2023-10-14 18:28:56,599 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,600 Model: "SequenceTagger(
  (embeddings): ByT5Embeddings(
    (model): T5EncoderModel(
      (shared): Embedding(384, 1472)
      (encoder): T5Stack(
        (embed_tokens): Embedding(384, 1472)
        (block): ModuleList(
          (0): T5Block(
            (layer): ModuleList(
              (0): T5LayerSelfAttention(
                (SelfAttention): T5Attention(
                  (q): Linear(in_features=1472, out_features=384, bias=False)
                  (k): Linear(in_features=1472, out_features=384, bias=False)
                  (v): Linear(in_features=1472, out_features=384, bias=False)
                  (o): Linear(in_features=384, out_features=1472, bias=False)
                  (relative_attention_bias): Embedding(32, 6)
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (1): T5LayerFF(
                (DenseReluDense): T5DenseGatedActDense(
                  (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
                  (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
                  (wo): Linear(in_features=3584, out_features=1472, bias=False)
                  (dropout): Dropout(p=0.1, inplace=False)
                  (act): NewGELUActivation()
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
          (1-11): 11 x T5Block(
            (layer): ModuleList(
              (0): T5LayerSelfAttention(
                (SelfAttention): T5Attention(
                  (q): Linear(in_features=1472, out_features=384, bias=False)
                  (k): Linear(in_features=1472, out_features=384, bias=False)
                  (v): Linear(in_features=1472, out_features=384, bias=False)
                  (o): Linear(in_features=384, out_features=1472, bias=False)
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (1): T5LayerFF(
                (DenseReluDense): T5DenseGatedActDense(
                  (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
                  (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
                  (wo): Linear(in_features=3584, out_features=1472, bias=False)
                  (dropout): Dropout(p=0.1, inplace=False)
                  (act): NewGELUActivation()
                )
                (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
          )
        )
        (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=1472, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-14 18:28:56,600 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,600 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
2023-10-14 18:28:56,600 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,600 Train:  3575 sentences
2023-10-14 18:28:56,600         (train_with_dev=False, train_with_test=False)
2023-10-14 18:28:56,600 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,600 Training Params:
2023-10-14 18:28:56,600  - learning_rate: "0.00015" 
2023-10-14 18:28:56,601  - mini_batch_size: "8"
2023-10-14 18:28:56,601  - max_epochs: "10"
2023-10-14 18:28:56,601  - shuffle: "True"
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 Plugins:
2023-10-14 18:28:56,601  - TensorboardLogger
2023-10-14 18:28:56,601  - LinearScheduler | warmup_fraction: '0.1'
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 Final evaluation on model from best epoch (best-model.pt)
