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flair-co-funer-german_bert_base-bs16-e10-lr5e-05-3

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
hetzner
hetzner-gex44
hetzner-gpu
Model card Files Metrics Community
2
flair-co-funer-german_bert_base-bs16-e10-lr5e-05-3
File size: 26,535 Bytes 
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2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(30001, 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 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Corpus: 758 train + 94 dev + 96 test sentences
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Train:  758 sentences
2024-03-26 11:33:29,248         (train_with_dev=False, train_with_test=False)
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Training Params:
2024-03-26 11:33:29,248  - learning_rate: "5e-05" 
2024-03-26 11:33:29,248  - mini_batch_size: "16"
2024-03-26 11:33:29,248  - max_epochs: "10"
2024-03-26 11:33:29,248  - shuffle: "True"
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Plugins:
2024-03-26 11:33:29,248  - TensorboardLogger
2024-03-26 11:33:29,248  - LinearScheduler | warmup_fraction: '0.1'
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Final evaluation on model from best epoch (best-model.pt)
2024-03-26 11:33:29,248  - metric: "('micro avg', 'f1-score')"
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Computation:
2024-03-26 11:33:29,248  - compute on device: cuda:0
2024-03-26 11:33:29,248  - embedding storage: none
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,248 Model training base path: "flair-co-funer-german_bert_base-bs16-e10-lr5e-05-3"
2024-03-26 11:33:29,248 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,249 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:29,249 Logging anything other than scalars to TensorBoard is currently not supported.
2024-03-26 11:33:30,579 epoch 1 - iter 4/48 - loss 3.07489042 - time (sec): 1.33 - samples/sec: 2069.01 - lr: 0.000003 - momentum: 0.000000
2024-03-26 11:33:32,704 epoch 1 - iter 8/48 - loss 3.08771911 - time (sec): 3.46 - samples/sec: 1685.38 - lr: 0.000007 - momentum: 0.000000
2024-03-26 11:33:34,228 epoch 1 - iter 12/48 - loss 2.94390144 - time (sec): 4.98 - samples/sec: 1681.86 - lr: 0.000011 - momentum: 0.000000
2024-03-26 11:33:37,270 epoch 1 - iter 16/48 - loss 2.77162015 - time (sec): 8.02 - samples/sec: 1445.94 - lr: 0.000016 - momentum: 0.000000
2024-03-26 11:33:39,010 epoch 1 - iter 20/48 - loss 2.60962695 - time (sec): 9.76 - samples/sec: 1475.60 - lr: 0.000020 - momentum: 0.000000
2024-03-26 11:33:40,472 epoch 1 - iter 24/48 - loss 2.51067364 - time (sec): 11.22 - samples/sec: 1529.72 - lr: 0.000024 - momentum: 0.000000
2024-03-26 11:33:41,866 epoch 1 - iter 28/48 - loss 2.39999582 - time (sec): 12.62 - samples/sec: 1547.60 - lr: 0.000028 - momentum: 0.000000
2024-03-26 11:33:44,008 epoch 1 - iter 32/48 - loss 2.27938260 - time (sec): 14.76 - samples/sec: 1539.92 - lr: 0.000032 - momentum: 0.000000
