models/flair-sentiment-classifier/training.log

2023-02-06 08:28:45,031 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,036 Model: "TextClassifier(
  (decoder): Linear(in_features=512, out_features=3, bias=True)
  (dropout): Dropout(p=0.0, inplace=False)
  (locked_dropout): LockedDropout(p=0.0)
  (word_dropout): WordDropout(p=0.0)
  (loss_function): CrossEntropyLoss()
  (document_embeddings): DocumentLSTMEmbeddings(
    (embeddings): StackedEmbeddings(
      (list_embedding_0): FlairEmbeddings(
        (lm): LanguageModel(
          (drop): Dropout(p=0.25, inplace=False)
          (encoder): Embedding(275, 100)
          (rnn): LSTM(100, 1024)
          (decoder): Linear(in_features=1024, out_features=275, bias=True)
        )
      )
    )
    (word_reprojection_map): Linear(in_features=1024, out_features=256, bias=True)
    (rnn): GRU(256, 512)
    (dropout): Dropout(p=0.5, inplace=False)
  )
  (weights): None
  (weight_tensor) None
)"
2023-02-06 08:28:45,039 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,042 Corpus: "Corpus: 8500 train + 1500 dev + 359 test sentences"
2023-02-06 08:28:45,045 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,048 Parameters:
2023-02-06 08:28:45,051  - learning_rate: "0.010000"
2023-02-06 08:28:45,052  - mini_batch_size: "64"
2023-02-06 08:28:45,056  - patience: "3"
2023-02-06 08:28:45,057  - anneal_factor: "0.5"
2023-02-06 08:28:45,061  - max_epochs: "35"
2023-02-06 08:28:45,063  - shuffle: "True"
2023-02-06 08:28:45,069  - train_with_dev: "False"
2023-02-06 08:28:45,071  - batch_growth_annealing: "False"
2023-02-06 08:28:45,075 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,078 Model training base path: "/content/drive/MyDrive/Colab Notebooks/models/flair-sentiment-classifier"
2023-02-06 08:28:45,081 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,083 Device: cuda:0
2023-02-06 08:28:45,085 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:45,089 Embeddings storage mode: gpu
2023-02-06 08:28:45,091 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:46,882 epoch 1 - iter 13/133 - loss 0.01562834 - samples/sec: 514.51 - lr: 0.010000
2023-02-06 08:28:48,397 epoch 1 - iter 26/133 - loss 0.01481466 - samples/sec: 748.73 - lr: 0.010000
2023-02-06 08:28:49,638 epoch 1 - iter 39/133 - loss 0.01423043 - samples/sec: 772.85 - lr: 0.010000
2023-02-06 08:28:51,189 epoch 1 - iter 52/133 - loss 0.01381341 - samples/sec: 600.83 - lr: 0.010000
2023-02-06 08:28:52,402 epoch 1 - iter 65/133 - loss 0.01349711 - samples/sec: 796.47 - lr: 0.010000
2023-02-06 08:28:53,877 epoch 1 - iter 78/133 - loss 0.01323276 - samples/sec: 765.73 - lr: 0.010000
2023-02-06 08:28:55,144 epoch 1 - iter 91/133 - loss 0.01301969 - samples/sec: 759.35 - lr: 0.010000
2023-02-06 08:28:56,401 epoch 1 - iter 104/133 - loss 0.01283645 - samples/sec: 765.85 - lr: 0.010000
2023-02-06 08:28:57,895 epoch 1 - iter 117/133 - loss 0.01267868 - samples/sec: 760.35 - lr: 0.010000
2023-02-06 08:28:59,155 epoch 1 - iter 130/133 - loss 0.01254156 - samples/sec: 766.70 - lr: 0.010000
2023-02-06 08:28:59,435 ----------------------------------------------------------------------------------------------------
2023-02-06 08:28:59,440 EPOCH 1 done: loss 0.0125 - lr 0.010000
2023-02-06 08:29:02,345 Evaluating as a multi-label problem: False
2023-02-06 08:29:02,360 DEV : loss 0.01149754598736763 - f1-score (micro avg)  0.5393
2023-02-06 08:29:02,938 BAD EPOCHS (no improvement): 0
2023-02-06 08:29:02,944 saving best model
2023-02-06 08:29:03,019 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:04,350 epoch 2 - iter 13/133 - loss 0.01134125 - samples/sec: 713.12 - lr: 0.010000
2023-02-06 08:29:05,867 epoch 2 - iter 26/133 - loss 0.01133783 - samples/sec: 745.31 - lr: 0.010000
2023-02-06 08:29:07,096 epoch 2 - iter 39/133 - loss 0.01130036 - samples/sec: 785.18 - lr: 0.010000
2023-02-06 08:29:08,398 epoch 2 - iter 52/133 - loss 0.01126930 - samples/sec: 739.05 - lr: 0.010000
2023-02-06 08:29:10,210 epoch 2 - iter 65/133 - loss 0.01123679 - samples/sec: 611.49 - lr: 0.010000
2023-02-06 08:29:12,013 epoch 2 - iter 78/133 - loss 0.01119918 - samples/sec: 563.65 - lr: 0.010000
2023-02-06 08:29:13,567 epoch 2 - iter 91/133 - loss 0.01119660 - samples/sec: 722.13 - lr: 0.010000
2023-02-06 08:29:14,863 epoch 2 - iter 104/133 - loss 0.01118887 - samples/sec: 739.97 - lr: 0.010000
2023-02-06 08:29:16,413 epoch 2 - iter 117/133 - loss 0.01117338 - samples/sec: 736.63 - lr: 0.010000
2023-02-06 08:29:17,679 epoch 2 - iter 130/133 - loss 0.01115977 - samples/sec: 754.47 - lr: 0.010000
2023-02-06 08:29:17,964 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:17,967 EPOCH 2 done: loss 0.0112 - lr 0.010000
2023-02-06 08:29:20,384 Evaluating as a multi-label problem: False
2023-02-06 08:29:20,400 DEV : loss 0.011094754561781883 - f1-score (micro avg)  0.586
2023-02-06 08:29:20,760 BAD EPOCHS (no improvement): 0
2023-02-06 08:29:20,767 saving best model
2023-02-06 08:29:20,836 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:22,455 epoch 3 - iter 13/133 - loss 0.01100920 - samples/sec: 577.11 - lr: 0.010000
2023-02-06 08:29:23,739 epoch 3 - iter 26/133 - loss 0.01100166 - samples/sec: 748.26 - lr: 0.010000
2023-02-06 08:29:25,256 epoch 3 - iter 39/133 - loss 0.01099206 - samples/sec: 614.87 - lr: 0.010000
2023-02-06 08:29:26,543 epoch 3 - iter 52/133 - loss 0.01096206 - samples/sec: 747.35 - lr: 0.010000
2023-02-06 08:29:28,045 epoch 3 - iter 65/133 - loss 0.01093502 - samples/sec: 756.93 - lr: 0.010000
2023-02-06 08:29:29,300 epoch 3 - iter 78/133 - loss 0.01092986 - samples/sec: 767.76 - lr: 0.010000
2023-02-06 08:29:30,899 epoch 3 - iter 91/133 - loss 0.01094036 - samples/sec: 700.02 - lr: 0.010000
2023-02-06 08:29:32,167 epoch 3 - iter 104/133 - loss 0.01093898 - samples/sec: 759.88 - lr: 0.010000
2023-02-06 08:29:33,482 epoch 3 - iter 117/133 - loss 0.01092654 - samples/sec: 724.74 - lr: 0.010000
2023-02-06 08:29:34,996 epoch 3 - iter 130/133 - loss 0.01091851 - samples/sec: 747.17 - lr: 0.010000
2023-02-06 08:29:35,285 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:35,288 EPOCH 3 done: loss 0.0109 - lr 0.010000
2023-02-06 08:29:37,771 Evaluating as a multi-label problem: False
2023-02-06 08:29:37,787 DEV : loss 0.010945815593004227 - f1-score (micro avg)  0.5833
2023-02-06 08:29:38,166 BAD EPOCHS (no improvement): 1
2023-02-06 08:29:38,171 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:39,771 epoch 4 - iter 13/133 - loss 0.01088269 - samples/sec: 703.41 - lr: 0.010000
2023-02-06 08:29:41,093 epoch 4 - iter 26/133 - loss 0.01079631 - samples/sec: 727.27 - lr: 0.010000
2023-02-06 08:29:42,388 epoch 4 - iter 39/133 - loss 0.01080397 - samples/sec: 731.61 - lr: 0.010000
