training.log

2024-03-26 15:52:02,958 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 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 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Train:  758 sentences
2024-03-26 15:52:02,959         (train_with_dev=False, train_with_test=False)
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Training Params:
2024-03-26 15:52:02,959  - learning_rate: "5e-05" 
2024-03-26 15:52:02,959  - mini_batch_size: "16"
2024-03-26 15:52:02,959  - max_epochs: "10"
2024-03-26 15:52:02,959  - shuffle: "True"
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Plugins:
2024-03-26 15:52:02,959  - TensorboardLogger
2024-03-26 15:52:02,959  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 15:52:02,959  - metric: "('micro avg', 'f1-score')"
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Computation:
2024-03-26 15:52:02,959  - compute on device: cuda:0
2024-03-26 15:52:02,959  - embedding storage: none
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Model training base path: "flair-co-funer-german_dbmdz_bert_base-bs16-e10-lr5e-05-3"
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:02,959 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 15:52:04,198 epoch 1 - iter 4/48 - loss 3.36871580 - time (sec): 1.24 - samples/sec: 2223.21 - lr: 0.000003 - momentum: 0.000000
2024-03-26 15:52:06,153 epoch 1 - iter 8/48 - loss 3.35491608 - time (sec): 3.19 - samples/sec: 1823.73 - lr: 0.000007 - momentum: 0.000000
2024-03-26 15:52:07,676 epoch 1 - iter 12/48 - loss 3.27876324 - time (sec): 4.72 - samples/sec: 1775.38 - lr: 0.000011 - momentum: 0.000000
2024-03-26 15:52:10,553 epoch 1 - iter 16/48 - loss 3.11865705 - time (sec): 7.59 - samples/sec: 1527.33 - lr: 0.000016 - momentum: 0.000000
2024-03-26 15:52:12,193 epoch 1 - iter 20/48 - loss 2.92420891 - time (sec): 9.23 - samples/sec: 1559.91 - lr: 0.000020 - momentum: 0.000000
2024-03-26 15:52:13,621 epoch 1 - iter 24/48 - loss 2.79486934 - time (sec): 10.66 - samples/sec: 1610.32 - lr: 0.000024 - momentum: 0.000000
2024-03-26 15:52:14,931 epoch 1 - iter 28/48 - loss 2.64942505 - time (sec): 11.97 - samples/sec: 1631.10 - lr: 0.000028 - momentum: 0.000000
2024-03-26 15:52:17,003 epoch 1 - iter 32/48 - loss 2.49591100 - time (sec): 14.04 - samples/sec: 1618.44 - lr: 0.000032 - momentum: 0.000000
2024-03-26 15:52:18,551 epoch 1 - iter 36/48 - loss 2.36472815 - time (sec): 15.59 - samples/sec: 1636.77 - lr: 0.000036 - momentum: 0.000000
2024-03-26 15:52:20,770 epoch 1 - iter 40/48 - loss 2.21807707 - time (sec): 17.81 - samples/sec: 1626.92 - lr: 0.000041 - momentum: 0.000000
2024-03-26 15:52:22,666 epoch 1 - iter 44/48 - loss 2.09300028 - time (sec): 19.71 - samples/sec: 1627.18 - lr: 0.000045 - momentum: 0.000000
2024-03-26 15:52:24,279 epoch 1 - iter 48/48 - loss 1.99414814 - time (sec): 21.32 - samples/sec: 1616.93 - lr: 0.000049 - momentum: 0.000000
2024-03-26 15:52:24,279 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:24,279 EPOCH 1 done: loss 1.9941 - lr: 0.000049
2024-03-26 15:52:25,089 DEV : loss 0.534384548664093 - f1-score (micro avg)  0.6359
2024-03-26 15:52:25,090 saving best model
2024-03-26 15:52:25,350 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:26,756 epoch 2 - iter 4/48 - loss 0.67744424 - time (sec): 1.41 - samples/sec: 1775.23 - lr: 0.000050 - momentum: 0.000000
