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---
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license: apache-2.0
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base_model: facebook/hubert-base-ls960
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: hubert-classifier-aug-fold-2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# hubert-classifier-aug-fold-2
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5511
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- Accuracy: 0.8720
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- Precision: 0.8843
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- Recall: 0.8720
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- F1: 0.8715
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- Binary: 0.9111
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 30
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| No log | 0.22 | 50 | 4.3908 | 0.0364 | 0.0036 | 0.0364 | 0.0055 | 0.2323 |
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| No log | 0.43 | 100 | 3.7968 | 0.0418 | 0.0037 | 0.0418 | 0.0062 | 0.3105 |
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| No log | 0.65 | 150 | 3.4223 | 0.0499 | 0.0034 | 0.0499 | 0.0062 | 0.3297 |
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| No log | 0.86 | 200 | 3.2649 | 0.0985 | 0.0263 | 0.0985 | 0.0345 | 0.3641 |
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| 3.8781 | 1.08 | 250 | 3.1085 | 0.1323 | 0.0462 | 0.1323 | 0.0530 | 0.3862 |
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| 3.8781 | 1.29 | 300 | 3.0185 | 0.1228 | 0.0541 | 0.1228 | 0.0555 | 0.3795 |
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| 3.8781 | 1.51 | 350 | 2.8481 | 0.1660 | 0.1305 | 0.1660 | 0.1055 | 0.4117 |
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| 3.8781 | 1.72 | 400 | 2.6281 | 0.2254 | 0.1349 | 0.2254 | 0.1446 | 0.4561 |
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| 3.8781 | 1.94 | 450 | 2.4175 | 0.2605 | 0.1934 | 0.2605 | 0.1835 | 0.4804 |
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| 3.0628 | 2.16 | 500 | 2.2785 | 0.2915 | 0.1990 | 0.2915 | 0.1961 | 0.5027 |
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| 3.0628 | 2.37 | 550 | 2.0868 | 0.3738 | 0.3460 | 0.3738 | 0.3058 | 0.5586 |
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| 3.0628 | 2.59 | 600 | 1.9295 | 0.4305 | 0.3815 | 0.4305 | 0.3534 | 0.6031 |
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| 3.0628 | 2.8 | 650 | 1.7881 | 0.4872 | 0.4541 | 0.4872 | 0.4192 | 0.6405 |
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| 2.478 | 3.02 | 700 | 1.6403 | 0.5304 | 0.4907 | 0.5304 | 0.4651 | 0.6715 |
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| 2.478 | 3.23 | 750 | 1.5843 | 0.5735 | 0.5809 | 0.5735 | 0.5262 | 0.7026 |
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| 2.478 | 3.45 | 800 | 1.4142 | 0.5789 | 0.5783 | 0.5789 | 0.5349 | 0.7040 |
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| 2.478 | 3.66 | 850 | 1.2960 | 0.6289 | 0.6269 | 0.6289 | 0.5828 | 0.7399 |
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| 2.478 | 3.88 | 900 | 1.2609 | 0.6680 | 0.6867 | 0.6680 | 0.6347 | 0.7686 |
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| 2.0301 | 4.09 | 950 | 1.1055 | 0.6991 | 0.7211 | 0.6991 | 0.6727 | 0.7906 |
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| 2.0301 | 4.31 | 1000 | 1.1323 | 0.7018 | 0.7338 | 0.7018 | 0.6907 | 0.7918 |