2023-02-06 08:29:43,901 epoch 4 - iter 52/133 - loss 0.01079299 - samples/sec: 748.17 - lr: 0.010000
2023-02-06 08:29:45,173 epoch 4 - iter 65/133 - loss 0.01080453 - samples/sec: 756.93 - lr: 0.010000
2023-02-06 08:29:46,704 epoch 4 - iter 78/133 - loss 0.01078781 - samples/sec: 742.45 - lr: 0.010000
2023-02-06 08:29:48,004 epoch 4 - iter 91/133 - loss 0.01077505 - samples/sec: 739.13 - lr: 0.010000
2023-02-06 08:29:49,566 epoch 4 - iter 104/133 - loss 0.01077253 - samples/sec: 719.85 - lr: 0.010000
2023-02-06 08:29:50,799 epoch 4 - iter 117/133 - loss 0.01077099 - samples/sec: 782.87 - lr: 0.010000
2023-02-06 08:29:52,309 epoch 4 - iter 130/133 - loss 0.01079835 - samples/sec: 751.86 - lr: 0.010000
2023-02-06 08:29:52,598 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:52,600 EPOCH 4 done: loss 0.0108 - lr 0.010000
2023-02-06 08:29:54,806 Evaluating as a multi-label problem: False
2023-02-06 08:29:54,823 DEV : loss 0.010844088159501553 - f1-score (micro avg)  0.5867
2023-02-06 08:29:55,416 BAD EPOCHS (no improvement): 0
2023-02-06 08:29:55,422 saving best model
2023-02-06 08:29:55,494 ----------------------------------------------------------------------------------------------------
2023-02-06 08:29:56,823 epoch 5 - iter 13/133 - loss 0.01067723 - samples/sec: 718.01 - lr: 0.010000
2023-02-06 08:29:58,344 epoch 5 - iter 26/133 - loss 0.01059902 - samples/sec: 743.92 - lr: 0.010000
2023-02-06 08:29:59,632 epoch 5 - iter 39/133 - loss 0.01065991 - samples/sec: 752.37 - lr: 0.010000
2023-02-06 08:30:01,148 epoch 5 - iter 52/133 - loss 0.01066396 - samples/sec: 742.67 - lr: 0.010000
2023-02-06 08:30:02,462 epoch 5 - iter 65/133 - loss 0.01067246 - samples/sec: 721.77 - lr: 0.010000
2023-02-06 08:30:03,991 epoch 5 - iter 78/133 - loss 0.01067908 - samples/sec: 764.43 - lr: 0.010000
2023-02-06 08:30:05,279 epoch 5 - iter 91/133 - loss 0.01070105 - samples/sec: 746.56 - lr: 0.010000
2023-02-06 08:30:06,504 epoch 5 - iter 104/133 - loss 0.01071328 - samples/sec: 788.19 - lr: 0.010000
2023-02-06 08:30:07,981 epoch 5 - iter 117/133 - loss 0.01069141 - samples/sec: 768.06 - lr: 0.010000
2023-02-06 08:30:09,278 epoch 5 - iter 130/133 - loss 0.01069258 - samples/sec: 736.98 - lr: 0.010000
2023-02-06 08:30:09,584 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:09,591 EPOCH 5 done: loss 0.0107 - lr 0.010000
2023-02-06 08:30:12,079 Evaluating as a multi-label problem: False
2023-02-06 08:30:12,096 DEV : loss 0.010764073580503464 - f1-score (micro avg)  0.59
2023-02-06 08:30:12,666 BAD EPOCHS (no improvement): 0
2023-02-06 08:30:12,684 saving best model
2023-02-06 08:30:12,758 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:14,153 epoch 6 - iter 13/133 - loss 0.01071924 - samples/sec: 682.06 - lr: 0.010000
2023-02-06 08:30:15,468 epoch 6 - iter 26/133 - loss 0.01071274 - samples/sec: 726.60 - lr: 0.010000
2023-02-06 08:30:16,995 epoch 6 - iter 39/133 - loss 0.01072073 - samples/sec: 745.06 - lr: 0.010000
2023-02-06 08:30:18,256 epoch 6 - iter 52/133 - loss 0.01069609 - samples/sec: 766.36 - lr: 0.010000
2023-02-06 08:30:19,760 epoch 6 - iter 65/133 - loss 0.01067637 - samples/sec: 621.63 - lr: 0.010000
2023-02-06 08:30:21,150 epoch 6 - iter 78/133 - loss 0.01069997 - samples/sec: 698.03 - lr: 0.010000
2023-02-06 08:30:22,666 epoch 6 - iter 91/133 - loss 0.01067124 - samples/sec: 747.15 - lr: 0.010000
2023-02-06 08:30:23,937 epoch 6 - iter 104/133 - loss 0.01065074 - samples/sec: 756.77 - lr: 0.010000
2023-02-06 08:30:25,196 epoch 6 - iter 117/133 - loss 0.01065426 - samples/sec: 759.07 - lr: 0.010000
2023-02-06 08:30:26,726 epoch 6 - iter 130/133 - loss 0.01065274 - samples/sec: 739.37 - lr: 0.010000
2023-02-06 08:30:27,011 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:27,016 EPOCH 6 done: loss 0.0107 - lr 0.010000
2023-02-06 08:30:29,488 Evaluating as a multi-label problem: False
2023-02-06 08:30:29,504 DEV : loss 0.010709869675338268 - f1-score (micro avg)  0.5953
2023-02-06 08:30:29,884 BAD EPOCHS (no improvement): 0
2023-02-06 08:30:29,891 saving best model
2023-02-06 08:30:29,962 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:31,568 epoch 7 - iter 13/133 - loss 0.01074821 - samples/sec: 588.47 - lr: 0.010000
2023-02-06 08:30:32,838 epoch 7 - iter 26/133 - loss 0.01067320 - samples/sec: 752.01 - lr: 0.010000
2023-02-06 08:30:34,326 epoch 7 - iter 39/133 - loss 0.01067867 - samples/sec: 764.30 - lr: 0.010000
2023-02-06 08:30:35,675 epoch 7 - iter 52/133 - loss 0.01066511 - samples/sec: 715.33 - lr: 0.010000
2023-02-06 08:30:37,160 epoch 7 - iter 65/133 - loss 0.01066500 - samples/sec: 774.66 - lr: 0.010000
2023-02-06 08:30:38,384 epoch 7 - iter 78/133 - loss 0.01064703 - samples/sec: 786.21 - lr: 0.010000
2023-02-06 08:30:39,656 epoch 7 - iter 91/133 - loss 0.01064059 - samples/sec: 752.36 - lr: 0.010000
2023-02-06 08:30:41,183 epoch 7 - iter 104/133 - loss 0.01064299 - samples/sec: 741.82 - lr: 0.010000
2023-02-06 08:30:42,411 epoch 7 - iter 117/133 - loss 0.01063663 - samples/sec: 782.15 - lr: 0.010000
2023-02-06 08:30:43,918 epoch 7 - iter 130/133 - loss 0.01062067 - samples/sec: 746.86 - lr: 0.010000
2023-02-06 08:30:44,207 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:44,212 EPOCH 7 done: loss 0.0106 - lr 0.010000
2023-02-06 08:30:46,609 Evaluating as a multi-label problem: False
2023-02-06 08:30:46,625 DEV : loss 0.010663843713700771 - f1-score (micro avg)  0.604
2023-02-06 08:30:46,984 BAD EPOCHS (no improvement): 0
2023-02-06 08:30:46,991 saving best model
2023-02-06 08:30:47,072 ----------------------------------------------------------------------------------------------------
2023-02-06 08:30:48,638 epoch 8 - iter 13/133 - loss 0.01043837 - samples/sec: 601.87 - lr: 0.010000
2023-02-06 08:30:49,893 epoch 8 - iter 26/133 - loss 0.01051882 - samples/sec: 760.45 - lr: 0.010000
2023-02-06 08:30:51,166 epoch 8 - iter 39/133 - loss 0.01054135 - samples/sec: 761.95 - lr: 0.010000
2023-02-06 08:30:52,624 epoch 8 - iter 52/133 - loss 0.01056330 - samples/sec: 784.40 - lr: 0.010000
2023-02-06 08:30:53,864 epoch 8 - iter 65/133 - loss 0.01058674 - samples/sec: 788.25 - lr: 0.010000
2023-02-06 08:30:55,348 epoch 8 - iter 78/133 - loss 0.01057453 - samples/sec: 632.25 - lr: 0.010000
2023-02-06 08:30:56,643 epoch 8 - iter 91/133 - loss 0.01056818 - samples/sec: 743.01 - lr: 0.010000
2023-02-06 08:30:58,165 epoch 8 - iter 104/133 - loss 0.01056378 - samples/sec: 754.99 - lr: 0.010000
2023-02-06 08:30:59,473 epoch 8 - iter 117/133 - loss 0.01055726 - samples/sec: 740.74 - lr: 0.010000
2023-02-06 08:31:00,790 epoch 8 - iter 130/133 - loss 0.01054234 - samples/sec: 730.40 - lr: 0.010000
2023-02-06 08:31:01,299 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:01,301 EPOCH 8 done: loss 0.0106 - lr 0.010000
