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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: 0.4752
  • Accuracy: 0.8895
  • Precision: 0.9022
  • Recall: 0.8895
  • F1: 0.8885
  • Binary: 0.9222

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 0.13 50 3.9894 0.0472 0.0049 0.0472 0.0080 0.3132
No log 0.27 100 3.4226 0.0970 0.0691 0.0970 0.0477 0.3615
No log 0.4 150 3.1173 0.1509 0.0772 0.1509 0.0759 0.3985
No log 0.54 200 2.7885 0.2439 0.1966 0.2439 0.1769 0.4664
No log 0.67 250 2.4226 0.3477 0.2627 0.3477 0.2654 0.5412
No log 0.81 300 2.0733 0.4515 0.4074 0.4515 0.3860 0.6143
No log 0.94 350 1.8531 0.5512 0.5247 0.5512 0.4904 0.6844
3.0592 1.08 400 1.6649 0.5809 0.5680 0.5809 0.5365 0.7046
3.0592 1.21 450 1.4351 0.6199 0.6214 0.6199 0.5796 0.7327
3.0592 1.35 500 1.2309 0.6954 0.6842 0.6954 0.6636 0.7864
3.0592 1.48 550 1.1706 0.7089 0.7292 0.7089 0.6899 0.7947
3.0592 1.62 600 1.0363 0.7264 0.7374 0.7264 0.7076 0.8078
3.0592 1.75 650 0.9977 0.7642 0.7769 0.7642 0.7508 0.8344
3.0592 1.89 700 0.9239 0.7655 0.7900 0.7655 0.7497 0.8367
1.4103 2.02 750 0.8664 0.7722 0.7836 0.7722 0.7587 0.8398
1.4103 2.16 800 0.8351 0.7844 0.7920 0.7844 0.7716 0.8480
1.4103 2.29 850 0.7602 0.7965 0.8065 0.7965 0.7904 0.8578
1.4103 2.43 900 0.7829 0.8100 0.8364 0.8100 0.8052 0.8678
1.4103 2.56 950 0.7197 0.8005 0.8158 0.8005 0.7903 0.8616
1.4103 2.7 1000 0.7602 0.8113 0.8297 0.8113 0.8059 0.8683
1.4103 2.83 1050 0.6590 0.8342 0.8489 0.8342 0.8266 0.8850
1.4103 2.97 1100 0.6178 0.8396 0.8546 0.8396 0.8320 0.8880
0.8796 3.1 1150 0.6263 0.8396 0.8577 0.8396 0.8344 0.8871
0.8796 3.24 1200 0.6099 0.8369 0.8532 0.8369 0.8302 0.8861
0.8796 3.37 1250 0.6266 0.8329 0.8505 0.8329 0.8289 0.8815
0.8796 3.51 1300 0.5873 0.8437 0.8564 0.8437 0.8409 0.8896
0.8796 3.64 1350 0.6750 0.8396 0.8572 0.8396 0.8359 0.8875
0.8796 3.78 1400 0.6247 0.8383 0.8534 0.8383 0.8356 0.8856
0.8796 3.91 1450 0.6302 0.8369 0.8483 0.8369 0.8328 0.8852
0.6486 4.05 1500 0.6063 0.8558 0.8670 0.8558 0.8504 0.9001
0.6486 4.18 1550 0.6152 0.8410 0.8564 0.8410 0.8359 0.8884
0.6486 4.32 1600 0.6123 0.8504 0.8653 0.8504 0.8438 0.8949
0.6486 4.45 1650 0.6590 0.8464 0.8608 0.8464 0.8410 0.8934
0.6486 4.59 1700 0.6929 0.8464 0.8599 0.8464 0.8419 0.8937
0.6486 4.72 1750 0.5531 0.8598 0.8753 0.8598 0.8574 0.9020
0.6486 4.86 1800 0.6241 0.8504 0.8635 0.8504 0.8446 0.8949