2024-03-26 11:33:45,593 epoch 1 - iter 36/48 - loss 2.16989687 - time (sec): 16.34 - samples/sec: 1561.40 - lr: 0.000036 - momentum: 0.000000
2024-03-26 11:33:47,867 epoch 1 - iter 40/48 - loss 2.03915890 - time (sec): 18.62 - samples/sec: 1556.37 - lr: 0.000041 - momentum: 0.000000
2024-03-26 11:33:49,800 epoch 1 - iter 44/48 - loss 1.93179330 - time (sec): 20.55 - samples/sec: 1560.33 - lr: 0.000045 - momentum: 0.000000
2024-03-26 11:33:51,477 epoch 1 - iter 48/48 - loss 1.84076884 - time (sec): 22.23 - samples/sec: 1550.84 - lr: 0.000049 - momentum: 0.000000
2024-03-26 11:33:51,477 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:51,477 EPOCH 1 done: loss 1.8408 - lr: 0.000049
2024-03-26 11:33:52,341 DEV : loss 0.5940399169921875 - f1-score (micro avg)  0.635
2024-03-26 11:33:52,342 saving best model
2024-03-26 11:33:52,617 ----------------------------------------------------------------------------------------------------
2024-03-26 11:33:54,062 epoch 2 - iter 4/48 - loss 0.72316569 - time (sec): 1.44 - samples/sec: 1727.06 - lr: 0.000050 - momentum: 0.000000
2024-03-26 11:33:55,558 epoch 2 - iter 8/48 - loss 0.57588712 - time (sec): 2.94 - samples/sec: 1659.65 - lr: 0.000049 - momentum: 0.000000
2024-03-26 11:33:57,043 epoch 2 - iter 12/48 - loss 0.56563452 - time (sec): 4.43 - samples/sec: 1737.03 - lr: 0.000049 - momentum: 0.000000
2024-03-26 11:33:59,008 epoch 2 - iter 16/48 - loss 0.52469522 - time (sec): 6.39 - samples/sec: 1683.10 - lr: 0.000048 - momentum: 0.000000
2024-03-26 11:34:01,353 epoch 2 - iter 20/48 - loss 0.50473970 - time (sec): 8.74 - samples/sec: 1623.17 - lr: 0.000048 - momentum: 0.000000
2024-03-26 11:34:03,440 epoch 2 - iter 24/48 - loss 0.47203328 - time (sec): 10.82 - samples/sec: 1602.62 - lr: 0.000047 - momentum: 0.000000
2024-03-26 11:34:06,292 epoch 2 - iter 28/48 - loss 0.46192324 - time (sec): 13.68 - samples/sec: 1530.16 - lr: 0.000047 - momentum: 0.000000
2024-03-26 11:34:08,529 epoch 2 - iter 32/48 - loss 0.44576921 - time (sec): 15.91 - samples/sec: 1500.06 - lr: 0.000046 - momentum: 0.000000
2024-03-26 11:34:10,295 epoch 2 - iter 36/48 - loss 0.43726579 - time (sec): 17.68 - samples/sec: 1494.39 - lr: 0.000046 - momentum: 0.000000
2024-03-26 11:34:12,061 epoch 2 - iter 40/48 - loss 0.43733009 - time (sec): 19.44 - samples/sec: 1500.28 - lr: 0.000046 - momentum: 0.000000
2024-03-26 11:34:14,381 epoch 2 - iter 44/48 - loss 0.42304062 - time (sec): 21.76 - samples/sec: 1488.86 - lr: 0.000045 - momentum: 0.000000
2024-03-26 11:34:15,946 epoch 2 - iter 48/48 - loss 0.41515240 - time (sec): 23.33 - samples/sec: 1477.66 - lr: 0.000045 - momentum: 0.000000
2024-03-26 11:34:15,946 ----------------------------------------------------------------------------------------------------
2024-03-26 11:34:15,946 EPOCH 2 done: loss 0.4152 - lr: 0.000045
2024-03-26 11:34:16,914 DEV : loss 0.26913678646087646 - f1-score (micro avg)  0.8164
2024-03-26 11:34:16,917 saving best model
