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flair-co-funer-gbert_base-bs16-e10-lr5e-05-2

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-bs16-e10-lr5e-05-2
File size: 26,566 Bytes 
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2024-03-26 09:47:48,954 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,954 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 09:47:48,954 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Train:  758 sentences
2024-03-26 09:47:48,955         (train_with_dev=False, train_with_test=False)
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Training Params:
2024-03-26 09:47:48,955  - learning_rate: "5e-05" 
2024-03-26 09:47:48,955  - mini_batch_size: "16"
2024-03-26 09:47:48,955  - max_epochs: "10"
2024-03-26 09:47:48,955  - shuffle: "True"
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Plugins:
2024-03-26 09:47:48,955  - TensorboardLogger
2024-03-26 09:47:48,955  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 09:47:48,955  - metric: "('micro avg', 'f1-score')"
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Computation:
2024-03-26 09:47:48,955  - compute on device: cuda:0
2024-03-26 09:47:48,955  - embedding storage: none
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Model training base path: "flair-co-funer-gbert_base-bs16-e10-lr5e-05-2"
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 ----------------------------------------------------------------------------------------------------
2024-03-26 09:47:48,955 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 09:47:50,666 epoch 1 - iter 4/48 - loss 3.53642249 - time (sec): 1.71 - samples/sec: 1765.99 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:47:52,742 epoch 1 - iter 8/48 - loss 3.42388110 - time (sec): 3.79 - samples/sec: 1639.55 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:47:54,571 epoch 1 - iter 12/48 - loss 3.28212646 - time (sec): 5.62 - samples/sec: 1587.46 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:47:56,564 epoch 1 - iter 16/48 - loss 3.05656396 - time (sec): 7.61 - samples/sec: 1594.65 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:47:58,739 epoch 1 - iter 20/48 - loss 2.85927097 - time (sec): 9.78 - samples/sec: 1561.84 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:48:01,755 epoch 1 - iter 24/48 - loss 2.71663771 - time (sec): 12.80 - samples/sec: 1420.28 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:48:04,147 epoch 1 - iter 28/48 - loss 2.57661348 - time (sec): 15.19 - samples/sec: 1402.94 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:48:04,965 epoch 1 - iter 32/48 - loss 2.47900786 - time (sec): 16.01 - samples/sec: 1458.52 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:48:06,218 epoch 1 - iter 36/48 - loss 2.36200311 - time (sec): 17.26 - samples/sec: 1514.86 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:48:08,077 epoch 1 - iter 40/48 - loss 2.25770330 - time (sec): 19.12 - samples/sec: 1521.40 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:48:09,948 epoch 1 - iter 44/48 - loss 2.14726220 - time (sec): 20.99 - samples/sec: 1521.98 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:48:11,291 epoch 1 - iter 48/48 - loss 2.05694147 - time (sec): 22.34 - samples/sec: 1543.33 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:48:11,292 ----------------------------------------------------------------------------------------------------
2024-03-26 09:48:11,292 EPOCH 1 done: loss 2.0569 - lr: 0.000049
2024-03-26 09:48:12,093 DEV : loss 0.5836076736450195 - f1-score (micro avg)  0.6019
2024-03-26 09:48:12,094 saving best model
2024-03-26 09:48:12,373 ----------------------------------------------------------------------------------------------------
2024-03-26 09:48:13,675 epoch 2 - iter 4/48 - loss 0.88111172 - time (sec): 1.30 - samples/sec: 2230.04 - lr: 0.000050 - momentum: 0.000000
