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hubert-classifier-aug-fold-4

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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-4
    results: []

hubert-classifier-aug-fold-4

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: 0.5994
  • Accuracy: 0.8908
  • Precision: 0.9054
  • Recall: 0.8908
  • F1: 0.8902
  • Binary: 0.9252

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 0.13 50 4.4211 0.0256 0.0066 0.0256 0.0094 0.2032
No log 0.27 100 4.3448 0.0499 0.0239 0.0499 0.0181 0.2625
No log 0.4 150 3.9589 0.1120 0.0582 0.1120 0.0534 0.3738
No log 0.54 200 3.6032 0.1727 0.0912 0.1727 0.0968 0.4177
No log 0.67 250 3.2149 0.2321 0.1419 0.2321 0.1452 0.4592
No log 0.81 300 2.8786 0.3441 0.2742 0.3441 0.2581 0.5387
No log 0.94 350 2.5253 0.4211 0.3438 0.4211 0.3380 0.5941
3.7437 1.08 400 2.1778 0.4588 0.4083 0.4588 0.3896 0.6201
3.7437 1.21 450 1.8620 0.5709 0.5259 0.5709 0.5166 0.6992
3.7437 1.35 500 1.6172 0.5803 0.5498 0.5803 0.5168 0.7062
3.7437 1.48 550 1.3691 0.6640 0.6471 0.6640 0.6287 0.7633
3.7437 1.62 600 1.2425 0.6910 0.6704 0.6910 0.6541 0.7837
3.7437 1.75 650 1.1155 0.7193 0.7205 0.7193 0.6936 0.8038
3.7437 1.89 700 0.9569 0.7463 0.7599 0.7463 0.7287 0.8225
1.7895 2.02 750 0.9260 0.7584 0.7657 0.7584 0.7389 0.8321
1.7895 2.16 800 0.8667 0.7787 0.8008 0.7787 0.7639 0.8452
1.7895 2.29 850 0.7438 0.8138 0.8159 0.8138 0.8047 0.8696
1.7895 2.43 900 0.7958 0.8016 0.8175 0.8016 0.7917 0.8602
1.7895 2.56 950 0.6627 0.8327 0.8449 0.8327 0.8296 0.8829
1.7895 2.7 1000 0.7242 0.7976 0.8152 0.7976 0.7882 0.8592
1.7895 2.83 1050 0.6745 0.8165 0.8337 0.8165 0.8123 0.8719
1.7895 2.96 1100 0.6795 0.8192 0.8388 0.8192 0.8158 0.8761
1.0205 3.1 1150 0.6546 0.8354 0.8575 0.8354 0.8319 0.8835
1.0205 3.23 1200 0.6165 0.8394 0.8489 0.8394 0.8365 0.8868
1.0205 3.37 1250 0.7041 0.8232 0.8490 0.8232 0.8202 0.8775
1.0205 3.5 1300 0.5767 0.8516 0.8626 0.8516 0.8485 0.8957
1.0205 3.64 1350 0.5831 0.8448 0.8609 0.8448 0.8404 0.8910
1.0205 3.77 1400 0.5623 0.8650 0.8761 0.8650 0.8624 0.9051
1.0205 3.91 1450 0.5696 0.8650 0.8757 0.8650 0.8630 0.9047
0.7175 4.04 1500 0.5455 0.8543 0.8756 0.8543 0.8522 0.8981
0.7175 4.18 1550 0.5209 0.8650 0.8785 0.8650 0.8592 0.9053
0.7175 4.31 1600 0.6185 0.8435 0.8606 0.8435 0.8415 0.8908
0.7175 4.45 1650 0.5434 0.8677 0.8797 0.8677 0.8644 0.9066
0.7175 4.58 1700 0.6622 0.8489 0.8728 0.8489 0.8444 0.8945
0.7175 4.72 1750 0.5668 0.8677 0.8798 0.8677 0.8662 0.9070
0.7175 4.85 1800 0.5375 0.8812 0.8934 0.8812 0.8804 0.9179
0.7175 4.99 1850 0.5550 0.8677 0.8780 0.8677 0.8640 0.9080
0.5694 5.12 1900 0.5739 0.8691 0.8811 0.8691 0.8647 0.9089
0.5694 5.26 1950 0.5325 0.8826 0.8923 0.8826 0.8818 0.9174
0.5694 5.39 2000 0.5496 0.8772 0.8885 0.8772 0.8747 0.9147
0.5694 5.53 2050 0.6038 0.8745 0.8854 0.8745 0.8726 0.9123
0.5694 5.66 2100 0.5606 0.8826 0.8936 0.8826 0.8816 0.9194
0.5694 5.8 2150 0.5655 0.8745 0.8885 0.8745 0.8741 0.9128
0.5694 5.93 2200 0.5588 0.8785 0.8912 0.8785 0.8775 0.9157
0.4761 6.06 2250 0.6021 0.8637 0.8802 0.8637 0.8617 0.9047
0.4761 6.2 2300 0.5785 0.8839 0.8956 0.8839 0.8840 0.9194
0.4761 6.33 2350 0.6397 0.8691 0.8831 0.8691 0.8677 0.9090
0.4761 6.47 2400 0.5376 0.8880 0.8998 0.8880 0.8866 0.9238
0.4761 6.6 2450 0.5669 0.8920 0.9025 0.8920 0.8904 0.9255
0.4761 6.74 2500 0.6968 0.8543 0.8723 0.8543 0.8522 0.8987
0.4761 6.87 2550 0.5628 0.8839 0.8952 0.8839 0.8829 0.9194
0.4178 7.01 2600 0.5975 0.8772 0.8861 0.8772 0.8755 0.9167
0.4178 7.14 2650 0.5967 0.8853 0.8919 0.8853 0.8834 0.9219
0.4178 7.28 2700 0.6271 0.8799 0.8921 0.8799 0.8783 0.9166
0.4178 7.41 2750 0.6047 0.8799 0.8916 0.8799 0.8784 0.9170
0.4178 7.55 2800 0.5336 0.8853 0.8978 0.8853 0.8829 0.9204
0.4178 7.68 2850 0.5722 0.8988 0.9097 0.8988 0.8988 0.9298
0.4178 7.82 2900 0.5478 0.8866 0.8987 0.8866 0.8853 0.9213
0.4178 7.95 2950 0.5176 0.8907 0.9016 0.8907 0.8897 0.9242
0.3642 8.09 3000 0.5172 0.8947 0.9030 0.8947 0.8938 0.9279
0.3642 8.22 3050 0.6341 0.8799 0.8932 0.8799 0.8787 0.9157
0.3642 8.36 3100 0.6011 0.8812 0.8897 0.8812 0.8797 0.9181
0.3642 8.49 3150 0.5807 0.8745 0.8864 0.8745 0.8733 0.9132
0.3642 8.63 3200 0.5931 0.8799 0.8942 0.8799 0.8795 0.9157
0.3642 8.76 3250 0.6045 0.8812 0.8955 0.8812 0.8818 0.9175
0.3642 8.89 3300 0.5473 0.8934 0.9047 0.8934 0.8927 0.9260
0.3326 9.03 3350 0.5111 0.8934 0.9058 0.8934 0.8924 0.9266

Framework versions

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