2024-03-26 11:34:17,390 ----------------------------------------------------------------------------------------------------
2024-03-26 11:34:18,964 epoch 3 - iter 4/48 - loss 0.27840730 - time (sec): 1.57 - samples/sec: 1558.05 - lr: 0.000044 - momentum: 0.000000
2024-03-26 11:34:21,788 epoch 3 - iter 8/48 - loss 0.22660795 - time (sec): 4.40 - samples/sec: 1303.04 - lr: 0.000044 - momentum: 0.000000
2024-03-26 11:34:23,083 epoch 3 - iter 12/48 - loss 0.22777721 - time (sec): 5.69 - samples/sec: 1431.27 - lr: 0.000043 - momentum: 0.000000
2024-03-26 11:34:24,492 epoch 3 - iter 16/48 - loss 0.20623331 - time (sec): 7.10 - samples/sec: 1554.57 - lr: 0.000043 - momentum: 0.000000
2024-03-26 11:34:26,009 epoch 3 - iter 20/48 - loss 0.20655534 - time (sec): 8.62 - samples/sec: 1567.86 - lr: 0.000042 - momentum: 0.000000
2024-03-26 11:34:28,814 epoch 3 - iter 24/48 - loss 0.20077536 - time (sec): 11.42 - samples/sec: 1462.28 - lr: 0.000042 - momentum: 0.000000
2024-03-26 11:34:30,804 epoch 3 - iter 28/48 - loss 0.20515197 - time (sec): 13.41 - samples/sec: 1477.75 - lr: 0.000041 - momentum: 0.000000
2024-03-26 11:34:33,323 epoch 3 - iter 32/48 - loss 0.19788947 - time (sec): 15.93 - samples/sec: 1431.16 - lr: 0.000041 - momentum: 0.000000
2024-03-26 11:34:35,287 epoch 3 - iter 36/48 - loss 0.20165014 - time (sec): 17.90 - samples/sec: 1429.28 - lr: 0.000040 - momentum: 0.000000
2024-03-26 11:34:37,666 epoch 3 - iter 40/48 - loss 0.19334685 - time (sec): 20.27 - samples/sec: 1408.84 - lr: 0.000040 - momentum: 0.000000
2024-03-26 11:34:40,145 epoch 3 - iter 44/48 - loss 0.20288087 - time (sec): 22.75 - samples/sec: 1398.52 - lr: 0.000040 - momentum: 0.000000
2024-03-26 11:34:42,579 epoch 3 - iter 48/48 - loss 0.19661772 - time (sec): 25.19 - samples/sec: 1368.61 - lr: 0.000039 - momentum: 0.000000
2024-03-26 11:34:42,579 ----------------------------------------------------------------------------------------------------
2024-03-26 11:34:42,579 EPOCH 3 done: loss 0.1966 - lr: 0.000039
2024-03-26 11:34:43,613 DEV : loss 0.21700386703014374 - f1-score (micro avg)  0.8608
2024-03-26 11:34:43,614 saving best model
2024-03-26 11:34:44,036 ----------------------------------------------------------------------------------------------------
2024-03-26 11:34:45,449 epoch 4 - iter 4/48 - loss 0.14641328 - time (sec): 1.41 - samples/sec: 1773.85 - lr: 0.000039 - momentum: 0.000000
2024-03-26 11:34:47,395 epoch 4 - iter 8/48 - loss 0.13808574 - time (sec): 3.36 - samples/sec: 1595.81 - lr: 0.000038 - momentum: 0.000000
2024-03-26 11:34:50,115 epoch 4 - iter 12/48 - loss 0.12876595 - time (sec): 6.08 - samples/sec: 1388.37 - lr: 0.000038 - momentum: 0.000000
2024-03-26 11:34:52,081 epoch 4 - iter 16/48 - loss 0.13156323 - time (sec): 8.04 - samples/sec: 1407.85 - lr: 0.000037 - momentum: 0.000000
2024-03-26 11:34:54,572 epoch 4 - iter 20/48 - loss 0.12428123 - time (sec): 10.53 - samples/sec: 1394.75 - lr: 0.000037 - momentum: 0.000000