2023-10-14 18:28:56,601  - metric: "('micro avg', 'f1-score')"
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 Computation:
2023-10-14 18:28:56,601  - compute on device: cuda:0
2023-10-14 18:28:56,601  - embedding storage: none
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 Model training base path: "hmbench-hipe2020/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1"
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 ----------------------------------------------------------------------------------------------------
2023-10-14 18:28:56,601 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-14 18:29:13,026 epoch 1 - iter 44/447 - loss 3.04968209 - time (sec): 16.42 - samples/sec: 558.83 - lr: 0.000014 - momentum: 0.000000
2023-10-14 18:29:27,978 epoch 1 - iter 88/447 - loss 3.03220833 - time (sec): 31.38 - samples/sec: 550.16 - lr: 0.000029 - momentum: 0.000000
2023-10-14 18:29:43,363 epoch 1 - iter 132/447 - loss 2.98006060 - time (sec): 46.76 - samples/sec: 545.03 - lr: 0.000044 - momentum: 0.000000
2023-10-14 18:29:58,651 epoch 1 - iter 176/447 - loss 2.85571004 - time (sec): 62.05 - samples/sec: 546.60 - lr: 0.000059 - momentum: 0.000000
2023-10-14 18:30:13,328 epoch 1 - iter 220/447 - loss 2.71064544 - time (sec): 76.73 - samples/sec: 540.41 - lr: 0.000073 - momentum: 0.000000
2023-10-14 18:30:28,322 epoch 1 - iter 264/447 - loss 2.53638057 - time (sec): 91.72 - samples/sec: 539.60 - lr: 0.000088 - momentum: 0.000000
2023-10-14 18:30:44,206 epoch 1 - iter 308/447 - loss 2.32818224 - time (sec): 107.60 - samples/sec: 545.92 - lr: 0.000103 - momentum: 0.000000
2023-10-14 18:30:59,374 epoch 1 - iter 352/447 - loss 2.14903375 - time (sec): 122.77 - samples/sec: 546.91 - lr: 0.000118 - momentum: 0.000000
2023-10-14 18:31:17,104 epoch 1 - iter 396/447 - loss 1.94372981 - time (sec): 140.50 - samples/sec: 551.15 - lr: 0.000133 - momentum: 0.000000
2023-10-14 18:31:32,315 epoch 1 - iter 440/447 - loss 1.81241981 - time (sec): 155.71 - samples/sec: 547.13 - lr: 0.000147 - momentum: 0.000000
2023-10-14 18:31:34,703 ----------------------------------------------------------------------------------------------------
2023-10-14 18:31:34,703 EPOCH 1 done: loss 1.7931 - lr: 0.000147
2023-10-14 18:31:57,276 DEV : loss 0.4706111252307892 - f1-score (micro avg)  0.0
2023-10-14 18:31:57,301 ----------------------------------------------------------------------------------------------------
2023-10-14 18:32:12,593 epoch 2 - iter 44/447 - loss 0.51221402 - time (sec): 15.29 - samples/sec: 553.41 - lr: 0.000148 - momentum: 0.000000
2023-10-14 18:32:27,785 epoch 2 - iter 88/447 - loss 0.49327832 - time (sec): 30.48 - samples/sec: 552.35 - lr: 0.000147 - momentum: 0.000000
2023-10-14 18:32:43,466 epoch 2 - iter 132/447 - loss 0.45252612 - time (sec): 46.16 - samples/sec: 567.85 - lr: 0.000145 - momentum: 0.000000
2023-10-14 18:33:00,409 epoch 2 - iter 176/447 - loss 0.42537516 - time (sec): 63.11 - samples/sec: 564.16 - lr: 0.000143 - momentum: 0.000000
2023-10-14 18:33:15,852 epoch 2 - iter 220/447 - loss 0.40524817 - time (sec): 78.55 - samples/sec: 562.93 - lr: 0.000142 - momentum: 0.000000
2023-10-14 18:33:31,601 epoch 2 - iter 264/447 - loss 0.38456250 - time (sec): 94.30 - samples/sec: 561.96 - lr: 0.000140 - momentum: 0.000000
