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flair-co-funer-gbert_base-bs16-e10-lr5e-05-1

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-05-1
File size: 26,652 Bytes 
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2024-03-26 09:31:31,802 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 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 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Train:  758 sentences
2024-03-26 09:31:31,803         (train_with_dev=False, train_with_test=False)
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Training Params:
2024-03-26 09:31:31,803  - learning_rate: "5e-05" 
2024-03-26 09:31:31,803  - mini_batch_size: "16"
2024-03-26 09:31:31,803  - max_epochs: "10"
2024-03-26 09:31:31,803  - shuffle: "True"
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Plugins:
2024-03-26 09:31:31,803  - TensorboardLogger
2024-03-26 09:31:31,803  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 09:31:31,803  - metric: "('micro avg', 'f1-score')"
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Computation:
2024-03-26 09:31:31,803  - compute on device: cuda:0
2024-03-26 09:31:31,803  - embedding storage: none
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Model training base path: "flair-co-funer-gbert_base-bs16-e10-lr5e-05-1"
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:31,803 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 09:31:33,778 epoch 1 - iter 4/48 - loss 3.13464684 - time (sec): 1.97 - samples/sec: 1375.34 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:31:35,058 epoch 1 - iter 8/48 - loss 3.04063711 - time (sec): 3.25 - samples/sec: 1655.82 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:31:37,983 epoch 1 - iter 12/48 - loss 2.87498959 - time (sec): 6.18 - samples/sec: 1408.24 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:31:41,134 epoch 1 - iter 16/48 - loss 2.71452658 - time (sec): 9.33 - samples/sec: 1306.73 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:31:43,588 epoch 1 - iter 20/48 - loss 2.54396549 - time (sec): 11.78 - samples/sec: 1305.27 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:31:45,279 epoch 1 - iter 24/48 - loss 2.41159667 - time (sec): 13.48 - samples/sec: 1353.38 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:31:46,852 epoch 1 - iter 28/48 - loss 2.30256979 - time (sec): 15.05 - samples/sec: 1375.61 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:31:48,927 epoch 1 - iter 32/48 - loss 2.20655569 - time (sec): 17.12 - samples/sec: 1380.31 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:31:49,904 epoch 1 - iter 36/48 - loss 2.12627397 - time (sec): 18.10 - samples/sec: 1439.10 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:31:51,838 epoch 1 - iter 40/48 - loss 2.02856966 - time (sec): 20.03 - samples/sec: 1453.30 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:31:53,824 epoch 1 - iter 44/48 - loss 1.93943712 - time (sec): 22.02 - samples/sec: 1438.68 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:31:55,285 epoch 1 - iter 48/48 - loss 1.84110524 - time (sec): 23.48 - samples/sec: 1468.03 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:31:55,285 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:55,286 EPOCH 1 done: loss 1.8411 - lr: 0.000049
2024-03-26 09:31:56,090 DEV : loss 0.5449312925338745 - f1-score (micro avg)  0.6421
2024-03-26 09:31:56,091 saving best model
2024-03-26 09:31:56,350 ----------------------------------------------------------------------------------------------------
2024-03-26 09:31:58,809 epoch 2 - iter 4/48 - loss 0.63065975 - time (sec): 2.46 - samples/sec: 1262.10 - lr: 0.000050 - momentum: 0.000000
2024-03-26 09:32:00,855 epoch 2 - iter 8/48 - loss 0.61141871 - time (sec): 4.50 - samples/sec: 1467.84 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:32:03,090 epoch 2 - iter 12/48 - loss 0.58530365 - time (sec): 6.74 - samples/sec: 1373.84 - lr: 0.000049 - momentum: 0.000000