2024-03-26 15:52:28,206 epoch 2 - iter 8/48 - loss 0.55139495 - time (sec): 2.85 - samples/sec: 1709.87 - lr: 0.000049 - momentum: 0.000000
2024-03-26 15:52:29,612 epoch 2 - iter 12/48 - loss 0.54765888 - time (sec): 4.26 - samples/sec: 1803.91 - lr: 0.000049 - momentum: 0.000000
2024-03-26 15:52:31,456 epoch 2 - iter 16/48 - loss 0.50626208 - time (sec): 6.11 - samples/sec: 1761.98 - lr: 0.000048 - momentum: 0.000000
2024-03-26 15:52:33,777 epoch 2 - iter 20/48 - loss 0.49532790 - time (sec): 8.43 - samples/sec: 1682.91 - lr: 0.000048 - momentum: 0.000000
2024-03-26 15:52:35,771 epoch 2 - iter 24/48 - loss 0.46590322 - time (sec): 10.42 - samples/sec: 1664.63 - lr: 0.000047 - momentum: 0.000000
2024-03-26 15:52:38,456 epoch 2 - iter 28/48 - loss 0.45169069 - time (sec): 13.10 - samples/sec: 1596.77 - lr: 0.000047 - momentum: 0.000000
2024-03-26 15:52:40,618 epoch 2 - iter 32/48 - loss 0.43566903 - time (sec): 15.27 - samples/sec: 1563.38 - lr: 0.000046 - momentum: 0.000000
2024-03-26 15:52:42,312 epoch 2 - iter 36/48 - loss 0.43297444 - time (sec): 16.96 - samples/sec: 1557.48 - lr: 0.000046 - momentum: 0.000000
2024-03-26 15:52:43,999 epoch 2 - iter 40/48 - loss 0.43365283 - time (sec): 18.65 - samples/sec: 1564.35 - lr: 0.000046 - momentum: 0.000000
2024-03-26 15:52:46,130 epoch 2 - iter 44/48 - loss 0.42306738 - time (sec): 20.78 - samples/sec: 1559.38 - lr: 0.000045 - momentum: 0.000000
2024-03-26 15:52:47,625 epoch 2 - iter 48/48 - loss 0.41433491 - time (sec): 22.27 - samples/sec: 1547.60 - lr: 0.000045 - momentum: 0.000000
2024-03-26 15:52:47,626 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:47,626 EPOCH 2 done: loss 0.4143 - lr: 0.000045
2024-03-26 15:52:48,507 DEV : loss 0.27958521246910095 - f1-score (micro avg)  0.8259
2024-03-26 15:52:48,508 saving best model
2024-03-26 15:52:48,954 ----------------------------------------------------------------------------------------------------
2024-03-26 15:52:50,440 epoch 3 - iter 4/48 - loss 0.27629199 - time (sec): 1.48 - samples/sec: 1650.03 - lr: 0.000044 - momentum: 0.000000
2024-03-26 15:52:53,216 epoch 3 - iter 8/48 - loss 0.21900262 - time (sec): 4.26 - samples/sec: 1344.64 - lr: 0.000044 - momentum: 0.000000
2024-03-26 15:52:54,448 epoch 3 - iter 12/48 - loss 0.23721159 - time (sec): 5.49 - samples/sec: 1483.07 - lr: 0.000043 - momentum: 0.000000
2024-03-26 15:52:55,799 epoch 3 - iter 16/48 - loss 0.22015402 - time (sec): 6.84 - samples/sec: 1612.92 - lr: 0.000043 - momentum: 0.000000
2024-03-26 15:52:57,237 epoch 3 - iter 20/48 - loss 0.22188495 - time (sec): 8.28 - samples/sec: 1631.55 - lr: 0.000042 - momentum: 0.000000
2024-03-26 15:52:59,912 epoch 3 - iter 24/48 - loss 0.21454789 - time (sec): 10.96 - samples/sec: 1524.48 - lr: 0.000042 - momentum: 0.000000
2024-03-26 15:53:01,801 epoch 3 - iter 28/48 - loss 0.21846474 - time (sec): 12.85 - samples/sec: 1542.93 - lr: 0.000041 - momentum: 0.000000
2024-03-26 15:53:04,292 epoch 3 - iter 32/48 - loss 0.20921952 - time (sec): 15.34 - samples/sec: 1486.76 - lr: 0.000041 - momentum: 0.000000
2024-03-26 15:53:06,201 epoch 3 - iter 36/48 - loss 0.21499627 - time (sec): 17.25 - samples/sec: 1483.18 - lr: 0.000040 - momentum: 0.000000
