2023-10-18 18:33:37,538 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,538 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 128)
        (position_embeddings): Embedding(512, 128)
        (token_type_embeddings): Embedding(2, 128)
        (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-1): 2 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=128, out_features=128, bias=True)
                (key): Linear(in_features=128, out_features=128, bias=True)
                (value): Linear(in_features=128, out_features=128, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=128, out_features=128, bias=True)
                (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=128, out_features=512, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=512, out_features=128, bias=True)
              (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=128, out_features=128, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=128, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-18 18:33:37,538 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,538 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
 - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Train:  3575 sentences
2023-10-18 18:33:37,539         (train_with_dev=False, train_with_test=False)
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Training Params:
2023-10-18 18:33:37,539  - learning_rate: "5e-05" 
2023-10-18 18:33:37,539  - mini_batch_size: "8"
2023-10-18 18:33:37,539  - max_epochs: "10"
2023-10-18 18:33:37,539  - shuffle: "True"
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Plugins:
2023-10-18 18:33:37,539  - TensorboardLogger
2023-10-18 18:33:37,539  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 18:33:37,539  - metric: "('micro avg', 'f1-score')"
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Computation:
2023-10-18 18:33:37,539  - compute on device: cuda:0
2023-10-18 18:33:37,539  - embedding storage: none
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-tiny-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:37,539 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 18:33:38,584 epoch 1 - iter 44/447 - loss 3.33635249 - time (sec): 1.04 - samples/sec: 9031.98 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:33:39,596 epoch 1 - iter 88/447 - loss 3.23369667 - time (sec): 2.06 - samples/sec: 8843.47 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:33:40,624 epoch 1 - iter 132/447 - loss 3.00613659 - time (sec): 3.08 - samples/sec: 8783.54 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:33:41,663 epoch 1 - iter 176/447 - loss 2.71396245 - time (sec): 4.12 - samples/sec: 8518.45 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:33:42,733 epoch 1 - iter 220/447 - loss 2.38046964 - time (sec): 5.19 - samples/sec: 8459.77 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:33:43,772 epoch 1 - iter 264/447 - loss 2.12925483 - time (sec): 6.23 - samples/sec: 8330.58 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:33:44,841 epoch 1 - iter 308/447 - loss 1.91320497 - time (sec): 7.30 - samples/sec: 8317.29 - lr: 0.000034 - momentum: 0.000000
2023-10-18 18:33:45,840 epoch 1 - iter 352/447 - loss 1.76692254 - time (sec): 8.30 - samples/sec: 8304.96 - lr: 0.000039 - momentum: 0.000000
2023-10-18 18:33:46,840 epoch 1 - iter 396/447 - loss 1.64812306 - time (sec): 9.30 - samples/sec: 8302.11 - lr: 0.000044 - momentum: 0.000000
2023-10-18 18:33:47,765 epoch 1 - iter 440/447 - loss 1.54768279 - time (sec): 10.23 - samples/sec: 8350.68 - lr: 0.000049 - momentum: 0.000000
2023-10-18 18:33:47,917 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:47,917 EPOCH 1 done: loss 1.5345 - lr: 0.000049
2023-10-18 18:33:50,187 DEV : loss 0.43991518020629883 - f1-score (micro avg)  0.0
2023-10-18 18:33:50,215 ----------------------------------------------------------------------------------------------------
2023-10-18 18:33:51,236 epoch 2 - iter 44/447 - loss 0.53951636 - time (sec): 1.02 - samples/sec: 9399.42 - lr: 0.000049 - momentum: 0.000000
2023-10-18 18:33:52,237 epoch 2 - iter 88/447 - loss 0.54048392 - time (sec): 2.02 - samples/sec: 9161.68 - lr: 0.000049 - momentum: 0.000000