2024-03-26 09:32:05,122 epoch 2 - iter 16/48 - loss 0.56230651 - time (sec): 8.77 - samples/sec: 1358.57 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:32:07,226 epoch 2 - iter 20/48 - loss 0.54060094 - time (sec): 10.88 - samples/sec: 1378.87 - lr: 0.000048 - momentum: 0.000000
2024-03-26 09:32:10,380 epoch 2 - iter 24/48 - loss 0.49961752 - time (sec): 14.03 - samples/sec: 1318.89 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:32:12,732 epoch 2 - iter 28/48 - loss 0.48484578 - time (sec): 16.38 - samples/sec: 1314.96 - lr: 0.000047 - momentum: 0.000000
2024-03-26 09:32:14,439 epoch 2 - iter 32/48 - loss 0.47215469 - time (sec): 18.09 - samples/sec: 1333.85 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:32:15,461 epoch 2 - iter 36/48 - loss 0.46303288 - time (sec): 19.11 - samples/sec: 1384.22 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:32:17,319 epoch 2 - iter 40/48 - loss 0.45128966 - time (sec): 20.97 - samples/sec: 1402.01 - lr: 0.000046 - momentum: 0.000000
2024-03-26 09:32:19,328 epoch 2 - iter 44/48 - loss 0.44327526 - time (sec): 22.98 - samples/sec: 1397.39 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:32:20,773 epoch 2 - iter 48/48 - loss 0.43448719 - time (sec): 24.42 - samples/sec: 1411.53 - lr: 0.000045 - momentum: 0.000000
2024-03-26 09:32:20,773 ----------------------------------------------------------------------------------------------------
2024-03-26 09:32:20,773 EPOCH 2 done: loss 0.4345 - lr: 0.000045
2024-03-26 09:32:21,662 DEV : loss 0.2701604962348938 - f1-score (micro avg)  0.812
2024-03-26 09:32:21,663 saving best model
2024-03-26 09:32:22,173 ----------------------------------------------------------------------------------------------------
2024-03-26 09:32:24,643 epoch 3 - iter 4/48 - loss 0.31097284 - time (sec): 2.47 - samples/sec: 1236.92 - lr: 0.000044 - momentum: 0.000000
2024-03-26 09:32:26,455 epoch 3 - iter 8/48 - loss 0.27100056 - time (sec): 4.28 - samples/sec: 1371.11 - lr: 0.000044 - momentum: 0.000000
2024-03-26 09:32:28,248 epoch 3 - iter 12/48 - loss 0.27588384 - time (sec): 6.07 - samples/sec: 1447.17 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:32:30,630 epoch 3 - iter 16/48 - loss 0.25168783 - time (sec): 8.45 - samples/sec: 1444.54 - lr: 0.000043 - momentum: 0.000000
2024-03-26 09:32:32,057 epoch 3 - iter 20/48 - loss 0.26133874 - time (sec): 9.88 - samples/sec: 1497.43 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:32:34,963 epoch 3 - iter 24/48 - loss 0.24628684 - time (sec): 12.79 - samples/sec: 1478.89 - lr: 0.000042 - momentum: 0.000000
2024-03-26 09:32:35,719 epoch 3 - iter 28/48 - loss 0.23985620 - time (sec): 13.54 - samples/sec: 1553.61 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:32:38,236 epoch 3 - iter 32/48 - loss 0.22794958 - time (sec): 16.06 - samples/sec: 1495.88 - lr: 0.000041 - momentum: 0.000000
2024-03-26 09:32:40,224 epoch 3 - iter 36/48 - loss 0.21907449 - time (sec): 18.05 - samples/sec: 1488.15 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:32:42,104 epoch 3 - iter 40/48 - loss 0.21907616 - time (sec): 19.93 - samples/sec: 1476.48 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:32:44,222 epoch 3 - iter 44/48 - loss 0.21043578 - time (sec): 22.05 - samples/sec: 1479.42 - lr: 0.000040 - momentum: 0.000000
2024-03-26 09:32:45,439 epoch 3 - iter 48/48 - loss 0.20906826 - time (sec): 23.26 - samples/sec: 1481.82 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:32:45,439 ----------------------------------------------------------------------------------------------------
2024-03-26 09:32:45,439 EPOCH 3 done: loss 0.2091 - lr: 0.000039
2024-03-26 09:32:46,322 DEV : loss 0.2270229309797287 - f1-score (micro avg)  0.8544
2024-03-26 09:32:46,323 saving best model
