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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: 0.5873
  • Accuracy: 0.8787
  • Precision: 0.8925
  • Recall: 0.8787
  • F1: 0.8784
  • Binary: 0.9162

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 4.4206 0.0195 0.0007 0.0195 0.0014 0.1390
No log 0.48 100 4.3006 0.0442 0.0114 0.0442 0.0127 0.2528
No log 0.72 150 3.9867 0.0472 0.0033 0.0472 0.0061 0.3276
No log 0.96 200 3.6925 0.0712 0.0116 0.0712 0.0180 0.3447
4.2438 1.2 250 3.4305 0.0854 0.0508 0.0854 0.0319 0.3580
4.2438 1.44 300 3.2405 0.1071 0.0689 0.1071 0.0432 0.3730
4.2438 1.68 350 3.0535 0.1491 0.1053 0.1491 0.0823 0.3999
4.2438 1.92 400 2.7897 0.2419 0.2020 0.2419 0.1678 0.4667
3.3411 2.16 450 2.4987 0.3303 0.2416 0.3303 0.2457 0.5288
3.3411 2.4 500 2.1588 0.4779 0.3998 0.4779 0.4078 0.6354
3.3411 2.63 550 1.8909 0.5273 0.4768 0.5273 0.4604 0.6688
3.3411 2.87 600 1.6458 0.5708 0.5612 0.5708 0.5191 0.6994
2.4102 3.11 650 1.4630 0.6187 0.6002 0.6187 0.5757 0.7327
2.4102 3.35 700 1.2770 0.6764 0.6582 0.6764 0.6409 0.7730
2.4102 3.59 750 1.1875 0.6966 0.6830 0.6966 0.6696 0.7884
2.4102 3.83 800 1.0563 0.7228 0.7372 0.7228 0.7012 0.8073
1.6409 4.07 850 0.9471 0.7506 0.7688 0.7506 0.7322 0.8260
1.6409 4.31 900 0.9012 0.7588 0.7677 0.7588 0.7471 0.8313
1.6409 4.55 950 0.8540 0.7768 0.8025 0.7768 0.7685 0.8435
1.6409 4.79 1000 0.7910 0.7828 0.7915 0.7828 0.7723 0.8479
1.2621 5.03 1050 0.7229 0.7918 0.7952 0.7918 0.7804 0.8542
1.2621 5.27 1100 0.7388 0.8067 0.8250 0.8067 0.8031 0.8650
1.2621 5.51 1150 0.7315 0.8090 0.8298 0.8090 0.8029 0.8672
1.2621 5.75 1200 0.7357 0.7903 0.8053 0.7903 0.7856 0.8533
1.2621 5.99 1250 0.7088 0.8090 0.8240 0.8090 0.8037 0.8672
1.0138 6.23 1300 0.6828 0.8112 0.8209 0.8112 0.8077 0.8684
1.0138 6.47 1350 0.7561 0.8082 0.8229 0.8082 0.8032 0.8678
1.0138 6.71 1400 0.6640 0.8292 0.8415 0.8292 0.8250 0.8812
1.0138 6.95 1450 0.6330 0.8315 0.8453 0.8315 0.8282 0.8828
0.9058 7.19 1500 0.6482 0.8217 0.8331 0.8217 0.8189 0.8764
0.9058 7.43 1550 0.7005 0.8187 0.8330 0.8187 0.8135 0.8736
0.9058 7.66 1600 0.5902 0.8562 0.8645 0.8562 0.8533 0.8998
0.9058 7.9 1650 0.5481 0.8607 0.8723 0.8607 0.8594 0.9019
0.7905 8.14 1700 0.6131 0.8427 0.8534 0.8427 0.8394 0.8899
0.7905 8.38 1750 0.6664 0.8419 0.8541 0.8419 0.8394 0.8897
0.7905 8.62 1800 0.6453 0.8330 0.8473 0.8330 0.8293 0.8842
0.7905 8.86 1850 0.6178 0.8390 0.8553 0.8390 0.8362 0.8873
0.7208 9.1 1900 0.6779 0.8412 0.8540 0.8412 0.8379 0.8895
0.7208 9.34 1950 0.5752 0.8607 0.8690 0.8607 0.8581 0.9031
0.7208 9.58 2000 0.6717 0.8434 0.8544 0.8434 0.8408 0.8909
0.7208 9.82 2050 0.6790 0.8345 0.8500 0.8345 0.8321 0.8848
0.6476 10.06 2100 0.6429 0.8494 0.8631 0.8494 0.8472 0.8954
0.6476 10.3 2150 0.6006 0.8577 0.8668 0.8577 0.8558 0.9007
0.6476 10.54 2200 0.5987 0.8532 0.8634 0.8532 0.8519 0.8974
0.6476 10.78 2250 0.6524 0.8472 0.8594 0.8472 0.8443 0.8934
0.6156 11.02 2300 0.6748 0.8412 0.8529 0.8412 0.8386 0.8904
0.6156 11.26 2350 0.5571 0.8577 0.8644 0.8577 0.8547 0.9011
0.6156 11.5 2400 0.6081 0.8502 0.8607 0.8502 0.8468 0.8959
