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hubert-classifier-aug-fold-2

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metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-classifier-aug-fold-2
    results: []

hubert-classifier-aug-fold-2

This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7842
  • Accuracy: 0.8652
  • Precision: 0.8801
  • Recall: 0.8652
  • F1: 0.8644
  • Binary: 0.9058

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.24 50 4.4178 0.0232 0.0049 0.0232 0.0073 0.1830
No log 0.48 100 4.3135 0.0787 0.0707 0.0787 0.0397 0.2956
No log 0.72 150 3.9934 0.1124 0.0716 0.1124 0.0606 0.3658
No log 0.96 200 3.6655 0.1506 0.1302 0.1506 0.0909 0.3913
4.2392 1.2 250 3.3326 0.2262 0.1653 0.2262 0.1582 0.4506
4.2392 1.44 300 2.9883 0.3401 0.2439 0.3401 0.2525 0.5362
4.2392 1.68 350 2.6683 0.3700 0.3123 0.3700 0.2952 0.5566
4.2392 1.92 400 2.4107 0.4052 0.3684 0.4052 0.3301 0.5785
3.1531 2.16 450 2.0225 0.4966 0.4535 0.4966 0.4251 0.6475
3.1531 2.4 500 1.7604 0.5618 0.5154 0.5618 0.4974 0.6929
3.1531 2.63 550 1.5309 0.6090 0.6103 0.6090 0.5608 0.7267
3.1531 2.87 600 1.3523 0.6599 0.6317 0.6599 0.6202 0.7609
2.1046 3.11 650 1.2380 0.6742 0.6824 0.6742 0.6423 0.7706
2.1046 3.35 700 1.1780 0.7004 0.7396 0.7004 0.6836 0.7886
2.1046 3.59 750 0.9850 0.7483 0.7666 0.7483 0.7323 0.8229
2.1046 3.83 800 0.9012 0.7715 0.7962 0.7715 0.7633 0.8399
1.4918 4.07 850 0.8522 0.7700 0.7916 0.7700 0.7603 0.8387
1.4918 4.31 900 0.7412 0.7948 0.8061 0.7948 0.7856 0.8564
1.4918 4.55 950 0.7650 0.8015 0.8137 0.8015 0.7906 0.8622
1.4918 4.79 1000 0.7356 0.8082 0.8232 0.8082 0.8019 0.8652
1.1749 5.03 1050 0.6688 0.8097 0.8209 0.8097 0.8051 0.8678
1.1749 5.27 1100 0.6470 0.8082 0.8308 0.8082 0.8040 0.8661
1.1749 5.51 1150 0.6289 0.8262 0.8388 0.8262 0.8228 0.8781
1.1749 5.75 1200 0.7243 0.8045 0.8206 0.8045 0.7992 0.8633
1.1749 5.99 1250 0.5897 0.8367 0.8500 0.8367 0.8356 0.8870
0.9784 6.23 1300 0.6412 0.8255 0.8397 0.8255 0.8249 0.8789
0.9784 6.47 1350 0.5967 0.8367 0.8462 0.8367 0.8331 0.8860
0.9784 6.71 1400 0.6342 0.8165 0.8356 0.8165 0.8142 0.8724
0.9784 6.95 1450 0.6780 0.8232 0.8386 0.8232 0.8207 0.8776
0.8541 7.19 1500 0.6008 0.8404 0.8532 0.8404 0.8382 0.8892
0.8541 7.43 1550 0.5507 0.8524 0.8615 0.8524 0.8505 0.8964
0.8541 7.66 1600 0.6125 0.8292 0.8435 0.8292 0.8267 0.8810
0.8541 7.9 1650 0.5411 0.8577 0.8701 0.8577 0.8575 0.9013
0.7624 8.14 1700 0.5236 0.8614 0.8693 0.8614 0.8603 0.9044
0.7624 8.38 1750 0.5491 0.8472 0.8562 0.8472 0.8450 0.8948
