loss 0.07305274 - time (sec): 3.32 - samples/sec: 1887.57 - lr: 0.000032 - momentum: 0.000000
2024-03-26 10:13:30,235 epoch 5 - iter 27/95 - loss 0.08372766 - time (sec): 4.67 - samples/sec: 1931.87 - lr: 0.000032 - momentum: 0.000000
2024-03-26 10:13:32,114 epoch 5 - iter 36/95 - loss 0.08762107 - time (sec): 6.55 - samples/sec: 1872.88 - lr: 0.000031 - momentum: 0.000000
2024-03-26 10:13:34,321 epoch 5 - iter 45/95 - loss 0.08538004 - time (sec): 8.76 - samples/sec: 1858.35 - lr: 0.000031 - momentum: 0.000000
2024-03-26 10:13:36,754 epoch 5 - iter 54/95 - loss 0.08449088 - time (sec): 11.19 - samples/sec: 1814.50 - lr: 0.000030 - momentum: 0.000000
2024-03-26 10:13:38,420 epoch 5 - iter 63/95 - loss 0.08194794 - time (sec): 12.86 - samples/sec: 1806.64 - lr: 0.000030 - momentum: 0.000000
2024-03-26 10:13:40,188 epoch 5 - iter 72/95 - loss 0.08370354 - time (sec): 14.63 - samples/sec: 1808.03 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:13:42,386 epoch 5 - iter 81/95 - loss 0.08624660 - time (sec): 16.83 - samples/sec: 1793.37 - lr: 0.000029 - momentum: 0.000000
2024-03-26 10:13:43,778 epoch 5 - iter 90/95 - loss 0.08725992 - time (sec): 18.22 - samples/sec: 1809.23 - lr: 0.000028 - momentum: 0.000000
2024-03-26 10:13:44,549 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:44,549 EPOCH 5 done: loss 0.0853 - lr: 0.000028
2024-03-26 10:13:45,528 DEV : loss 0.15603235363960266 - f1-score (micro avg)  0.9191
2024-03-26 10:13:45,529 saving best model
2024-03-26 10:13:45,955 ----------------------------------------------------------------------------------------------------
2024-03-26 10:13:47,889 epoch 6 - iter 9/95 - loss 0.05189760 - time (sec): 1.93 - samples/sec: 1805.09 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:13:49,440 epoch 6 - iter 18/95 - loss 0.05290575 - time (sec): 3.48 - samples/sec: 1826.05 - lr: 0.000027 - momentum: 0.000000
2024-03-26 10:13:51,348 epoch 6 - iter 27/95 - loss 0.05365903 - time (sec): 5.39 - samples/sec: 1834.17 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:13:52,910 epoch 6 - iter 36/95 - loss 0.05604796 - time (sec): 6.95 - samples/sec: 1834.10 - lr: 0.000026 - momentum: 0.000000
2024-03-26 10:13:54,366 epoch 6 - iter 45/95 - loss 0.05515355 - time (sec): 8.41 - samples/sec: 1870.09 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:13:55,813 epoch 6 - iter 54/95 - loss 0.05376542 - time (sec): 9.86 - samples/sec: 1868.77 - lr: 0.000025 - momentum: 0.000000
2024-03-26 10:13:57,095 epoch 6 - iter 63/95 - loss 0.05273952 - time (sec): 11.14 - samples/sec: 1930.27 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:13:59,339 epoch 6 - iter 72/95 - loss 0.06092265 - time (sec): 13.38 - samples/sec: 1897.59 - lr: 0.000024 - momentum: 0.000000
2024-03-26 10:14:00,927 epoch 6 - iter 81/95 - loss 0.05912352 - time (sec): 14.97 - samples/sec: 1914.17 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:14:02,632 epoch 6 - iter 90/95 - loss 0.06035882 - time (sec): 16.68 - samples/sec: 1932.85 - lr: 0.000023 - momentum: 0.000000
2024-03-26 10:14:03,897 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:03,897 EPOCH 6 done: loss 0.0603 - lr: 0.000023
2024-03-26 10:14:04,793 DEV : loss 0.16718925535678864 - f1-score (micro avg)  0.9201
2024-03-26 10:14:04,794 saving best model
2024-03-26 10:14:05,238 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:07,130 epoch 7 - iter 9/95 - loss 0.05321789 - time (sec): 1.89 - samples/sec: 1679.94 - lr: 0.000022 - momentum: 0.000000
2024-03-26 10:14:09,176 epoch 7 - iter 18/95 - loss 0.03684477 - time (sec): 3.94 - samples/sec: 1665.45 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:14:10,709 epoch 7 - iter 27/95 - loss 0.03258736 - time (sec): 5.47 - samples/sec: 1788.71 - lr: 0.000021 - momentum: 0.000000