2024-03-26 11:34:57,507 epoch 4 - iter 24/48 - loss 0.11793333 - time (sec): 13.47 - samples/sec: 1353.70 - lr: 0.000036 - momentum: 0.000000
2024-03-26 11:34:58,691 epoch 4 - iter 28/48 - loss 0.11608881 - time (sec): 14.65 - samples/sec: 1386.24 - lr: 0.000036 - momentum: 0.000000
2024-03-26 11:35:01,804 epoch 4 - iter 32/48 - loss 0.11458934 - time (sec): 17.77 - samples/sec: 1329.30 - lr: 0.000035 - momentum: 0.000000
2024-03-26 11:35:03,594 epoch 4 - iter 36/48 - loss 0.11846800 - time (sec): 19.56 - samples/sec: 1360.43 - lr: 0.000035 - momentum: 0.000000
2024-03-26 11:35:06,500 epoch 4 - iter 40/48 - loss 0.12511856 - time (sec): 22.46 - samples/sec: 1330.40 - lr: 0.000034 - momentum: 0.000000
2024-03-26 11:35:07,417 epoch 4 - iter 44/48 - loss 0.12629946 - time (sec): 23.38 - samples/sec: 1376.32 - lr: 0.000034 - momentum: 0.000000
2024-03-26 11:35:08,913 epoch 4 - iter 48/48 - loss 0.12787118 - time (sec): 24.88 - samples/sec: 1385.74 - lr: 0.000034 - momentum: 0.000000
2024-03-26 11:35:08,914 ----------------------------------------------------------------------------------------------------
2024-03-26 11:35:08,914 EPOCH 4 done: loss 0.1279 - lr: 0.000034
2024-03-26 11:35:09,895 DEV : loss 0.18662486970424652 - f1-score (micro avg)  0.8817
2024-03-26 11:35:09,897 saving best model
2024-03-26 11:35:10,332 ----------------------------------------------------------------------------------------------------
2024-03-26 11:35:12,809 epoch 5 - iter 4/48 - loss 0.06830634 - time (sec): 2.48 - samples/sec: 1283.35 - lr: 0.000033 - momentum: 0.000000
2024-03-26 11:35:14,268 epoch 5 - iter 8/48 - loss 0.08573760 - time (sec): 3.94 - samples/sec: 1446.25 - lr: 0.000033 - momentum: 0.000000
2024-03-26 11:35:15,788 epoch 5 - iter 12/48 - loss 0.08684380 - time (sec): 5.46 - samples/sec: 1510.47 - lr: 0.000032 - momentum: 0.000000
2024-03-26 11:35:18,018 epoch 5 - iter 16/48 - loss 0.08965837 - time (sec): 7.69 - samples/sec: 1431.53 - lr: 0.000032 - momentum: 0.000000
2024-03-26 11:35:20,150 epoch 5 - iter 20/48 - loss 0.10094467 - time (sec): 9.82 - samples/sec: 1434.23 - lr: 0.000031 - momentum: 0.000000
2024-03-26 11:35:22,751 epoch 5 - iter 24/48 - loss 0.09379417 - time (sec): 12.42 - samples/sec: 1417.55 - lr: 0.000031 - momentum: 0.000000
2024-03-26 11:35:25,342 epoch 5 - iter 28/48 - loss 0.08737934 - time (sec): 15.01 - samples/sec: 1402.35 - lr: 0.000030 - momentum: 0.000000
2024-03-26 11:35:27,327 epoch 5 - iter 32/48 - loss 0.08652809 - time (sec): 16.99 - samples/sec: 1401.19 - lr: 0.000030 - momentum: 0.000000
2024-03-26 11:35:29,218 epoch 5 - iter 36/48 - loss 0.08405259 - time (sec): 18.89 - samples/sec: 1400.33 - lr: 0.000029 - momentum: 0.000000
2024-03-26 11:35:31,655 epoch 5 - iter 40/48 - loss 0.08424074 - time (sec): 21.32 - samples/sec: 1385.12 - lr: 0.000029 - momentum: 0.000000
2024-03-26 11:35:33,674 epoch 5 - iter 44/48 - loss 0.08675698 - time (sec): 23.34 - samples/sec: 1383.79 - lr: 0.000029 - momentum: 0.000000