0.7624 8.62 1800 0.5398 0.8614 0.8686 0.8614 0.8583 0.9042
0.7624 8.86 1850 0.5902 0.8509 0.8627 0.8509 0.8497 0.8969
0.6922 9.1 1900 0.5808 0.8547 0.8686 0.8547 0.8543 0.8989
0.6922 9.34 1950 0.6115 0.8390 0.8513 0.8390 0.8368 0.8886
0.6922 9.58 2000 0.5479 0.8667 0.8759 0.8667 0.8645 0.9073
0.6922 9.82 2050 0.6070 0.8487 0.8610 0.8487 0.8476 0.8953
0.6251 10.06 2100 0.5700 0.8592 0.8724 0.8592 0.8589 0.9013
0.6251 10.3 2150 0.5362 0.8742 0.8846 0.8742 0.8742 0.9122
0.6251 10.54 2200 0.6288 0.8494 0.8669 0.8494 0.8499 0.8952
0.6251 10.78 2250 0.5886 0.8569 0.8692 0.8569 0.8555 0.8995
0.6044 11.02 2300 0.6453 0.8577 0.8712 0.8577 0.8556 0.9021
0.6044 11.26 2350 0.6322 0.8479 0.8609 0.8479 0.8461 0.8950
0.6044 11.5 2400 0.5856 0.8629 0.8713 0.8629 0.8619 0.9052
0.6044 11.74 2450 0.5970 0.8569 0.8670 0.8569 0.8561 0.9013
0.6044 11.98 2500 0.5703 0.8689 0.8789 0.8689 0.8676 0.9091
0.5484 12.22 2550 0.6249 0.8629 0.8739 0.8629 0.8627 0.9043
0.5484 12.46 2600 0.6848 0.8434 0.8565 0.8434 0.8404 0.8918
0.5484 12.69 2650 0.5845 0.8659 0.8766 0.8659 0.8658 0.9072
0.5484 12.93 2700 0.6151 0.8592 0.8695 0.8592 0.8579 0.9028
0.5297 13.17 2750 0.5739 0.8734 0.8820 0.8734 0.8728 0.9122
0.5297 13.41 2800 0.5720 0.8682 0.8797 0.8682 0.8671 0.9088
0.5297 13.65 2850 0.5494 0.8779 0.8874 0.8779 0.8764 0.9158
0.5297 13.89 2900 0.5730 0.8727 0.8813 0.8727 0.8719 0.9112
0.5084 14.13 2950 0.6109 0.8652 0.8757 0.8652 0.8629 0.9073
0.5084 14.37 3000 0.6417 0.8652 0.8762 0.8652 0.8642 0.9067
0.5084 14.61 3050 0.5735 0.8712 0.8788 0.8712 0.8701 0.9101
0.5084 14.85 3100 0.5614 0.8757 0.8835 0.8757 0.8743 0.9139
0.4813 15.09 3150 0.6592 0.8644 0.8735 0.8644 0.8632 0.9064
0.4813 15.33 3200 0.5960 0.8719 0.8786 0.8719 0.8702 0.9112
0.4813 15.57 3250 0.5824 0.8742 0.8815 0.8742 0.8735 0.9127
0.4813 15.81 3300 0.6188 0.8674 0.8767 0.8674 0.8668 0.9082
0.4615 16.05 3350 0.5480 0.8749 0.8832 0.8749 0.8742 0.9131
0.4615 16.29 3400 0.5980 0.8764 0.8844 0.8764 0.8760 0.9140
0.4615 16.53 3450 0.5855 0.8742 0.8815 0.8742 0.8732 0.9120
0.4615 16.77 3500 0.5869 0.8719 0.8832 0.8719 0.8717 0.9114
0.4421 17.01 3550 0.6259 0.8674 0.8760 0.8674 0.8657 0.9077
0.4421 17.25 3600 0.6427 0.8577 0.8684 0.8577 0.8569 0.9012
0.4421 17.49 3650 0.6241 0.8749 0.8847 0.8749 0.8739 0.9133
0.4421 17.72 3700 0.6669 0.8592 0.8688 0.8592 0.8574 0.9026
0.4421 17.96 3750 0.5774 0.8757 0.8834 0.8757 0.8741 0.9140
0.4298 18.2 3800 0.6578 0.8704 0.8766 0.8704 0.8681 0.9100