2024-03-26 15:53:08,534 epoch 3 - iter 40/48 - loss 0.20811360 - time (sec): 19.58 - samples/sec: 1458.97 - lr: 0.000040 - momentum: 0.000000
2024-03-26 15:53:10,945 epoch 3 - iter 44/48 - loss 0.21735434 - time (sec): 21.99 - samples/sec: 1447.14 - lr: 0.000040 - momentum: 0.000000
2024-03-26 15:53:13,249 epoch 3 - iter 48/48 - loss 0.20978841 - time (sec): 24.29 - samples/sec: 1418.97 - lr: 0.000039 - momentum: 0.000000
2024-03-26 15:53:13,249 ----------------------------------------------------------------------------------------------------
2024-03-26 15:53:13,249 EPOCH 3 done: loss 0.2098 - lr: 0.000039
2024-03-26 15:53:14,147 DEV : loss 0.1994694024324417 - f1-score (micro avg)  0.8687
2024-03-26 15:53:14,149 saving best model
2024-03-26 15:53:14,593 ----------------------------------------------------------------------------------------------------
2024-03-26 15:53:15,971 epoch 4 - iter 4/48 - loss 0.15914292 - time (sec): 1.38 - samples/sec: 1820.14 - lr: 0.000039 - momentum: 0.000000
2024-03-26 15:53:17,861 epoch 4 - iter 8/48 - loss 0.14834117 - time (sec): 3.27 - samples/sec: 1640.83 - lr: 0.000038 - momentum: 0.000000
2024-03-26 15:53:20,373 epoch 4 - iter 12/48 - loss 0.13533886 - time (sec): 5.78 - samples/sec: 1460.32 - lr: 0.000038 - momentum: 0.000000
2024-03-26 15:53:22,252 epoch 4 - iter 16/48 - loss 0.14253572 - time (sec): 7.66 - samples/sec: 1478.91 - lr: 0.000037 - momentum: 0.000000
2024-03-26 15:53:24,628 epoch 4 - iter 20/48 - loss 0.13383594 - time (sec): 10.03 - samples/sec: 1464.44 - lr: 0.000037 - momentum: 0.000000
2024-03-26 15:53:27,500 epoch 4 - iter 24/48 - loss 0.12723340 - time (sec): 12.91 - samples/sec: 1412.93 - lr: 0.000036 - momentum: 0.000000
2024-03-26 15:53:28,611 epoch 4 - iter 28/48 - loss 0.12587698 - time (sec): 14.02 - samples/sec: 1449.28 - lr: 0.000036 - momentum: 0.000000
2024-03-26 15:53:31,598 epoch 4 - iter 32/48 - loss 0.12620216 - time (sec): 17.00 - samples/sec: 1389.04 - lr: 0.000035 - momentum: 0.000000
2024-03-26 15:53:33,309 epoch 4 - iter 36/48 - loss 0.12972818 - time (sec): 18.71 - samples/sec: 1421.66 - lr: 0.000035 - momentum: 0.000000
2024-03-26 15:53:36,115 epoch 4 - iter 40/48 - loss 0.13833744 - time (sec): 21.52 - samples/sec: 1388.68 - lr: 0.000034 - momentum: 0.000000
2024-03-26 15:53:37,019 epoch 4 - iter 44/48 - loss 0.14040303 - time (sec): 22.42 - samples/sec: 1434.97 - lr: 0.000034 - momentum: 0.000000
2024-03-26 15:53:38,491 epoch 4 - iter 48/48 - loss 0.14126333 - time (sec): 23.90 - samples/sec: 1442.56 - lr: 0.000034 - momentum: 0.000000
2024-03-26 15:53:38,491 ----------------------------------------------------------------------------------------------------
2024-03-26 15:53:38,491 EPOCH 4 done: loss 0.1413 - lr: 0.000034
2024-03-26 15:53:39,390 DEV : loss 0.18417491018772125 - f1-score (micro avg)  0.9023
2024-03-26 15:53:39,391 saving best model
2024-03-26 15:53:39,808 ----------------------------------------------------------------------------------------------------
2024-03-26 15:53:42,254 epoch 5 - iter 4/48 - loss 0.08062461 - time (sec): 2.44 - samples/sec: 1300.88 - lr: 0.000033 - momentum: 0.000000