2023-02-06 08:31:03,556 Evaluating as a multi-label problem: False
2023-02-06 08:31:03,573 DEV : loss 0.010619796812534332 - f1-score (micro avg)  0.602
2023-02-06 08:31:04,148 BAD EPOCHS (no improvement): 1
2023-02-06 08:31:04,153 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:05,493 epoch 9 - iter 13/133 - loss 0.01055787 - samples/sec: 714.38 - lr: 0.010000
2023-02-06 08:31:06,991 epoch 9 - iter 26/133 - loss 0.01055880 - samples/sec: 756.84 - lr: 0.010000
2023-02-06 08:31:08,220 epoch 9 - iter 39/133 - loss 0.01058857 - samples/sec: 784.11 - lr: 0.010000
2023-02-06 08:31:09,733 epoch 9 - iter 52/133 - loss 0.01050204 - samples/sec: 743.77 - lr: 0.010000
2023-02-06 08:31:11,006 epoch 9 - iter 65/133 - loss 0.01049462 - samples/sec: 757.49 - lr: 0.010000
2023-02-06 08:31:12,320 epoch 9 - iter 78/133 - loss 0.01049927 - samples/sec: 726.38 - lr: 0.010000
2023-02-06 08:31:13,836 epoch 9 - iter 91/133 - loss 0.01053675 - samples/sec: 753.53 - lr: 0.010000
2023-02-06 08:31:15,093 epoch 9 - iter 104/133 - loss 0.01051643 - samples/sec: 763.35 - lr: 0.010000
2023-02-06 08:31:16,571 epoch 9 - iter 117/133 - loss 0.01051333 - samples/sec: 770.43 - lr: 0.010000
2023-02-06 08:31:17,840 epoch 9 - iter 130/133 - loss 0.01052863 - samples/sec: 764.73 - lr: 0.010000
2023-02-06 08:31:18,111 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:18,112 EPOCH 9 done: loss 0.0105 - lr 0.010000
2023-02-06 08:31:20,513 Evaluating as a multi-label problem: False
2023-02-06 08:31:20,529 DEV : loss 0.010611701756715775 - f1-score (micro avg)  0.6127
2023-02-06 08:31:21,099 BAD EPOCHS (no improvement): 0
2023-02-06 08:31:21,105 saving best model
2023-02-06 08:31:21,180 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:22,488 epoch 10 - iter 13/133 - loss 0.01068018 - samples/sec: 735.19 - lr: 0.010000
2023-02-06 08:31:23,814 epoch 10 - iter 26/133 - loss 0.01048881 - samples/sec: 719.40 - lr: 0.010000
2023-02-06 08:31:25,348 epoch 10 - iter 39/133 - loss 0.01057341 - samples/sec: 607.48 - lr: 0.010000
2023-02-06 08:31:26,612 epoch 10 - iter 52/133 - loss 0.01053287 - samples/sec: 762.66 - lr: 0.010000
2023-02-06 08:31:28,080 epoch 10 - iter 65/133 - loss 0.01053009 - samples/sec: 780.80 - lr: 0.010000
2023-02-06 08:31:29,348 epoch 10 - iter 78/133 - loss 0.01049424 - samples/sec: 765.03 - lr: 0.010000
2023-02-06 08:31:30,907 epoch 10 - iter 91/133 - loss 0.01049714 - samples/sec: 602.01 - lr: 0.010000
2023-02-06 08:31:32,299 epoch 10 - iter 104/133 - loss 0.01051002 - samples/sec: 688.23 - lr: 0.010000
2023-02-06 08:31:33,585 epoch 10 - iter 117/133 - loss 0.01050991 - samples/sec: 751.61 - lr: 0.010000
2023-02-06 08:31:35,136 epoch 10 - iter 130/133 - loss 0.01049037 - samples/sec: 721.95 - lr: 0.010000
2023-02-06 08:31:35,429 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:35,431 EPOCH 10 done: loss 0.0105 - lr 0.010000
2023-02-06 08:31:37,895 Evaluating as a multi-label problem: False
2023-02-06 08:31:37,910 DEV : loss 0.010555021464824677 - f1-score (micro avg)  0.612
2023-02-06 08:31:38,250 BAD EPOCHS (no improvement): 1
2023-02-06 08:31:38,266 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:39,815 epoch 11 - iter 13/133 - loss 0.01014661 - samples/sec: 717.23 - lr: 0.010000
2023-02-06 08:31:41,095 epoch 11 - iter 26/133 - loss 0.01037157 - samples/sec: 747.91 - lr: 0.010000
2023-02-06 08:31:42,350 epoch 11 - iter 39/133 - loss 0.01039031 - samples/sec: 771.90 - lr: 0.010000
2023-02-06 08:31:43,850 epoch 11 - iter 52/133 - loss 0.01048393 - samples/sec: 763.72 - lr: 0.010000
2023-02-06 08:31:45,153 epoch 11 - iter 65/133 - loss 0.01050528 - samples/sec: 737.89 - lr: 0.010000
2023-02-06 08:31:46,661 epoch 11 - iter 78/133 - loss 0.01048936 - samples/sec: 752.50 - lr: 0.010000
2023-02-06 08:31:47,936 epoch 11 - iter 91/133 - loss 0.01046032 - samples/sec: 756.43 - lr: 0.010000
2023-02-06 08:31:49,443 epoch 11 - iter 104/133 - loss 0.01049842 - samples/sec: 750.46 - lr: 0.010000
2023-02-06 08:31:50,667 epoch 11 - iter 117/133 - loss 0.01048207 - samples/sec: 788.09 - lr: 0.010000
2023-02-06 08:31:51,915 epoch 11 - iter 130/133 - loss 0.01047867 - samples/sec: 764.34 - lr: 0.010000
2023-02-06 08:31:52,443 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:52,447 EPOCH 11 done: loss 0.0105 - lr 0.010000
2023-02-06 08:31:54,642 Evaluating as a multi-label problem: False
2023-02-06 08:31:54,659 DEV : loss 0.010583124123513699 - f1-score (micro avg)  0.618
2023-02-06 08:31:55,235 BAD EPOCHS (no improvement): 0
2023-02-06 08:31:55,243 saving best model
2023-02-06 08:31:55,324 ----------------------------------------------------------------------------------------------------
2023-02-06 08:31:56,599 epoch 12 - iter 13/133 - loss 0.01034765 - samples/sec: 758.67 - lr: 0.010000
2023-02-06 08:31:58,049 epoch 12 - iter 26/133 - loss 0.01038689 - samples/sec: 649.83 - lr: 0.010000
2023-02-06 08:31:59,293 epoch 12 - iter 39/133 - loss 0.01041491 - samples/sec: 771.48 - lr: 0.010000
2023-02-06 08:32:00,827 epoch 12 - iter 52/133 - loss 0.01039636 - samples/sec: 749.22 - lr: 0.010000
2023-02-06 08:32:02,125 epoch 12 - iter 65/133 - loss 0.01039765 - samples/sec: 734.58 - lr: 0.010000
2023-02-06 08:32:03,651 epoch 12 - iter 78/133 - loss 0.01037797 - samples/sec: 742.60 - lr: 0.010000
2023-02-06 08:32:04,912 epoch 12 - iter 91/133 - loss 0.01037713 - samples/sec: 767.06 - lr: 0.010000
2023-02-06 08:32:06,155 epoch 12 - iter 104/133 - loss 0.01039333 - samples/sec: 778.01 - lr: 0.010000
2023-02-06 08:32:07,679 epoch 12 - iter 117/133 - loss 0.01039785 - samples/sec: 612.76 - lr: 0.010000
2023-02-06 08:32:08,958 epoch 12 - iter 130/133 - loss 0.01041191 - samples/sec: 749.49 - lr: 0.010000
2023-02-06 08:32:09,221 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:09,225 EPOCH 12 done: loss 0.0104 - lr 0.010000
2023-02-06 08:32:11,743 Evaluating as a multi-label problem: False
2023-02-06 08:32:11,761 DEV : loss 0.010490193963050842 - f1-score (micro avg)  0.6233
2023-02-06 08:32:12,343 BAD EPOCHS (no improvement): 0
2023-02-06 08:32:12,347 saving best model
2023-02-06 08:32:12,425 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:13,768 epoch 13 - iter 13/133 - loss 0.01044893 - samples/sec: 711.51 - lr: 0.010000
2023-02-06 08:32:15,331 epoch 13 - iter 26/133 - loss 0.01037701 - samples/sec: 597.05 - lr: 0.010000
2023-02-06 08:32:16,636 epoch 13 - iter 39/133 - loss 0.01041042 - samples/sec: 729.64 - lr: 0.010000
2023-02-06 08:32:18,179 epoch 13 - iter 52/133 - loss 0.01039558 - samples/sec: 639.59 - lr: 0.010000
2023-02-06 08:32:20,357 epoch 13 - iter 65/133 - loss 0.01036643 - samples/sec: 453.47 - lr: 0.010000
2023-02-06 08:32:22,076 epoch 13 - iter 78/133 - loss 0.01034448 - samples/sec: 576.82 - lr: 0.010000
