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hubert-classifier-aug-fold-0

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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-0
    results: []

hubert-classifier-aug-fold-0

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: 0.6722
  • Accuracy: 0.8706
  • Precision: 0.8828
  • Recall: 0.8706
  • F1: 0.8708
  • Binary: 0.9097

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: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.24 50 4.4197 0.0210 0.0174 0.0210 0.0057 0.1713
No log 0.48 100 4.2999 0.0479 0.0457 0.0479 0.0233 0.3186
No log 0.72 150 3.9495 0.0517 0.0304 0.0517 0.0221 0.3297
No log 0.96 200 3.6566 0.0779 0.0264 0.0779 0.0319 0.3483
4.2316 1.2 250 3.4461 0.0944 0.0410 0.0944 0.0381 0.3633
4.2316 1.44 300 3.2464 0.1266 0.0635 0.1266 0.0624 0.3869
4.2316 1.68 350 3.0578 0.1476 0.0942 0.1476 0.0816 0.3983
4.2316 1.92 400 2.7652 0.2210 0.1506 0.2210 0.1354 0.4527
3.3453 2.16 450 2.4759 0.3026 0.2342 0.3026 0.2120 0.5108
3.3453 2.4 500 2.1916 0.3925 0.3092 0.3925 0.3043 0.5742
3.3453 2.63 550 1.9549 0.4524 0.3866 0.4524 0.3861 0.6169
3.3453 2.87 600 1.7926 0.4891 0.4796 0.4891 0.4231 0.6419
2.4259 3.11 650 1.5900 0.5700 0.5456 0.5700 0.5217 0.6991
2.4259 3.35 700 1.3724 0.6180 0.6275 0.6180 0.5730 0.7328
2.4259 3.59 750 1.2748 0.6502 0.6406 0.6502 0.6102 0.7560
2.4259 3.83 800 1.1681 0.6704 0.6791 0.6704 0.6384 0.7703
1.7305 4.07 850 1.0720 0.7139 0.7255 0.7139 0.6889 0.8001
1.7305 4.31 900 1.0337 0.7146 0.7298 0.7146 0.6921 0.7993
1.7305 4.55 950 0.9137 0.7423 0.7541 0.7423 0.7231 0.8199
1.7305 4.79 1000 0.8462 0.7633 0.7716 0.7633 0.7494 0.8345
1.3376 5.03 1050 0.8048 0.7790 0.7985 0.7790 0.7685 0.8462
1.3376 5.27 1100 0.7739 0.7850 0.7900 0.7850 0.7706 0.8493
1.3376 5.51 1150 0.7713 0.7955 0.8096 0.7955 0.7892 0.8569
1.3376 5.75 1200 0.7841 0.7925 0.8059 0.7925 0.7866 0.8550
1.3376 5.99 1250 0.7026 0.8007 0.8249 0.8007 0.7966 0.8609
1.0806 6.23 1300 0.6965 0.8112 0.8240 0.8112 0.8078 0.8685
1.0806 6.47 1350 0.6891 0.8142 0.8312 0.8142 0.8097 0.8697
1.0806 6.71 1400 0.6624 0.8262 0.8387 0.8262 0.8214 0.8781
1.0806 6.95 1450 0.6302 0.8337 0.8441 0.8337 0.8299 0.8834
0.9458 7.19 1500 0.6213 0.8367 0.8468 0.8367 0.8321 0.8854
0.9458 7.43 1550 0.6815 0.8195 0.8331 0.8195 0.8155 0.8738
0.9458 7.66 1600 0.6206 0.8427 0.8538 0.8427 0.8408 0.8902
0.9458 7.9 1650 0.5314 0.8577 0.8687 0.8577 0.8556 0.9007
0.8202 8.14 1700 0.5861 0.8390 0.8505 0.8390 0.8369 0.8874
0.8202 8.38 1750 0.5927 0.8532 0.8661 0.8532 0.8519 0.8975
0.8202 8.62 1800 0.6158 0.8449 0.8592 0.8449 0.8420 0.8919
0.8202 8.86 1850 0.5726 0.8457 0.8569 0.8457 0.8416 0.8918