2023-10-18 18:33:53,244 epoch 2 - iter 132/447 - loss 0.51411769 - time (sec): 3.03 - samples/sec: 8848.25 - lr: 0.000048 - momentum: 0.000000
2023-10-18 18:33:54,269 epoch 2 - iter 176/447 - loss 0.48625964 - time (sec): 4.05 - samples/sec: 8700.98 - lr: 0.000048 - momentum: 0.000000
2023-10-18 18:33:55,244 epoch 2 - iter 220/447 - loss 0.48222774 - time (sec): 5.03 - samples/sec: 8567.74 - lr: 0.000047 - momentum: 0.000000
2023-10-18 18:33:56,313 epoch 2 - iter 264/447 - loss 0.47749236 - time (sec): 6.10 - samples/sec: 8403.28 - lr: 0.000047 - momentum: 0.000000
2023-10-18 18:33:57,401 epoch 2 - iter 308/447 - loss 0.47712866 - time (sec): 7.19 - samples/sec: 8360.14 - lr: 0.000046 - momentum: 0.000000
2023-10-18 18:33:58,455 epoch 2 - iter 352/447 - loss 0.47982226 - time (sec): 8.24 - samples/sec: 8354.58 - lr: 0.000046 - momentum: 0.000000
2023-10-18 18:33:59,477 epoch 2 - iter 396/447 - loss 0.47767121 - time (sec): 9.26 - samples/sec: 8313.03 - lr: 0.000045 - momentum: 0.000000
2023-10-18 18:34:00,473 epoch 2 - iter 440/447 - loss 0.47472382 - time (sec): 10.26 - samples/sec: 8331.69 - lr: 0.000045 - momentum: 0.000000
2023-10-18 18:34:00,631 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:00,632 EPOCH 2 done: loss 0.4744 - lr: 0.000045
2023-10-18 18:34:05,863 DEV : loss 0.34927892684936523 - f1-score (micro avg)  0.1013
2023-10-18 18:34:05,892 saving best model
2023-10-18 18:34:05,926 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:06,986 epoch 3 - iter 44/447 - loss 0.41430857 - time (sec): 1.06 - samples/sec: 8227.86 - lr: 0.000044 - momentum: 0.000000
2023-10-18 18:34:08,037 epoch 3 - iter 88/447 - loss 0.42596292 - time (sec): 2.11 - samples/sec: 8062.22 - lr: 0.000043 - momentum: 0.000000
2023-10-18 18:34:09,114 epoch 3 - iter 132/447 - loss 0.41735810 - time (sec): 3.19 - samples/sec: 8032.54 - lr: 0.000043 - momentum: 0.000000
2023-10-18 18:34:10,154 epoch 3 - iter 176/447 - loss 0.40121953 - time (sec): 4.23 - samples/sec: 7956.82 - lr: 0.000042 - momentum: 0.000000
2023-10-18 18:34:11,246 epoch 3 - iter 220/447 - loss 0.40989488 - time (sec): 5.32 - samples/sec: 7989.71 - lr: 0.000042 - momentum: 0.000000
2023-10-18 18:34:12,282 epoch 3 - iter 264/447 - loss 0.40691409 - time (sec): 6.36 - samples/sec: 7992.85 - lr: 0.000041 - momentum: 0.000000
2023-10-18 18:34:13,367 epoch 3 - iter 308/447 - loss 0.40246807 - time (sec): 7.44 - samples/sec: 8082.52 - lr: 0.000041 - momentum: 0.000000
2023-10-18 18:34:14,381 epoch 3 - iter 352/447 - loss 0.39964777 - time (sec): 8.45 - samples/sec: 8073.16 - lr: 0.000040 - momentum: 0.000000
2023-10-18 18:34:15,444 epoch 3 - iter 396/447 - loss 0.40686685 - time (sec): 9.52 - samples/sec: 8104.67 - lr: 0.000040 - momentum: 0.000000
2023-10-18 18:34:16,474 epoch 3 - iter 440/447 - loss 0.40199107 - time (sec): 10.55 - samples/sec: 8099.80 - lr: 0.000039 - momentum: 0.000000
2023-10-18 18:34:16,641 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:16,641 EPOCH 3 done: loss 0.4005 - lr: 0.000039
2023-10-18 18:34:21,882 DEV : loss 0.3129710257053375 - f1-score (micro avg)  0.2991
2023-10-18 18:34:21,910 saving best model
2023-10-18 18:34:21,943 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:23,020 epoch 4 - iter 44/447 - loss 0.34905134 - time (sec): 1.08 - samples/sec: 8302.97 - lr: 0.000038 - momentum: 0.000000
2023-10-18 18:34:24,089 epoch 4 - iter 88/447 - loss 0.33784904 - time (sec): 2.15 - samples/sec: 8202.40 - lr: 0.000038 - momentum: 0.000000
2023-10-18 18:34:25,178 epoch 4 - iter 132/447 - loss 0.34129737 - time (sec): 3.23 - samples/sec: 8397.94 - lr: 0.000037 - momentum: 0.000000
2023-10-18 18:34:26,221 epoch 4 - iter 176/447 - loss 0.35267194 - time (sec): 4.28 - samples/sec: 8371.63 - lr: 0.000037 - momentum: 0.000000
2023-10-18 18:34:27,255 epoch 4 - iter 220/447 - loss 0.35297139 - time (sec): 5.31 - samples/sec: 8230.26 - lr: 0.000036 - momentum: 0.000000
2023-10-18 18:34:28,308 epoch 4 - iter 264/447 - loss 0.35619649 - time (sec): 6.36 - samples/sec: 8280.36 - lr: 0.000036 - momentum: 0.000000
2023-10-18 18:34:29,274 epoch 4 - iter 308/447 - loss 0.35922624 - time (sec): 7.33 - samples/sec: 8299.85 - lr: 0.000035 - momentum: 0.000000