2024-03-26 15:53:43,670 epoch 5 - iter 8/48 - loss 0.10869288 - time (sec): 3.86 - samples/sec: 1475.04 - lr: 0.000033 - momentum: 0.000000
2024-03-26 15:53:45,120 epoch 5 - iter 12/48 - loss 0.10565853 - time (sec): 5.31 - samples/sec: 1552.27 - lr: 0.000032 - momentum: 0.000000
2024-03-26 15:53:47,297 epoch 5 - iter 16/48 - loss 0.10167389 - time (sec): 7.49 - samples/sec: 1469.81 - lr: 0.000032 - momentum: 0.000000
2024-03-26 15:53:49,350 epoch 5 - iter 20/48 - loss 0.10838379 - time (sec): 9.54 - samples/sec: 1476.06 - lr: 0.000031 - momentum: 0.000000
2024-03-26 15:53:51,814 epoch 5 - iter 24/48 - loss 0.10181658 - time (sec): 12.00 - samples/sec: 1466.58 - lr: 0.000031 - momentum: 0.000000
2024-03-26 15:53:54,360 epoch 5 - iter 28/48 - loss 0.09694413 - time (sec): 14.55 - samples/sec: 1446.70 - lr: 0.000030 - momentum: 0.000000
2024-03-26 15:53:56,226 epoch 5 - iter 32/48 - loss 0.09530949 - time (sec): 16.42 - samples/sec: 1450.67 - lr: 0.000030 - momentum: 0.000000
2024-03-26 15:53:58,030 epoch 5 - iter 36/48 - loss 0.09236130 - time (sec): 18.22 - samples/sec: 1451.54 - lr: 0.000029 - momentum: 0.000000
2024-03-26 15:54:00,314 epoch 5 - iter 40/48 - loss 0.09219478 - time (sec): 20.50 - samples/sec: 1440.45 - lr: 0.000029 - momentum: 0.000000
2024-03-26 15:54:02,252 epoch 5 - iter 44/48 - loss 0.09581412 - time (sec): 22.44 - samples/sec: 1439.37 - lr: 0.000029 - momentum: 0.000000
2024-03-26 15:54:03,297 epoch 5 - iter 48/48 - loss 0.09520551 - time (sec): 23.49 - samples/sec: 1467.77 - lr: 0.000028 - momentum: 0.000000
2024-03-26 15:54:03,297 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:03,297 EPOCH 5 done: loss 0.0952 - lr: 0.000028
2024-03-26 15:54:04,196 DEV : loss 0.1844843178987503 - f1-score (micro avg)  0.9094
2024-03-26 15:54:04,198 saving best model
2024-03-26 15:54:04,638 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:07,219 epoch 6 - iter 4/48 - loss 0.06778703 - time (sec): 2.58 - samples/sec: 1232.60 - lr: 0.000028 - momentum: 0.000000
2024-03-26 15:54:09,206 epoch 6 - iter 8/48 - loss 0.06680921 - time (sec): 4.57 - samples/sec: 1285.84 - lr: 0.000027 - momentum: 0.000000
2024-03-26 15:54:10,772 epoch 6 - iter 12/48 - loss 0.06684490 - time (sec): 6.13 - samples/sec: 1438.61 - lr: 0.000027 - momentum: 0.000000
2024-03-26 15:54:12,722 epoch 6 - iter 16/48 - loss 0.06208855 - time (sec): 8.08 - samples/sec: 1438.89 - lr: 0.000026 - momentum: 0.000000
2024-03-26 15:54:13,791 epoch 6 - iter 20/48 - loss 0.06558676 - time (sec): 9.15 - samples/sec: 1526.83 - lr: 0.000026 - momentum: 0.000000
2024-03-26 15:54:15,711 epoch 6 - iter 24/48 - loss 0.06594786 - time (sec): 11.07 - samples/sec: 1509.68 - lr: 0.000025 - momentum: 0.000000
2024-03-26 15:54:16,852 epoch 6 - iter 28/48 - loss 0.06539093 - time (sec): 12.21 - samples/sec: 1557.62 - lr: 0.000025 - momentum: 0.000000
2024-03-26 15:54:18,609 epoch 6 - iter 32/48 - loss 0.06402909 - time (sec): 13.97 - samples/sec: 1577.02 - lr: 0.000024 - momentum: 0.000000
2024-03-26 15:54:20,995 epoch 6 - iter 36/48 - loss 0.07604529 - time (sec): 16.36 - samples/sec: 1552.56 - lr: 0.000024 - momentum: 0.000000