0.6486 4.99 1850 0.6102 0.8531 0.8632 0.8531 0.8484 0.8966
0.5302 5.12 1900 0.6633 0.8612 0.8764 0.8612 0.8541 0.9026
0.5302 5.26 1950 0.6566 0.8531 0.8633 0.8531 0.8489 0.8968
0.5302 5.39 2000 0.7052 0.8342 0.8598 0.8342 0.8312 0.8836
0.5302 5.53 2050 0.6527 0.8518 0.8618 0.8518 0.8491 0.8958
0.5302 5.66 2100 0.6170 0.8598 0.8714 0.8598 0.8558 0.9011
0.5302 5.8 2150 0.6832 0.8491 0.8648 0.8491 0.8462 0.8939
0.5302 5.93 2200 0.6431 0.8571 0.8733 0.8571 0.8540 0.8992
0.4492 6.07 2250 0.6744 0.8437 0.8664 0.8437 0.8408 0.8898
0.4492 6.2 2300 0.6106 0.8544 0.8641 0.8544 0.8499 0.8977
0.4492 6.34 2350 0.5262 0.8652 0.8794 0.8652 0.8636 0.9053
0.4492 6.47 2400 0.6457 0.8544 0.8689 0.8544 0.8519 0.8981
0.4492 6.61 2450 0.6413 0.8625 0.8688 0.8625 0.8584 0.9034
0.4492 6.74 2500 0.6634 0.8504 0.8634 0.8504 0.8476 0.8941
0.4492 6.88 2550 0.6251 0.8612 0.8708 0.8612 0.8560 0.9024
0.4054 7.01 2600 0.6368 0.8558 0.8682 0.8558 0.8501 0.8988
0.4054 7.15 2650 0.6141 0.8720 0.8825 0.8720 0.8680 0.9097
0.4054 7.28 2700 0.6492 0.8558 0.8711 0.8558 0.8531 0.8988
0.4054 7.42 2750 0.6126 0.8760 0.8842 0.8760 0.8720 0.9135
0.4054 7.55 2800 0.6686 0.8720 0.8827 0.8720 0.8683 0.9089
0.4054 7.69 2850 0.6718 0.8652 0.8774 0.8652 0.8629 0.9053
0.4054 7.82 2900 0.6315 0.8706 0.8832 0.8706 0.8681 0.9092
0.4054 7.96 2950 0.7499 0.8558 0.8692 0.8558 0.8512 0.8980
0.3625 8.09 3000 0.6539 0.8639 0.8819 0.8639 0.8606 0.9047
0.3625 8.23 3050 0.6250 0.8787 0.8910 0.8787 0.8766 0.9151
0.3625 8.36 3100 0.6734 0.8639 0.8709 0.8639 0.8594 0.9053
0.3625 8.5 3150 0.7044 0.8571 0.8673 0.8571 0.8519 0.9001
0.3625 8.63 3200 0.7766 0.8558 0.8705 0.8558 0.8531 0.8993
0.3625 8.77 3250 0.6379 0.8585 0.8723 0.8585 0.8555 0.9001
0.3625 8.9 3300 0.6725 0.8612 0.8786 0.8612 0.8577 0.9030
0.3322 9.04 3350 0.6457 0.8679 0.8835 0.8679 0.8649 0.9067
0.3322 9.17 3400 0.7583 0.8558 0.8671 0.8558 0.8526 0.8982
0.3322 9.31 3450 0.6929 0.8598 0.8737 0.8598 0.8538 0.9007
0.3322 9.44 3500 0.7696 0.8491 0.8615 0.8491 0.8447 0.8927
0.3322 9.58 3550 0.7551 0.8531 0.8725 0.8531 0.8467 0.8973
0.3322 9.71 3600 0.6499 0.8598 0.8725 0.8598 0.8565 0.9011
0.3322 9.84 3650 0.6816 0.8558 0.8702 0.8558 0.8537 0.8992
0.3322 9.98 3700 0.6956 0.8598 0.8739 0.8598 0.8566 0.9011

Framework versions

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