2023-10-14 18:33:46,668 epoch 2 - iter 308/447 - loss 0.38166992 - time (sec): 109.37 - samples/sec: 557.13 - lr: 0.000139 - momentum: 0.000000
2023-10-14 18:34:02,008 epoch 2 - iter 352/447 - loss 0.37044403 - time (sec): 124.71 - samples/sec: 557.20 - lr: 0.000137 - momentum: 0.000000
2023-10-14 18:34:17,604 epoch 2 - iter 396/447 - loss 0.36100765 - time (sec): 140.30 - samples/sec: 555.12 - lr: 0.000135 - momentum: 0.000000
2023-10-14 18:34:32,442 epoch 2 - iter 440/447 - loss 0.35295013 - time (sec): 155.14 - samples/sec: 551.20 - lr: 0.000134 - momentum: 0.000000
2023-10-14 18:34:34,704 ----------------------------------------------------------------------------------------------------
2023-10-14 18:34:34,705 EPOCH 2 done: loss 0.3534 - lr: 0.000134
2023-10-14 18:34:59,145 DEV : loss 0.24648237228393555 - f1-score (micro avg)  0.4615
2023-10-14 18:34:59,170 saving best model
2023-10-14 18:34:59,966 ----------------------------------------------------------------------------------------------------
2023-10-14 18:35:15,514 epoch 3 - iter 44/447 - loss 0.28031449 - time (sec): 15.55 - samples/sec: 529.00 - lr: 0.000132 - momentum: 0.000000
2023-10-14 18:35:30,601 epoch 3 - iter 88/447 - loss 0.24660222 - time (sec): 30.63 - samples/sec: 533.31 - lr: 0.000130 - momentum: 0.000000
2023-10-14 18:35:46,488 epoch 3 - iter 132/447 - loss 0.24011989 - time (sec): 46.52 - samples/sec: 535.83 - lr: 0.000128 - momentum: 0.000000
2023-10-14 18:36:01,757 epoch 3 - iter 176/447 - loss 0.23680025 - time (sec): 61.79 - samples/sec: 538.05 - lr: 0.000127 - momentum: 0.000000
2023-10-14 18:36:19,186 epoch 3 - iter 220/447 - loss 0.22759569 - time (sec): 79.22 - samples/sec: 547.03 - lr: 0.000125 - momentum: 0.000000
2023-10-14 18:36:34,393 epoch 3 - iter 264/447 - loss 0.22429489 - time (sec): 94.43 - samples/sec: 545.33 - lr: 0.000124 - momentum: 0.000000
2023-10-14 18:36:49,787 epoch 3 - iter 308/447 - loss 0.21942290 - time (sec): 109.82 - samples/sec: 543.54 - lr: 0.000122 - momentum: 0.000000
2023-10-14 18:37:04,772 epoch 3 - iter 352/447 - loss 0.21213137 - time (sec): 124.80 - samples/sec: 541.12 - lr: 0.000120 - momentum: 0.000000
2023-10-14 18:37:20,495 epoch 3 - iter 396/447 - loss 0.20683047 - time (sec): 140.53 - samples/sec: 543.46 - lr: 0.000119 - momentum: 0.000000
2023-10-14 18:37:35,931 epoch 3 - iter 440/447 - loss 0.20185030 - time (sec): 155.96 - samples/sec: 545.25 - lr: 0.000117 - momentum: 0.000000
2023-10-14 18:37:38,436 ----------------------------------------------------------------------------------------------------
2023-10-14 18:37:38,437 EPOCH 3 done: loss 0.2006 - lr: 0.000117
2023-10-14 18:38:03,004 DEV : loss 0.17484012246131897 - f1-score (micro avg)  0.6667
2023-10-14 18:38:03,030 saving best model
2023-10-14 18:38:03,867 ----------------------------------------------------------------------------------------------------
2023-10-14 18:38:19,427 epoch 4 - iter 44/447 - loss 0.16075713 - time (sec): 15.56 - samples/sec: 535.32 - lr: 0.000115 - momentum: 0.000000
2023-10-14 18:38:34,365 epoch 4 - iter 88/447 - loss 0.15141283 - time (sec): 30.50 - samples/sec: 526.90 - lr: 0.000113 - momentum: 0.000000
2023-10-14 18:38:49,421 epoch 4 - iter 132/447 - loss 0.14651235 - time (sec): 45.55 - samples/sec: 528.07 - lr: 0.000112 - momentum: 0.000000