2024-03-26 09:48:15,489 epoch 2 - iter 8/48 - loss 0.72062731 - time (sec): 3.11 - samples/sec: 1957.65 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:48:18,899 epoch 2 - iter 12/48 - loss 0.62239608 - time (sec): 6.52 - samples/sec: 1559.76 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:48:21,358 epoch 2 - iter 16/48 - loss 0.57386908 - time (sec): 8.98 - samples/sec: 1482.50 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:48:23,990 epoch 2 - iter 20/48 - loss 0.53883182 - time (sec): 11.62 - samples/sec: 1430.14 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:48:25,862 epoch 2 - iter 24/48 - loss 0.50635336 - time (sec): 13.49 - samples/sec: 1429.34 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:48:27,632 epoch 2 - iter 28/48 - loss 0.50264568 - time (sec): 15.26 - samples/sec: 1437.42 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:48:29,329 epoch 2 - iter 32/48 - loss 0.49297626 - time (sec): 16.95 - samples/sec: 1451.15 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:48:31,161 epoch 2 - iter 36/48 - loss 0.48197253 - time (sec): 18.79 - samples/sec: 1460.12 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:48:32,170 epoch 2 - iter 40/48 - loss 0.47295079 - time (sec): 19.80 - samples/sec: 1507.97 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:48:33,591 epoch 2 - iter 44/48 - loss 0.47014713 - time (sec): 21.22 - samples/sec: 1527.80 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:48:35,108 epoch 2 - iter 48/48 - loss 0.45670664 - time (sec): 22.73 - samples/sec: 1516.34 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:48:35,108 ----------------------------------------------------------------------------------------------------
2024-03-26 09:48:35,108 EPOCH 2 done: loss 0.4567 - lr: 0.000045
2024-03-26 09:48:36,014 DEV : loss 0.2943086624145508 - f1-score (micro avg)  0.8037
2024-03-26 09:48:36,017 saving best model
2024-03-26 09:48:36,504 ----------------------------------------------------------------------------------------------------
2024-03-26 09:48:39,054 epoch 3 - iter 4/48 - loss 0.28683702 - time (sec): 2.55 - samples/sec: 1180.57 - lr: 0.000044 - momentum: 0.000000
2024-03-26 09:48:41,186 epoch 3 - iter 8/48 - loss 0.28361676 - time (sec): 4.68 - samples/sec: 1356.68 - lr: 0.000044 - momentum: 0.000000
2024-03-26 09:48:42,755 epoch 3 - iter 12/48 - loss 0.29235243 - time (sec): 6.25 - samples/sec: 1419.71 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:48:44,487 epoch 3 - iter 16/48 - loss 0.27063291 - time (sec): 7.98 - samples/sec: 1423.98 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:48:45,629 epoch 3 - iter 20/48 - loss 0.27417182 - time (sec): 9.12 - samples/sec: 1499.70 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:48:47,465 epoch 3 - iter 24/48 - loss 0.28225090 - time (sec): 10.96 - samples/sec: 1501.48 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:48:49,904 epoch 3 - iter 28/48 - loss 0.27774390 - time (sec): 13.40 - samples/sec: 1443.98 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:48:51,742 epoch 3 - iter 32/48 - loss 0.27232927 - time (sec): 15.24 - samples/sec: 1453.05 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:48:53,165 epoch 3 - iter 36/48 - loss 0.26448046 - time (sec): 16.66 - samples/sec: 1487.41 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:48:55,424 epoch 3 - iter 40/48 - loss 0.25424281 - time (sec): 18.92 - samples/sec: 1459.40 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:48:58,678 epoch 3 - iter 44/48 - loss 0.23593615 - time (sec): 22.17 - samples/sec: 1453.31 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:48:59,937 epoch 3 - iter 48/48 - loss 0.23057082 - time (sec): 23.43 - samples/sec: 1471.19 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:48:59,937 ----------------------------------------------------------------------------------------------------
2024-03-26 09:48:59,937 EPOCH 3 done: loss 0.2306 - lr: 0.000039
2024-03-26 09:49:00,848 DEV : loss 0.22804519534111023 - f1-score (micro avg)  0.8586
