Hugging Face's logo

fydhfzh
/
hubert-classifier-aug-fold-2-old

Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
hubert-classifier-aug-fold-2-old / README.md
fydhfzh's picture
fydhfzh
End of training
ffcafef verified
preview code
|
raw
history blame contribute delete
11.7 kB
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-2
    results: []

hubert-classifier-aug-fold-2

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: 0.5511
  • Accuracy: 0.8720
  • Precision: 0.8843
  • Recall: 0.8720
  • F1: 0.8715
  • Binary: 0.9111

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: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.22 50 4.3908 0.0364 0.0036 0.0364 0.0055 0.2323
No log 0.43 100 3.7968 0.0418 0.0037 0.0418 0.0062 0.3105
No log 0.65 150 3.4223 0.0499 0.0034 0.0499 0.0062 0.3297
No log 0.86 200 3.2649 0.0985 0.0263 0.0985 0.0345 0.3641
3.8781 1.08 250 3.1085 0.1323 0.0462 0.1323 0.0530 0.3862
3.8781 1.29 300 3.0185 0.1228 0.0541 0.1228 0.0555 0.3795
3.8781 1.51 350 2.8481 0.1660 0.1305 0.1660 0.1055 0.4117
3.8781 1.72 400 2.6281 0.2254 0.1349 0.2254 0.1446 0.4561
3.8781 1.94 450 2.4175 0.2605 0.1934 0.2605 0.1835 0.4804
3.0628 2.16 500 2.2785 0.2915 0.1990 0.2915 0.1961 0.5027
3.0628 2.37 550 2.0868 0.3738 0.3460 0.3738 0.3058 0.5586
3.0628 2.59 600 1.9295 0.4305 0.3815 0.4305 0.3534 0.6031
3.0628 2.8 650 1.7881 0.4872 0.4541 0.4872 0.4192 0.6405
2.478 3.02 700 1.6403 0.5304 0.4907 0.5304 0.4651 0.6715
2.478 3.23 750 1.5843 0.5735 0.5809 0.5735 0.5262 0.7026
2.478 3.45 800 1.4142 0.5789 0.5783 0.5789 0.5349 0.7040
2.478 3.66 850 1.2960 0.6289 0.6269 0.6289 0.5828 0.7399
2.478 3.88 900 1.2609 0.6680 0.6867 0.6680 0.6347 0.7686
2.0301 4.09 950 1.1055 0.6991 0.7211 0.6991 0.6727 0.7906
2.0301 4.31 1000 1.1323 0.7018 0.7338 0.7018 0.6907 0.7918
2.0301 4.53 1050 0.9934 0.7314 0.7459 0.7314 0.7152 0.8112
2.0301 4.74 1100 0.9489 0.7395 0.7599 0.7395 0.7213 0.8174
2.0301 4.96 1150 0.9370 0.7355 0.7633 0.7355 0.7260 0.8151
1.7314 5.17 1200 0.8762 0.7679 0.7919 0.7679 0.7548 0.8359
1.7314 5.39 1250 0.7944 0.7868 0.8047 0.7868 0.7783 0.8520
1.7314 5.6 1300 0.7927 0.7881 0.8091 0.7881 0.7804 0.8518
1.7314 5.82 1350 0.7881 0.7895 0.8087 0.7895 0.7830 0.8533
1.5038 6.03 1400 0.7181 0.8057 0.8260 0.8057 0.8012 0.8661
1.5038 6.25 1450 0.7339 0.8084 0.8205 0.8084 0.8006 0.8665
1.5038 6.47 1500 0.6845 0.8192 0.8371 0.8192 0.8160 0.8737
1.5038 6.68 1550 0.6878 0.8246 0.8463 0.8246 0.8239 0.8776
1.5038 6.9 1600 0.6396 0.8300 0.8439 0.8300 0.8277 0.8803
1.3718 7.11 1650 0.6743 0.8232 0.8458 0.8232 0.8225 0.8772
1.3718 7.33 1700 0.6018 0.8435 0.8644 0.8435 0.8419 0.8912
1.3718 7.54 1750 0.6461 0.8300 0.8494 0.8300 0.8293 0.8811
1.3718 7.76 1800 0.6181 0.8178 0.8385 0.8178 0.8157 0.8731
1.3718 7.97 1850 0.5696 0.8421 0.8608 0.8421 0.8425 0.8904
1.2723 8.19 1900 0.5924 0.8354 0.8550 0.8354 0.8335 0.8858
1.2723 8.41 1950 0.6176 0.8313 0.8464 0.8313 0.8281 0.8821
1.2723 8.62 2000 0.6529 0.8246 0.8496 0.8246 0.8239 0.8773
1.2723 8.84 2050 0.5532 0.8489 0.8680 0.8489 0.8485 0.8953
1.1706 9.05 2100 0.5791 0.8502 0.8673 0.8502 0.8502 0.8957
1.1706 9.27 2150 0.5871 0.8448 0.8624 0.8448 0.8434 0.8916
1.1706 9.48 2200 0.5853 0.8394 0.8605 0.8394 0.8394 0.8887
1.1706 9.7 2250 0.5420 0.8502 0.8675 0.8502 0.8494 0.8962