2024-03-26 15:54:23,030 epoch 6 - iter 40/48 - loss 0.07312409 - time (sec): 18.39 - samples/sec: 1545.07 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:54:24,891 epoch 6 - iter 44/48 - loss 0.07627946 - time (sec): 20.25 - samples/sec: 1553.26 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:54:26,419 epoch 6 - iter 48/48 - loss 0.07671114 - time (sec): 21.78 - samples/sec: 1582.82 - lr: 0.000023 - momentum: 0.000000
2024-03-26 15:54:26,419 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:26,419 EPOCH 6 done: loss 0.0767 - lr: 0.000023
2024-03-26 15:54:27,324 DEV : loss 0.17374055087566376 - f1-score (micro avg)  0.9099
2024-03-26 15:54:27,326 saving best model
2024-03-26 15:54:27,747 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:29,906 epoch 7 - iter 4/48 - loss 0.07425281 - time (sec): 2.16 - samples/sec: 1281.28 - lr: 0.000022 - momentum: 0.000000
2024-03-26 15:54:31,595 epoch 7 - iter 8/48 - loss 0.05887528 - time (sec): 3.85 - samples/sec: 1493.64 - lr: 0.000022 - momentum: 0.000000
2024-03-26 15:54:33,643 epoch 7 - iter 12/48 - loss 0.04957316 - time (sec): 5.89 - samples/sec: 1453.44 - lr: 0.000021 - momentum: 0.000000
2024-03-26 15:54:36,191 epoch 7 - iter 16/48 - loss 0.04756465 - time (sec): 8.44 - samples/sec: 1399.92 - lr: 0.000021 - momentum: 0.000000
2024-03-26 15:54:38,842 epoch 7 - iter 20/48 - loss 0.05175879 - time (sec): 11.09 - samples/sec: 1408.93 - lr: 0.000020 - momentum: 0.000000
2024-03-26 15:54:40,355 epoch 7 - iter 24/48 - loss 0.05180407 - time (sec): 12.61 - samples/sec: 1429.71 - lr: 0.000020 - momentum: 0.000000
2024-03-26 15:54:42,438 epoch 7 - iter 28/48 - loss 0.04908208 - time (sec): 14.69 - samples/sec: 1448.25 - lr: 0.000019 - momentum: 0.000000
2024-03-26 15:54:44,585 epoch 7 - iter 32/48 - loss 0.05229406 - time (sec): 16.84 - samples/sec: 1452.08 - lr: 0.000019 - momentum: 0.000000
2024-03-26 15:54:46,786 epoch 7 - iter 36/48 - loss 0.05610937 - time (sec): 19.04 - samples/sec: 1437.97 - lr: 0.000018 - momentum: 0.000000
2024-03-26 15:54:48,353 epoch 7 - iter 40/48 - loss 0.05349840 - time (sec): 20.60 - samples/sec: 1447.06 - lr: 0.000018 - momentum: 0.000000
2024-03-26 15:54:49,993 epoch 7 - iter 44/48 - loss 0.05687802 - time (sec): 22.24 - samples/sec: 1466.35 - lr: 0.000017 - momentum: 0.000000
2024-03-26 15:54:51,315 epoch 7 - iter 48/48 - loss 0.05675112 - time (sec): 23.57 - samples/sec: 1462.78 - lr: 0.000017 - momentum: 0.000000
2024-03-26 15:54:51,315 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:51,315 EPOCH 7 done: loss 0.0568 - lr: 0.000017
2024-03-26 15:54:52,224 DEV : loss 0.1754644811153412 - f1-score (micro avg)  0.9258
2024-03-26 15:54:52,225 saving best model
2024-03-26 15:54:52,663 ----------------------------------------------------------------------------------------------------
2024-03-26 15:54:55,003 epoch 8 - iter 4/48 - loss 0.02736370 - time (sec): 2.34 - samples/sec: 1256.96 - lr: 0.000017 - momentum: 0.000000
2024-03-26 15:54:57,540 epoch 8 - iter 8/48 - loss 0.02700390 - time (sec): 4.87 - samples/sec: 1356.81 - lr: 0.000016 - momentum: 0.000000
2024-03-26 15:54:59,525 epoch 8 - iter 12/48 - loss 0.02802816 - time (sec): 6.86 - samples/sec: 1341.50 - lr: 0.000016 - momentum: 0.000000
2024-03-26 15:55:01,510 epoch 8 - iter 16/48 - loss 0.02894184 - time (sec): 8.84 - samples/sec: 1355.67 - lr: 0.000015 - momentum: 0.000000