2024-03-26 11:35:34,768 epoch 5 - iter 48/48 - loss 0.08578516 - time (sec): 24.44 - samples/sec: 1410.70 - lr: 0.000028 - momentum: 0.000000
2024-03-26 11:35:34,769 ----------------------------------------------------------------------------------------------------
2024-03-26 11:35:34,769 EPOCH 5 done: loss 0.0858 - lr: 0.000028
2024-03-26 11:35:35,716 DEV : loss 0.17283330857753754 - f1-score (micro avg)  0.8942
2024-03-26 11:35:35,717 saving best model
2024-03-26 11:35:36,174 ----------------------------------------------------------------------------------------------------
2024-03-26 11:35:38,850 epoch 6 - iter 4/48 - loss 0.05365840 - time (sec): 2.68 - samples/sec: 1188.40 - lr: 0.000028 - momentum: 0.000000
2024-03-26 11:35:40,878 epoch 6 - iter 8/48 - loss 0.05720723 - time (sec): 4.70 - samples/sec: 1248.47 - lr: 0.000027 - momentum: 0.000000
2024-03-26 11:35:42,486 epoch 6 - iter 12/48 - loss 0.06649040 - time (sec): 6.31 - samples/sec: 1397.93 - lr: 0.000027 - momentum: 0.000000
2024-03-26 11:35:44,519 epoch 6 - iter 16/48 - loss 0.06053967 - time (sec): 8.34 - samples/sec: 1393.76 - lr: 0.000026 - momentum: 0.000000
2024-03-26 11:35:45,652 epoch 6 - iter 20/48 - loss 0.05981345 - time (sec): 9.48 - samples/sec: 1474.40 - lr: 0.000026 - momentum: 0.000000
2024-03-26 11:35:47,585 epoch 6 - iter 24/48 - loss 0.05907532 - time (sec): 11.41 - samples/sec: 1464.86 - lr: 0.000025 - momentum: 0.000000
2024-03-26 11:35:48,768 epoch 6 - iter 28/48 - loss 0.06005105 - time (sec): 12.59 - samples/sec: 1510.48 - lr: 0.000025 - momentum: 0.000000
2024-03-26 11:35:50,567 epoch 6 - iter 32/48 - loss 0.05586702 - time (sec): 14.39 - samples/sec: 1530.72 - lr: 0.000024 - momentum: 0.000000
2024-03-26 11:35:53,101 epoch 6 - iter 36/48 - loss 0.06595337 - time (sec): 16.93 - samples/sec: 1500.21 - lr: 0.000024 - momentum: 0.000000
2024-03-26 11:35:55,243 epoch 6 - iter 40/48 - loss 0.06371281 - time (sec): 19.07 - samples/sec: 1490.16 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:35:57,163 epoch 6 - iter 44/48 - loss 0.06590059 - time (sec): 20.99 - samples/sec: 1498.80 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:35:58,837 epoch 6 - iter 48/48 - loss 0.06662131 - time (sec): 22.66 - samples/sec: 1521.17 - lr: 0.000023 - momentum: 0.000000
2024-03-26 11:35:58,837 ----------------------------------------------------------------------------------------------------
2024-03-26 11:35:58,837 EPOCH 6 done: loss 0.0666 - lr: 0.000023
2024-03-26 11:35:59,808 DEV : loss 0.15879587829113007 - f1-score (micro avg)  0.9341
2024-03-26 11:35:59,809 saving best model
2024-03-26 11:36:00,267 ----------------------------------------------------------------------------------------------------
2024-03-26 11:36:02,501 epoch 7 - iter 4/48 - loss 0.07284967 - time (sec): 2.23 - samples/sec: 1237.82 - lr: 0.000022 - momentum: 0.000000
2024-03-26 11:36:04,244 epoch 7 - iter 8/48 - loss 0.05810049 - time (sec): 3.98 - samples/sec: 1445.13 - lr: 0.000022 - momentum: 0.000000