2024-03-26 09:32:46,755 ----------------------------------------------------------------------------------------------------
2024-03-26 09:32:48,235 epoch 4 - iter 4/48 - loss 0.16865347 - time (sec): 1.48 - samples/sec: 1844.61 - lr: 0.000039 - momentum: 0.000000
2024-03-26 09:32:50,630 epoch 4 - iter 8/48 - loss 0.16492162 - time (sec): 3.87 - samples/sec: 1481.26 - lr: 0.000038 - momentum: 0.000000
2024-03-26 09:32:52,697 epoch 4 - iter 12/48 - loss 0.16216988 - time (sec): 5.94 - samples/sec: 1470.92 - lr: 0.000038 - momentum: 0.000000
2024-03-26 09:32:54,836 epoch 4 - iter 16/48 - loss 0.14441421 - time (sec): 8.08 - samples/sec: 1481.99 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:32:57,827 epoch 4 - iter 20/48 - loss 0.13635337 - time (sec): 11.07 - samples/sec: 1399.22 - lr: 0.000037 - momentum: 0.000000
2024-03-26 09:32:59,236 epoch 4 - iter 24/48 - loss 0.13563064 - time (sec): 12.48 - samples/sec: 1444.20 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:33:00,732 epoch 4 - iter 28/48 - loss 0.13427894 - time (sec): 13.98 - samples/sec: 1483.96 - lr: 0.000036 - momentum: 0.000000
2024-03-26 09:33:03,174 epoch 4 - iter 32/48 - loss 0.13956275 - time (sec): 16.42 - samples/sec: 1476.35 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:33:04,151 epoch 4 - iter 36/48 - loss 0.13898618 - time (sec): 17.39 - samples/sec: 1527.52 - lr: 0.000035 - momentum: 0.000000
2024-03-26 09:33:06,496 epoch 4 - iter 40/48 - loss 0.13510467 - time (sec): 19.74 - samples/sec: 1479.50 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:33:08,262 epoch 4 - iter 44/48 - loss 0.13614949 - time (sec): 21.51 - samples/sec: 1500.31 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:33:09,593 epoch 4 - iter 48/48 - loss 0.13528628 - time (sec): 22.84 - samples/sec: 1509.49 - lr: 0.000034 - momentum: 0.000000
2024-03-26 09:33:09,593 ----------------------------------------------------------------------------------------------------
2024-03-26 09:33:09,593 EPOCH 4 done: loss 0.1353 - lr: 0.000034
2024-03-26 09:33:10,485 DEV : loss 0.17390906810760498 - f1-score (micro avg)  0.8874
2024-03-26 09:33:10,486 saving best model
2024-03-26 09:33:10,918 ----------------------------------------------------------------------------------------------------
2024-03-26 09:33:12,819 epoch 5 - iter 4/48 - loss 0.10556470 - time (sec): 1.90 - samples/sec: 1468.92 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:33:15,231 epoch 5 - iter 8/48 - loss 0.09265636 - time (sec): 4.31 - samples/sec: 1377.48 - lr: 0.000033 - momentum: 0.000000
2024-03-26 09:33:17,165 epoch 5 - iter 12/48 - loss 0.09936621 - time (sec): 6.24 - samples/sec: 1372.04 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:33:19,146 epoch 5 - iter 16/48 - loss 0.10301987 - time (sec): 8.23 - samples/sec: 1404.36 - lr: 0.000032 - momentum: 0.000000
2024-03-26 09:33:21,038 epoch 5 - iter 20/48 - loss 0.10466271 - time (sec): 10.12 - samples/sec: 1414.52 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:33:22,531 epoch 5 - iter 24/48 - loss 0.11156514 - time (sec): 11.61 - samples/sec: 1464.98 - lr: 0.000031 - momentum: 0.000000
2024-03-26 09:33:24,700 epoch 5 - iter 28/48 - loss 0.11115749 - time (sec): 13.78 - samples/sec: 1461.94 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:33:27,280 epoch 5 - iter 32/48 - loss 0.10915818 - time (sec): 16.36 - samples/sec: 1447.00 - lr: 0.000030 - momentum: 0.000000
2024-03-26 09:33:29,609 epoch 5 - iter 36/48 - loss 0.10469563 - time (sec): 18.69 - samples/sec: 1452.08 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:33:30,483 epoch 5 - iter 40/48 - loss 0.10615079 - time (sec): 19.56 - samples/sec: 1495.65 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:33:33,063 epoch 5 - iter 44/48 - loss 0.10402087 - time (sec): 22.14 - samples/sec: 1462.55 - lr: 0.000029 - momentum: 0.000000
