2023-10-18 18:15:44,757 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 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 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 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 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Train:  3575 sentences
2023-10-18 18:15:44,758         (train_with_dev=False, train_with_test=False)
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Training Params:
2023-10-18 18:15:44,758  - learning_rate: "5e-05" 
2023-10-18 18:15:44,758  - mini_batch_size: "4"
2023-10-18 18:15:44,758  - max_epochs: "10"
2023-10-18 18:15:44,758  - shuffle: "True"
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Plugins:
2023-10-18 18:15:44,758  - TensorboardLogger
2023-10-18 18:15:44,758  - LinearScheduler | warmup_fraction: '0.1'
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Final evaluation on model from best epoch (best-model.pt)
2023-10-18 18:15:44,758  - metric: "('micro avg', 'f1-score')"
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Computation:
2023-10-18 18:15:44,758  - compute on device: cuda:0
2023-10-18 18:15:44,758  - embedding storage: none
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:44,759 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-18 18:15:46,187 epoch 1 - iter 89/894 - loss 4.25883135 - time (sec): 1.43 - samples/sec: 5802.08 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:15:47,561 epoch 1 - iter 178/894 - loss 3.88846431 - time (sec): 2.80 - samples/sec: 6012.25 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:15:48,974 epoch 1 - iter 267/894 - loss 3.39207820 - time (sec): 4.21 - samples/sec: 6314.28 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:15:50,370 epoch 1 - iter 356/894 - loss 2.85985897 - time (sec): 5.61 - samples/sec: 6345.58 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:15:51,764 epoch 1 - iter 445/894 - loss 2.42305557 - time (sec): 7.01 - samples/sec: 6428.18 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:15:53,024 epoch 1 - iter 534/894 - loss 2.14394411 - time (sec): 8.27 - samples/sec: 6485.91 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:15:54,306 epoch 1 - iter 623/894 - loss 1.93877586 - time (sec): 9.55 - samples/sec: 6461.23 - lr: 0.000035 - momentum: 0.000000
2023-10-18 18:15:55,643 epoch 1 - iter 712/894 - loss 1.78000923 - time (sec): 10.88 - samples/sec: 6416.44 - lr: 0.000040 - momentum: 0.000000
2023-10-18 18:15:57,005 epoch 1 - iter 801/894 - loss 1.66000782 - time (sec): 12.25 - samples/sec: 6339.69 - lr: 0.000045 - momentum: 0.000000
2023-10-18 18:15:58,322 epoch 1 - iter 890/894 - loss 1.54698441 - time (sec): 13.56 - samples/sec: 6357.22 - lr: 0.000050 - momentum: 0.000000
2023-10-18 18:15:58,378 ----------------------------------------------------------------------------------------------------
2023-10-18 18:15:58,378 EPOCH 1 done: loss 1.5432 - lr: 0.000050
2023-10-18 18:16:00,654 DEV : loss 0.3984837830066681 - f1-score (micro avg)  0.0
2023-10-18 18:16:00,682 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:02,073 epoch 2 - iter 89/894 - loss 0.52594792 - time (sec): 1.39 - samples/sec: 6369.10 - lr: 0.000049 - momentum: 0.000000
2023-10-18 18:16:03,433 epoch 2 - iter 178/894 - loss 0.50071493 - time (sec): 2.75 - samples/sec: 6264.97 - lr: 0.000049 - momentum: 0.000000