2024-03-26 11:36:06,352 epoch 7 - iter 12/48 - loss 0.04687988 - time (sec): 6.08 - samples/sec: 1408.13 - lr: 0.000021 - momentum: 0.000000
2024-03-26 11:36:09,051 epoch 7 - iter 16/48 - loss 0.04388564 - time (sec): 8.78 - samples/sec: 1345.71 - lr: 0.000021 - momentum: 0.000000
2024-03-26 11:36:11,822 epoch 7 - iter 20/48 - loss 0.04451078 - time (sec): 11.55 - samples/sec: 1352.70 - lr: 0.000020 - momentum: 0.000000
2024-03-26 11:36:13,366 epoch 7 - iter 24/48 - loss 0.04438006 - time (sec): 13.10 - samples/sec: 1376.06 - lr: 0.000020 - momentum: 0.000000
2024-03-26 11:36:15,571 epoch 7 - iter 28/48 - loss 0.04184705 - time (sec): 15.30 - samples/sec: 1390.16 - lr: 0.000019 - momentum: 0.000000
2024-03-26 11:36:17,773 epoch 7 - iter 32/48 - loss 0.04525184 - time (sec): 17.51 - samples/sec: 1396.63 - lr: 0.000019 - momentum: 0.000000
2024-03-26 11:36:20,032 epoch 7 - iter 36/48 - loss 0.04947562 - time (sec): 19.76 - samples/sec: 1385.10 - lr: 0.000018 - momentum: 0.000000
2024-03-26 11:36:21,659 epoch 7 - iter 40/48 - loss 0.04694456 - time (sec): 21.39 - samples/sec: 1393.87 - lr: 0.000018 - momentum: 0.000000
2024-03-26 11:36:23,419 epoch 7 - iter 44/48 - loss 0.05016226 - time (sec): 23.15 - samples/sec: 1408.92 - lr: 0.000017 - momentum: 0.000000
2024-03-26 11:36:24,776 epoch 7 - iter 48/48 - loss 0.04985462 - time (sec): 24.51 - samples/sec: 1406.54 - lr: 0.000017 - momentum: 0.000000
2024-03-26 11:36:24,777 ----------------------------------------------------------------------------------------------------
2024-03-26 11:36:24,777 EPOCH 7 done: loss 0.0499 - lr: 0.000017
2024-03-26 11:36:25,828 DEV : loss 0.16802552342414856 - f1-score (micro avg)  0.9333
2024-03-26 11:36:25,830 ----------------------------------------------------------------------------------------------------
2024-03-26 11:36:28,176 epoch 8 - iter 4/48 - loss 0.02523109 - time (sec): 2.35 - samples/sec: 1253.12 - lr: 0.000017 - momentum: 0.000000
2024-03-26 11:36:30,775 epoch 8 - iter 8/48 - loss 0.02208519 - time (sec): 4.94 - samples/sec: 1337.73 - lr: 0.000016 - momentum: 0.000000
2024-03-26 11:36:32,833 epoch 8 - iter 12/48 - loss 0.02316165 - time (sec): 7.00 - samples/sec: 1314.10 - lr: 0.000016 - momentum: 0.000000
2024-03-26 11:36:34,948 epoch 8 - iter 16/48 - loss 0.02270118 - time (sec): 9.12 - samples/sec: 1315.04 - lr: 0.000015 - momentum: 0.000000
2024-03-26 11:36:36,478 epoch 8 - iter 20/48 - loss 0.02510760 - time (sec): 10.65 - samples/sec: 1342.76 - lr: 0.000015 - momentum: 0.000000
2024-03-26 11:36:38,968 epoch 8 - iter 24/48 - loss 0.02440371 - time (sec): 13.14 - samples/sec: 1319.99 - lr: 0.000014 - momentum: 0.000000
2024-03-26 11:36:41,209 epoch 8 - iter 28/48 - loss 0.02404743 - time (sec): 15.38 - samples/sec: 1310.63 - lr: 0.000014 - momentum: 0.000000
2024-03-26 11:36:43,580 epoch 8 - iter 32/48 - loss 0.03201898 - time (sec): 17.75 - samples/sec: 1322.64 - lr: 0.000013 - momentum: 0.000000