2023-10-18 18:34:30,258 epoch 4 - iter 352/447 - loss 0.36302459 - time (sec): 8.31 - samples/sec: 8260.86 - lr: 0.000035 - momentum: 0.000000
2023-10-18 18:34:31,297 epoch 4 - iter 396/447 - loss 0.36296842 - time (sec): 9.35 - samples/sec: 8231.92 - lr: 0.000034 - momentum: 0.000000
2023-10-18 18:34:32,321 epoch 4 - iter 440/447 - loss 0.36140456 - time (sec): 10.38 - samples/sec: 8228.75 - lr: 0.000033 - momentum: 0.000000
2023-10-18 18:34:32,474 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:32,474 EPOCH 4 done: loss 0.3624 - lr: 0.000033
2023-10-18 18:34:37,831 DEV : loss 0.3036978840827942 - f1-score (micro avg)  0.3289
2023-10-18 18:34:37,862 saving best model
2023-10-18 18:34:37,896 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:38,871 epoch 5 - iter 44/447 - loss 0.38380203 - time (sec): 0.97 - samples/sec: 7851.81 - lr: 0.000033 - momentum: 0.000000
2023-10-18 18:34:39,858 epoch 5 - iter 88/447 - loss 0.35069929 - time (sec): 1.96 - samples/sec: 8000.92 - lr: 0.000032 - momentum: 0.000000
2023-10-18 18:34:40,869 epoch 5 - iter 132/447 - loss 0.35025750 - time (sec): 2.97 - samples/sec: 7989.62 - lr: 0.000032 - momentum: 0.000000
2023-10-18 18:34:41,927 epoch 5 - iter 176/447 - loss 0.32746992 - time (sec): 4.03 - samples/sec: 8316.64 - lr: 0.000031 - momentum: 0.000000
2023-10-18 18:34:42,999 epoch 5 - iter 220/447 - loss 0.32093703 - time (sec): 5.10 - samples/sec: 8417.39 - lr: 0.000031 - momentum: 0.000000
2023-10-18 18:34:44,092 epoch 5 - iter 264/447 - loss 0.32327443 - time (sec): 6.20 - samples/sec: 8408.21 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:34:45,141 epoch 5 - iter 308/447 - loss 0.32315435 - time (sec): 7.24 - samples/sec: 8342.28 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:34:46,173 epoch 5 - iter 352/447 - loss 0.32526981 - time (sec): 8.28 - samples/sec: 8271.09 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:34:47,179 epoch 5 - iter 396/447 - loss 0.32725129 - time (sec): 9.28 - samples/sec: 8276.80 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:34:48,202 epoch 5 - iter 440/447 - loss 0.32840326 - time (sec): 10.31 - samples/sec: 8274.56 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:34:48,370 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:48,370 EPOCH 5 done: loss 0.3282 - lr: 0.000028
2023-10-18 18:34:53,324 DEV : loss 0.30492323637008667 - f1-score (micro avg)  0.3499
2023-10-18 18:34:53,354 saving best model
2023-10-18 18:34:53,390 ----------------------------------------------------------------------------------------------------
2023-10-18 18:34:54,517 epoch 6 - iter 44/447 - loss 0.28364922 - time (sec): 1.13 - samples/sec: 7540.80 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:34:55,545 epoch 6 - iter 88/447 - loss 0.30524148 - time (sec): 2.15 - samples/sec: 7993.72 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:34:56,617 epoch 6 - iter 132/447 - loss 0.30377453 - time (sec): 3.23 - samples/sec: 8341.74 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:34:57,582 epoch 6 - iter 176/447 - loss 0.31103907 - time (sec): 4.19 - samples/sec: 8421.94 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:34:58,566 epoch 6 - iter 220/447 - loss 0.31842430 - time (sec): 5.17 - samples/sec: 8286.96 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:34:59,543 epoch 6 - iter 264/447 - loss 0.31506307 - time (sec): 6.15 - samples/sec: 8275.98 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:35:00,549 epoch 6 - iter 308/447 - loss 0.31532303 - time (sec): 7.16 - samples/sec: 8328.75 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:35:01,530 epoch 6 - iter 352/447 - loss 0.31862513 - time (sec): 8.14 - samples/sec: 8355.13 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:35:02,529 epoch 6 - iter 396/447 - loss 0.31647241 - time (sec): 9.14 - samples/sec: 8376.43 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:35:03,527 epoch 6 - iter 440/447 - loss 0.31494212 - time (sec): 10.14 - samples/sec: 8404.30 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:35:03,684 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:03,684 EPOCH 6 done: loss 0.3141 - lr: 0.000022