1.1706 9.91 2300 0.5913 0.8286 0.8515 0.8286 0.8275 0.8822
1.102 10.13 2350 0.5674 0.8448 0.8614 0.8448 0.8426 0.8927
1.102 10.34 2400 0.5948 0.8502 0.8675 0.8502 0.8491 0.8966
1.102 10.56 2450 0.5462 0.8543 0.8725 0.8543 0.8544 0.8969
1.102 10.78 2500 0.5774 0.8462 0.8662 0.8462 0.8469 0.8928
1.102 10.99 2550 0.5601 0.8448 0.8671 0.8448 0.8460 0.8938
1.0635 11.21 2600 0.5655 0.8570 0.8744 0.8570 0.8572 0.8988
1.0635 11.42 2650 0.5635 0.8516 0.8688 0.8516 0.8506 0.8950
1.0635 11.64 2700 0.6053 0.8327 0.8547 0.8327 0.8325 0.8822
1.0635 11.85 2750 0.6079 0.8408 0.8621 0.8408 0.8390 0.8896
1.0079 12.07 2800 0.5631 0.8489 0.8657 0.8489 0.8489 0.8962
1.0079 12.28 2850 0.5383 0.8691 0.8828 0.8691 0.8685 0.9093
1.0079 12.5 2900 0.5289 0.8623 0.8760 0.8623 0.8620 0.9040
1.0079 12.72 2950 0.5366 0.8650 0.8841 0.8650 0.8661 0.9065
1.0079 12.93 3000 0.5507 0.8516 0.8715 0.8516 0.8507 0.8965
0.9768 13.15 3050 0.5615 0.8596 0.8734 0.8596 0.8586 0.9036
0.9768 13.36 3100 0.5332 0.8583 0.8769 0.8583 0.8591 0.9016
0.9768 13.58 3150 0.5017 0.8691 0.8825 0.8691 0.8687 0.9086
0.9768 13.79 3200 0.5430 0.8623 0.8753 0.8623 0.8619 0.9034
0.9196 14.01 3250 0.5612 0.8556 0.8706 0.8556 0.8536 0.8992
0.9196 14.22 3300 0.5152 0.8664 0.8817 0.8664 0.8652 0.9062
0.9196 14.44 3350 0.5082 0.8745 0.8852 0.8745 0.8744 0.9126
0.9196 14.66 3400 0.5131 0.8745 0.8824 0.8745 0.8744 0.9128
0.9196 14.87 3450 0.5532 0.8543 0.8691 0.8543 0.8529 0.8992
0.8897 15.09 3500 0.5611 0.8556 0.8682 0.8556 0.8544 0.8987
0.8897 15.3 3550 0.5185 0.8664 0.8742 0.8664 0.8646 0.9062
0.8897 15.52 3600 0.5576 0.8556 0.8713 0.8556 0.8547 0.8988
0.8897 15.73 3650 0.5190 0.8650 0.8758 0.8650 0.8631 0.9053
0.8897 15.95 3700 0.5215 0.8623 0.8753 0.8623 0.8613 0.9031
0.8483 16.16 3750 0.5424 0.8543 0.8708 0.8543 0.8524 0.8978
0.8483 16.38 3800 0.5499 0.8691 0.8814 0.8691 0.8698 0.9090
0.8483 16.59 3850 0.5676 0.8556 0.8711 0.8556 0.8530 0.8997
0.8483 16.81 3900 0.5817 0.8570 0.8750 0.8570 0.8565 0.8996
0.8359 17.03 3950 0.5602 0.8583 0.8759 0.8583 0.8576 0.9005
0.8359 17.24 4000 0.5563 0.8596 0.8760 0.8596 0.8589 0.9016
0.8359 17.46 4050 0.5181 0.8637 0.8754 0.8637 0.8620 0.9039
0.8359 17.67 4100 0.5184 0.8731 0.8886 0.8731 0.8710 0.9119
0.8359 17.89 4150 0.5222 0.8664 0.8755 0.8664 0.8636 0.9076
0.7893 18.1 4200 0.5228 0.8691 0.8800 0.8691 0.8683 0.9086
0.7893 18.32 4250 0.5159 0.8745 0.8879 0.8745 0.8743 0.9128
0.7893 18.53 4300 0.5181 0.8745 0.8843 0.8745 0.8724 0.9117
0.7893 18.75 4350 0.5315 0.8772 0.8884 0.8772 0.8759 0.9134
0.7893 18.97 4400 0.5018 0.8772 0.8880 0.8772 0.8756 0.9132
0.768 19.18 4450 0.5367 0.8677 0.8787 0.8677 0.8682 0.9067
0.768 19.4 4500 0.5166 0.8745 0.8855 0.8745 0.8733 0.9120
0.768 19.61 4550 0.5010 0.8758 0.8850 0.8758 0.8748 0.9128
0.768 19.83 4600 0.5553 0.8704 0.8813 0.8704 0.8694 0.9096
0.7463 20.04 4650 0.5474 0.8637 0.8755 0.8637 0.8625 0.9053
0.7463 20.26 4700 0.5482 0.8664 0.8795 0.8664 0.8661 0.9062
0.7463 20.47 4750 0.5528 0.8650 0.8782 0.8650 0.8649 0.9058
0.7463 20.69 4800 0.5490 0.8650 0.8782 0.8650 0.8640 0.9053
0.7463 20.91 4850 0.5362 0.8637 0.8746 0.8637 0.8620 0.9043
0.7261 21.12 4900 0.5296 0.8691 0.8830 0.8691 0.8681 0.9077

Framework versions

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