2023-10-18 18:16:04,866 epoch 2 - iter 267/894 - loss 0.49628263 - time (sec): 4.18 - samples/sec: 6123.09 - lr: 0.000048 - momentum: 0.000000
2023-10-18 18:16:06,248 epoch 2 - iter 356/894 - loss 0.48832046 - time (sec): 5.57 - samples/sec: 6098.08 - lr: 0.000048 - momentum: 0.000000
2023-10-18 18:16:07,641 epoch 2 - iter 445/894 - loss 0.48124232 - time (sec): 6.96 - samples/sec: 6088.49 - lr: 0.000047 - momentum: 0.000000
2023-10-18 18:16:09,013 epoch 2 - iter 534/894 - loss 0.47516643 - time (sec): 8.33 - samples/sec: 6006.41 - lr: 0.000047 - momentum: 0.000000
2023-10-18 18:16:10,390 epoch 2 - iter 623/894 - loss 0.46557034 - time (sec): 9.71 - samples/sec: 6057.11 - lr: 0.000046 - momentum: 0.000000
2023-10-18 18:16:11,800 epoch 2 - iter 712/894 - loss 0.45087699 - time (sec): 11.12 - samples/sec: 6183.99 - lr: 0.000046 - momentum: 0.000000
2023-10-18 18:16:13,191 epoch 2 - iter 801/894 - loss 0.44947313 - time (sec): 12.51 - samples/sec: 6218.03 - lr: 0.000045 - momentum: 0.000000
2023-10-18 18:16:14,558 epoch 2 - iter 890/894 - loss 0.44333084 - time (sec): 13.88 - samples/sec: 6210.45 - lr: 0.000044 - momentum: 0.000000
2023-10-18 18:16:14,616 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:14,616 EPOCH 2 done: loss 0.4443 - lr: 0.000044
2023-10-18 18:16:19,949 DEV : loss 0.3260151147842407 - f1-score (micro avg)  0.2507
2023-10-18 18:16:19,976 saving best model
2023-10-18 18:16:20,012 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:21,412 epoch 3 - iter 89/894 - loss 0.37344402 - time (sec): 1.40 - samples/sec: 6354.88 - lr: 0.000044 - momentum: 0.000000
2023-10-18 18:16:22,774 epoch 3 - iter 178/894 - loss 0.35634230 - time (sec): 2.76 - samples/sec: 6244.10 - lr: 0.000043 - momentum: 0.000000
2023-10-18 18:16:24,157 epoch 3 - iter 267/894 - loss 0.36909354 - time (sec): 4.14 - samples/sec: 6261.41 - lr: 0.000043 - momentum: 0.000000
2023-10-18 18:16:25,496 epoch 3 - iter 356/894 - loss 0.37521279 - time (sec): 5.48 - samples/sec: 6213.43 - lr: 0.000042 - momentum: 0.000000
2023-10-18 18:16:26,874 epoch 3 - iter 445/894 - loss 0.37028765 - time (sec): 6.86 - samples/sec: 6242.59 - lr: 0.000042 - momentum: 0.000000
2023-10-18 18:16:28,313 epoch 3 - iter 534/894 - loss 0.36555683 - time (sec): 8.30 - samples/sec: 6357.70 - lr: 0.000041 - momentum: 0.000000
2023-10-18 18:16:29,728 epoch 3 - iter 623/894 - loss 0.37443295 - time (sec): 9.72 - samples/sec: 6334.47 - lr: 0.000041 - momentum: 0.000000
2023-10-18 18:16:31,122 epoch 3 - iter 712/894 - loss 0.36744880 - time (sec): 11.11 - samples/sec: 6287.03 - lr: 0.000040 - momentum: 0.000000
2023-10-18 18:16:32,488 epoch 3 - iter 801/894 - loss 0.36894331 - time (sec): 12.48 - samples/sec: 6224.81 - lr: 0.000039 - momentum: 0.000000
2023-10-18 18:16:33,882 epoch 3 - iter 890/894 - loss 0.36997707 - time (sec): 13.87 - samples/sec: 6210.54 - lr: 0.000039 - momentum: 0.000000
2023-10-18 18:16:33,941 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:33,942 EPOCH 3 done: loss 0.3694 - lr: 0.000039
2023-10-18 18:16:39,229 DEV : loss 0.3036574423313141 - f1-score (micro avg)  0.3299
2023-10-18 18:16:39,257 saving best model
2023-10-18 18:16:39,295 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:40,691 epoch 4 - iter 89/894 - loss 0.31548673 - time (sec): 1.39 - samples/sec: 5546.15 - lr: 0.000038 - momentum: 0.000000