2024-03-26 09:49:00,849 saving best model
2024-03-26 09:49:01,314 ----------------------------------------------------------------------------------------------------
2024-03-26 09:49:02,868 epoch 4 - iter 4/48 - loss 0.21189672 - time (sec): 1.55 - samples/sec: 1642.81 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:49:05,156 epoch 4 - iter 8/48 - loss 0.17986344 - time (sec): 3.84 - samples/sec: 1560.59 - lr: 0.000038 - momentum: 0.000000
2024-03-26 09:49:06,395 epoch 4 - iter 12/48 - loss 0.17779364 - time (sec): 5.08 - samples/sec: 1645.64 - lr: 0.000038 - momentum: 0.000000
2024-03-26 09:49:08,607 epoch 4 - iter 16/48 - loss 0.17969234 - time (sec): 7.29 - samples/sec: 1546.22 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:49:11,130 epoch 4 - iter 20/48 - loss 0.16736457 - time (sec): 9.81 - samples/sec: 1424.67 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:49:13,147 epoch 4 - iter 24/48 - loss 0.17458547 - time (sec): 11.83 - samples/sec: 1422.92 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:49:15,272 epoch 4 - iter 28/48 - loss 0.17017034 - time (sec): 13.96 - samples/sec: 1425.49 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:49:17,828 epoch 4 - iter 32/48 - loss 0.16887576 - time (sec): 16.51 - samples/sec: 1396.54 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:49:20,631 epoch 4 - iter 36/48 - loss 0.16050873 - time (sec): 19.32 - samples/sec: 1384.91 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:49:22,312 epoch 4 - iter 40/48 - loss 0.15575668 - time (sec): 21.00 - samples/sec: 1385.70 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:49:24,301 epoch 4 - iter 44/48 - loss 0.15602446 - time (sec): 22.98 - samples/sec: 1388.89 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:49:25,962 epoch 4 - iter 48/48 - loss 0.15352497 - time (sec): 24.65 - samples/sec: 1398.71 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:49:25,962 ----------------------------------------------------------------------------------------------------
2024-03-26 09:49:25,962 EPOCH 4 done: loss 0.1535 - lr: 0.000034
2024-03-26 09:49:26,859 DEV : loss 0.18054579198360443 - f1-score (micro avg)  0.8954
2024-03-26 09:49:26,860 saving best model
2024-03-26 09:49:27,332 ----------------------------------------------------------------------------------------------------
2024-03-26 09:49:28,158 epoch 5 - iter 4/48 - loss 0.08079552 - time (sec): 0.82 - samples/sec: 2226.15 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:49:29,524 epoch 5 - iter 8/48 - loss 0.10472471 - time (sec): 2.19 - samples/sec: 2030.78 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:49:32,260 epoch 5 - iter 12/48 - loss 0.10165691 - time (sec): 4.93 - samples/sec: 1620.07 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:49:35,210 epoch 5 - iter 16/48 - loss 0.09744302 - time (sec): 7.88 - samples/sec: 1432.77 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:49:36,587 epoch 5 - iter 20/48 - loss 0.10007066 - time (sec): 9.25 - samples/sec: 1483.65 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:49:39,035 epoch 5 - iter 24/48 - loss 0.09831137 - time (sec): 11.70 - samples/sec: 1432.01 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:49:41,096 epoch 5 - iter 28/48 - loss 0.09668980 - time (sec): 13.76 - samples/sec: 1419.78 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:49:43,343 epoch 5 - iter 32/48 - loss 0.09776114 - time (sec): 16.01 - samples/sec: 1446.91 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:49:44,798 epoch 5 - iter 36/48 - loss 0.10267083 - time (sec): 17.46 - samples/sec: 1470.89 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:49:47,328 epoch 5 - iter 40/48 - loss 0.09952572 - time (sec): 19.99 - samples/sec: 1420.97 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:49:49,405 epoch 5 - iter 44/48 - loss 0.09960116 - time (sec): 22.07 - samples/sec: 1433.36 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:49:51,340 epoch 5 - iter 48/48 - loss 0.09992566 - time (sec): 24.01 - samples/sec: 1435.95 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:49:51,341 ----------------------------------------------------------------------------------------------------