2023-02-06 08:32:23,544 epoch 13 - iter 91/133 - loss 0.01037494 - samples/sec: 776.14 - lr: 0.010000
2023-02-06 08:32:24,857 epoch 13 - iter 104/133 - loss 0.01038988 - samples/sec: 723.98 - lr: 0.010000
2023-02-06 08:32:26,302 epoch 13 - iter 117/133 - loss 0.01037204 - samples/sec: 803.02 - lr: 0.010000
2023-02-06 08:32:27,579 epoch 13 - iter 130/133 - loss 0.01037025 - samples/sec: 756.57 - lr: 0.010000
2023-02-06 08:32:27,857 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:27,858 EPOCH 13 done: loss 0.0104 - lr 0.010000
2023-02-06 08:32:30,369 Evaluating as a multi-label problem: False
2023-02-06 08:32:30,385 DEV : loss 0.010483094491064548 - f1-score (micro avg)  0.6207
2023-02-06 08:32:30,744 BAD EPOCHS (no improvement): 1
2023-02-06 08:32:30,752 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:32,325 epoch 14 - iter 13/133 - loss 0.01026158 - samples/sec: 717.41 - lr: 0.010000
2023-02-06 08:32:33,586 epoch 14 - iter 26/133 - loss 0.01037027 - samples/sec: 762.17 - lr: 0.010000
2023-02-06 08:32:35,070 epoch 14 - iter 39/133 - loss 0.01036379 - samples/sec: 774.03 - lr: 0.010000
2023-02-06 08:32:36,383 epoch 14 - iter 52/133 - loss 0.01035813 - samples/sec: 729.67 - lr: 0.010000
2023-02-06 08:32:37,889 epoch 14 - iter 65/133 - loss 0.01031572 - samples/sec: 748.65 - lr: 0.010000
2023-02-06 08:32:39,182 epoch 14 - iter 78/133 - loss 0.01034433 - samples/sec: 743.14 - lr: 0.010000
2023-02-06 08:32:40,475 epoch 14 - iter 91/133 - loss 0.01037816 - samples/sec: 742.25 - lr: 0.010000
2023-02-06 08:32:41,954 epoch 14 - iter 104/133 - loss 0.01039795 - samples/sec: 782.25 - lr: 0.010000
2023-02-06 08:32:43,225 epoch 14 - iter 117/133 - loss 0.01038837 - samples/sec: 761.71 - lr: 0.010000
2023-02-06 08:32:44,737 epoch 14 - iter 130/133 - loss 0.01036175 - samples/sec: 745.28 - lr: 0.010000
2023-02-06 08:32:45,035 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:45,040 EPOCH 14 done: loss 0.0104 - lr 0.010000
2023-02-06 08:32:47,510 Evaluating as a multi-label problem: False
2023-02-06 08:32:47,528 DEV : loss 0.010432829149067402 - f1-score (micro avg)  0.626
2023-02-06 08:32:47,863 BAD EPOCHS (no improvement): 0
2023-02-06 08:32:47,871 saving best model
2023-02-06 08:32:47,944 ----------------------------------------------------------------------------------------------------
2023-02-06 08:32:49,537 epoch 15 - iter 13/133 - loss 0.01019386 - samples/sec: 698.75 - lr: 0.010000
2023-02-06 08:32:50,800 epoch 15 - iter 26/133 - loss 0.01019158 - samples/sec: 758.25 - lr: 0.010000
2023-02-06 08:32:52,065 epoch 15 - iter 39/133 - loss 0.01015538 - samples/sec: 759.68 - lr: 0.010000
2023-02-06 08:32:53,509 epoch 15 - iter 52/133 - loss 0.01023823 - samples/sec: 804.56 - lr: 0.010000
2023-02-06 08:32:54,834 epoch 15 - iter 65/133 - loss 0.01026660 - samples/sec: 722.21 - lr: 0.010000
2023-02-06 08:32:56,442 epoch 15 - iter 78/133 - loss 0.01025501 - samples/sec: 717.72 - lr: 0.010000
2023-02-06 08:32:57,718 epoch 15 - iter 91/133 - loss 0.01024956 - samples/sec: 756.16 - lr: 0.010000
2023-02-06 08:32:59,173 epoch 15 - iter 104/133 - loss 0.01028768 - samples/sec: 783.99 - lr: 0.010000
2023-02-06 08:33:00,444 epoch 15 - iter 117/133 - loss 0.01027921 - samples/sec: 758.72 - lr: 0.010000
2023-02-06 08:33:01,708 epoch 15 - iter 130/133 - loss 0.01030246 - samples/sec: 760.06 - lr: 0.010000
2023-02-06 08:33:02,242 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:02,248 EPOCH 15 done: loss 0.0103 - lr 0.010000
2023-02-06 08:33:04,549 Evaluating as a multi-label problem: False
2023-02-06 08:33:04,568 DEV : loss 0.010397534817457199 - f1-score (micro avg)  0.6247
2023-02-06 08:33:05,171 BAD EPOCHS (no improvement): 1
2023-02-06 08:33:05,181 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:06,547 epoch 16 - iter 13/133 - loss 0.01041342 - samples/sec: 696.13 - lr: 0.010000
2023-02-06 08:33:08,089 epoch 16 - iter 26/133 - loss 0.01036971 - samples/sec: 726.77 - lr: 0.010000
2023-02-06 08:33:09,375 epoch 16 - iter 39/133 - loss 0.01029897 - samples/sec: 745.27 - lr: 0.010000
2023-02-06 08:33:10,662 epoch 16 - iter 52/133 - loss 0.01030162 - samples/sec: 746.88 - lr: 0.010000
2023-02-06 08:33:12,125 epoch 16 - iter 65/133 - loss 0.01031824 - samples/sec: 780.89 - lr: 0.010000
2023-02-06 08:33:13,387 epoch 16 - iter 78/133 - loss 0.01031728 - samples/sec: 754.65 - lr: 0.010000
2023-02-06 08:33:14,895 epoch 16 - iter 91/133 - loss 0.01027123 - samples/sec: 757.20 - lr: 0.010000
2023-02-06 08:33:16,162 epoch 16 - iter 104/133 - loss 0.01027054 - samples/sec: 764.32 - lr: 0.010000
2023-02-06 08:33:17,639 epoch 16 - iter 117/133 - loss 0.01024219 - samples/sec: 633.84 - lr: 0.010000
2023-02-06 08:33:18,899 epoch 16 - iter 130/133 - loss 0.01026610 - samples/sec: 758.90 - lr: 0.010000
2023-02-06 08:33:19,184 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:19,188 EPOCH 16 done: loss 0.0103 - lr 0.010000
2023-02-06 08:33:21,632 Evaluating as a multi-label problem: False
2023-02-06 08:33:21,649 DEV : loss 0.010406638495624065 - f1-score (micro avg)  0.6253
2023-02-06 08:33:22,222 BAD EPOCHS (no improvement): 2
2023-02-06 08:33:22,234 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:23,528 epoch 17 - iter 13/133 - loss 0.01033387 - samples/sec: 738.38 - lr: 0.010000
2023-02-06 08:33:24,760 epoch 17 - iter 26/133 - loss 0.01031095 - samples/sec: 781.92 - lr: 0.010000
2023-02-06 08:33:26,300 epoch 17 - iter 39/133 - loss 0.01028919 - samples/sec: 746.79 - lr: 0.010000
2023-02-06 08:33:27,590 epoch 17 - iter 52/133 - loss 0.01030368 - samples/sec: 739.39 - lr: 0.010000
2023-02-06 08:33:29,070 epoch 17 - iter 65/133 - loss 0.01029647 - samples/sec: 634.36 - lr: 0.010000
2023-02-06 08:33:30,298 epoch 17 - iter 78/133 - loss 0.01029278 - samples/sec: 782.85 - lr: 0.010000
2023-02-06 08:33:31,598 epoch 17 - iter 91/133 - loss 0.01028871 - samples/sec: 734.99 - lr: 0.010000
2023-02-06 08:33:33,082 epoch 17 - iter 104/133 - loss 0.01029995 - samples/sec: 762.60 - lr: 0.010000
2023-02-06 08:33:34,331 epoch 17 - iter 117/133 - loss 0.01028484 - samples/sec: 776.27 - lr: 0.010000
2023-02-06 08:33:35,784 epoch 17 - iter 130/133 - loss 0.01027576 - samples/sec: 785.80 - lr: 0.010000
2023-02-06 08:33:36,070 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:36,071 EPOCH 17 done: loss 0.0103 - lr 0.010000
2023-02-06 08:33:38,498 Evaluating as a multi-label problem: False
2023-02-06 08:33:38,515 DEV : loss 0.01034807600080967 - f1-score (micro avg)  0.63
2023-02-06 08:33:38,856 BAD EPOCHS (no improvement): 0
2023-02-06 08:33:38,862 saving best model
2023-02-06 08:33:38,939 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:40,462 epoch 18 - iter 13/133 - loss 0.01017348 - samples/sec: 740.64 - lr: 0.010000