0.6156 11.74 2450 0.5866 0.8592 0.8692 0.8592 0.8575 0.9022
0.6156 11.98 2500 0.6205 0.8517 0.8630 0.8517 0.8501 0.8966
0.5738 12.22 2550 0.6544 0.8562 0.8704 0.8562 0.8549 0.8996
0.5738 12.46 2600 0.6792 0.8427 0.8545 0.8427 0.8385 0.8906
0.5738 12.69 2650 0.6009 0.8569 0.8676 0.8569 0.8557 0.9008
0.5738 12.93 2700 0.6580 0.8524 0.8621 0.8524 0.8490 0.8972
0.5416 13.17 2750 0.6781 0.8532 0.8639 0.8532 0.8504 0.8977
0.5416 13.41 2800 0.5903 0.8659 0.8749 0.8659 0.8646 0.9084
0.5416 13.65 2850 0.5766 0.8644 0.8728 0.8644 0.8620 0.9064
0.5416 13.89 2900 0.6674 0.8592 0.8688 0.8592 0.8565 0.9027
0.5213 14.13 2950 0.6256 0.8652 0.8751 0.8652 0.8635 0.9067
0.5213 14.37 3000 0.6518 0.8622 0.8704 0.8622 0.8602 0.9051
0.5213 14.61 3050 0.6694 0.8547 0.8661 0.8547 0.8531 0.8999
0.5213 14.85 3100 0.6153 0.8719 0.8799 0.8719 0.8710 0.9125
0.4856 15.09 3150 0.6067 0.8727 0.8821 0.8727 0.8715 0.9106
0.4856 15.33 3200 0.6354 0.8592 0.8712 0.8592 0.8581 0.9019
0.4856 15.57 3250 0.6773 0.8532 0.8623 0.8532 0.8507 0.8988
0.4856 15.81 3300 0.6356 0.8682 0.8759 0.8682 0.8660 0.9088
0.4631 16.05 3350 0.6139 0.8712 0.8783 0.8712 0.8700 0.9102
0.4631 16.29 3400 0.6589 0.8622 0.8730 0.8622 0.8612 0.9049
0.4631 16.53 3450 0.6439 0.8539 0.8660 0.8539 0.8516 0.8982
0.4631 16.77 3500 0.6727 0.8689 0.8757 0.8689 0.8673 0.9091
0.4605 17.01 3550 0.6359 0.8712 0.8793 0.8712 0.8703 0.9103
0.4605 17.25 3600 0.6926 0.8547 0.8647 0.8547 0.8534 0.8999
0.4605 17.49 3650 0.6937 0.8562 0.8687 0.8562 0.8544 0.9008
0.4605 17.72 3700 0.6625 0.8659 0.8777 0.8659 0.8649 0.9068
0.4605 17.96 3750 0.6542 0.8674 0.8784 0.8674 0.8655 0.9090
0.4371 18.2 3800 0.5719 0.8742 0.8831 0.8742 0.8727 0.9121
0.4371 18.44 3850 0.6245 0.8734 0.8811 0.8734 0.8727 0.9124
0.4371 18.68 3900 0.6993 0.8577 0.8680 0.8577 0.8559 0.9018
0.4371 18.92 3950 0.6896 0.8592 0.8681 0.8592 0.8573 0.9028
0.4277 19.16 4000 0.6869 0.8517 0.8640 0.8517 0.8507 0.8973
0.4277 19.4 4050 0.6963 0.8599 0.8692 0.8599 0.8587 0.9021
0.4277 19.64 4100 0.5527 0.8831 0.8898 0.8831 0.8819 0.9184
0.4277 19.88 4150 0.6925 0.8592 0.8699 0.8592 0.8580 0.9025
0.401 20.12 4200 0.6998 0.8592 0.8719 0.8592 0.8582 0.9040
0.401 20.36 4250 0.6390 0.8757 0.8849 0.8757 0.8743 0.9139
0.401 20.6 4300 0.6792 0.8659 0.8762 0.8659 0.8641 0.9075
0.401 20.84 4350 0.6946 0.8554 0.8662 0.8554 0.8529 0.8990
0.3945 21.08 4400 0.8223 0.8427 0.8559 0.8427 0.8409 0.8903
0.3945 21.32 4450 0.7841 0.8622 0.8710 0.8622 0.8599 0.9040
0.3945 21.56 4500 0.6545 0.8697 0.8766 0.8697 0.8687 0.9093
0.3945 21.8 4550 0.7135 0.8652 0.8710 0.8652 0.8630 0.9072
0.3829 22.04 4600 0.6901 0.8622 0.8705 0.8622 0.8610 0.9046
0.3829 22.28 4650 0.6960 0.8599 0.8688 0.8599 0.8579 0.9035
0.3829 22.51 4700 0.7047 0.8644 0.8752 0.8644 0.8630 0.9061
0.3829 22.75 4750 0.6855 0.8674 0.8784 0.8674 0.8662 0.9094
0.3829 22.99 4800 0.7315 0.8539 0.8652 0.8539 0.8516 0.8993
0.3695 23.23 4850 0.7299 0.8569 0.8663 0.8569 0.8545 0.9005

Framework versions

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