2024-03-26 09:49:51,341 EPOCH 5 done: loss 0.0999 - lr: 0.000028
2024-03-26 09:49:52,240 DEV : loss 0.16526347398757935 - f1-score (micro avg)  0.898
2024-03-26 09:49:52,241 saving best model
2024-03-26 09:49:52,672 ----------------------------------------------------------------------------------------------------
2024-03-26 09:49:54,247 epoch 6 - iter 4/48 - loss 0.07695899 - time (sec): 1.57 - samples/sec: 1583.06 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:49:56,630 epoch 6 - iter 8/48 - loss 0.08135486 - time (sec): 3.96 - samples/sec: 1617.83 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:49:58,553 epoch 6 - iter 12/48 - loss 0.08287219 - time (sec): 5.88 - samples/sec: 1540.90 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:50:00,545 epoch 6 - iter 16/48 - loss 0.08040351 - time (sec): 7.87 - samples/sec: 1540.60 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:50:03,274 epoch 6 - iter 20/48 - loss 0.08138121 - time (sec): 10.60 - samples/sec: 1507.26 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:50:04,781 epoch 6 - iter 24/48 - loss 0.09010974 - time (sec): 12.11 - samples/sec: 1528.95 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:50:06,134 epoch 6 - iter 28/48 - loss 0.09003451 - time (sec): 13.46 - samples/sec: 1535.59 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:50:07,290 epoch 6 - iter 32/48 - loss 0.08811901 - time (sec): 14.62 - samples/sec: 1556.50 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:50:08,750 epoch 6 - iter 36/48 - loss 0.08255487 - time (sec): 16.08 - samples/sec: 1588.29 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:50:10,642 epoch 6 - iter 40/48 - loss 0.08293763 - time (sec): 17.97 - samples/sec: 1577.18 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:50:12,807 epoch 6 - iter 44/48 - loss 0.07987206 - time (sec): 20.13 - samples/sec: 1597.11 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:50:14,448 epoch 6 - iter 48/48 - loss 0.07932191 - time (sec): 21.77 - samples/sec: 1583.18 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:50:14,448 ----------------------------------------------------------------------------------------------------
2024-03-26 09:50:14,448 EPOCH 6 done: loss 0.0793 - lr: 0.000023
2024-03-26 09:50:15,345 DEV : loss 0.15160244703292847 - f1-score (micro avg)  0.9208
2024-03-26 09:50:15,346 saving best model
2024-03-26 09:50:15,779 ----------------------------------------------------------------------------------------------------
2024-03-26 09:50:17,398 epoch 7 - iter 4/48 - loss 0.03393528 - time (sec): 1.62 - samples/sec: 1506.68 - lr: 0.000022 - momentum: 0.000000
2024-03-26 09:50:18,991 epoch 7 - iter 8/48 - loss 0.04828518 - time (sec): 3.21 - samples/sec: 1543.28 - lr: 0.000022 - momentum: 0.000000
2024-03-26 09:50:21,091 epoch 7 - iter 12/48 - loss 0.05131597 - time (sec): 5.31 - samples/sec: 1482.33 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:50:23,098 epoch 7 - iter 16/48 - loss 0.05011794 - time (sec): 7.32 - samples/sec: 1522.87 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:50:23,732 epoch 7 - iter 20/48 - loss 0.04799400 - time (sec): 7.95 - samples/sec: 1630.05 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:50:25,308 epoch 7 - iter 24/48 - loss 0.04996161 - time (sec): 9.53 - samples/sec: 1608.33 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:50:28,145 epoch 7 - iter 28/48 - loss 0.04902908 - time (sec): 12.36 - samples/sec: 1506.35 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:50:30,895 epoch 7 - iter 32/48 - loss 0.04822722 - time (sec): 15.11 - samples/sec: 1433.54 