2023-10-14 18:39:04,810 epoch 4 - iter 176/447 - loss 0.14574267 - time (sec): 60.94 - samples/sec: 528.80 - lr: 0.000110 - momentum: 0.000000
2023-10-14 18:39:19,959 epoch 4 - iter 220/447 - loss 0.13882476 - time (sec): 76.09 - samples/sec: 527.66 - lr: 0.000109 - momentum: 0.000000
2023-10-14 18:39:35,657 epoch 4 - iter 264/447 - loss 0.13265973 - time (sec): 91.79 - samples/sec: 535.86 - lr: 0.000107 - momentum: 0.000000
2023-10-14 18:39:50,775 epoch 4 - iter 308/447 - loss 0.12641890 - time (sec): 106.91 - samples/sec: 535.20 - lr: 0.000105 - momentum: 0.000000
2023-10-14 18:40:05,921 epoch 4 - iter 352/447 - loss 0.12439053 - time (sec): 122.05 - samples/sec: 535.52 - lr: 0.000104 - momentum: 0.000000
2023-10-14 18:40:23,362 epoch 4 - iter 396/447 - loss 0.12260346 - time (sec): 139.49 - samples/sec: 539.84 - lr: 0.000102 - momentum: 0.000000
2023-10-14 18:40:39,750 epoch 4 - iter 440/447 - loss 0.11757667 - time (sec): 155.88 - samples/sec: 544.25 - lr: 0.000100 - momentum: 0.000000
2023-10-14 18:40:42,378 ----------------------------------------------------------------------------------------------------
2023-10-14 18:40:42,378 EPOCH 4 done: loss 0.1161 - lr: 0.000100
2023-10-14 18:41:07,153 DEV : loss 0.16356223821640015 - f1-score (micro avg)  0.725
2023-10-14 18:41:07,179 saving best model
2023-10-14 18:41:11,734 ----------------------------------------------------------------------------------------------------
2023-10-14 18:41:26,859 epoch 5 - iter 44/447 - loss 0.07023867 - time (sec): 15.12 - samples/sec: 506.11 - lr: 0.000098 - momentum: 0.000000
2023-10-14 18:41:42,294 epoch 5 - iter 88/447 - loss 0.06478001 - time (sec): 30.56 - samples/sec: 523.37 - lr: 0.000097 - momentum: 0.000000
2023-10-14 18:41:58,072 epoch 5 - iter 132/447 - loss 0.06412258 - time (sec): 46.34 - samples/sec: 536.84 - lr: 0.000095 - momentum: 0.000000
2023-10-14 18:42:13,295 epoch 5 - iter 176/447 - loss 0.07024363 - time (sec): 61.56 - samples/sec: 538.35 - lr: 0.000094 - momentum: 0.000000
2023-10-14 18:42:28,441 epoch 5 - iter 220/447 - loss 0.06745593 - time (sec): 76.70 - samples/sec: 541.44 - lr: 0.000092 - momentum: 0.000000
2023-10-14 18:42:45,723 epoch 5 - iter 264/447 - loss 0.07148378 - time (sec): 93.99 - samples/sec: 544.05 - lr: 0.000090 - momentum: 0.000000
2023-10-14 18:43:00,596 epoch 5 - iter 308/447 - loss 0.07209831 - time (sec): 108.86 - samples/sec: 542.81 - lr: 0.000089 - momentum: 0.000000
2023-10-14 18:43:15,817 epoch 5 - iter 352/447 - loss 0.07099360 - time (sec): 124.08 - samples/sec: 544.94 - lr: 0.000087 - momentum: 0.000000
2023-10-14 18:43:31,278 epoch 5 - iter 396/447 - loss 0.07091432 - time (sec): 139.54 - samples/sec: 548.72 - lr: 0.000085 - momentum: 0.000000
2023-10-14 18:43:46,569 epoch 5 - iter 440/447 - loss 0.07134499 - time (sec): 154.83 - samples/sec: 550.12 - lr: 0.000084 - momentum: 0.000000
2023-10-14 18:43:48,967 ----------------------------------------------------------------------------------------------------
2023-10-14 18:43:48,967 EPOCH 5 done: loss 0.0719 - lr: 0.000084
2023-10-14 18:44:13,629 DEV : loss 0.1595887392759323 - f1-score (micro avg)  0.7469
2023-10-14 18:44:13,655 saving best model
2023-10-14 18:44:18,225 ----------------------------------------------------------------------------------------------------
2023-10-14 18:44:33,929 epoch 6 - iter 44/447 - loss 0.02958638 - time (sec): 15.70 - samples/sec: 542.37 - lr: 0.000082 - momentum: 0.000000
