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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
    results: []

hubert-classifier

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: 1.1320
  • Accuracy: 0.7724
  • Precision: 0.8107
  • Recall: 0.7724
  • F1: 0.7633
  • Binary: 0.8448

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: 3e-05
  • 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 0.17 50 4.2981 0.0242 0.0171 0.0242 0.0108 0.2760
No log 0.35 100 3.9571 0.0315 0.0038 0.0315 0.0059 0.3133
No log 0.52 150 3.7311 0.0678 0.0243 0.0678 0.0277 0.3424
No log 0.69 200 3.5663 0.0896 0.0557 0.0896 0.0513 0.3593
No log 0.86 250 3.4934 0.0944 0.0408 0.0944 0.0477 0.3545
No log 1.04 300 3.3705 0.1235 0.0864 0.1235 0.0706 0.3748
No log 1.21 350 3.2630 0.1429 0.1042 0.1429 0.0811 0.3906
No log 1.38 400 3.1551 0.1574 0.1413 0.1574 0.1118 0.4029
No log 1.55 450 3.0426 0.2349 0.1580 0.2349 0.1585 0.4593
3.6339 1.73 500 2.9462 0.2542 0.1854 0.2542 0.1806 0.4736
3.6339 1.9 550 2.8439 0.2663 0.2105 0.2663 0.2084 0.4814
3.6339 2.07 600 2.7192 0.3051 0.2797 0.3051 0.2411 0.5092
3.6339 2.24 650 2.6390 0.3293 0.3167 0.3293 0.2650 0.5274
3.6339 2.42 700 2.5491 0.3753 0.3731 0.3753 0.3290 0.5600
3.6339 2.59 750 2.4728 0.4092 0.3891 0.4092 0.3595 0.5823
3.6339 2.76 800 2.3395 0.4431 0.4205 0.4431 0.3917 0.6082
3.6339 2.93 850 2.2685 0.4552 0.4355 0.4552 0.4028 0.6160
3.6339 3.11 900 2.1883 0.4915 0.4680 0.4915 0.4387 0.6414
3.6339 3.28 950 2.1182 0.4843 0.5102 0.4843 0.4440 0.6363
2.6665 3.45 1000 2.0197 0.5448 0.5629 0.5448 0.5028 0.6804
2.6665 3.62 1050 1.9782 0.5327 0.5532 0.5327 0.4935 0.6712
2.6665 3.8 1100 1.9313 0.5593 0.5486 0.5593 0.5156 0.6930
2.6665 3.97 1150 1.8627 0.5908 0.5893 0.5908 0.5513 0.7119
2.6665 4.14 1200 1.8169 0.5908 0.5834 0.5908 0.5543 0.7128
2.6665 4.31 1250 1.7702 0.5835 0.5843 0.5835 0.5487 0.7077
2.6665 4.49 1300 1.7007 0.6344 0.6857 0.6344 0.6124 0.7438
2.6665 4.66 1350 1.6638 0.6199 0.6156 0.6199 0.5850 0.7354
2.6665 4.83 1400 1.6198 0.6368 0.6325 0.6368 0.6004 0.7482
2.6665 5.0 1450 1.5672 0.6804 0.6888 0.6804 0.6529 0.7753
2.0909 5.18 1500 1.5308 0.6683 0.6870 0.6683 0.6437 0.7692
2.0909 5.35 1550 1.4946 0.6877 0.6969 0.6877 0.6632 0.7811
2.0909 5.52 1600 1.4698 0.6755 0.6767 0.6755 0.6454 0.7743
2.0909 5.69 1650 1.4228 0.6804 0.7066 0.6804 0.6612 0.7785
2.0909 5.87 1700 1.3709 0.7312 0.7432 0.7312 0.7128 0.8140
2.0909 6.04 1750 1.3780 0.7215 0.7356 0.7215 0.7010 0.8082
2.0909 6.21 1800 1.3291 0.7215 0.7370 0.7215 0.7007 0.8090
2.0909 6.38 1850 1.3296 0.7191 0.7333 0.7191 0.7028 0.8056
2.0909 6.56 1900 1.3195 0.7191 0.7584 0.7191 0.7069 0.8048
2.0909 6.73 1950 1.2939 0.7191 0.7609 0.7191 0.7019 0.8065
1.75 6.9 2000 1.2800 0.7191 0.7353 0.7191 0.7018 0.8065
1.75 7.08 2050 1.2767 0.7094 0.7175 0.7094 0.6920 0.7998
1.75 7.25 2100 1.2280 0.7264 0.7689 0.7264 0.7148 0.8116
1.75 7.42 2150 1.2231 0.7385 0.7585 0.7385 0.7246 0.8201
1.75 7.59 2200 1.2198 0.7385 0.7563 0.7385 0.7248 0.8201
1.75 7.77 2250 1.1782 0.7482 0.7634 0.7482 0.7352 0.8269
1.75 7.94 2300 1.1848 0.7579 0.7900 0.7579 0.7519 0.8337
1.75 8.11 2350 1.1773 0.7579 0.7875 0.7579 0.7484 0.8346
1.75 8.28 2400 1.1752 0.7676 0.7965 0.7676 0.7594 0.8404
1.75 8.46 2450 1.1563 0.7724 0.8048 0.7724 0.7649 0.8438
1.5635 8.63 2500 1.1320 0.7724 0.8107 0.7724 0.7633 0.8448
1.5635 8.8 2550 1.1194 0.7700 0.8018 0.7700 0.7601 0.8421
1.5635 8.97 2600 1.1268 0.7554 0.7756 0.7554 0.7448 0.8329
1.5635 9.15 2650 1.1176 0.7676 0.7844 0.7676 0.7567 0.8404

Framework versions

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