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:50:33,612 epoch 7 - iter 36/48 - loss 0.05162724 - time (sec): 17.83 - samples/sec: 1445.88 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:50:35,573 epoch 7 - iter 40/48 - loss 0.05440209 - time (sec): 19.79 - samples/sec: 1452.52 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:50:38,106 epoch 7 - iter 44/48 - loss 0.05663287 - time (sec): 22.32 - samples/sec: 1426.85 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:50:39,841 epoch 7 - iter 48/48 - loss 0.05640716 - time (sec): 24.06 - samples/sec: 1432.78 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:50:39,842 ----------------------------------------------------------------------------------------------------
2024-03-26 09:50:39,842 EPOCH 7 done: loss 0.0564 - lr: 0.000017
2024-03-26 09:50:40,741 DEV : loss 0.1558217704296112 - f1-score (micro avg)  0.9138
2024-03-26 09:50:40,742 ----------------------------------------------------------------------------------------------------
2024-03-26 09:50:43,456 epoch 8 - iter 4/48 - loss 0.05526487 - time (sec): 2.71 - samples/sec: 1217.07 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:50:45,503 epoch 8 - iter 8/48 - loss 0.04552815 - time (sec): 4.76 - samples/sec: 1232.56 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:50:48,677 epoch 8 - iter 12/48 - loss 0.04385440 - time (sec): 7.93 - samples/sec: 1221.41 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:50:50,591 epoch 8 - iter 16/48 - loss 0.05071680 - time (sec): 9.85 - samples/sec: 1253.33 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:50:52,022 epoch 8 - iter 20/48 - loss 0.04816494 - time (sec): 11.28 - samples/sec: 1301.96 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:50:54,424 epoch 8 - iter 24/48 - loss 0.04840064 - time (sec): 13.68 - samples/sec: 1304.28 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:50:56,158 epoch 8 - iter 28/48 - loss 0.05089514 - time (sec): 15.42 - samples/sec: 1341.41 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:50:57,810 epoch 8 - iter 32/48 - loss 0.04859750 - time (sec): 17.07 - samples/sec: 1363.04 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:50:59,089 epoch 8 - iter 36/48 - loss 0.04663531 - time (sec): 18.35 - samples/sec: 1394.40 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:51:01,385 epoch 8 - iter 40/48 - loss 0.04551566 - time (sec): 20.64 - samples/sec: 1404.50 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:51:04,178 epoch 8 - iter 44/48 - loss 0.04325259 - time (sec): 23.44 - samples/sec: 1374.72 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:51:06,078 epoch 8 - iter 48/48 - loss 0.04367567 - time (sec): 25.34 - samples/sec: 1360.59 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:51:06,079 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:06,079 EPOCH 8 done: loss 0.0437 - lr: 0.000011
2024-03-26 09:51:06,978 DEV : loss 0.1572684496641159 - f1-score (micro avg)  0.9364
2024-03-26 09:51:06,979 saving best model
2024-03-26 09:51:07,403 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:09,207 epoch 9 - iter 4/48 - loss 0.03484799 - time (sec): 1.80 - samples/sec: 1578.51 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:51:11,602 epoch 9 - iter 8/48 - loss 0.02955485 - time (sec): 4.20 - samples/sec: 1461.06 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:51:13,938 epoch 9 - iter 12/48 - loss 0.04064986 - time (sec): 6.53 - samples/sec: 1413.01 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:51:15,963 epoch 9 - iter 16/48 - loss 0.04182541 - time (sec): 8.56 - samples/sec: 1413.34 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:51:17,412 epoch 9 - iter 20/48 - loss 0.03717904 - time (sec): 10.01 - samples/sec: 1472.81 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:51:18,604 epoch 9 - iter 24/48 - loss 0.03459590 - time (sec): 11.20 - samples/sec: 1521.25 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:51:20,288 epoch 