2024-03-26 10:14:12,654 epoch 7 - iter 36/95 - loss 0.03305511 - time (sec): 7.41 - samples/sec: 1777.21 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:14:15,035 epoch 7 - iter 45/95 - loss 0.03992535 - time (sec): 9.80 - samples/sec: 1770.36 - lr: 0.000020 - momentum: 0.000000
2024-03-26 10:14:16,548 epoch 7 - iter 54/95 - loss 0.03978942 - time (sec): 11.31 - samples/sec: 1778.83 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:14:18,735 epoch 7 - iter 63/95 - loss 0.04213624 - time (sec): 13.49 - samples/sec: 1784.59 - lr: 0.000019 - momentum: 0.000000
2024-03-26 10:14:20,525 epoch 7 - iter 72/95 - loss 0.04587297 - time (sec): 15.29 - samples/sec: 1790.89 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:14:21,947 epoch 7 - iter 81/95 - loss 0.04361343 - time (sec): 16.71 - samples/sec: 1802.97 - lr: 0.000018 - momentum: 0.000000
2024-03-26 10:14:23,915 epoch 7 - iter 90/95 - loss 0.04536795 - time (sec): 18.68 - samples/sec: 1783.65 - lr: 0.000017 - momentum: 0.000000
2024-03-26 10:14:24,399 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:24,399 EPOCH 7 done: loss 0.0462 - lr: 0.000017
2024-03-26 10:14:25,307 DEV : loss 0.16716967523097992 - f1-score (micro avg)  0.9411
2024-03-26 10:14:25,308 saving best model
2024-03-26 10:14:25,747 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:27,627 epoch 8 - iter 9/95 - loss 0.01951604 - time (sec): 1.88 - samples/sec: 1708.16 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:14:30,113 epoch 8 - iter 18/95 - loss 0.01840664 - time (sec): 4.36 - samples/sec: 1697.69 - lr: 0.000016 - momentum: 0.000000
2024-03-26 10:14:31,879 epoch 8 - iter 27/95 - loss 0.02281365 - time (sec): 6.13 - samples/sec: 1736.13 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:14:33,422 epoch 8 - iter 36/95 - loss 0.02354290 - time (sec): 7.67 - samples/sec: 1728.02 - lr: 0.000015 - momentum: 0.000000
2024-03-26 10:14:34,930 epoch 8 - iter 45/95 - loss 0.02180613 - time (sec): 9.18 - samples/sec: 1759.24 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:14:36,592 epoch 8 - iter 54/95 - loss 0.02226186 - time (sec): 10.84 - samples/sec: 1779.13 - lr: 0.000014 - momentum: 0.000000
2024-03-26 10:14:38,783 epoch 8 - iter 63/95 - loss 0.03092783 - time (sec): 13.03 - samples/sec: 1775.62 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:14:41,032 epoch 8 - iter 72/95 - loss 0.03257996 - time (sec): 15.28 - samples/sec: 1756.12 - lr: 0.000013 - momentum: 0.000000
2024-03-26 10:14:42,690 epoch 8 - iter 81/95 - loss 0.03612619 - time (sec): 16.94 - samples/sec: 1758.38 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:14:43,981 epoch 8 - iter 90/95 - loss 0.03615101 - time (sec): 18.23 - samples/sec: 1800.86 - lr: 0.000012 - momentum: 0.000000
2024-03-26 10:14:44,878 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:44,878 EPOCH 8 done: loss 0.0350 - lr: 0.000012
2024-03-26 10:14:45,778 DEV : loss 0.1608782857656479 - f1-score (micro avg)  0.9517
2024-03-26 10:14:45,779 saving best model
2024-03-26 10:14:46,223 ----------------------------------------------------------------------------------------------------
2024-03-26 10:14:48,197 epoch 9 - iter 9/95 - loss 0.01453760 - time (sec): 1.97 - samples/sec: 1787.94 - lr: 0.000011 - momentum: 0.000000
2024-03-26 10:14:49,915 epoch 9 - iter 18/95 - loss 0.02446034 - time (sec): 3.69 - samples/sec: 1813.90 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:14:51,796 epoch 9 - iter 27/95 - loss 0.02473284 - time (sec): 5.57 - samples/sec: 1834.03 - lr: 0.000010 - momentum: 0.000000
2024-03-26 10:14:53,651 epoch 9 - iter 36/95 - loss 0.02318813 - time (sec): 7.43 - samples/sec: 1828.97 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:14:55,908 epoch 9 - iter 45/95 - loss 0.02045251 - time (sec): 9.68 - samples/sec: 1750.46 - lr: 0.000009 - momentum: 0.000000