2023-02-06 08:33:41,723 epoch 18 - iter 26/133 - loss 0.01019914 - samples/sec: 764.13 - lr: 0.010000
2023-02-06 08:33:43,017 epoch 18 - iter 39/133 - loss 0.01021330 - samples/sec: 737.55 - lr: 0.010000
2023-02-06 08:33:44,471 epoch 18 - iter 52/133 - loss 0.01018504 - samples/sec: 782.18 - lr: 0.010000
2023-02-06 08:33:45,771 epoch 18 - iter 65/133 - loss 0.01017944 - samples/sec: 740.07 - lr: 0.010000
2023-02-06 08:33:47,306 epoch 18 - iter 78/133 - loss 0.01020170 - samples/sec: 734.11 - lr: 0.010000
2023-02-06 08:33:48,590 epoch 18 - iter 91/133 - loss 0.01021375 - samples/sec: 743.50 - lr: 0.010000
2023-02-06 08:33:50,106 epoch 18 - iter 104/133 - loss 0.01019670 - samples/sec: 745.19 - lr: 0.010000
2023-02-06 08:33:51,371 epoch 18 - iter 117/133 - loss 0.01023334 - samples/sec: 762.98 - lr: 0.010000
2023-02-06 08:33:52,871 epoch 18 - iter 130/133 - loss 0.01022335 - samples/sec: 764.73 - lr: 0.010000
2023-02-06 08:33:53,154 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:53,156 EPOCH 18 done: loss 0.0102 - lr 0.010000
2023-02-06 08:33:55,611 Evaluating as a multi-label problem: False
2023-02-06 08:33:55,629 DEV : loss 0.010320308618247509 - f1-score (micro avg)  0.6313
2023-02-06 08:33:55,977 BAD EPOCHS (no improvement): 0
2023-02-06 08:33:55,987 saving best model
2023-02-06 08:33:56,065 ----------------------------------------------------------------------------------------------------
2023-02-06 08:33:57,370 epoch 19 - iter 13/133 - loss 0.01029091 - samples/sec: 738.06 - lr: 0.010000
2023-02-06 08:33:58,890 epoch 19 - iter 26/133 - loss 0.01023014 - samples/sec: 750.92 - lr: 0.010000
2023-02-06 08:34:00,150 epoch 19 - iter 39/133 - loss 0.01020864 - samples/sec: 762.09 - lr: 0.010000
2023-02-06 08:34:01,681 epoch 19 - iter 52/133 - loss 0.01024439 - samples/sec: 741.73 - lr: 0.010000
2023-02-06 08:34:02,906 epoch 19 - iter 65/133 - loss 0.01023181 - samples/sec: 790.97 - lr: 0.010000
2023-02-06 08:34:04,433 epoch 19 - iter 78/133 - loss 0.01022412 - samples/sec: 613.31 - lr: 0.010000
2023-02-06 08:34:05,780 epoch 19 - iter 91/133 - loss 0.01019693 - samples/sec: 712.09 - lr: 0.010000
2023-02-06 08:34:07,187 epoch 19 - iter 104/133 - loss 0.01022081 - samples/sec: 673.66 - lr: 0.010000
2023-02-06 08:34:08,668 epoch 19 - iter 117/133 - loss 0.01022890 - samples/sec: 785.83 - lr: 0.010000
2023-02-06 08:34:09,910 epoch 19 - iter 130/133 - loss 0.01020723 - samples/sec: 777.53 - lr: 0.010000
2023-02-06 08:34:10,200 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:10,207 EPOCH 19 done: loss 0.0102 - lr 0.010000
2023-02-06 08:34:12,679 Evaluating as a multi-label problem: False
2023-02-06 08:34:12,700 DEV : loss 0.010296817868947983 - f1-score (micro avg)  0.64
2023-02-06 08:34:13,311 BAD EPOCHS (no improvement): 0
2023-02-06 08:34:13,316 saving best model
2023-02-06 08:34:13,390 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:14,758 epoch 20 - iter 13/133 - loss 0.01021864 - samples/sec: 703.40 - lr: 0.010000
2023-02-06 08:34:16,077 epoch 20 - iter 26/133 - loss 0.01028711 - samples/sec: 725.84 - lr: 0.010000
2023-02-06 08:34:17,634 epoch 20 - iter 39/133 - loss 0.01022971 - samples/sec: 745.04 - lr: 0.010000
2023-02-06 08:34:18,921 epoch 20 - iter 52/133 - loss 0.01026002 - samples/sec: 750.90 - lr: 0.010000
2023-02-06 08:34:20,467 epoch 20 - iter 65/133 - loss 0.01026057 - samples/sec: 602.55 - lr: 0.010000
2023-02-06 08:34:21,753 epoch 20 - iter 78/133 - loss 0.01026928 - samples/sec: 751.33 - lr: 0.010000
2023-02-06 08:34:23,292 epoch 20 - iter 91/133 - loss 0.01025076 - samples/sec: 736.16 - lr: 0.010000
2023-02-06 08:34:24,579 epoch 20 - iter 104/133 - loss 0.01020640 - samples/sec: 746.82 - lr: 0.010000
2023-02-06 08:34:25,909 epoch 20 - iter 117/133 - loss 0.01019171 - samples/sec: 719.60 - lr: 0.010000
2023-02-06 08:34:27,438 epoch 20 - iter 130/133 - loss 0.01017225 - samples/sec: 738.30 - lr: 0.010000
2023-02-06 08:34:27,739 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:27,744 EPOCH 20 done: loss 0.0102 - lr 0.010000
2023-02-06 08:34:30,257 Evaluating as a multi-label problem: False
2023-02-06 08:34:30,274 DEV : loss 0.010265583172440529 - f1-score (micro avg)  0.6413
2023-02-06 08:34:30,654 BAD EPOCHS (no improvement): 0
2023-02-06 08:34:30,660 saving best model
2023-02-06 08:34:30,739 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:32,330 epoch 21 - iter 13/133 - loss 0.01014521 - samples/sec: 710.15 - lr: 0.010000
2023-02-06 08:34:33,625 epoch 21 - iter 26/133 - loss 0.01018544 - samples/sec: 743.43 - lr: 0.010000
2023-02-06 08:34:35,128 epoch 21 - iter 39/133 - loss 0.01014949 - samples/sec: 755.29 - lr: 0.010000
2023-02-06 08:34:36,388 epoch 21 - iter 52/133 - loss 0.01015701 - samples/sec: 758.49 - lr: 0.010000
2023-02-06 08:34:37,625 epoch 21 - iter 65/133 - loss 0.01014343 - samples/sec: 781.03 - lr: 0.010000
2023-02-06 08:34:39,127 epoch 21 - iter 78/133 - loss 0.01011384 - samples/sec: 758.01 - lr: 0.010000
2023-02-06 08:34:40,496 epoch 21 - iter 91/133 - loss 0.01012422 - samples/sec: 690.35 - lr: 0.010000
2023-02-06 08:34:42,023 epoch 21 - iter 104/133 - loss 0.01014283 - samples/sec: 761.00 - lr: 0.010000
2023-02-06 08:34:43,304 epoch 21 - iter 117/133 - loss 0.01013442 - samples/sec: 748.07 - lr: 0.010000
2023-02-06 08:34:44,869 epoch 21 - iter 130/133 - loss 0.01012862 - samples/sec: 730.48 - lr: 0.010000
2023-02-06 08:34:45,169 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:45,175 EPOCH 21 done: loss 0.0101 - lr 0.010000
2023-02-06 08:34:47,720 Evaluating as a multi-label problem: False
2023-02-06 08:34:47,743 DEV : loss 0.010291438549757004 - f1-score (micro avg)  0.6373
2023-02-06 08:34:48,104 BAD EPOCHS (no improvement): 1
2023-02-06 08:34:48,124 ----------------------------------------------------------------------------------------------------
2023-02-06 08:34:49,559 epoch 22 - iter 13/133 - loss 0.00996709 - samples/sec: 657.64 - lr: 0.010000
2023-02-06 08:34:51,095 epoch 22 - iter 26/133 - loss 0.01001206 - samples/sec: 739.55 - lr: 0.010000
2023-02-06 08:34:52,347 epoch 22 - iter 39/133 - loss 0.01004049 - samples/sec: 767.48 - lr: 0.010000
2023-02-06 08:34:53,920 epoch 22 - iter 52/133 - loss 0.01005317 - samples/sec: 722.99 - lr: 0.010000
2023-02-06 08:34:55,196 epoch 22 - iter 65/133 - loss 0.01010477 - samples/sec: 747.76 - lr: 0.010000
2023-02-06 08:34:56,737 epoch 22 - iter 78/133 - loss 0.01010079 - samples/sec: 608.24 - lr: 0.010000
2023-02-06 08:34:58,059 epoch 22 - iter 91/133 - loss 0.01013500 - samples/sec: 727.48 - lr: 0.010000
2023-02-06 08:34:59,404 epoch 22 - iter 104/133 - loss 0.01013831 - samples/sec: 712.32 - lr: 0.010000
2023-02-06 08:35:00,950 epoch 22 - iter 117/133 - loss 0.01014236 - samples/sec: 741.47 - lr: 0.010000