2023-10-18 18:16:41,893 epoch 4 - iter 178/894 - loss 0.33092983 - time (sec): 2.60 - samples/sec: 6132.05 - lr: 0.000038 - momentum: 0.000000
2023-10-18 18:16:43,135 epoch 4 - iter 267/894 - loss 0.35748682 - time (sec): 3.84 - samples/sec: 6337.31 - lr: 0.000037 - momentum: 0.000000
2023-10-18 18:16:44,610 epoch 4 - iter 356/894 - loss 0.35730649 - time (sec): 5.31 - samples/sec: 6200.84 - lr: 0.000037 - momentum: 0.000000
2023-10-18 18:16:46,065 epoch 4 - iter 445/894 - loss 0.34162987 - time (sec): 6.77 - samples/sec: 6260.27 - lr: 0.000036 - momentum: 0.000000
2023-10-18 18:16:47,448 epoch 4 - iter 534/894 - loss 0.33088873 - time (sec): 8.15 - samples/sec: 6268.61 - lr: 0.000036 - momentum: 0.000000
2023-10-18 18:16:48,891 epoch 4 - iter 623/894 - loss 0.32593421 - time (sec): 9.59 - samples/sec: 6322.04 - lr: 0.000035 - momentum: 0.000000
2023-10-18 18:16:50,274 epoch 4 - iter 712/894 - loss 0.32575031 - time (sec): 10.98 - samples/sec: 6348.62 - lr: 0.000034 - momentum: 0.000000
2023-10-18 18:16:51,707 epoch 4 - iter 801/894 - loss 0.32238412 - time (sec): 12.41 - samples/sec: 6285.42 - lr: 0.000034 - momentum: 0.000000
2023-10-18 18:16:53,074 epoch 4 - iter 890/894 - loss 0.32559948 - time (sec): 13.78 - samples/sec: 6261.46 - lr: 0.000033 - momentum: 0.000000
2023-10-18 18:16:53,135 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:53,135 EPOCH 4 done: loss 0.3265 - lr: 0.000033
2023-10-18 18:16:58,438 DEV : loss 0.3067420423030853 - f1-score (micro avg)  0.3352
2023-10-18 18:16:58,466 saving best model
2023-10-18 18:16:58,498 ----------------------------------------------------------------------------------------------------
2023-10-18 18:16:59,905 epoch 5 - iter 89/894 - loss 0.31476768 - time (sec): 1.41 - samples/sec: 6326.63 - lr: 0.000033 - momentum: 0.000000
2023-10-18 18:17:01,274 epoch 5 - iter 178/894 - loss 0.29060870 - time (sec): 2.78 - samples/sec: 6049.80 - lr: 0.000032 - momentum: 0.000000
2023-10-18 18:17:02,622 epoch 5 - iter 267/894 - loss 0.29162793 - time (sec): 4.12 - samples/sec: 6349.59 - lr: 0.000032 - momentum: 0.000000
2023-10-18 18:17:04,072 epoch 5 - iter 356/894 - loss 0.29757568 - time (sec): 5.57 - samples/sec: 6274.00 - lr: 0.000031 - momentum: 0.000000
2023-10-18 18:17:05,440 epoch 5 - iter 445/894 - loss 0.29353139 - time (sec): 6.94 - samples/sec: 6306.29 - lr: 0.000031 - momentum: 0.000000
2023-10-18 18:17:06,820 epoch 5 - iter 534/894 - loss 0.29628911 - time (sec): 8.32 - samples/sec: 6270.32 - lr: 0.000030 - momentum: 0.000000
2023-10-18 18:17:08,186 epoch 5 - iter 623/894 - loss 0.30268124 - time (sec): 9.69 - samples/sec: 6181.30 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:17:09,575 epoch 5 - iter 712/894 - loss 0.30243496 - time (sec): 11.08 - samples/sec: 6145.96 - lr: 0.000029 - momentum: 0.000000
2023-10-18 18:17:10,966 epoch 5 - iter 801/894 - loss 0.29872617 - time (sec): 12.47 - samples/sec: 6122.41 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:17:12,436 epoch 5 - iter 890/894 - loss 0.30206675 - time (sec): 13.94 - samples/sec: 6192.20 - lr: 0.000028 - momentum: 0.000000
2023-10-18 18:17:12,491 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:12,491 EPOCH 5 done: loss 0.3021 - lr: 0.000028