2024-03-26 11:36:46,883 epoch 8 - iter 36/48 - loss 0.03651999 - time (sec): 21.05 - samples/sec: 1274.74 - lr: 0.000013 - momentum: 0.000000
2024-03-26 11:36:48,978 epoch 8 - iter 40/48 - loss 0.04125936 - time (sec): 23.15 - samples/sec: 1278.60 - lr: 0.000012 - momentum: 0.000000
2024-03-26 11:36:49,806 epoch 8 - iter 44/48 - loss 0.04000375 - time (sec): 23.98 - samples/sec: 1325.12 - lr: 0.000012 - momentum: 0.000000
2024-03-26 11:36:51,680 epoch 8 - iter 48/48 - loss 0.03939075 - time (sec): 25.85 - samples/sec: 1333.53 - lr: 0.000011 - momentum: 0.000000
2024-03-26 11:36:51,681 ----------------------------------------------------------------------------------------------------
2024-03-26 11:36:51,681 EPOCH 8 done: loss 0.0394 - lr: 0.000011
2024-03-26 11:36:52,644 DEV : loss 0.18046870827674866 - f1-score (micro avg)  0.9294
2024-03-26 11:36:52,647 ----------------------------------------------------------------------------------------------------
2024-03-26 11:36:55,407 epoch 9 - iter 4/48 - loss 0.01368922 - time (sec): 2.76 - samples/sec: 1195.26 - lr: 0.000011 - momentum: 0.000000
2024-03-26 11:36:57,113 epoch 9 - iter 8/48 - loss 0.02194754 - time (sec): 4.47 - samples/sec: 1284.97 - lr: 0.000011 - momentum: 0.000000
2024-03-26 11:36:59,325 epoch 9 - iter 12/48 - loss 0.02259365 - time (sec): 6.68 - samples/sec: 1344.23 - lr: 0.000010 - momentum: 0.000000
2024-03-26 11:37:01,488 epoch 9 - iter 16/48 - loss 0.02747245 - time (sec): 8.84 - samples/sec: 1369.80 - lr: 0.000010 - momentum: 0.000000
2024-03-26 11:37:03,868 epoch 9 - iter 20/48 - loss 0.02337877 - time (sec): 11.22 - samples/sec: 1348.38 - lr: 0.000009 - momentum: 0.000000
2024-03-26 11:37:05,818 epoch 9 - iter 24/48 - loss 0.02369286 - time (sec): 13.17 - samples/sec: 1344.53 - lr: 0.000009 - momentum: 0.000000
2024-03-26 11:37:09,104 epoch 9 - iter 28/48 - loss 0.02677139 - time (sec): 16.46 - samples/sec: 1298.13 - lr: 0.000008 - momentum: 0.000000
2024-03-26 11:37:10,540 epoch 9 - iter 32/48 - loss 0.02760924 - time (sec): 17.89 - samples/sec: 1335.17 - lr: 0.000008 - momentum: 0.000000
2024-03-26 11:37:13,071 epoch 9 - iter 36/48 - loss 0.02778603 - time (sec): 20.42 - samples/sec: 1319.50 - lr: 0.000007 - momentum: 0.000000
2024-03-26 11:37:14,592 epoch 9 - iter 40/48 - loss 0.02879429 - time (sec): 21.94 - samples/sec: 1336.09 - lr: 0.000007 - momentum: 0.000000
2024-03-26 11:37:16,189 epoch 9 - iter 44/48 - loss 0.03224152 - time (sec): 23.54 - samples/sec: 1351.25 - lr: 0.000006 - momentum: 0.000000
2024-03-26 11:37:17,651 epoch 9 - iter 48/48 - loss 0.03182031 - time (sec): 25.00 - samples/sec: 1378.69 - lr: 0.000006 - momentum: 0.000000
2024-03-26 11:37:17,651 ----------------------------------------------------------------------------------------------------
2024-03-26 11:37:17,651 EPOCH 9 done: loss 0.0318 - lr: 0.000006
2024-03-26 11:37:18,590 DEV : loss 0.18695427477359772 - f1-score (micro avg)  0.9263