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| 2.0301 | 4.53 | 1050 | 0.9934 | 0.7314 | 0.7459 | 0.7314 | 0.7152 | 0.8112 |
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| 2.0301 | 4.74 | 1100 | 0.9489 | 0.7395 | 0.7599 | 0.7395 | 0.7213 | 0.8174 |
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| 2.0301 | 4.96 | 1150 | 0.9370 | 0.7355 | 0.7633 | 0.7355 | 0.7260 | 0.8151 |
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| 1.7314 | 5.17 | 1200 | 0.8762 | 0.7679 | 0.7919 | 0.7679 | 0.7548 | 0.8359 |
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| 1.7314 | 5.39 | 1250 | 0.7944 | 0.7868 | 0.8047 | 0.7868 | 0.7783 | 0.8520 |
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| 1.7314 | 5.6 | 1300 | 0.7927 | 0.7881 | 0.8091 | 0.7881 | 0.7804 | 0.8518 |
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| 1.7314 | 5.82 | 1350 | 0.7881 | 0.7895 | 0.8087 | 0.7895 | 0.7830 | 0.8533 |
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| 1.5038 | 6.03 | 1400 | 0.7181 | 0.8057 | 0.8260 | 0.8057 | 0.8012 | 0.8661 |
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| 1.5038 | 6.25 | 1450 | 0.7339 | 0.8084 | 0.8205 | 0.8084 | 0.8006 | 0.8665 |
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| 1.5038 | 6.47 | 1500 | 0.6845 | 0.8192 | 0.8371 | 0.8192 | 0.8160 | 0.8737 |
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| 1.5038 | 6.68 | 1550 | 0.6878 | 0.8246 | 0.8463 | 0.8246 | 0.8239 | 0.8776 |
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| 1.5038 | 6.9 | 1600 | 0.6396 | 0.8300 | 0.8439 | 0.8300 | 0.8277 | 0.8803 |
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| 1.3718 | 7.11 | 1650 | 0.6743 | 0.8232 | 0.8458 | 0.8232 | 0.8225 | 0.8772 |
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| 1.3718 | 7.33 | 1700 | 0.6018 | 0.8435 | 0.8644 | 0.8435 | 0.8419 | 0.8912 |
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| 1.3718 | 7.54 | 1750 | 0.6461 | 0.8300 | 0.8494 | 0.8300 | 0.8293 | 0.8811 |
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| 1.3718 | 7.76 | 1800 | 0.6181 | 0.8178 | 0.8385 | 0.8178 | 0.8157 | 0.8731 |
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| 1.3718 | 7.97 | 1850 | 0.5696 | 0.8421 | 0.8608 | 0.8421 | 0.8425 | 0.8904 |
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| 1.2723 | 8.19 | 1900 | 0.5924 | 0.8354 | 0.8550 | 0.8354 | 0.8335 | 0.8858 |
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| 1.2723 | 8.41 | 1950 | 0.6176 | 0.8313 | 0.8464 | 0.8313 | 0.8281 | 0.8821 |
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| 1.2723 | 8.62 | 2000 | 0.6529 | 0.8246 | 0.8496 | 0.8246 | 0.8239 | 0.8773 |
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| 1.2723 | 8.84 | 2050 | 0.5532 | 0.8489 | 0.8680 | 0.8489 | 0.8485 | 0.8953 |
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| 1.1706 | 9.05 | 2100 | 0.5791 | 0.8502 | 0.8673 | 0.8502 | 0.8502 | 0.8957 |
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| 1.1706 | 9.27 | 2150 | 0.5871 | 0.8448 | 0.8624 | 0.8448 | 0.8434 | 0.8916 |
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| 1.1706 | 9.48 | 2200 | 0.5853 | 0.8394 | 0.8605 | 0.8394 | 0.8394 | 0.8887 |
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| 1.1706 | 9.7 | 2250 | 0.5420 | 0.8502 | 0.8675 | 0.8502 | 0.8494 | 0.8962 |
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| 1.1706 | 9.91 | 2300 | 0.5913 | 0.8286 | 0.8515 | 0.8286 | 0.8275 | 0.8822 |
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| 1.102 | 10.13 | 2350 | 0.5674 | 0.8448 | 0.8614 | 0.8448 | 0.8426 | 0.8927 |