2023-10-14 18:44:49,057 epoch 6 - iter 88/447 - loss 0.03872661 - time (sec): 30.83 - samples/sec: 545.68 - lr: 0.000080 - momentum: 0.000000
2023-10-14 18:45:04,426 epoch 6 - iter 132/447 - loss 0.04324198 - time (sec): 46.20 - samples/sec: 546.14 - lr: 0.000079 - momentum: 0.000000
2023-10-14 18:45:19,712 epoch 6 - iter 176/447 - loss 0.04411436 - time (sec): 61.48 - samples/sec: 549.13 - lr: 0.000077 - momentum: 0.000000
2023-10-14 18:45:34,792 epoch 6 - iter 220/447 - loss 0.04536235 - time (sec): 76.56 - samples/sec: 545.39 - lr: 0.000075 - momentum: 0.000000
2023-10-14 18:45:51,942 epoch 6 - iter 264/447 - loss 0.04612584 - time (sec): 93.71 - samples/sec: 546.82 - lr: 0.000074 - momentum: 0.000000
2023-10-14 18:46:07,599 epoch 6 - iter 308/447 - loss 0.04566728 - time (sec): 109.37 - samples/sec: 551.32 - lr: 0.000072 - momentum: 0.000000
2023-10-14 18:46:23,331 epoch 6 - iter 352/447 - loss 0.04571960 - time (sec): 125.10 - samples/sec: 549.12 - lr: 0.000070 - momentum: 0.000000
2023-10-14 18:46:38,316 epoch 6 - iter 396/447 - loss 0.04798319 - time (sec): 140.09 - samples/sec: 546.83 - lr: 0.000069 - momentum: 0.000000
2023-10-14 18:46:53,874 epoch 6 - iter 440/447 - loss 0.04832325 - time (sec): 155.65 - samples/sec: 547.34 - lr: 0.000067 - momentum: 0.000000
2023-10-14 18:46:56,276 ----------------------------------------------------------------------------------------------------
2023-10-14 18:46:56,277 EPOCH 6 done: loss 0.0481 - lr: 0.000067
2023-10-14 18:47:20,963 DEV : loss 0.1803191751241684 - f1-score (micro avg)  0.7481
2023-10-14 18:47:20,988 saving best model
2023-10-14 18:47:25,327 ----------------------------------------------------------------------------------------------------
2023-10-14 18:47:42,568 epoch 7 - iter 44/447 - loss 0.04512986 - time (sec): 17.24 - samples/sec: 561.18 - lr: 0.000065 - momentum: 0.000000
2023-10-14 18:47:58,148 epoch 7 - iter 88/447 - loss 0.03940830 - time (sec): 32.82 - samples/sec: 558.82 - lr: 0.000064 - momentum: 0.000000
2023-10-14 18:48:13,106 epoch 7 - iter 132/447 - loss 0.04551032 - time (sec): 47.78 - samples/sec: 547.80 - lr: 0.000062 - momentum: 0.000000
2023-10-14 18:48:28,259 epoch 7 - iter 176/447 - loss 0.04250563 - time (sec): 62.93 - samples/sec: 547.24 - lr: 0.000060 - momentum: 0.000000
2023-10-14 18:48:43,733 epoch 7 - iter 220/447 - loss 0.03900809 - time (sec): 78.40 - samples/sec: 549.82 - lr: 0.000059 - momentum: 0.000000
2023-10-14 18:48:59,886 epoch 7 - iter 264/447 - loss 0.03682204 - time (sec): 94.56 - samples/sec: 549.51 - lr: 0.000057 - momentum: 0.000000
2023-10-14 18:49:15,131 epoch 7 - iter 308/447 - loss 0.03689685 - time (sec): 109.80 - samples/sec: 549.10 - lr: 0.000055 - momentum: 0.000000
2023-10-14 18:49:30,170 epoch 7 - iter 352/447 - loss 0.03473793 - time (sec): 124.84 - samples/sec: 548.47 - lr: 0.000054 - momentum: 0.000000
2023-10-14 18:49:45,552 epoch 7 - iter 396/447 - loss 0.03496683 - time (sec): 140.22 - samples/sec: 550.10 - lr: 0.000052 - momentum: 0.000000
2023-10-14 18:50:00,748 epoch 7 - iter 440/447 - loss 0.03374463 - time (sec): 155.42 - samples/sec: 548.61 - lr: 0.000050 - momentum: 0.000000
2023-10-14 18:50:03,115 ----------------------------------------------------------------------------------------------------