2024-03-26 11:37:18,591 ----------------------------------------------------------------------------------------------------
2024-03-26 11:37:21,015 epoch 10 - iter 4/48 - loss 0.02264444 - time (sec): 2.42 - samples/sec: 1358.51 - lr: 0.000006 - momentum: 0.000000
2024-03-26 11:37:22,976 epoch 10 - iter 8/48 - loss 0.01694052 - time (sec): 4.38 - samples/sec: 1333.02 - lr: 0.000005 - momentum: 0.000000
2024-03-26 11:37:24,212 epoch 10 - iter 12/48 - loss 0.02510073 - time (sec): 5.62 - samples/sec: 1484.16 - lr: 0.000005 - momentum: 0.000000
2024-03-26 11:37:25,824 epoch 10 - iter 16/48 - loss 0.02807504 - time (sec): 7.23 - samples/sec: 1551.36 - lr: 0.000004 - momentum: 0.000000
2024-03-26 11:37:27,540 epoch 10 - iter 20/48 - loss 0.02802998 - time (sec): 8.95 - samples/sec: 1595.24 - lr: 0.000004 - momentum: 0.000000
2024-03-26 11:37:29,677 epoch 10 - iter 24/48 - loss 0.02579472 - time (sec): 11.09 - samples/sec: 1537.39 - lr: 0.000003 - momentum: 0.000000
2024-03-26 11:37:31,826 epoch 10 - iter 28/48 - loss 0.02330368 - time (sec): 13.23 - samples/sec: 1507.27 - lr: 0.000003 - momentum: 0.000000
2024-03-26 11:37:34,002 epoch 10 - iter 32/48 - loss 0.02482044 - time (sec): 15.41 - samples/sec: 1513.99 - lr: 0.000002 - momentum: 0.000000
2024-03-26 11:37:35,409 epoch 10 - iter 36/48 - loss 0.02437032 - time (sec): 16.82 - samples/sec: 1516.24 - lr: 0.000002 - momentum: 0.000000
2024-03-26 11:37:38,098 epoch 10 - iter 40/48 - loss 0.02266370 - time (sec): 19.51 - samples/sec: 1479.35 - lr: 0.000001 - momentum: 0.000000
2024-03-26 11:37:40,623 epoch 10 - iter 44/48 - loss 0.02605707 - time (sec): 22.03 - samples/sec: 1459.52 - lr: 0.000001 - momentum: 0.000000
2024-03-26 11:37:42,293 epoch 10 - iter 48/48 - loss 0.02700708 - time (sec): 23.70 - samples/sec: 1454.42 - lr: 0.000000 - momentum: 0.000000
2024-03-26 11:37:42,293 ----------------------------------------------------------------------------------------------------
2024-03-26 11:37:42,293 EPOCH 10 done: loss 0.0270 - lr: 0.000000
2024-03-26 11:37:43,246 DEV : loss 0.17998817563056946 - f1-score (micro avg)  0.9326
2024-03-26 11:37:43,555 ----------------------------------------------------------------------------------------------------
2024-03-26 11:37:43,555 Loading model from best epoch ...
2024-03-26 11:37:44,460 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 11:37:45,303 
Results:
- F-score (micro) 0.9052
- F-score (macro) 0.6892
- Accuracy 0.8279

By class:
              precision    recall  f1-score   support

 Unternehmen     0.9027    0.8722    0.8872       266
 Auslagerung     0.8775    0.8916    0.8845       249
         Ort     0.9779    0.9925    0.9852       134
    Software     0.0000    0.0000    0.0000         0

   micro avg     0.9059    0.9045    0.9052       649
   macro avg     0.6895    0.6891    0.6892       649
weighted avg     0.9086    0.9045    0.9064       649

2024-03-26 11:37:45,304 ----------------------------------------------------------------------------------------------------