2024-03-26 09:33:34,511 epoch 5 - iter 48/48 - loss 0.10402272 - time (sec): 23.59 - samples/sec: 1461.29 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:33:34,511 ----------------------------------------------------------------------------------------------------
2024-03-26 09:33:34,511 EPOCH 5 done: loss 0.1040 - lr: 0.000028
2024-03-26 09:33:35,399 DEV : loss 0.16777649521827698 - f1-score (micro avg)  0.9018
2024-03-26 09:33:35,400 saving best model
2024-03-26 09:33:35,831 ----------------------------------------------------------------------------------------------------
2024-03-26 09:33:37,818 epoch 6 - iter 4/48 - loss 0.04935121 - time (sec): 1.98 - samples/sec: 1332.68 - lr: 0.000028 - momentum: 0.000000
2024-03-26 09:33:39,894 epoch 6 - iter 8/48 - loss 0.07739319 - time (sec): 4.06 - samples/sec: 1362.42 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:33:41,679 epoch 6 - iter 12/48 - loss 0.07870801 - time (sec): 5.85 - samples/sec: 1478.51 - lr: 0.000027 - momentum: 0.000000
2024-03-26 09:33:43,866 epoch 6 - iter 16/48 - loss 0.07730179 - time (sec): 8.03 - samples/sec: 1430.42 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:33:45,592 epoch 6 - iter 20/48 - loss 0.08151266 - time (sec): 9.76 - samples/sec: 1438.08 - lr: 0.000026 - momentum: 0.000000
2024-03-26 09:33:48,008 epoch 6 - iter 24/48 - loss 0.07871227 - time (sec): 12.17 - samples/sec: 1414.34 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:33:49,815 epoch 6 - iter 28/48 - loss 0.08023292 - time (sec): 13.98 - samples/sec: 1414.68 - lr: 0.000025 - momentum: 0.000000
2024-03-26 09:33:52,231 epoch 6 - iter 32/48 - loss 0.07986238 - time (sec): 16.40 - samples/sec: 1393.96 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:33:55,591 epoch 6 - iter 36/48 - loss 0.07669859 - time (sec): 19.76 - samples/sec: 1349.74 - lr: 0.000024 - momentum: 0.000000
2024-03-26 09:33:57,177 epoch 6 - iter 40/48 - loss 0.07499192 - time (sec): 21.34 - samples/sec: 1384.54 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:33:58,970 epoch 6 - iter 44/48 - loss 0.07360792 - time (sec): 23.14 - samples/sec: 1388.05 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:34:00,234 epoch 6 - iter 48/48 - loss 0.07617279 - time (sec): 24.40 - samples/sec: 1412.75 - lr: 0.000023 - momentum: 0.000000
2024-03-26 09:34:00,235 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:00,235 EPOCH 6 done: loss 0.0762 - lr: 0.000023
2024-03-26 09:34:01,134 DEV : loss 0.16763538122177124 - f1-score (micro avg)  0.9099
2024-03-26 09:34:01,134 saving best model
2024-03-26 09:34:01,569 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:03,193 epoch 7 - iter 4/48 - loss 0.09393644 - time (sec): 1.62 - samples/sec: 1694.32 - lr: 0.000022 - momentum: 0.000000
2024-03-26 09:34:05,334 epoch 7 - iter 8/48 - loss 0.07306259 - time (sec): 3.76 - samples/sec: 1429.27 - lr: 0.000022 - momentum: 0.000000
2024-03-26 09:34:07,604 epoch 7 - iter 12/48 - loss 0.07212888 - time (sec): 6.03 - samples/sec: 1376.15 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:34:10,146 epoch 7 - iter 16/48 - loss 0.06332659 - time (sec): 8.58 - samples/sec: 1345.98 - lr: 0.000021 - momentum: 0.000000
2024-03-26 09:34:12,382 epoch 7 - iter 20/48 - loss 0.06418990 - time (sec): 10.81 - samples/sec: 1351.94 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:34:13,717 epoch 7 - iter 24/48 - loss 0.06067116 - time (sec): 12.15 - samples/sec: 1408.53 - lr: 0.000020 - momentum: 0.000000
2024-03-26 09:34:15,098 epoch 7 - iter 28/48 - loss 0.06052551 - time (sec): 13.53 - samples/sec: 1474.63 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:34:17,052 epoch 7 - iter 32/48 - loss 0.05922220 - time (sec): 15.48 - samples/sec: 1465.11 - lr: 0.000019 - momentum: 0.000000
2024-03-26 09:34:19,180 epoch 7 - iter 36/48 - loss 0.05681445 - time (sec): 17.61 - samples/sec: 1454.57 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:34:21,604 epoch 7 - iter 40/48 - loss 0.05828898 - time (sec): 20.03 - samples/sec: 1434.70 - lr: 0.000018 - momentum: 0.000000