2023-10-14 18:50:03,116 EPOCH 7 done: loss 0.0338 - lr: 0.000050
2023-10-14 18:50:27,933 DEV : loss 0.1989319771528244 - f1-score (micro avg)  0.7592
2023-10-14 18:50:27,958 saving best model
2023-10-14 18:50:32,398 ----------------------------------------------------------------------------------------------------
2023-10-14 18:50:47,481 epoch 8 - iter 44/447 - loss 0.02816068 - time (sec): 15.08 - samples/sec: 542.31 - lr: 0.000049 - momentum: 0.000000
2023-10-14 18:51:03,151 epoch 8 - iter 88/447 - loss 0.03674012 - time (sec): 30.75 - samples/sec: 546.65 - lr: 0.000047 - momentum: 0.000000
2023-10-14 18:51:18,134 epoch 8 - iter 132/447 - loss 0.03190669 - time (sec): 45.73 - samples/sec: 540.12 - lr: 0.000045 - momentum: 0.000000
2023-10-14 18:51:33,859 epoch 8 - iter 176/447 - loss 0.02926721 - time (sec): 61.46 - samples/sec: 552.77 - lr: 0.000044 - momentum: 0.000000
2023-10-14 18:51:49,605 epoch 8 - iter 220/447 - loss 0.02779927 - time (sec): 77.21 - samples/sec: 558.46 - lr: 0.000042 - momentum: 0.000000
2023-10-14 18:52:05,003 epoch 8 - iter 264/447 - loss 0.02652766 - time (sec): 92.60 - samples/sec: 551.05 - lr: 0.000040 - momentum: 0.000000
2023-10-14 18:52:21,852 epoch 8 - iter 308/447 - loss 0.02801775 - time (sec): 109.45 - samples/sec: 549.38 - lr: 0.000039 - momentum: 0.000000
2023-10-14 18:52:36,870 epoch 8 - iter 352/447 - loss 0.02681118 - time (sec): 124.47 - samples/sec: 548.52 - lr: 0.000037 - momentum: 0.000000
2023-10-14 18:52:52,122 epoch 8 - iter 396/447 - loss 0.02608349 - time (sec): 139.72 - samples/sec: 548.04 - lr: 0.000035 - momentum: 0.000000
2023-10-14 18:53:07,402 epoch 8 - iter 440/447 - loss 0.02505040 - time (sec): 155.00 - samples/sec: 549.68 - lr: 0.000034 - momentum: 0.000000
2023-10-14 18:53:09,819 ----------------------------------------------------------------------------------------------------
2023-10-14 18:53:09,819 EPOCH 8 done: loss 0.0249 - lr: 0.000034
2023-10-14 18:53:34,610 DEV : loss 0.20397181808948517 - f1-score (micro avg)  0.7593
2023-10-14 18:53:34,635 saving best model
2023-10-14 18:53:38,825 ----------------------------------------------------------------------------------------------------
2023-10-14 18:53:56,216 epoch 9 - iter 44/447 - loss 0.03413553 - time (sec): 17.39 - samples/sec: 559.05 - lr: 0.000032 - momentum: 0.000000
2023-10-14 18:54:12,137 epoch 9 - iter 88/447 - loss 0.02537551 - time (sec): 33.31 - samples/sec: 563.45 - lr: 0.000030 - momentum: 0.000000
2023-10-14 18:54:27,605 epoch 9 - iter 132/447 - loss 0.02230186 - time (sec): 48.78 - samples/sec: 561.25 - lr: 0.000029 - momentum: 0.000000
2023-10-14 18:54:43,093 epoch 9 - iter 176/447 - loss 0.02191161 - time (sec): 64.27 - samples/sec: 562.30 - lr: 0.000027 - momentum: 0.000000
2023-10-14 18:54:57,966 epoch 9 - iter 220/447 - loss 0.02003936 - time (sec): 79.14 - samples/sec: 553.96 - lr: 0.000025 - momentum: 0.000000
2023-10-14 18:55:13,466 epoch 9 - iter 264/447 - loss 0.02206598 - time (sec): 94.64 - samples/sec: 550.03 - lr: 0.000024 - momentum: 0.000000
2023-10-14 18:55:28,451 epoch 9 - iter 308/447 - loss 0.02063661 - time (sec): 109.62 - samples/sec: 545.60 - lr: 0.000022 - momentum: 0.000000
2023-10-14 18:55:43,831 epoch 9 - iter 352/447 - loss 0.02036125 - time (sec): 125.00 - samples/sec: 545.41 - lr: 0.000020 - momentum: 0.000000