2023-10-18 18:17:17,514 DEV : loss 0.29209402203559875 - f1-score (micro avg)  0.3512
2023-10-18 18:17:17,541 saving best model
2023-10-18 18:17:17,576 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:18,938 epoch 6 - iter 89/894 - loss 0.28333048 - time (sec): 1.36 - samples/sec: 5836.96 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:17:20,633 epoch 6 - iter 178/894 - loss 0.25675668 - time (sec): 3.06 - samples/sec: 5679.16 - lr: 0.000027 - momentum: 0.000000
2023-10-18 18:17:21,994 epoch 6 - iter 267/894 - loss 0.25036742 - time (sec): 4.42 - samples/sec: 5706.85 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:17:23,384 epoch 6 - iter 356/894 - loss 0.26571766 - time (sec): 5.81 - samples/sec: 5964.57 - lr: 0.000026 - momentum: 0.000000
2023-10-18 18:17:24,788 epoch 6 - iter 445/894 - loss 0.26798149 - time (sec): 7.21 - samples/sec: 6082.58 - lr: 0.000025 - momentum: 0.000000
2023-10-18 18:17:26,140 epoch 6 - iter 534/894 - loss 0.27319482 - time (sec): 8.56 - samples/sec: 6074.22 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:17:27,493 epoch 6 - iter 623/894 - loss 0.27136640 - time (sec): 9.92 - samples/sec: 6064.28 - lr: 0.000024 - momentum: 0.000000
2023-10-18 18:17:28,873 epoch 6 - iter 712/894 - loss 0.27289306 - time (sec): 11.30 - samples/sec: 6174.47 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:17:30,260 epoch 6 - iter 801/894 - loss 0.27008042 - time (sec): 12.68 - samples/sec: 6133.47 - lr: 0.000023 - momentum: 0.000000
2023-10-18 18:17:31,622 epoch 6 - iter 890/894 - loss 0.28116086 - time (sec): 14.04 - samples/sec: 6138.46 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:17:31,678 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:31,678 EPOCH 6 done: loss 0.2811 - lr: 0.000022
2023-10-18 18:17:36,730 DEV : loss 0.2932937443256378 - f1-score (micro avg)  0.3493
2023-10-18 18:17:36,758 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:38,169 epoch 7 - iter 89/894 - loss 0.22158974 - time (sec): 1.41 - samples/sec: 6593.41 - lr: 0.000022 - momentum: 0.000000
2023-10-18 18:17:39,537 epoch 7 - iter 178/894 - loss 0.25346068 - time (sec): 2.78 - samples/sec: 6309.08 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:17:41,035 epoch 7 - iter 267/894 - loss 0.27860276 - time (sec): 4.28 - samples/sec: 6398.57 - lr: 0.000021 - momentum: 0.000000
2023-10-18 18:17:42,457 epoch 7 - iter 356/894 - loss 0.27970492 - time (sec): 5.70 - samples/sec: 6335.40 - lr: 0.000020 - momentum: 0.000000
2023-10-18 18:17:43,845 epoch 7 - iter 445/894 - loss 0.27912838 - time (sec): 7.09 - samples/sec: 6296.72 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:17:45,223 epoch 7 - iter 534/894 - loss 0.27591335 - time (sec): 8.46 - samples/sec: 6216.40 - lr: 0.000019 - momentum: 0.000000
2023-10-18 18:17:46,594 epoch 7 - iter 623/894 - loss 0.26892576 - time (sec): 9.84 - samples/sec: 6180.69 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:17:47,986 epoch 7 - iter 712/894 - loss 0.26882585 - time (sec): 11.23 - samples/sec: 6174.19 - lr: 0.000018 - momentum: 0.000000
2023-10-18 18:17:49,380 epoch 7 - iter 801/894 - loss 0.26356169 - time (sec): 12.62 - samples/sec: 6177.37 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:17:50,736 epoch 7 - iter 890/894 - loss 0.26609943 - time (sec): 13.98 - samples/sec: 6167.03 - lr: 0.000017 - momentum: 0.000000