2024-03-26 15:55:03,025 epoch 8 - iter 20/48 - loss 0.03150964 - time (sec): 10.36 - samples/sec: 1380.11 - lr: 0.000015 - momentum: 0.000000
2024-03-26 15:55:05,354 epoch 8 - iter 24/48 - loss 0.02969630 - time (sec): 12.69 - samples/sec: 1366.69 - lr: 0.000014 - momentum: 0.000000
2024-03-26 15:55:07,489 epoch 8 - iter 28/48 - loss 0.02992001 - time (sec): 14.82 - samples/sec: 1359.70 - lr: 0.000014 - momentum: 0.000000
2024-03-26 15:55:09,783 epoch 8 - iter 32/48 - loss 0.03878451 - time (sec): 17.12 - samples/sec: 1371.42 - lr: 0.000013 - momentum: 0.000000
2024-03-26 15:55:12,948 epoch 8 - iter 36/48 - loss 0.04273473 - time (sec): 20.28 - samples/sec: 1323.14 - lr: 0.000013 - momentum: 0.000000
2024-03-26 15:55:14,918 epoch 8 - iter 40/48 - loss 0.04616562 - time (sec): 22.25 - samples/sec: 1329.97 - lr: 0.000012 - momentum: 0.000000
2024-03-26 15:55:15,712 epoch 8 - iter 44/48 - loss 0.04479597 - time (sec): 23.05 - samples/sec: 1378.50 - lr: 0.000012 - momentum: 0.000000
2024-03-26 15:55:17,517 epoch 8 - iter 48/48 - loss 0.04471448 - time (sec): 24.85 - samples/sec: 1387.13 - lr: 0.000011 - momentum: 0.000000
2024-03-26 15:55:17,517 ----------------------------------------------------------------------------------------------------
2024-03-26 15:55:17,517 EPOCH 8 done: loss 0.0447 - lr: 0.000011
2024-03-26 15:55:18,427 DEV : loss 0.1700660139322281 - f1-score (micro avg)  0.9317
2024-03-26 15:55:18,428 saving best model
2024-03-26 15:55:18,873 ----------------------------------------------------------------------------------------------------
2024-03-26 15:55:21,552 epoch 9 - iter 4/48 - loss 0.02576288 - time (sec): 2.68 - samples/sec: 1231.65 - lr: 0.000011 - momentum: 0.000000
2024-03-26 15:55:23,217 epoch 9 - iter 8/48 - loss 0.02794343 - time (sec): 4.34 - samples/sec: 1321.42 - lr: 0.000011 - momentum: 0.000000
2024-03-26 15:55:25,320 epoch 9 - iter 12/48 - loss 0.03166695 - time (sec): 6.45 - samples/sec: 1392.61 - lr: 0.000010 - momentum: 0.000000
2024-03-26 15:55:27,386 epoch 9 - iter 16/48 - loss 0.03166024 - time (sec): 8.51 - samples/sec: 1422.73 - lr: 0.000010 - momentum: 0.000000
2024-03-26 15:55:29,689 epoch 9 - iter 20/48 - loss 0.02724723 - time (sec): 10.82 - samples/sec: 1398.87 - lr: 0.000009 - momentum: 0.000000
2024-03-26 15:55:31,590 epoch 9 - iter 24/48 - loss 0.03010259 - time (sec): 12.72 - samples/sec: 1392.59 - lr: 0.000009 - momentum: 0.000000
2024-03-26 15:55:34,763 epoch 9 - iter 28/48 - loss 0.03210630 - time (sec): 15.89 - samples/sec: 1344.54 - lr: 0.000008 - momentum: 0.000000
2024-03-26 15:55:36,120 epoch 9 - iter 32/48 - loss 0.03325750 - time (sec): 17.25 - samples/sec: 1385.30 - lr: 0.000008 - momentum: 0.000000
2024-03-26 15:55:38,466 epoch 9 - iter 36/48 - loss 0.03263536 - time (sec): 19.59 - samples/sec: 1375.54 - lr: 0.000007 - momentum: 0.000000
2024-03-26 15:55:39,916 epoch 9 - iter 40/48 - loss 0.03437591 - time (sec): 21.04 - samples/sec: 1393.42 - lr: 0.000007 - momentum: 0.000000
2024-03-26 15:55:41,411 epoch 9 - iter 44/48 - loss 0.03798605 - time (sec): 22.54 - samples/sec: 1411.50 - lr: 0.000006 - momentum: 0.000000
2024-03-26 15:55:42,804 epoch 9 - iter 48/48 - loss 0.03764149 - time (sec): 23.93 - samples/sec: 1440.58 - lr: 0.000006 - momentum: 0.000000