0.7454 9.1 1900 0.6392 0.8360 0.8528 0.8360 0.8315 0.8858
0.7454 9.34 1950 0.5566 0.8577 0.8710 0.8577 0.8569 0.9006
0.7454 9.58 2000 0.5260 0.8592 0.8693 0.8592 0.8561 0.9010
0.7454 9.82 2050 0.5470 0.8659 0.8760 0.8659 0.8651 0.9058
0.6472 10.06 2100 0.5692 0.8554 0.8643 0.8554 0.8541 0.9001
0.6472 10.3 2150 0.5730 0.8599 0.8683 0.8599 0.8574 0.9016
0.6472 10.54 2200 0.5408 0.8637 0.8715 0.8637 0.8619 0.9048
0.6472 10.78 2250 0.5869 0.8652 0.8739 0.8652 0.8635 0.9052
0.6204 11.02 2300 0.6284 0.8539 0.8638 0.8539 0.8511 0.8985
0.6204 11.26 2350 0.5792 0.8599 0.8674 0.8599 0.8565 0.9024
0.6204 11.5 2400 0.6085 0.8592 0.8704 0.8592 0.8568 0.9011
0.6204 11.74 2450 0.6259 0.8517 0.8590 0.8517 0.8493 0.8958
0.6204 11.98 2500 0.6429 0.8494 0.8634 0.8494 0.8474 0.8945
0.5797 12.22 2550 0.6478 0.8502 0.8596 0.8502 0.8480 0.8960
0.5797 12.46 2600 0.5734 0.8652 0.8737 0.8652 0.8619 0.9055
0.5797 12.69 2650 0.6109 0.8569 0.8667 0.8569 0.8528 0.9003
0.5797 12.93 2700 0.5982 0.8652 0.8784 0.8652 0.8632 0.9058
0.542 13.17 2750 0.6024 0.8539 0.8655 0.8539 0.8527 0.8975
0.542 13.41 2800 0.5819 0.8629 0.8707 0.8629 0.8609 0.9056
0.542 13.65 2850 0.5870 0.8689 0.8781 0.8689 0.8680 0.9085
0.542 13.89 2900 0.5818 0.8637 0.8710 0.8637 0.8619 0.9042
0.5116 14.13 2950 0.5965 0.8599 0.8709 0.8599 0.8590 0.9035
0.5116 14.37 3000 0.6023 0.8607 0.8675 0.8607 0.8581 0.9029
0.5116 14.61 3050 0.6432 0.8637 0.8745 0.8637 0.8620 0.9040
0.5116 14.85 3100 0.6255 0.8584 0.8703 0.8584 0.8574 0.9014
0.4756 15.09 3150 0.6000 0.8629 0.8710 0.8629 0.8615 0.9040
0.4756 15.33 3200 0.6462 0.8689 0.8793 0.8689 0.8682 0.9082
0.4756 15.57 3250 0.6419 0.8539 0.8641 0.8539 0.8518 0.8984
0.4756 15.81 3300 0.6592 0.8569 0.8624 0.8569 0.8538 0.9012
0.4492 16.05 3350 0.6195 0.8607 0.8687 0.8607 0.8591 0.9034
0.4492 16.29 3400 0.6042 0.8697 0.8803 0.8697 0.8687 0.9090
0.4492 16.53 3450 0.6235 0.8562 0.8664 0.8562 0.8544 0.8998
0.4492 16.77 3500 0.6332 0.8674 0.8756 0.8674 0.8659 0.9069
0.4383 17.01 3550 0.6278 0.8584 0.8661 0.8584 0.8566 0.9011
0.4383 17.25 3600 0.5924 0.8719 0.8806 0.8719 0.8709 0.9100
0.4383 17.49 3650 0.6176 0.8712 0.8817 0.8712 0.8696 0.9105
0.4383 17.72 3700 0.6186 0.8712 0.8788 0.8712 0.8694 0.9106
0.4383 17.96 3750 0.6185 0.8749 0.8849 0.8749 0.8736 0.9124
0.4249 18.2 3800 0.6101 0.8742 0.8820 0.8742 0.8735 0.9116
0.4249 18.44 3850 0.6121 0.8689 0.8802 0.8689 0.8682 0.9085
0.4249 18.68 3900 0.6568 0.8614 0.8719 0.8614 0.8599 0.9031
0.4249 18.92 3950 0.6292 0.8697 0.8797 0.8697 0.8688 0.9091
0.4073 19.16 4000 0.6200 0.8719 0.8822 0.8719 0.8702 0.9103
0.4073 19.4 4050 0.6544 0.8644 0.8740 0.8644 0.8635 0.9052