2023-10-14 18:55:59,355 epoch 9 - iter 396/447 - loss 0.01930381 - time (sec): 140.53 - samples/sec: 546.14 - lr: 0.000019 - momentum: 0.000000
2023-10-14 18:56:14,934 epoch 9 - iter 440/447 - loss 0.02015557 - time (sec): 156.11 - samples/sec: 545.94 - lr: 0.000017 - momentum: 0.000000
2023-10-14 18:56:17,335 ----------------------------------------------------------------------------------------------------
2023-10-14 18:56:17,335 EPOCH 9 done: loss 0.0201 - lr: 0.000017
2023-10-14 18:56:42,347 DEV : loss 0.2131572663784027 - f1-score (micro avg)  0.7503
2023-10-14 18:56:42,372 ----------------------------------------------------------------------------------------------------
2023-10-14 18:56:57,773 epoch 10 - iter 44/447 - loss 0.02127213 - time (sec): 15.40 - samples/sec: 568.28 - lr: 0.000015 - momentum: 0.000000
2023-10-14 18:57:12,525 epoch 10 - iter 88/447 - loss 0.01811628 - time (sec): 30.15 - samples/sec: 544.58 - lr: 0.000014 - momentum: 0.000000
2023-10-14 18:57:27,600 epoch 10 - iter 132/447 - loss 0.01611064 - time (sec): 45.23 - samples/sec: 545.36 - lr: 0.000012 - momentum: 0.000000
2023-10-14 18:57:43,311 epoch 10 - iter 176/447 - loss 0.01554251 - time (sec): 60.94 - samples/sec: 551.21 - lr: 0.000010 - momentum: 0.000000
2023-10-14 18:58:01,072 epoch 10 - iter 220/447 - loss 0.01884512 - time (sec): 78.70 - samples/sec: 556.12 - lr: 0.000009 - momentum: 0.000000
2023-10-14 18:58:16,786 epoch 10 - iter 264/447 - loss 0.01782274 - time (sec): 94.41 - samples/sec: 551.69 - lr: 0.000007 - momentum: 0.000000
2023-10-14 18:58:31,821 epoch 10 - iter 308/447 - loss 0.01717610 - time (sec): 109.45 - samples/sec: 547.71 - lr: 0.000005 - momentum: 0.000000
2023-10-14 18:58:46,654 epoch 10 - iter 352/447 - loss 0.01630284 - time (sec): 124.28 - samples/sec: 543.75 - lr: 0.000004 - momentum: 0.000000
2023-10-14 18:59:02,118 epoch 10 - iter 396/447 - loss 0.01612965 - time (sec): 139.74 - samples/sec: 545.16 - lr: 0.000002 - momentum: 0.000000
2023-10-14 18:59:18,081 epoch 10 - iter 440/447 - loss 0.01766576 - time (sec): 155.71 - samples/sec: 547.01 - lr: 0.000001 - momentum: 0.000000
2023-10-14 18:59:20,506 ----------------------------------------------------------------------------------------------------
2023-10-14 18:59:20,506 EPOCH 10 done: loss 0.0180 - lr: 0.000001
2023-10-14 18:59:45,871 DEV : loss 0.22141988575458527 - f1-score (micro avg)  0.7508
2023-10-14 18:59:46,688 ----------------------------------------------------------------------------------------------------
2023-10-14 18:59:46,689 Loading model from best epoch ...
2023-10-14 18:59:49,776 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
2023-10-14 19:00:11,248 
Results:
- F-score (micro) 0.7519
- F-score (macro) 0.6501
- Accuracy 0.6163

By class:
              precision    recall  f1-score   support

         loc     0.8339    0.8674    0.8503       596
        pers     0.6772    0.7748    0.7227       333
         org     0.5263    0.5303    0.5283       132
        prod     0.6296    0.5152    0.5667        66
        time     0.5556    0.6122    0.5825        49

   micro avg     0.7319    0.7730    0.7519      1176
   macro avg     0.6445    0.6600    0.6501      1176
weighted avg     0.7319    0.7730    0.7510      1176

2023-10-14 19:00:11,249 ----------------------------------------------------------------------------------------------------