0.4298 18.44 3850 0.6365 0.8712 0.8796 0.8712 0.8712 0.9088
0.4298 18.68 3900 0.5537 0.8779 0.8845 0.8779 0.8759 0.9155
0.4298 18.92 3950 0.6618 0.8757 0.8848 0.8757 0.8740 0.9129
0.4154 19.16 4000 0.6952 0.8569 0.8681 0.8569 0.8555 0.9005
0.4154 19.4 4050 0.6206 0.8727 0.8797 0.8727 0.8717 0.9116
0.4154 19.64 4100 0.6469 0.8734 0.8846 0.8734 0.8726 0.9121
0.4154 19.88 4150 0.6405 0.8674 0.8788 0.8674 0.8665 0.9081
0.39 20.12 4200 0.6393 0.8809 0.8888 0.8809 0.8796 0.9176
0.39 20.36 4250 0.6617 0.8779 0.8881 0.8779 0.8777 0.9145
0.39 20.6 4300 0.6272 0.8697 0.8820 0.8697 0.8696 0.9098
0.39 20.84 4350 0.6635 0.8704 0.8818 0.8704 0.8707 0.9098
0.4019 21.08 4400 0.5965 0.8846 0.8943 0.8846 0.8849 0.9203
0.4019 21.32 4450 0.6427 0.8764 0.8872 0.8764 0.8764 0.9145
0.4019 21.56 4500 0.6726 0.8712 0.8799 0.8712 0.8699 0.9101
0.4019 21.8 4550 0.5973 0.8772 0.8854 0.8772 0.8766 0.9159
0.3718 22.04 4600 0.6342 0.8764 0.8868 0.8764 0.8759 0.9139
0.3718 22.28 4650 0.6081 0.8846 0.8945 0.8846 0.8848 0.9193
0.3718 22.51 4700 0.6140 0.8779 0.8900 0.8779 0.8767 0.9150
0.3718 22.75 4750 0.6561 0.8719 0.8840 0.8719 0.8715 0.9116
0.3718 22.99 4800 0.5921 0.8757 0.8853 0.8757 0.8746 0.9139
0.3638 23.23 4850 0.6855 0.8719 0.8826 0.8719 0.8702 0.9106
0.3638 23.47 4900 0.5923 0.8816 0.8933 0.8816 0.8813 0.9170
0.3638 23.71 4950 0.6988 0.8629 0.8761 0.8629 0.8608 0.9049
0.3638 23.95 5000 0.7042 0.8734 0.8840 0.8734 0.8730 0.9124
0.3447 24.19 5050 0.7146 0.8667 0.8755 0.8667 0.8647 0.9071
0.3447 24.43 5100 0.7134 0.8659 0.8754 0.8659 0.8647 0.9066
0.3447 24.67 5150 0.6893 0.8682 0.8784 0.8682 0.8672 0.9083
0.3447 24.91 5200 0.6617 0.8757 0.8887 0.8757 0.8750 0.9140
0.3387 25.15 5250 0.6747 0.8652 0.8772 0.8652 0.8636 0.9050
0.3387 25.39 5300 0.6693 0.8697 0.8794 0.8697 0.8684 0.9099
0.3387 25.63 5350 0.7019 0.8727 0.8863 0.8727 0.8720 0.9121
0.3387 25.87 5400 0.7221 0.8637 0.8793 0.8637 0.8626 0.9057
0.3303 26.11 5450 0.6852 0.8734 0.8821 0.8734 0.8723 0.9130
0.3303 26.35 5500 0.6092 0.8801 0.8883 0.8801 0.8796 0.9166
0.3303 26.59 5550 0.6416 0.8801 0.8881 0.8801 0.8795 0.9170
0.3303 26.83 5600 0.6762 0.8667 0.8763 0.8667 0.8646 0.9077
0.3253 27.07 5650 0.6886 0.8742 0.8841 0.8742 0.8737 0.9121
0.3253 27.31 5700 0.7574 0.8629 0.8742 0.8629 0.8602 0.9049
0.3253 27.54 5750 0.6952 0.8749 0.8836 0.8749 0.8745 0.9143
0.3253 27.78 5800 0.7068 0.8667 0.8786 0.8667 0.8663 0.9095
0.3233 28.02 5850 0.6912 0.8749 0.8867 0.8749 0.8741 0.9141

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1