0.4073 19.64 4100 0.6441 0.8652 0.8731 0.8652 0.8639 0.9061
0.4073 19.88 4150 0.6056 0.8779 0.8836 0.8779 0.8764 0.9146
0.3797 20.12 4200 0.6192 0.8742 0.8815 0.8742 0.8728 0.9117
0.3797 20.36 4250 0.5936 0.8787 0.8864 0.8787 0.8775 0.9156
0.3797 20.6 4300 0.6288 0.8749 0.8836 0.8749 0.8736 0.9124
0.3797 20.84 4350 0.6280 0.8734 0.8812 0.8734 0.8717 0.9116
0.3727 21.08 4400 0.6542 0.8712 0.8782 0.8712 0.8694 0.9097
0.3727 21.32 4450 0.6506 0.8667 0.8761 0.8667 0.8643 0.9063
0.3727 21.56 4500 0.6217 0.8727 0.8789 0.8727 0.8707 0.9105
0.3727 21.8 4550 0.6120 0.8779 0.8836 0.8779 0.8769 0.9142
0.3495 22.04 4600 0.6275 0.8704 0.8786 0.8704 0.8689 0.9092
0.3495 22.28 4650 0.6258 0.8794 0.8862 0.8794 0.8777 0.9153
0.3495 22.51 4700 0.6255 0.8682 0.8770 0.8682 0.8663 0.9079
0.3495 22.75 4750 0.6442 0.8689 0.8772 0.8689 0.8667 0.9085
0.3495 22.99 4800 0.6274 0.8727 0.8816 0.8727 0.8716 0.9109
0.3363 23.23 4850 0.6241 0.8712 0.8783 0.8712 0.8693 0.9103
0.3363 23.47 4900 0.5921 0.8824 0.8886 0.8824 0.8811 0.9175
0.3363 23.71 4950 0.6452 0.8749 0.8832 0.8749 0.8732 0.9124
0.3363 23.95 5000 0.6247 0.8757 0.8851 0.8757 0.8739 0.9129
0.3218 24.19 5050 0.6176 0.8816 0.8897 0.8816 0.8797 0.9173
0.3218 24.43 5100 0.6232 0.8772 0.8846 0.8772 0.8753 0.9139
0.3218 24.67 5150 0.6267 0.8757 0.8833 0.8757 0.8742 0.9131
0.3218 24.91 5200 0.6109 0.8749 0.8825 0.8749 0.8736 0.9124
0.3173 25.15 5250 0.6192 0.8801 0.8878 0.8801 0.8786 0.9160
0.3173 25.39 5300 0.6303 0.8764 0.8853 0.8764 0.8750 0.9134
0.3173 25.63 5350 0.6552 0.8742 0.8818 0.8742 0.8726 0.9115
0.3173 25.87 5400 0.6291 0.8712 0.8782 0.8712 0.8697 0.9094
0.316 26.11 5450 0.6041 0.8816 0.8874 0.8816 0.8805 0.9169
0.316 26.35 5500 0.6254 0.8809 0.8887 0.8809 0.8792 0.9166
0.316 26.59 5550 0.6147 0.8801 0.8868 0.8801 0.8789 0.9160
0.316 26.83 5600 0.6255 0.8794 0.8866 0.8794 0.8780 0.9155
0.2917 27.07 5650 0.5997 0.8824 0.8893 0.8824 0.8811 0.9173
0.2917 27.31 5700 0.5993 0.8831 0.8906 0.8831 0.8817 0.9181
0.2917 27.54 5750 0.6007 0.8809 0.8889 0.8809 0.8796 0.9166
0.2917 27.78 5800 0.6041 0.8787 0.8871 0.8787 0.8772 0.9152
0.2896 28.02 5850 0.5977 0.8854 0.8921 0.8854 0.8844 0.9196
0.2896 28.26 5900 0.5875 0.8869 0.8937 0.8869 0.8858 0.9210
0.2896 28.5 5950 0.6133 0.8764 0.8843 0.8764 0.8750 0.9136
0.2896 28.74 6000 0.6153 0.8794 0.8874 0.8794 0.8783 0.9157
0.2896 28.98 6050 0.6031 0.8816 0.8891 0.8816 0.8799 0.9173
0.2821 29.22 6100 0.6034 0.8839 0.8908 0.8839 0.8823 0.9189
0.2821 29.46 6150 0.6003 0.8831 0.8895 0.8831 0.8815 0.9184
0.2821 29.7 6200 0.6013 0.8846 0.8911 0.8846 0.8832 0.9194

Framework versions

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