2024-03-26 15:55:42,804 ----------------------------------------------------------------------------------------------------
2024-03-26 15:55:42,804 EPOCH 9 done: loss 0.0376 - lr: 0.000006
2024-03-26 15:55:43,716 DEV : loss 0.19358719885349274 - f1-score (micro avg)  0.9372
2024-03-26 15:55:43,717 saving best model
2024-03-26 15:55:44,161 ----------------------------------------------------------------------------------------------------
2024-03-26 15:55:46,521 epoch 10 - iter 4/48 - loss 0.01698527 - time (sec): 2.36 - samples/sec: 1395.97 - lr: 0.000006 - momentum: 0.000000
2024-03-26 15:55:48,421 epoch 10 - iter 8/48 - loss 0.01570125 - time (sec): 4.26 - samples/sec: 1372.71 - lr: 0.000005 - momentum: 0.000000
2024-03-26 15:55:49,592 epoch 10 - iter 12/48 - loss 0.03071953 - time (sec): 5.43 - samples/sec: 1536.69 - lr: 0.000005 - momentum: 0.000000
2024-03-26 15:55:51,101 epoch 10 - iter 16/48 - loss 0.03178666 - time (sec): 6.94 - samples/sec: 1617.13 - lr: 0.000004 - momentum: 0.000000
2024-03-26 15:55:52,789 epoch 10 - iter 20/48 - loss 0.03140645 - time (sec): 8.63 - samples/sec: 1654.91 - lr: 0.000004 - momentum: 0.000000
2024-03-26 15:55:54,792 epoch 10 - iter 24/48 - loss 0.02924738 - time (sec): 10.63 - samples/sec: 1603.43 - lr: 0.000003 - momentum: 0.000000
2024-03-26 15:55:56,896 epoch 10 - iter 28/48 - loss 0.02706663 - time (sec): 12.73 - samples/sec: 1566.63 - lr: 0.000003 - momentum: 0.000000
2024-03-26 15:55:58,995 epoch 10 - iter 32/48 - loss 0.02812544 - time (sec): 14.83 - samples/sec: 1573.00 - lr: 0.000002 - momentum: 0.000000
2024-03-26 15:56:00,384 epoch 10 - iter 36/48 - loss 0.02759213 - time (sec): 16.22 - samples/sec: 1572.08 - lr: 0.000002 - momentum: 0.000000
2024-03-26 15:56:03,004 epoch 10 - iter 40/48 - loss 0.02639293 - time (sec): 18.84 - samples/sec: 1531.69 - lr: 0.000001 - momentum: 0.000000
2024-03-26 15:56:05,520 epoch 10 - iter 44/48 - loss 0.02901644 - time (sec): 21.36 - samples/sec: 1505.63 - lr: 0.000001 - momentum: 0.000000
2024-03-26 15:56:07,109 epoch 10 - iter 48/48 - loss 0.02914343 - time (sec): 22.95 - samples/sec: 1502.34 - lr: 0.000000 - momentum: 0.000000
2024-03-26 15:56:07,109 ----------------------------------------------------------------------------------------------------
2024-03-26 15:56:07,109 EPOCH 10 done: loss 0.0291 - lr: 0.000000
2024-03-26 15:56:08,014 DEV : loss 0.18885844945907593 - f1-score (micro avg)  0.9335
2024-03-26 15:56:08,280 ----------------------------------------------------------------------------------------------------
2024-03-26 15:56:08,280 Loading model from best epoch ...
2024-03-26 15:56:09,218 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 15:56:09,964 
Results:
- F-score (micro) 0.9065
- F-score (macro) 0.6888
- Accuracy 0.8312

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9004    0.8835    0.8918       266
 Auslagerung     0.8609    0.9197    0.8893       249
         Ort     0.9635    0.9851    0.9742       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.8949    0.9183    0.9065       649
   macro avg     0.6812    0.6971    0.6888       649
weighted avg     0.8983    0.9183    0.9079       649

2024-03-26 15:56:09,964 ----------------------------------------------------------------------------------------------------