2024-03-26 09:34:23,409 epoch 7 - iter 44/48 - loss 0.05798258 - time (sec): 21.84 - samples/sec: 1451.65 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:34:25,294 epoch 7 - iter 48/48 - loss 0.05651592 - time (sec): 23.72 - samples/sec: 1453.11 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:34:25,294 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:25,294 EPOCH 7 done: loss 0.0565 - lr: 0.000017
2024-03-26 09:34:26,192 DEV : loss 0.16629981994628906 - f1-score (micro avg)  0.9159
2024-03-26 09:34:26,193 saving best model
2024-03-26 09:34:26,624 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:28,581 epoch 8 - iter 4/48 - loss 0.05104273 - time (sec): 1.95 - samples/sec: 1383.16 - lr: 0.000017 - momentum: 0.000000
2024-03-26 09:34:31,363 epoch 8 - iter 8/48 - loss 0.03876510 - time (sec): 4.74 - samples/sec: 1172.92 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:34:32,635 epoch 8 - iter 12/48 - loss 0.04249942 - time (sec): 6.01 - samples/sec: 1328.57 - lr: 0.000016 - momentum: 0.000000
2024-03-26 09:34:35,040 epoch 8 - iter 16/48 - loss 0.05026022 - time (sec): 8.41 - samples/sec: 1337.94 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:34:37,549 epoch 8 - iter 20/48 - loss 0.04212980 - time (sec): 10.92 - samples/sec: 1379.56 - lr: 0.000015 - momentum: 0.000000
2024-03-26 09:34:38,829 epoch 8 - iter 24/48 - loss 0.04271362 - time (sec): 12.20 - samples/sec: 1458.44 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:34:42,077 epoch 8 - iter 28/48 - loss 0.04376621 - time (sec): 15.45 - samples/sec: 1412.35 - lr: 0.000014 - momentum: 0.000000
2024-03-26 09:34:44,069 epoch 8 - iter 32/48 - loss 0.04516094 - time (sec): 17.44 - samples/sec: 1413.41 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:34:45,121 epoch 8 - iter 36/48 - loss 0.04602552 - time (sec): 18.49 - samples/sec: 1451.86 - lr: 0.000013 - momentum: 0.000000
2024-03-26 09:34:46,782 epoch 8 - iter 40/48 - loss 0.04504686 - time (sec): 20.16 - samples/sec: 1450.07 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:34:48,353 epoch 8 - iter 44/48 - loss 0.04621049 - time (sec): 21.73 - samples/sec: 1470.63 - lr: 0.000012 - momentum: 0.000000
2024-03-26 09:34:50,296 epoch 8 - iter 48/48 - loss 0.04680647 - time (sec): 23.67 - samples/sec: 1456.41 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:34:50,296 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:50,296 EPOCH 8 done: loss 0.0468 - lr: 0.000011
2024-03-26 09:34:51,194 DEV : loss 0.1683352291584015 - f1-score (micro avg)  0.9131
2024-03-26 09:34:51,196 ----------------------------------------------------------------------------------------------------
2024-03-26 09:34:53,032 epoch 9 - iter 4/48 - loss 0.03239823 - time (sec): 1.84 - samples/sec: 1458.83 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:34:56,183 epoch 9 - iter 8/48 - loss 0.02376028 - time (sec): 4.99 - samples/sec: 1252.19 - lr: 0.000011 - momentum: 0.000000
2024-03-26 09:34:57,833 epoch 9 - iter 12/48 - loss 0.02807453 - time (sec): 6.64 - samples/sec: 1309.51 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:35:00,070 epoch 9 - iter 16/48 - loss 0.02893077 - time (sec): 8.87 - samples/sec: 1298.35 - lr: 0.000010 - momentum: 0.000000
2024-03-26 09:35:02,333 epoch 9 - iter 20/48 - loss 0.03399391 - time (sec): 11.14 - samples/sec: 1328.52 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:35:04,499 epoch 9 - iter 24/48 - loss 0.03527515 - time (sec): 13.30 - samples/sec: 1344.48 - lr: 0.000009 - momentum: 0.000000
2024-03-26 09:35:06,861 epoch 9 - iter 28/48 - loss 0.03279158 - time (sec): 15.66 - samples/sec: 1337.95 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:35:09,187 epoch 9 - iter 32/48 - loss 0.03328617 - time (sec): 17.99 - samples/sec: 1334.74 - lr: 0.000008 - momentum: 0.000000