9 - iter 28/48 - loss 0.03321645 - time (sec): 12.88 - samples/sec: 1535.14 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:51:22,527 epoch 9 - iter 32/48 - loss 0.03736319 - time (sec): 15.12 - samples/sec: 1520.59 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:51:25,183 epoch 9 - iter 36/48 - loss 0.03761193 - time (sec): 17.78 - samples/sec: 1469.33 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:51:28,071 epoch 9 - iter 40/48 - loss 0.03806719 - time (sec): 20.67 - samples/sec: 1426.22 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:51:29,859 epoch 9 - iter 44/48 - loss 0.03734658 - time (sec): 22.45 - samples/sec: 1442.09 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:51:30,881 epoch 9 - iter 48/48 - loss 0.03713892 - time (sec): 23.48 - samples/sec: 1468.37 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:51:30,882 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:30,882 EPOCH 9 done: loss 0.0371 - lr: 0.000006
2024-03-26 09:51:31,787 DEV : loss 0.15151749551296234 - f1-score (micro avg)  0.933
2024-03-26 09:51:31,788 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:34,073 epoch 10 - iter 4/48 - loss 0.01152605 - time (sec): 2.28 - samples/sec: 1445.53 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:51:36,120 epoch 10 - iter 8/48 - loss 0.01842762 - time (sec): 4.33 - samples/sec: 1426.37 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:51:38,030 epoch 10 - iter 12/48 - loss 0.02046314 - time (sec): 6.24 - samples/sec: 1413.64 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:51:39,263 epoch 10 - iter 16/48 - loss 0.02028408 - time (sec): 7.47 - samples/sec: 1474.31 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:51:41,165 epoch 10 - iter 20/48 - loss 0.02706232 - time (sec): 9.38 - samples/sec: 1461.90 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:51:43,375 epoch 10 - iter 24/48 - loss 0.03307105 - time (sec): 11.59 - samples/sec: 1433.29 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:51:44,257 epoch 10 - iter 28/48 - loss 0.03239851 - time (sec): 12.47 - samples/sec: 1506.88 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:51:45,511 epoch 10 - iter 32/48 - loss 0.03121566 - time (sec): 13.72 - samples/sec: 1547.75 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:51:48,258 epoch 10 - iter 36/48 - loss 0.03064417 - time (sec): 16.47 - samples/sec: 1499.26 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:51:50,642 epoch 10 - iter 40/48 - loss 0.03093211 - time (sec): 18.85 - samples/sec: 1525.02 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:51:53,171 epoch 10 - iter 44/48 - loss 0.03125066 - time (sec): 21.38 - samples/sec: 1499.97 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:51:55,073 epoch 10 - iter 48/48 - loss 0.03079486 - time (sec): 23.28 - samples/sec: 1480.44 - lr: 0.000000 - momentum: 0.000000
2024-03-26 09:51:55,074 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:55,074 EPOCH 10 done: loss 0.0308 - lr: 0.000000
2024-03-26 09:51:55,970 DEV : loss 0.15536418557167053 - f1-score (micro avg)  0.9251
2024-03-26 09:51:56,252 ----------------------------------------------------------------------------------------------------
2024-03-26 09:51:56,252 Loading model from best epoch ...
2024-03-26 09:51:57,139 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 09:51:57,975 
Results:
- F-score (micro) 0.9103
- F-score (macro) 0.6921
- Accuracy 0.8378

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9080    0.8910    0.8994       266
 Auslagerung     0.8577    0.9197    0.8876       249
         Ort     0.9708    0.9925    0.9815       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.8981    0.9230    0.9103       649
   macro avg     0.6841    0.7008    0.6921       649
weighted avg     0.9017    0.9230    0.9118       649

2024-03-26 09:51:57,975 ----------------------------------------------------------------------------------------------------