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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: 0.7155
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- Accuracy: 0.8625
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- Precision: 0.8774
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- Recall: 0.8625
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- F1: 0.8616
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- Binary: 0.9049
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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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- lr_scheduler_warmup_steps: 500
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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 | 0.24 | 50 | 4.4213 | 0.0142 | 0.0068 | 0.0142 | 0.0043 | 0.1424 |
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| No log | 0.48 | 100 | 4.3075 | 0.0442 | 0.0196 | 0.0442 | 0.0177 | 0.3070 |
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| No log | 0.72 | 150 | 4.0138 | 0.0502 | 0.0284 | 0.0502 | 0.0150 | 0.3229 |
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| No log | 0.96 | 200 | 3.7089 | 0.1049 | 0.0547 | 0.1049 | 0.0490 | 0.3679 |
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| 4.2506 | 1.2 | 250 | 3.4627 | 0.1498 | 0.0995 | 0.1498 | 0.0882 | 0.3998 |
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| 4.2506 | 1.44 | 300 | 3.2210 | 0.1753 | 0.0964 | 0.1753 | 0.0970 | 0.4209 |
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| 4.2506 | 1.68 | 350 | 2.9579 | 0.2412 | 0.1495 | 0.2412 | 0.1554 | 0.4661 |
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| 4.2506 | 1.92 | 400 | 2.6469 | 0.2869 | 0.2258 | 0.2869 | 0.1944 | 0.5003 |
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| 3.3165 | 2.16 | 450 | 2.2886 | 0.4097 | 0.3315 | 0.4097 | 0.3156 | 0.5854 |
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| 3.3165 | 2.4 | 500 | 2.0450 | 0.4757 | 0.4368 | 0.4757 | 0.4139 | 0.6314 |
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| 3.3165 | 2.63 | 550 | 1.7683 | 0.5453 | 0.5445 | 0.5453 | 0.4891 | 0.6812 |
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| 3.3165 | 2.87 | 600 | 1.6279 | 0.6090 | 0.6005 | 0.6090 | 0.5677 | 0.7214 |
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| 2.2876 | 3.11 | 650 | 1.3926 | 0.6404 | 0.6494 | 0.6404 | 0.6015 | 0.7466 |
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| 2.2876 | 3.35 | 700 | 1.2807 | 0.6689 | 0.6921 | 0.6689 | 0.6386 | 0.7679 |
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| 2.2876 | 3.59 | 750 | 1.0819 | 0.7161 | 0.7222 | 0.7161 | 0.6956 | 0.8023 |
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| 2.2876 | 3.83 | 800 | 1.0035 | 0.7326 | 0.7524 | 0.7326 | 0.7131 | 0.8132 |
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| 1.6056 | 4.07 | 850 | 0.9128 | 0.7648 | 0.7692 | 0.7648 | 0.7466 | 0.8362 |
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| 1.6056 | 4.31 | 900 | 0.8266 | 0.7910 | 0.7986 | 0.7910 | 0.7817 | 0.8542 |
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| 1.6056 | 4.55 | 950 | 0.7800 | 0.7955 | 0.8165 | 0.7955 | 0.7839 | 0.8587 |
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| 1.6056 | 4.79 | 1000 | 0.8136 | 0.7873 | 0.8078 | 0.7873 | 0.7747 | 0.8520 |
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| 1.2478 | 5.03 | 1050 | 0.8057 | 0.7783 | 0.7988 | 0.7783 | 0.7707 | 0.8451 |
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| 1.2478 | 5.27 | 1100 | 0.6884 | 0.8082 | 0.8287 | 0.8082 | 0.8051 | 0.8658 |
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| 1.2478 | 5.51 | 1150 | 0.6515 | 0.8217 | 0.8312 | 0.8217 | 0.8173 | 0.8766 |
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| 1.2478 | 5.75 | 1200 | 0.7116 | 0.8120 | 0.8295 | 0.8120 | 0.8047 | 0.8694 |
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| 1.2478 | 5.99 | 1250 | 0.6343 | 0.8360 | 0.8492 | 0.8360 | 0.8337 | 0.8856 |
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| 1.0252 | 6.23 | 1300 | 0.6608 | 0.8060 | 0.8252 | 0.8060 | 0.8055 | 0.8654 |