2023-02-06 08:35:02,201 epoch 22 - iter 130/133 - loss 0.01013044 - samples/sec: 762.45 - lr: 0.010000
2023-02-06 08:35:02,483 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:02,485 EPOCH 22 done: loss 0.0101 - lr 0.010000
2023-02-06 08:35:04,928 Evaluating as a multi-label problem: False
2023-02-06 08:35:04,944 DEV : loss 0.010224188677966595 - f1-score (micro avg)  0.6413
2023-02-06 08:35:05,519 BAD EPOCHS (no improvement): 0
2023-02-06 08:35:05,528 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:06,856 epoch 23 - iter 13/133 - loss 0.01005563 - samples/sec: 720.28 - lr: 0.010000
2023-02-06 08:35:08,375 epoch 23 - iter 26/133 - loss 0.01002448 - samples/sec: 616.23 - lr: 0.010000
2023-02-06 08:35:09,661 epoch 23 - iter 39/133 - loss 0.01015809 - samples/sec: 739.97 - lr: 0.010000
2023-02-06 08:35:10,916 epoch 23 - iter 52/133 - loss 0.01016788 - samples/sec: 764.29 - lr: 0.010000
2023-02-06 08:35:12,458 epoch 23 - iter 65/133 - loss 0.01009484 - samples/sec: 739.26 - lr: 0.010000
2023-02-06 08:35:13,705 epoch 23 - iter 78/133 - loss 0.01008399 - samples/sec: 770.62 - lr: 0.010000
2023-02-06 08:35:15,187 epoch 23 - iter 91/133 - loss 0.01007382 - samples/sec: 769.82 - lr: 0.010000
2023-02-06 08:35:16,466 epoch 23 - iter 104/133 - loss 0.01006859 - samples/sec: 747.87 - lr: 0.010000
2023-02-06 08:35:18,033 epoch 23 - iter 117/133 - loss 0.01007952 - samples/sec: 718.89 - lr: 0.010000
2023-02-06 08:35:19,305 epoch 23 - iter 130/133 - loss 0.01007932 - samples/sec: 752.56 - lr: 0.010000
2023-02-06 08:35:19,591 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:19,593 EPOCH 23 done: loss 0.0101 - lr 0.010000
2023-02-06 08:35:22,099 Evaluating as a multi-label problem: False
2023-02-06 08:35:22,116 DEV : loss 0.010190014727413654 - f1-score (micro avg)  0.644
2023-02-06 08:35:22,476 BAD EPOCHS (no improvement): 0
2023-02-06 08:35:22,484 saving best model
2023-02-06 08:35:22,562 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:24,159 epoch 24 - iter 13/133 - loss 0.01033764 - samples/sec: 707.79 - lr: 0.010000
2023-02-06 08:35:25,436 epoch 24 - iter 26/133 - loss 0.01039924 - samples/sec: 753.06 - lr: 0.010000
2023-02-06 08:35:26,953 epoch 24 - iter 39/133 - loss 0.01019226 - samples/sec: 761.33 - lr: 0.010000
2023-02-06 08:35:28,217 epoch 24 - iter 52/133 - loss 0.01010421 - samples/sec: 759.92 - lr: 0.010000
2023-02-06 08:35:29,503 epoch 24 - iter 65/133 - loss 0.01008049 - samples/sec: 751.64 - lr: 0.010000
2023-02-06 08:35:31,044 epoch 24 - iter 78/133 - loss 0.01005312 - samples/sec: 735.12 - lr: 0.010000
2023-02-06 08:35:32,319 epoch 24 - iter 91/133 - loss 0.01005299 - samples/sec: 759.51 - lr: 0.010000
2023-02-06 08:35:33,822 epoch 24 - iter 104/133 - loss 0.01003790 - samples/sec: 760.19 - lr: 0.010000
2023-02-06 08:35:35,097 epoch 24 - iter 117/133 - loss 0.01003322 - samples/sec: 752.83 - lr: 0.010000
2023-02-06 08:35:36,616 epoch 24 - iter 130/133 - loss 0.01004728 - samples/sec: 748.80 - lr: 0.010000
2023-02-06 08:35:36,902 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:36,903 EPOCH 24 done: loss 0.0101 - lr 0.010000
2023-02-06 08:35:39,354 Evaluating as a multi-label problem: False
2023-02-06 08:35:39,371 DEV : loss 0.010158772580325603 - f1-score (micro avg)  0.6487
2023-02-06 08:35:39,720 BAD EPOCHS (no improvement): 0
2023-02-06 08:35:39,727 saving best model
2023-02-06 08:35:39,802 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:41,120 epoch 25 - iter 13/133 - loss 0.00981064 - samples/sec: 733.84 - lr: 0.010000
2023-02-06 08:35:42,650 epoch 25 - iter 26/133 - loss 0.00984903 - samples/sec: 745.83 - lr: 0.010000
2023-02-06 08:35:43,929 epoch 25 - iter 39/133 - loss 0.00991121 - samples/sec: 755.02 - lr: 0.010000
2023-02-06 08:35:45,467 epoch 25 - iter 52/133 - loss 0.01000175 - samples/sec: 740.89 - lr: 0.010000
2023-02-06 08:35:46,750 epoch 25 - iter 65/133 - loss 0.01000978 - samples/sec: 746.54 - lr: 0.010000
2023-02-06 08:35:48,252 epoch 25 - iter 78/133 - loss 0.01001222 - samples/sec: 770.84 - lr: 0.010000
2023-02-06 08:35:49,515 epoch 25 - iter 91/133 - loss 0.01000999 - samples/sec: 759.97 - lr: 0.010000
2023-02-06 08:35:51,036 epoch 25 - iter 104/133 - loss 0.00999015 - samples/sec: 616.30 - lr: 0.010000
2023-02-06 08:35:52,290 epoch 25 - iter 117/133 - loss 0.00998576 - samples/sec: 770.57 - lr: 0.010000
2023-02-06 08:35:53,576 epoch 25 - iter 130/133 - loss 0.00998035 - samples/sec: 745.63 - lr: 0.010000
2023-02-06 08:35:54,094 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:54,096 EPOCH 25 done: loss 0.0100 - lr 0.010000
2023-02-06 08:35:56,283 Evaluating as a multi-label problem: False
2023-02-06 08:35:56,301 DEV : loss 0.010137598030269146 - f1-score (micro avg)  0.6507
2023-02-06 08:35:56,866 BAD EPOCHS (no improvement): 0
2023-02-06 08:35:56,874 saving best model
2023-02-06 08:35:56,952 ----------------------------------------------------------------------------------------------------
2023-02-06 08:35:58,254 epoch 26 - iter 13/133 - loss 0.00981231 - samples/sec: 744.59 - lr: 0.010000
2023-02-06 08:35:59,716 epoch 26 - iter 26/133 - loss 0.00985499 - samples/sec: 779.93 - lr: 0.010000
2023-02-06 08:36:00,934 epoch 26 - iter 39/133 - loss 0.00986997 - samples/sec: 794.56 - lr: 0.010000
2023-02-06 08:36:02,450 epoch 26 - iter 52/133 - loss 0.00985057 - samples/sec: 750.90 - lr: 0.010000
2023-02-06 08:36:03,705 epoch 26 - iter 65/133 - loss 0.00991802 - samples/sec: 770.23 - lr: 0.010000
2023-02-06 08:36:04,983 epoch 26 - iter 78/133 - loss 0.00995249 - samples/sec: 752.52 - lr: 0.010000
2023-02-06 08:36:06,610 epoch 26 - iter 91/133 - loss 0.00994670 - samples/sec: 695.85 - lr: 0.010000
2023-02-06 08:36:07,854 epoch 26 - iter 104/133 - loss 0.00992897 - samples/sec: 766.60 - lr: 0.010000
2023-02-06 08:36:09,332 epoch 26 - iter 117/133 - loss 0.00995839 - samples/sec: 771.15 - lr: 0.010000
2023-02-06 08:36:10,585 epoch 26 - iter 130/133 - loss 0.00997863 - samples/sec: 763.77 - lr: 0.010000
2023-02-06 08:36:10,866 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:10,871 EPOCH 26 done: loss 0.0100 - lr 0.010000
2023-02-06 08:36:13,272 Evaluating as a multi-label problem: False
2023-02-06 08:36:13,289 DEV : loss 0.010154918767511845 - f1-score (micro avg)  0.6453
2023-02-06 08:36:13,875 BAD EPOCHS (no improvement): 1
2023-02-06 08:36:13,882 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:15,140 epoch 27 - iter 13/133 - loss 0.00982359 - samples/sec: 758.36 - lr: 0.010000
2023-02-06 08:36:16,393 epoch 27 - iter 26/133 - loss 0.00995263 - samples/sec: 765.88 - lr: 0.010000
2023-02-06 08:36:17,990 epoch 27 - iter 39/133 - loss 0.00994264 - samples/sec: 724.70 - lr: 0.010000
2023-02-06 08:36:19,734 epoch 27 - iter 52/133 - loss 0.00990056 - samples/sec: 584.51 - lr: 0.010000
