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hubert-classifier-aug

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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
    results: []

hubert-classifier-aug

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: 0.5264
  • Accuracy: 0.8625
  • Precision: 0.8662
  • Recall: 0.8625
  • F1: 0.8517
  • Binary: 0.9030

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
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.19 50 3.8945 0.0566 0.0077 0.0566 0.0127 0.3326
No log 0.38 100 3.4610 0.0701 0.0208 0.0701 0.0174 0.3418
No log 0.58 150 3.2223 0.1051 0.0294 0.1051 0.0364 0.3720
No log 0.77 200 3.1153 0.1294 0.0504 0.1294 0.0565 0.3795
No log 0.96 250 2.8292 0.1914 0.1010 0.1914 0.1073 0.4315
No log 1.15 300 2.7080 0.2264 0.1522 0.2264 0.1461 0.4496
No log 1.34 350 2.4083 0.2776 0.1986 0.2776 0.1896 0.4935
No log 1.53 400 2.2517 0.3720 0.2762 0.3720 0.2845 0.5580
No log 1.73 450 2.1201 0.3908 0.3501 0.3908 0.3098 0.5712
3.1927 1.92 500 1.9149 0.4582 0.3806 0.4582 0.3781 0.6210
3.1927 2.11 550 1.7920 0.5013 0.4684 0.5013 0.4456 0.6515
3.1927 2.3 600 1.5973 0.5418 0.4910 0.5418 0.4765 0.6803
3.1927 2.49 650 1.5067 0.5957 0.5572 0.5957 0.5409 0.7162
3.1927 2.68 700 1.3985 0.6253 0.6046 0.6253 0.5740 0.7361
3.1927 2.88 750 1.3198 0.6604 0.6224 0.6604 0.6114 0.7623
3.1927 3.07 800 1.2483 0.6685 0.6709 0.6685 0.6273 0.7674
3.1927 3.26 850 1.1560 0.7116 0.7063 0.7116 0.6710 0.7973
3.1927 3.45 900 1.0992 0.7197 0.7345 0.7197 0.6872 0.8030
3.1927 3.64 950 1.1148 0.7143 0.7477 0.7143 0.6918 0.7992
2.0117 3.84 1000 0.9688 0.7682 0.7634 0.7682 0.7404 0.8369
2.0117 4.03 1050 0.9990 0.7062 0.7148 0.7062 0.6717 0.7927
2.0117 4.22 1100 0.9516 0.7412 0.7619 0.7412 0.7229 0.8199
2.0117 4.41 1150 0.8740 0.7763 0.7947 0.7763 0.7582 0.8426
2.0117 4.6 1200 0.8611 0.7682 0.7800 0.7682 0.7469 0.8388
2.0117 4.79 1250 0.7992 0.7898 0.8228 0.7898 0.7775 0.8539
2.0117 4.99 1300 0.8161 0.7898 0.8209 0.7898 0.7756 0.8512
2.0117 5.18 1350 0.7420 0.7925 0.8144 0.7925 0.7768 0.8539
2.0117 5.37 1400 0.7420 0.7925 0.8070 0.7925 0.7712 0.8550
2.0117 5.56 1450 0.7126 0.8140 0.8187 0.8140 0.8017 0.8701
1.5617 5.75 1500 0.6797 0.8194 0.8436 0.8194 0.8086 0.8739
1.5617 5.94 1550 0.6877 0.8221 0.8279 0.8221 0.8028 0.8747
1.5617 6.14 1600 0.6547 0.8329 0.8525 0.8329 0.8230 0.8822
1.5617 6.33 1650 0.5935 0.8410 0.8589 0.8410 0.8270 0.8879
1.5617 6.52 1700 0.6423 0.8194 0.8255 0.8194 0.8052 0.8728
1.5617 6.71 1750 0.5980 0.8464 0.8610 0.8464 0.8322 0.8916
1.5617 6.9 1800 0.6111 0.8437 0.8543 0.8437 0.8287 0.8916
1.5617 7.09 1850 0.5835 0.8437 0.8588 0.8437 0.8336 0.8927
1.5617 7.29 1900 0.5804 0.8329 0.8461 0.8329 0.8210 0.8822
1.5617 7.48 1950 0.5711 0.8410 0.8580 0.8410 0.8290 0.8908
1.3255 7.67 2000 0.5468 0.8571 0.8633 0.8571 0.8457 0.9011
1.3255 7.86 2050 0.5384 0.8652 0.8720 0.8652 0.8553 0.9049
1.3255 8.05 2100 0.5673 0.8625 0.8684 0.8625 0.8547 0.9030
1.3255 8.25 2150 0.5450 0.8491 0.8582 0.8491 0.8403 0.8935
1.3255 8.44 2200 0.5278 0.8706 0.8770 0.8706 0.8630 0.9086
1.3255 8.63 2250 0.5339 0.8652 0.8692 0.8652 0.8542 0.9049
1.3255 8.82 2300 0.5469 0.8598 0.8648 0.8598 0.8489 0.9011
1.3255 9.01 2350 0.5404 0.8706 0.8747 0.8706 0.8602 0.9086
1.3255 9.2 2400 0.5455 0.8491 0.8565 0.8491 0.8378 0.8935
1.3255 9.4 2450 0.5317 0.8598 0.8664 0.8598 0.8479 0.9011
1.1934 9.59 2500 0.5227 0.8760 0.8798 0.8760 0.8657 0.9124
1.1934 9.78 2550 0.5278 0.8598 0.8653 0.8598 0.8481 0.9011
1.1934 9.97 2600 0.5264 0.8625 0.8662 0.8625 0.8517 0.9030

Framework versions

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