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| 1.102 | 10.34 | 2400 | 0.5948 | 0.8502 | 0.8675 | 0.8502 | 0.8491 | 0.8966 |
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| 1.102 | 10.56 | 2450 | 0.5462 | 0.8543 | 0.8725 | 0.8543 | 0.8544 | 0.8969 |
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| 1.102 | 10.78 | 2500 | 0.5774 | 0.8462 | 0.8662 | 0.8462 | 0.8469 | 0.8928 |
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| 1.102 | 10.99 | 2550 | 0.5601 | 0.8448 | 0.8671 | 0.8448 | 0.8460 | 0.8938 |
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| 1.0635 | 11.21 | 2600 | 0.5655 | 0.8570 | 0.8744 | 0.8570 | 0.8572 | 0.8988 |
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| 1.0635 | 11.42 | 2650 | 0.5635 | 0.8516 | 0.8688 | 0.8516 | 0.8506 | 0.8950 |
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| 1.0635 | 11.64 | 2700 | 0.6053 | 0.8327 | 0.8547 | 0.8327 | 0.8325 | 0.8822 |
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| 1.0635 | 11.85 | 2750 | 0.6079 | 0.8408 | 0.8621 | 0.8408 | 0.8390 | 0.8896 |
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| 1.0079 | 12.07 | 2800 | 0.5631 | 0.8489 | 0.8657 | 0.8489 | 0.8489 | 0.8962 |
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| 1.0079 | 12.28 | 2850 | 0.5383 | 0.8691 | 0.8828 | 0.8691 | 0.8685 | 0.9093 |
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| 1.0079 | 12.5 | 2900 | 0.5289 | 0.8623 | 0.8760 | 0.8623 | 0.8620 | 0.9040 |
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| 1.0079 | 12.72 | 2950 | 0.5366 | 0.8650 | 0.8841 | 0.8650 | 0.8661 | 0.9065 |
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| 1.0079 | 12.93 | 3000 | 0.5507 | 0.8516 | 0.8715 | 0.8516 | 0.8507 | 0.8965 |
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| 0.9768 | 13.15 | 3050 | 0.5615 | 0.8596 | 0.8734 | 0.8596 | 0.8586 | 0.9036 |
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| 0.9768 | 13.36 | 3100 | 0.5332 | 0.8583 | 0.8769 | 0.8583 | 0.8591 | 0.9016 |
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| 0.9768 | 13.58 | 3150 | 0.5017 | 0.8691 | 0.8825 | 0.8691 | 0.8687 | 0.9086 |
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| 0.9768 | 13.79 | 3200 | 0.5430 | 0.8623 | 0.8753 | 0.8623 | 0.8619 | 0.9034 |
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| 0.9196 | 14.01 | 3250 | 0.5612 | 0.8556 | 0.8706 | 0.8556 | 0.8536 | 0.8992 |
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| 0.9196 | 14.22 | 3300 | 0.5152 | 0.8664 | 0.8817 | 0.8664 | 0.8652 | 0.9062 |
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| 0.9196 | 14.44 | 3350 | 0.5082 | 0.8745 | 0.8852 | 0.8745 | 0.8744 | 0.9126 |
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| 0.9196 | 14.66 | 3400 | 0.5131 | 0.8745 | 0.8824 | 0.8745 | 0.8744 | 0.9128 |
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| 0.9196 | 14.87 | 3450 | 0.5532 | 0.8543 | 0.8691 | 0.8543 | 0.8529 | 0.8992 |
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| 0.8897 | 15.09 | 3500 | 0.5611 | 0.8556 | 0.8682 | 0.8556 | 0.8544 | 0.8987 |
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| 0.8897 | 15.3 | 3550 | 0.5185 | 0.8664 | 0.8742 | 0.8664 | 0.8646 | 0.9062 |
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| 0.8897 | 15.52 | 3600 | 0.5576 | 0.8556 | 0.8713 | 0.8556 | 0.8547 | 0.8988 |
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| 0.8897 | 15.73 | 3650 | 0.5190 | 0.8650 | 0.8758 | 0.8650 | 0.8631 | 0.9053 |