2023-02-06 08:36:21,871 epoch 27 - iter 65/133 - loss 0.00989842 - samples/sec: 547.01 - lr: 0.010000
2023-02-06 08:36:23,233 epoch 27 - iter 78/133 - loss 0.00990904 - samples/sec: 698.41 - lr: 0.010000
2023-02-06 08:36:24,750 epoch 27 - iter 91/133 - loss 0.00997472 - samples/sec: 741.54 - lr: 0.010000
2023-02-06 08:36:25,989 epoch 27 - iter 104/133 - loss 0.00996257 - samples/sec: 774.43 - lr: 0.010000
2023-02-06 08:36:27,217 epoch 27 - iter 117/133 - loss 0.00995437 - samples/sec: 781.46 - lr: 0.010000
2023-02-06 08:36:28,665 epoch 27 - iter 130/133 - loss 0.00995591 - samples/sec: 787.10 - lr: 0.010000
2023-02-06 08:36:28,936 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:28,941 EPOCH 27 done: loss 0.0100 - lr 0.010000
2023-02-06 08:36:31,339 Evaluating as a multi-label problem: False
2023-02-06 08:36:31,357 DEV : loss 0.010085121728479862 - f1-score (micro avg)  0.6567
2023-02-06 08:36:31,722 BAD EPOCHS (no improvement): 0
2023-02-06 08:36:31,732 saving best model
2023-02-06 08:36:31,827 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:33,404 epoch 28 - iter 13/133 - loss 0.01020064 - samples/sec: 708.07 - lr: 0.010000
2023-02-06 08:36:34,623 epoch 28 - iter 26/133 - loss 0.01013967 - samples/sec: 786.34 - lr: 0.010000
2023-02-06 08:36:36,131 epoch 28 - iter 39/133 - loss 0.01003563 - samples/sec: 758.40 - lr: 0.010000
2023-02-06 08:36:37,442 epoch 28 - iter 52/133 - loss 0.00998388 - samples/sec: 730.28 - lr: 0.010000
2023-02-06 08:36:38,722 epoch 28 - iter 65/133 - loss 0.00999888 - samples/sec: 748.06 - lr: 0.010000
2023-02-06 08:36:40,168 epoch 28 - iter 78/133 - loss 0.01003122 - samples/sec: 786.57 - lr: 0.010000
2023-02-06 08:36:41,456 epoch 28 - iter 91/133 - loss 0.01000777 - samples/sec: 744.48 - lr: 0.010000
2023-02-06 08:36:42,925 epoch 28 - iter 104/133 - loss 0.00998209 - samples/sec: 783.40 - lr: 0.010000
2023-02-06 08:36:44,160 epoch 28 - iter 117/133 - loss 0.00999413 - samples/sec: 777.17 - lr: 0.010000
2023-02-06 08:36:45,652 epoch 28 - iter 130/133 - loss 0.00996400 - samples/sec: 627.73 - lr: 0.010000
2023-02-06 08:36:45,936 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:45,937 EPOCH 28 done: loss 0.0100 - lr 0.010000
2023-02-06 08:36:48,370 Evaluating as a multi-label problem: False
2023-02-06 08:36:48,390 DEV : loss 0.010063917376101017 - f1-score (micro avg)  0.6567
2023-02-06 08:36:48,774 BAD EPOCHS (no improvement): 0
2023-02-06 08:36:48,783 ----------------------------------------------------------------------------------------------------
2023-02-06 08:36:50,112 epoch 29 - iter 13/133 - loss 0.01007614 - samples/sec: 715.39 - lr: 0.010000
2023-02-06 08:36:51,579 epoch 29 - iter 26/133 - loss 0.00997458 - samples/sec: 766.42 - lr: 0.010000
2023-02-06 08:36:52,814 epoch 29 - iter 39/133 - loss 0.00989408 - samples/sec: 781.38 - lr: 0.010000
2023-02-06 08:36:54,264 epoch 29 - iter 52/133 - loss 0.00989758 - samples/sec: 784.84 - lr: 0.010000
2023-02-06 08:36:55,532 epoch 29 - iter 65/133 - loss 0.00991688 - samples/sec: 756.52 - lr: 0.010000
2023-02-06 08:36:57,026 epoch 29 - iter 78/133 - loss 0.00991818 - samples/sec: 758.17 - lr: 0.010000
2023-02-06 08:36:58,307 epoch 29 - iter 91/133 - loss 0.00989315 - samples/sec: 756.41 - lr: 0.010000
2023-02-06 08:36:59,562 epoch 29 - iter 104/133 - loss 0.00987888 - samples/sec: 767.41 - lr: 0.010000
2023-02-06 08:37:01,023 epoch 29 - iter 117/133 - loss 0.00989864 - samples/sec: 642.50 - lr: 0.010000
2023-02-06 08:37:02,264 epoch 29 - iter 130/133 - loss 0.00992279 - samples/sec: 772.60 - lr: 0.010000
2023-02-06 08:37:02,549 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:02,550 EPOCH 29 done: loss 0.0099 - lr 0.010000
2023-02-06 08:37:05,071 Evaluating as a multi-label problem: False
2023-02-06 08:37:05,088 DEV : loss 0.010063448920845985 - f1-score (micro avg)  0.6533
2023-02-06 08:37:05,677 BAD EPOCHS (no improvement): 1
2023-02-06 08:37:05,684 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:06,997 epoch 30 - iter 13/133 - loss 0.01003949 - samples/sec: 732.41 - lr: 0.010000
2023-02-06 08:37:08,331 epoch 30 - iter 26/133 - loss 0.01015033 - samples/sec: 719.55 - lr: 0.010000
2023-02-06 08:37:09,806 epoch 30 - iter 39/133 - loss 0.01011311 - samples/sec: 636.91 - lr: 0.010000
2023-02-06 08:37:11,050 epoch 30 - iter 52/133 - loss 0.00997797 - samples/sec: 769.41 - lr: 0.010000
2023-02-06 08:37:12,552 epoch 30 - iter 65/133 - loss 0.00995678 - samples/sec: 763.55 - lr: 0.010000
2023-02-06 08:37:13,798 epoch 30 - iter 78/133 - loss 0.00991438 - samples/sec: 771.97 - lr: 0.010000
2023-02-06 08:37:15,297 epoch 30 - iter 91/133 - loss 0.00988681 - samples/sec: 753.64 - lr: 0.010000
2023-02-06 08:37:16,543 epoch 30 - iter 104/133 - loss 0.00986966 - samples/sec: 770.92 - lr: 0.010000
2023-02-06 08:37:17,762 epoch 30 - iter 117/133 - loss 0.00987746 - samples/sec: 785.58 - lr: 0.010000
2023-02-06 08:37:19,280 epoch 30 - iter 130/133 - loss 0.00989263 - samples/sec: 738.22 - lr: 0.010000
2023-02-06 08:37:19,569 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:19,571 EPOCH 30 done: loss 0.0099 - lr 0.010000
2023-02-06 08:37:22,147 Evaluating as a multi-label problem: False
2023-02-06 08:37:22,165 DEV : loss 0.010032457299530506 - f1-score (micro avg)  0.6627
2023-02-06 08:37:22,507 BAD EPOCHS (no improvement): 0
2023-02-06 08:37:22,515 saving best model
2023-02-06 08:37:22,593 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:24,034 epoch 31 - iter 13/133 - loss 0.00986991 - samples/sec: 795.89 - lr: 0.010000
2023-02-06 08:37:25,350 epoch 31 - iter 26/133 - loss 0.00980032 - samples/sec: 722.12 - lr: 0.010000
2023-02-06 08:37:26,804 epoch 31 - iter 39/133 - loss 0.00981070 - samples/sec: 788.11 - lr: 0.010000
2023-02-06 08:37:28,032 epoch 31 - iter 52/133 - loss 0.00978956 - samples/sec: 782.72 - lr: 0.010000
2023-02-06 08:37:29,262 epoch 31 - iter 65/133 - loss 0.00985205 - samples/sec: 776.53 - lr: 0.010000
2023-02-06 08:37:30,801 epoch 31 - iter 78/133 - loss 0.00986377 - samples/sec: 734.35 - lr: 0.010000
2023-02-06 08:37:32,105 epoch 31 - iter 91/133 - loss 0.00986039 - samples/sec: 737.43 - lr: 0.010000
2023-02-06 08:37:33,586 epoch 31 - iter 104/133 - loss 0.00986586 - samples/sec: 766.82 - lr: 0.010000
2023-02-06 08:37:34,867 epoch 31 - iter 117/133 - loss 0.00988794 - samples/sec: 748.41 - lr: 0.010000
2023-02-06 08:37:36,405 epoch 31 - iter 130/133 - loss 0.00986537 - samples/sec: 732.70 - lr: 0.010000
2023-02-06 08:37:36,707 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:36,713 EPOCH 31 done: loss 0.0099 - lr 0.010000
2023-02-06 08:37:39,148 Evaluating as a multi-label problem: False
2023-02-06 08:37:39,165 DEV : loss 0.010030188597738743 - f1-score (micro avg)  0.6507