2023-10-18 18:17:50,794 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:50,794 EPOCH 7 done: loss 0.2667 - lr: 0.000017
2023-10-18 18:17:56,124 DEV : loss 0.3029758632183075 - f1-score (micro avg)  0.3684
2023-10-18 18:17:56,153 saving best model
2023-10-18 18:17:56,190 ----------------------------------------------------------------------------------------------------
2023-10-18 18:17:57,395 epoch 8 - iter 89/894 - loss 0.25478181 - time (sec): 1.20 - samples/sec: 7916.80 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:17:58,758 epoch 8 - iter 178/894 - loss 0.24482678 - time (sec): 2.57 - samples/sec: 6943.02 - lr: 0.000016 - momentum: 0.000000
2023-10-18 18:18:00,134 epoch 8 - iter 267/894 - loss 0.25577237 - time (sec): 3.94 - samples/sec: 6715.16 - lr: 0.000015 - momentum: 0.000000
2023-10-18 18:18:01,491 epoch 8 - iter 356/894 - loss 0.26327040 - time (sec): 5.30 - samples/sec: 6517.93 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:18:02,866 epoch 8 - iter 445/894 - loss 0.26619424 - time (sec): 6.68 - samples/sec: 6430.84 - lr: 0.000014 - momentum: 0.000000
2023-10-18 18:18:04,249 epoch 8 - iter 534/894 - loss 0.26295140 - time (sec): 8.06 - samples/sec: 6363.38 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:18:05,610 epoch 8 - iter 623/894 - loss 0.25983363 - time (sec): 9.42 - samples/sec: 6292.77 - lr: 0.000013 - momentum: 0.000000
2023-10-18 18:18:07,002 epoch 8 - iter 712/894 - loss 0.25819637 - time (sec): 10.81 - samples/sec: 6295.11 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:18:08,360 epoch 8 - iter 801/894 - loss 0.25250941 - time (sec): 12.17 - samples/sec: 6291.86 - lr: 0.000012 - momentum: 0.000000
2023-10-18 18:18:09,708 epoch 8 - iter 890/894 - loss 0.25517432 - time (sec): 13.52 - samples/sec: 6368.90 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:18:09,767 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:09,768 EPOCH 8 done: loss 0.2542 - lr: 0.000011
2023-10-18 18:18:15,190 DEV : loss 0.30212923884391785 - f1-score (micro avg)  0.3703
2023-10-18 18:18:15,218 saving best model
2023-10-18 18:18:15,254 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:16,676 epoch 9 - iter 89/894 - loss 0.22003771 - time (sec): 1.42 - samples/sec: 5794.02 - lr: 0.000011 - momentum: 0.000000
2023-10-18 18:18:18,054 epoch 9 - iter 178/894 - loss 0.23577347 - time (sec): 2.80 - samples/sec: 5592.59 - lr: 0.000010 - momentum: 0.000000
2023-10-18 18:18:19,450 epoch 9 - iter 267/894 - loss 0.23253033 - time (sec): 4.19 - samples/sec: 5831.48 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:18:20,847 epoch 9 - iter 356/894 - loss 0.24026224 - time (sec): 5.59 - samples/sec: 5862.72 - lr: 0.000009 - momentum: 0.000000
2023-10-18 18:18:22,278 epoch 9 - iter 445/894 - loss 0.24192239 - time (sec): 7.02 - samples/sec: 5991.87 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:18:23,699 epoch 9 - iter 534/894 - loss 0.24368213 - time (sec): 8.44 - samples/sec: 6069.84 - lr: 0.000008 - momentum: 0.000000
2023-10-18 18:18:25,087 epoch 9 - iter 623/894 - loss 0.23891496 - time (sec): 9.83 - samples/sec: 6082.71 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:18:26,427 epoch 9 - iter 712/894 - loss 0.23933687 - time (sec): 11.17 - samples/sec: 6076.22 - lr: 0.000007 - momentum: 0.000000