2024-03-26 09:35:10,978 epoch 9 - iter 36/48 - loss 0.03552305 - time (sec): 19.78 - samples/sec: 1353.32 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:35:13,150 epoch 9 - iter 40/48 - loss 0.03717142 - time (sec): 21.95 - samples/sec: 1343.11 - lr: 0.000007 - momentum: 0.000000
2024-03-26 09:35:15,278 epoch 9 - iter 44/48 - loss 0.03618859 - time (sec): 24.08 - samples/sec: 1353.80 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:35:16,026 epoch 9 - iter 48/48 - loss 0.03659229 - time (sec): 24.83 - samples/sec: 1388.29 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:35:16,027 ----------------------------------------------------------------------------------------------------
2024-03-26 09:35:16,027 EPOCH 9 done: loss 0.0366 - lr: 0.000006
2024-03-26 09:35:16,937 DEV : loss 0.16806438565254211 - f1-score (micro avg)  0.9207
2024-03-26 09:35:16,938 saving best model
2024-03-26 09:35:17,369 ----------------------------------------------------------------------------------------------------
2024-03-26 09:35:19,113 epoch 10 - iter 4/48 - loss 0.01938598 - time (sec): 1.74 - samples/sec: 1508.81 - lr: 0.000006 - momentum: 0.000000
2024-03-26 09:35:21,045 epoch 10 - iter 8/48 - loss 0.02258557 - time (sec): 3.67 - samples/sec: 1507.88 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:35:23,658 epoch 10 - iter 12/48 - loss 0.02730836 - time (sec): 6.29 - samples/sec: 1387.97 - lr: 0.000005 - momentum: 0.000000
2024-03-26 09:35:25,570 epoch 10 - iter 16/48 - loss 0.03125997 - time (sec): 8.20 - samples/sec: 1399.21 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:35:27,402 epoch 10 - iter 20/48 - loss 0.03153887 - time (sec): 10.03 - samples/sec: 1442.05 - lr: 0.000004 - momentum: 0.000000
2024-03-26 09:35:29,043 epoch 10 - iter 24/48 - loss 0.03612120 - time (sec): 11.67 - samples/sec: 1452.94 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:35:30,786 epoch 10 - iter 28/48 - loss 0.03445545 - time (sec): 13.42 - samples/sec: 1474.83 - lr: 0.000003 - momentum: 0.000000
2024-03-26 09:35:31,976 epoch 10 - iter 32/48 - loss 0.03355325 - time (sec): 14.61 - samples/sec: 1507.95 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:35:34,947 epoch 10 - iter 36/48 - loss 0.03055986 - time (sec): 17.58 - samples/sec: 1457.31 - lr: 0.000002 - momentum: 0.000000
2024-03-26 09:35:37,702 epoch 10 - iter 40/48 - loss 0.03283648 - time (sec): 20.33 - samples/sec: 1430.36 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:35:40,457 epoch 10 - iter 44/48 - loss 0.03091020 - time (sec): 23.09 - samples/sec: 1398.36 - lr: 0.000001 - momentum: 0.000000
2024-03-26 09:35:42,052 epoch 10 - iter 48/48 - loss 0.03004462 - time (sec): 24.68 - samples/sec: 1396.65 - lr: 0.000000 - momentum: 0.000000
2024-03-26 09:35:42,053 ----------------------------------------------------------------------------------------------------
2024-03-26 09:35:42,053 EPOCH 10 done: loss 0.0300 - lr: 0.000000
2024-03-26 09:35:42,972 DEV : loss 0.1705980747938156 - f1-score (micro avg)  0.9214
2024-03-26 09:35:42,974 saving best model
2024-03-26 09:35:43,689 ----------------------------------------------------------------------------------------------------
2024-03-26 09:35:43,689 Loading model from best epoch ...
2024-03-26 09:35:44,576 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 09:35:45,419 
Results:
- F-score (micro) 0.9
- F-score (macro) 0.6847
- Accuracy 0.8239

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9046    0.8910    0.8977       266
 Auslagerung     0.8333    0.9036    0.8671       249
         Ort     0.9635    0.9851    0.9742       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.8852    0.9153    0.9000       649
   macro avg     0.6754    0.6949    0.6847       649
weighted avg     0.8894    0.9153    0.9017       649

2024-03-26 09:35:45,419 ----------------------------------------------------------------------------------------------------