2023-02-06 08:37:39,503 BAD EPOCHS (no improvement): 1
2023-02-06 08:37:39,509 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:40,819 epoch 32 - iter 13/133 - loss 0.00972815 - samples/sec: 729.87 - lr: 0.010000
2023-02-06 08:37:42,293 epoch 32 - iter 26/133 - loss 0.00993442 - samples/sec: 767.42 - lr: 0.010000
2023-02-06 08:37:43,527 epoch 32 - iter 39/133 - loss 0.00989563 - samples/sec: 777.81 - lr: 0.010000
2023-02-06 08:37:45,030 epoch 32 - iter 52/133 - loss 0.00987169 - samples/sec: 762.50 - lr: 0.010000
2023-02-06 08:37:46,312 epoch 32 - iter 65/133 - loss 0.00982198 - samples/sec: 744.56 - lr: 0.010000
2023-02-06 08:37:47,772 epoch 32 - iter 78/133 - loss 0.00984166 - samples/sec: 787.72 - lr: 0.010000
2023-02-06 08:37:49,052 epoch 32 - iter 91/133 - loss 0.00985835 - samples/sec: 753.47 - lr: 0.010000
2023-02-06 08:37:50,603 epoch 32 - iter 104/133 - loss 0.00985180 - samples/sec: 729.35 - lr: 0.010000
2023-02-06 08:37:51,833 epoch 32 - iter 117/133 - loss 0.00984472 - samples/sec: 780.52 - lr: 0.010000
2023-02-06 08:37:53,083 epoch 32 - iter 130/133 - loss 0.00982573 - samples/sec: 764.01 - lr: 0.010000
2023-02-06 08:37:53,402 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:53,407 EPOCH 32 done: loss 0.0098 - lr 0.010000
2023-02-06 08:37:55,865 Evaluating as a multi-label problem: False
2023-02-06 08:37:55,882 DEV : loss 0.009983059018850327 - f1-score (micro avg)  0.662
2023-02-06 08:37:56,448 BAD EPOCHS (no improvement): 2
2023-02-06 08:37:56,456 ----------------------------------------------------------------------------------------------------
2023-02-06 08:37:57,753 epoch 33 - iter 13/133 - loss 0.00986220 - samples/sec: 736.25 - lr: 0.010000
2023-02-06 08:37:59,189 epoch 33 - iter 26/133 - loss 0.00975062 - samples/sec: 802.53 - lr: 0.010000
2023-02-06 08:38:00,474 epoch 33 - iter 39/133 - loss 0.00986705 - samples/sec: 745.66 - lr: 0.010000
2023-02-06 08:38:01,738 epoch 33 - iter 52/133 - loss 0.00984679 - samples/sec: 754.60 - lr: 0.010000
2023-02-06 08:38:03,179 epoch 33 - iter 65/133 - loss 0.00978551 - samples/sec: 794.68 - lr: 0.010000
2023-02-06 08:38:04,429 epoch 33 - iter 78/133 - loss 0.00975048 - samples/sec: 765.26 - lr: 0.010000
2023-02-06 08:38:05,913 epoch 33 - iter 91/133 - loss 0.00976211 - samples/sec: 634.13 - lr: 0.010000
2023-02-06 08:38:07,200 epoch 33 - iter 104/133 - loss 0.00978878 - samples/sec: 740.16 - lr: 0.010000
2023-02-06 08:38:08,709 epoch 33 - iter 117/133 - loss 0.00982863 - samples/sec: 747.73 - lr: 0.010000
2023-02-06 08:38:10,026 epoch 33 - iter 130/133 - loss 0.00987417 - samples/sec: 722.74 - lr: 0.010000
2023-02-06 08:38:10,302 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:10,303 EPOCH 33 done: loss 0.0099 - lr 0.010000
2023-02-06 08:38:12,844 Evaluating as a multi-label problem: False
2023-02-06 08:38:12,862 DEV : loss 0.009982590563595295 - f1-score (micro avg)  0.66
2023-02-06 08:38:13,436 BAD EPOCHS (no improvement): 3
2023-02-06 08:38:13,443 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:14,816 epoch 34 - iter 13/133 - loss 0.00992892 - samples/sec: 690.92 - lr: 0.010000
2023-02-06 08:38:16,115 epoch 34 - iter 26/133 - loss 0.00987621 - samples/sec: 736.73 - lr: 0.010000
2023-02-06 08:38:17,696 epoch 34 - iter 39/133 - loss 0.00987597 - samples/sec: 734.20 - lr: 0.010000
2023-02-06 08:38:18,994 epoch 34 - iter 52/133 - loss 0.00991974 - samples/sec: 737.16 - lr: 0.010000
2023-02-06 08:38:20,513 epoch 34 - iter 65/133 - loss 0.00990955 - samples/sec: 619.03 - lr: 0.010000
2023-02-06 08:38:21,810 epoch 34 - iter 78/133 - loss 0.00985254 - samples/sec: 739.38 - lr: 0.010000
2023-02-06 08:38:23,273 epoch 34 - iter 91/133 - loss 0.00982011 - samples/sec: 779.25 - lr: 0.010000
2023-02-06 08:38:24,511 epoch 34 - iter 104/133 - loss 0.00980783 - samples/sec: 773.71 - lr: 0.010000
2023-02-06 08:38:25,739 epoch 34 - iter 117/133 - loss 0.00976338 - samples/sec: 780.95 - lr: 0.010000
2023-02-06 08:38:27,199 epoch 34 - iter 130/133 - loss 0.00978109 - samples/sec: 777.03 - lr: 0.010000
2023-02-06 08:38:27,468 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:27,472 EPOCH 34 done: loss 0.0098 - lr 0.010000
2023-02-06 08:38:29,920 Evaluating as a multi-label problem: False
2023-02-06 08:38:29,937 DEV : loss 0.009954135864973068 - f1-score (micro avg)  0.6647
2023-02-06 08:38:30,291 BAD EPOCHS (no improvement): 0
2023-02-06 08:38:30,298 saving best model
2023-02-06 08:38:30,373 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:31,950 epoch 35 - iter 13/133 - loss 0.00960910 - samples/sec: 708.10 - lr: 0.010000
2023-02-06 08:38:33,281 epoch 35 - iter 26/133 - loss 0.00965913 - samples/sec: 722.62 - lr: 0.010000
2023-02-06 08:38:34,761 epoch 35 - iter 39/133 - loss 0.00961002 - samples/sec: 636.49 - lr: 0.010000
2023-02-06 08:38:36,130 epoch 35 - iter 52/133 - loss 0.00968180 - samples/sec: 698.70 - lr: 0.010000
2023-02-06 08:38:37,349 epoch 35 - iter 65/133 - loss 0.00970834 - samples/sec: 786.80 - lr: 0.010000
2023-02-06 08:38:38,815 epoch 35 - iter 78/133 - loss 0.00976811 - samples/sec: 770.86 - lr: 0.010000
2023-02-06 08:38:40,015 epoch 35 - iter 91/133 - loss 0.00977029 - samples/sec: 805.65 - lr: 0.010000
2023-02-06 08:38:41,480 epoch 35 - iter 104/133 - loss 0.00976745 - samples/sec: 773.86 - lr: 0.010000
2023-02-06 08:38:42,701 epoch 35 - iter 117/133 - loss 0.00977891 - samples/sec: 787.95 - lr: 0.010000
2023-02-06 08:38:44,189 epoch 35 - iter 130/133 - loss 0.00974154 - samples/sec: 759.68 - lr: 0.010000
2023-02-06 08:38:44,483 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:44,489 EPOCH 35 done: loss 0.0098 - lr 0.010000
2023-02-06 08:38:46,913 Evaluating as a multi-label problem: False
2023-02-06 08:38:46,930 DEV : loss 0.009962853975594044 - f1-score (micro avg)  0.658
2023-02-06 08:38:47,267 BAD EPOCHS (no improvement): 1
2023-02-06 08:38:47,361 ----------------------------------------------------------------------------------------------------
2023-02-06 08:38:47,365 loading file /content/drive/MyDrive/Colab Notebooks/models/flair-sentiment-classifier/best-model.pt
2023-02-06 08:38:48,126 Evaluating as a multi-label problem: False
2023-02-06 08:38:48,139 0.6462	0.6462	0.6462	0.6462
2023-02-06 08:38:48,144 
Results:
- F-score (micro) 0.6462
- F-score (macro) 0.6426
- Accuracy 0.6462

By class:
              precision    recall  f1-score   support

           1     0.6291    0.7363    0.6785       182
           0     0.6712    0.5537    0.6068       177

    accuracy                         0.6462       359
   macro avg     0.6502    0.6450    0.6426       359
weighted avg     0.6499    0.6462    0.6431       359

2023-02-06 08:38:48,150 ----------------------------------------------------------------------------------------------------