2023-10-18 18:18:27,723 epoch 9 - iter 801/894 - loss 0.24261585 - time (sec): 12.47 - samples/sec: 6219.89 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:18:29,148 epoch 9 - iter 890/894 - loss 0.24459348 - time (sec): 13.89 - samples/sec: 6213.34 - lr: 0.000006 - momentum: 0.000000
2023-10-18 18:18:29,209 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:29,209 EPOCH 9 done: loss 0.2449 - lr: 0.000006
2023-10-18 18:18:34,561 DEV : loss 0.3134533762931824 - f1-score (micro avg)  0.3652
2023-10-18 18:18:34,588 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:35,969 epoch 10 - iter 89/894 - loss 0.28204273 - time (sec): 1.38 - samples/sec: 6010.18 - lr: 0.000005 - momentum: 0.000000
2023-10-18 18:18:37,357 epoch 10 - iter 178/894 - loss 0.25940032 - time (sec): 2.77 - samples/sec: 5955.00 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:18:38,751 epoch 10 - iter 267/894 - loss 0.24066822 - time (sec): 4.16 - samples/sec: 6023.31 - lr: 0.000004 - momentum: 0.000000
2023-10-18 18:18:40,128 epoch 10 - iter 356/894 - loss 0.23618971 - time (sec): 5.54 - samples/sec: 5981.87 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:18:41,496 epoch 10 - iter 445/894 - loss 0.24428962 - time (sec): 6.91 - samples/sec: 5907.36 - lr: 0.000003 - momentum: 0.000000
2023-10-18 18:18:42,898 epoch 10 - iter 534/894 - loss 0.23708175 - time (sec): 8.31 - samples/sec: 5973.97 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:18:44,191 epoch 10 - iter 623/894 - loss 0.23826333 - time (sec): 9.60 - samples/sec: 6039.64 - lr: 0.000002 - momentum: 0.000000
2023-10-18 18:18:45,476 epoch 10 - iter 712/894 - loss 0.24098653 - time (sec): 10.89 - samples/sec: 6299.71 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:18:46,713 epoch 10 - iter 801/894 - loss 0.24280119 - time (sec): 12.12 - samples/sec: 6351.83 - lr: 0.000001 - momentum: 0.000000
2023-10-18 18:18:47,960 epoch 10 - iter 890/894 - loss 0.24048726 - time (sec): 13.37 - samples/sec: 6433.47 - lr: 0.000000 - momentum: 0.000000
2023-10-18 18:18:48,018 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:48,018 EPOCH 10 done: loss 0.2407 - lr: 0.000000
2023-10-18 18:18:53,067 DEV : loss 0.3089354932308197 - f1-score (micro avg)  0.3602
2023-10-18 18:18:53,126 ----------------------------------------------------------------------------------------------------
2023-10-18 18:18:53,126 Loading model from best epoch ...
2023-10-18 18:18:53,208 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 18:18:55,501 
Results:
- F-score (micro) 0.3689
- F-score (macro) 0.2044
- Accuracy 0.2367

By class:
              precision    recall  f1-score   support

         loc     0.4930    0.5906    0.5374       596
        pers     0.1657    0.2462    0.1981       333
         org     1.0000    0.0076    0.0150       132
        time     0.3438    0.2245    0.2716        49
        prod     0.0000    0.0000    0.0000        66

   micro avg     0.3591    0.3793    0.3689      1176
   macro avg     0.4005    0.2138    0.2044      1176
weighted avg     0.4233    0.3793    0.3414      1176

2023-10-18 18:18:55,501 ----------------------------------------------------------------------------------------------------