2023-10-18 18:35:08,966 DEV : loss 0.3028465211391449 - f1-score (micro avg)  0.3604
2023-10-18 18:35:08,994 saving best model
2023-10-18 18:35:09,028 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:10,060 epoch 7 - iter 44/447 - loss 0.32923135 - time (sec): 1.03 - samples/sec: 8106.31 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:35:11,038 epoch 7 - iter 88/447 - loss 0.30360566 - time (sec): 2.01 - samples/sec: 8293.92 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:35:12,038 epoch 7 - iter 132/447 - loss 0.30120743 - time (sec): 3.01 - samples/sec: 8153.90 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:35:13,063 epoch 7 - iter 176/447 - loss 0.29389615 - time (sec): 4.03 - samples/sec: 8378.99 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:35:14,079 epoch 7 - iter 220/447 - loss 0.29926602 - time (sec): 5.05 - samples/sec: 8446.85 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:35:15,046 epoch 7 - iter 264/447 - loss 0.29100384 - time (sec): 6.02 - samples/sec: 8531.87 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:35:16,061 epoch 7 - iter 308/447 - loss 0.30005417 - time (sec): 7.03 - samples/sec: 8503.70 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:35:17,102 epoch 7 - iter 352/447 - loss 0.29977152 - time (sec): 8.07 - samples/sec: 8540.69 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:35:18,105 epoch 7 - iter 396/447 - loss 0.30209308 - time (sec): 9.08 - samples/sec: 8510.51 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:35:19,101 epoch 7 - iter 440/447 - loss 0.30016354 - time (sec): 10.07 - samples/sec: 8460.16 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:35:19,253 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:19,253 EPOCH 7 done: loss 0.3002 - lr: 0.000017
2023-10-18 18:35:24,552 DEV : loss 0.2917996644973755 - f1-score (micro avg)  0.3543
2023-10-18 18:35:24,583 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:25,619 epoch 8 - iter 44/447 - loss 0.30753885 - time (sec): 1.04 - samples/sec: 8181.07 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:35:26,616 epoch 8 - iter 88/447 - loss 0.29967643 - time (sec): 2.03 - samples/sec: 8244.97 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:35:27,649 epoch 8 - iter 132/447 - loss 0.29950276 - time (sec): 3.06 - samples/sec: 8325.53 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:35:28,633 epoch 8 - iter 176/447 - loss 0.29925959 - time (sec): 4.05 - samples/sec: 8497.58 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:35:29,642 epoch 8 - iter 220/447 - loss 0.29617713 - time (sec): 5.06 - samples/sec: 8453.57 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:35:30,733 epoch 8 - iter 264/447 - loss 0.29498884 - time (sec): 6.15 - samples/sec: 8516.03 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:35:31,700 epoch 8 - iter 308/447 - loss 0.29278015 - time (sec): 7.12 - samples/sec: 8480.48 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:35:32,710 epoch 8 - iter 352/447 - loss 0.29362748 - time (sec): 8.13 - samples/sec: 8493.47 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:35:33,733 epoch 8 - iter 396/447 - loss 0.28795118 - time (sec): 9.15 - samples/sec: 8504.46 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:35:34,698 epoch 8 - iter 440/447 - loss 0.29006311 - time (sec): 10.11 - samples/sec: 8454.88 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:35:34,845 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:34,845 EPOCH 8 done: loss 0.2907 - lr: 0.000011
2023-10-18 18:35:40,117 DEV : loss 0.29176047444343567 - f1-score (micro avg)  0.3645
2023-10-18 18:35:40,145 saving best model