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| 0.8897 | 15.95 | 3700 | 0.5215 | 0.8623 | 0.8753 | 0.8623 | 0.8613 | 0.9031 |
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| 0.8483 | 16.16 | 3750 | 0.5424 | 0.8543 | 0.8708 | 0.8543 | 0.8524 | 0.8978 |
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| 0.8483 | 16.38 | 3800 | 0.5499 | 0.8691 | 0.8814 | 0.8691 | 0.8698 | 0.9090 |
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| 0.8483 | 16.59 | 3850 | 0.5676 | 0.8556 | 0.8711 | 0.8556 | 0.8530 | 0.8997 |
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| 0.8483 | 16.81 | 3900 | 0.5817 | 0.8570 | 0.8750 | 0.8570 | 0.8565 | 0.8996 |
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| 0.8359 | 17.03 | 3950 | 0.5602 | 0.8583 | 0.8759 | 0.8583 | 0.8576 | 0.9005 |
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| 0.8359 | 17.24 | 4000 | 0.5563 | 0.8596 | 0.8760 | 0.8596 | 0.8589 | 0.9016 |
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| 0.8359 | 17.46 | 4050 | 0.5181 | 0.8637 | 0.8754 | 0.8637 | 0.8620 | 0.9039 |
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| 0.8359 | 17.67 | 4100 | 0.5184 | 0.8731 | 0.8886 | 0.8731 | 0.8710 | 0.9119 |
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| 0.8359 | 17.89 | 4150 | 0.5222 | 0.8664 | 0.8755 | 0.8664 | 0.8636 | 0.9076 |
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| 0.7893 | 18.1 | 4200 | 0.5228 | 0.8691 | 0.8800 | 0.8691 | 0.8683 | 0.9086 |
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| 0.7893 | 18.32 | 4250 | 0.5159 | 0.8745 | 0.8879 | 0.8745 | 0.8743 | 0.9128 |
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| 0.7893 | 18.53 | 4300 | 0.5181 | 0.8745 | 0.8843 | 0.8745 | 0.8724 | 0.9117 |
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| 0.7893 | 18.75 | 4350 | 0.5315 | 0.8772 | 0.8884 | 0.8772 | 0.8759 | 0.9134 |
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| 0.7893 | 18.97 | 4400 | 0.5018 | 0.8772 | 0.8880 | 0.8772 | 0.8756 | 0.9132 |
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| 0.768 | 19.18 | 4450 | 0.5367 | 0.8677 | 0.8787 | 0.8677 | 0.8682 | 0.9067 |
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| 0.768 | 19.4 | 4500 | 0.5166 | 0.8745 | 0.8855 | 0.8745 | 0.8733 | 0.9120 |
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| 0.768 | 19.61 | 4550 | 0.5010 | 0.8758 | 0.8850 | 0.8758 | 0.8748 | 0.9128 |
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| 0.768 | 19.83 | 4600 | 0.5553 | 0.8704 | 0.8813 | 0.8704 | 0.8694 | 0.9096 |
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| 0.7463 | 20.04 | 4650 | 0.5474 | 0.8637 | 0.8755 | 0.8637 | 0.8625 | 0.9053 |
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| 0.7463 | 20.26 | 4700 | 0.5482 | 0.8664 | 0.8795 | 0.8664 | 0.8661 | 0.9062 |
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| 0.7463 | 20.47 | 4750 | 0.5528 | 0.8650 | 0.8782 | 0.8650 | 0.8649 | 0.9058 |
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| 0.7463 | 20.69 | 4800 | 0.5490 | 0.8650 | 0.8782 | 0.8650 | 0.8640 | 0.9053 |
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| 0.7463 | 20.91 | 4850 | 0.5362 | 0.8637 | 0.8746 | 0.8637 | 0.8620 | 0.9043 |
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| 0.7261 | 21.12 | 4900 | 0.5296 | 0.8691 | 0.8830 | 0.8691 | 0.8681 | 0.9077 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.3.0
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- Datasets 2.19.1
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- Tokenizers 0.15.1
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