2024-03-26 10:14:57,835 epoch 9 - iter 54/95 - loss 0.02611207 - time (sec): 11.61 - samples/sec: 1738.74 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:14:59,723 epoch 9 - iter 63/95 - loss 0.02537917 - time (sec): 13.50 - samples/sec: 1749.31 - lr: 0.000008 - momentum: 0.000000
2024-03-26 10:15:01,602 epoch 9 - iter 72/95 - loss 0.02461298 - time (sec): 15.38 - samples/sec: 1752.48 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:15:02,853 epoch 9 - iter 81/95 - loss 0.02473164 - time (sec): 16.63 - samples/sec: 1775.52 - lr: 0.000007 - momentum: 0.000000
2024-03-26 10:15:04,257 epoch 9 - iter 90/95 - loss 0.02719568 - time (sec): 18.03 - samples/sec: 1797.34 - lr: 0.000006 - momentum: 0.000000
2024-03-26 10:15:05,204 ----------------------------------------------------------------------------------------------------
2024-03-26 10:15:05,204 EPOCH 9 done: loss 0.0265 - lr: 0.000006
2024-03-26 10:15:06,105 DEV : loss 0.18035191297531128 - f1-score (micro avg)  0.9468
2024-03-26 10:15:06,106 ----------------------------------------------------------------------------------------------------
2024-03-26 10:15:08,246 epoch 10 - iter 9/95 - loss 0.00557531 - time (sec): 2.14 - samples/sec: 1781.65 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:15:09,506 epoch 10 - iter 18/95 - loss 0.00880795 - time (sec): 3.40 - samples/sec: 1904.54 - lr: 0.000005 - momentum: 0.000000
2024-03-26 10:15:10,813 epoch 10 - iter 27/95 - loss 0.02526886 - time (sec): 4.71 - samples/sec: 2014.92 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:15:12,166 epoch 10 - iter 36/95 - loss 0.02295782 - time (sec): 6.06 - samples/sec: 2035.49 - lr: 0.000004 - momentum: 0.000000
2024-03-26 10:15:14,123 epoch 10 - iter 45/95 - loss 0.01953834 - time (sec): 8.02 - samples/sec: 1986.50 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:15:15,695 epoch 10 - iter 54/95 - loss 0.01921737 - time (sec): 9.59 - samples/sec: 1977.11 - lr: 0.000003 - momentum: 0.000000
2024-03-26 10:15:18,215 epoch 10 - iter 63/95 - loss 0.02060934 - time (sec): 12.11 - samples/sec: 1898.74 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:15:19,503 epoch 10 - iter 72/95 - loss 0.01949359 - time (sec): 13.40 - samples/sec: 1903.48 - lr: 0.000002 - momentum: 0.000000
2024-03-26 10:15:21,840 epoch 10 - iter 81/95 - loss 0.01845566 - time (sec): 15.73 - samples/sec: 1851.36 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:15:24,060 epoch 10 - iter 90/95 - loss 0.02088092 - time (sec): 17.95 - samples/sec: 1831.79 - lr: 0.000001 - momentum: 0.000000
2024-03-26 10:15:25,122 ----------------------------------------------------------------------------------------------------
2024-03-26 10:15:25,122 EPOCH 10 done: loss 0.0206 - lr: 0.000001
2024-03-26 10:15:26,019 DEV : loss 0.18286916613578796 - f1-score (micro avg)  0.9417
2024-03-26 10:15:26,304 ----------------------------------------------------------------------------------------------------
2024-03-26 10:15:26,304 Loading model from best epoch ...
2024-03-26 10:15:27,155 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 10:15:27,901 
Results:
- F-score (micro) 0.9163
- F-score (macro) 0.6959
- Accuracy 0.8504

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9173    0.8759    0.8962       266
 Auslagerung     0.8851    0.9277    0.9059       249
         Ort     0.9708    0.9925    0.9815       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9128    0.9199    0.9163       649
   macro avg     0.6933    0.6990    0.6959       649
weighted avg     0.9160    0.9199    0.9175       649

2024-03-26 10:15:27,901 ----------------------------------------------------------------------------------------------------