2023-10-18 18:35:40,180 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:41,161 epoch 9 - iter 44/447 - loss 0.27815427 - time (sec): 0.98 - samples/sec: 8007.69 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:35:42,203 epoch 9 - iter 88/447 - loss 0.29456349 - time (sec): 2.02 - samples/sec: 8744.68 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:35:43,193 epoch 9 - iter 132/447 - loss 0.30940547 - time (sec): 3.01 - samples/sec: 8679.69 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:35:44,197 epoch 9 - iter 176/447 - loss 0.29950930 - time (sec): 4.02 - samples/sec: 8612.02 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:35:45,156 epoch 9 - iter 220/447 - loss 0.29574842 - time (sec): 4.98 - samples/sec: 8552.28 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:35:46,233 epoch 9 - iter 264/447 - loss 0.29098159 - time (sec): 6.05 - samples/sec: 8608.97 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:35:47,237 epoch 9 - iter 308/447 - loss 0.28502560 - time (sec): 7.06 - samples/sec: 8656.29 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:35:48,230 epoch 9 - iter 352/447 - loss 0.28416310 - time (sec): 8.05 - samples/sec: 8593.79 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:35:49,241 epoch 9 - iter 396/447 - loss 0.28422952 - time (sec): 9.06 - samples/sec: 8574.72 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:35:50,207 epoch 9 - iter 440/447 - loss 0.28130013 - time (sec): 10.03 - samples/sec: 8520.33 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:35:50,362 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:50,362 EPOCH 9 done: loss 0.2808 - lr: 0.000006
2023-10-18 18:35:55,812 DEV : loss 0.298006534576416 - f1-score (micro avg)  0.3606
2023-10-18 18:35:55,841 ----------------------------------------------------------------------------------------------------
2023-10-18 18:35:56,799 epoch 10 - iter 44/447 - loss 0.23061533 - time (sec): 0.96 - samples/sec: 9276.23 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:35:57,825 epoch 10 - iter 88/447 - loss 0.24946824 - time (sec): 1.98 - samples/sec: 8725.00 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:35:58,817 epoch 10 - iter 132/447 - loss 0.24339995 - time (sec): 2.98 - samples/sec: 8314.11 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:35:59,824 epoch 10 - iter 176/447 - loss 0.25223271 - time (sec): 3.98 - samples/sec: 8273.66 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:36:00,855 epoch 10 - iter 220/447 - loss 0.25850329 - time (sec): 5.01 - samples/sec: 8212.99 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:36:01,880 epoch 10 - iter 264/447 - loss 0.26389720 - time (sec): 6.04 - samples/sec: 8151.84 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:36:02,949 epoch 10 - iter 308/447 - loss 0.26807520 - time (sec): 7.11 - samples/sec: 8067.37 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:36:04,057 epoch 10 - iter 352/447 - loss 0.27136731 - time (sec): 8.22 - samples/sec: 8137.26 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:36:05,175 epoch 10 - iter 396/447 - loss 0.26821511 - time (sec): 9.33 - samples/sec: 8233.20 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:36:06,174 epoch 10 - iter 440/447 - loss 0.27485413 - time (sec): 10.33 - samples/sec: 8242.45 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:36:06,341 ----------------------------------------------------------------------------------------------------
2023-10-18 18:36:06,341 EPOCH 10 done: loss 0.2758 - lr: 0.000000
2023-10-18 18:36:11,515 DEV : loss 0.2943832576274872 - f1-score (micro avg)  0.3628
2023-10-18 18:36:11,574 ----------------------------------------------------------------------------------------------------
2023-10-18 18:36:11,574 Loading model from best epoch ...
2023-10-18 18:36:11,658 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
2023-10-18 18:36:14,073 
Results:
- F-score (micro) 0.3459
- F-score (macro) 0.1566
- Accuracy 0.2208

By class:
              precision    recall  f1-score   support

         loc     0.4685    0.5487    0.5054       596
        pers     0.1655    0.2102    0.1852       333
         org     0.0000    0.0000    0.0000       132
        prod     0.0000    0.0000    0.0000        66
        time     0.1875    0.0612    0.0923        49

   micro avg     0.3518    0.3401    0.3459      1176
   macro avg     0.1643    0.1640    0.1566      1176
weighted avg     0.2921    0.3401    0.3124      1176

2023-10-18 18:36:14,073 ----------------------------------------------------------------------------------------------------