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| 1.0252 | 6.47 | 1350 | 0.6766 | 0.8097 | 0.8265 | 0.8097 | 0.8068 | 0.8667 |
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| 1.0252 | 6.71 | 1400 | 0.6744 | 0.8135 | 0.8298 | 0.8135 | 0.8108 | 0.8697 |
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| 1.0252 | 6.95 | 1450 | 0.6163 | 0.8404 | 0.8539 | 0.8404 | 0.8391 | 0.8902 |
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| 0.8918 | 7.19 | 1500 | 0.5671 | 0.8524 | 0.8627 | 0.8524 | 0.8517 | 0.8980 |
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| 0.8918 | 7.43 | 1550 | 0.5416 | 0.8562 | 0.8689 | 0.8562 | 0.8546 | 0.9006 |
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| 0.8918 | 7.66 | 1600 | 0.5710 | 0.8554 | 0.8648 | 0.8554 | 0.8534 | 0.8990 |
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| 0.8918 | 7.9 | 1650 | 0.5341 | 0.8524 | 0.8649 | 0.8524 | 0.8508 | 0.8982 |
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| 0.7857 | 8.14 | 1700 | 0.5764 | 0.8449 | 0.8554 | 0.8449 | 0.8429 | 0.8919 |
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| 0.7857 | 8.38 | 1750 | 0.5792 | 0.8562 | 0.8646 | 0.8562 | 0.8544 | 0.8997 |
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| 0.7857 | 8.62 | 1800 | 0.5455 | 0.8599 | 0.8689 | 0.8599 | 0.8566 | 0.9030 |
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| 0.7857 | 8.86 | 1850 | 0.5837 | 0.8547 | 0.8655 | 0.8547 | 0.8521 | 0.8993 |
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| 0.7152 | 9.1 | 1900 | 0.6607 | 0.8472 | 0.8592 | 0.8472 | 0.8459 | 0.8948 |
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| 0.7152 | 9.34 | 1950 | 0.6106 | 0.8547 | 0.8648 | 0.8547 | 0.8538 | 0.8994 |
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| 0.7152 | 9.58 | 2000 | 0.5548 | 0.8659 | 0.8762 | 0.8659 | 0.8648 | 0.9064 |
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| 0.7152 | 9.82 | 2050 | 0.6189 | 0.8532 | 0.8635 | 0.8532 | 0.8514 | 0.8990 |
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| 0.6356 | 10.06 | 2100 | 0.5869 | 0.8614 | 0.8718 | 0.8614 | 0.8601 | 0.9037 |
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| 0.6356 | 10.3 | 2150 | 0.5585 | 0.8622 | 0.8707 | 0.8622 | 0.8603 | 0.9049 |
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| 0.6356 | 10.54 | 2200 | 0.5857 | 0.8659 | 0.8781 | 0.8659 | 0.8642 | 0.9078 |
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| 0.6356 | 10.78 | 2250 | 0.5226 | 0.8704 | 0.8806 | 0.8704 | 0.8699 | 0.9103 |
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| 0.6081 | 11.02 | 2300 | 0.5549 | 0.8712 | 0.8849 | 0.8712 | 0.8703 | 0.9108 |
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| 0.6081 | 11.26 | 2350 | 0.5454 | 0.8712 | 0.8829 | 0.8712 | 0.8707 | 0.9112 |
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| 0.6081 | 11.5 | 2400 | 0.5374 | 0.8659 | 0.8737 | 0.8659 | 0.8648 | 0.9085 |
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| 0.6081 | 11.74 | 2450 | 0.5789 | 0.8584 | 0.8742 | 0.8584 | 0.8580 | 0.9020 |
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| 0.6081 | 11.98 | 2500 | 0.5650 | 0.8659 | 0.8779 | 0.8659 | 0.8653 | 0.9067 |
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| 0.5506 | 12.22 | 2550 | 0.5908 | 0.8652 | 0.8750 | 0.8652 | 0.8651 | 0.9057 |
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| 0.5506 | 12.46 | 2600 | 0.5970 | 0.8757 | 0.8809 | 0.8757 | 0.8749 | 0.9133 |
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| 0.5506 | 12.69 | 2650 | 0.5703 | 0.8727 | 0.8811 | 0.8727 | 0.8726 | 0.9117 |
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| 0.5506 | 12.93 | 2700 | 0.6146 | 0.8614 | 0.8716 | 0.8614 | 0.8607 | 0.9044 |
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| 0.529 | 13.17 | 2750 | 0.5766 | 0.8742 | 0.8850 | 0.8742 | 0.8732 | 0.9124 |
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| 0.529 | 13.41 | 2800 | 0.5620 | 0.8682 | 0.8778 | 0.8682 | 0.8670 | 0.9091 |
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| 0.529 | 13.65 | 2850 | 0.5397 | 0.8846 | 0.8928 | 0.8846 | 0.8844 | 0.9211 |
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| 0.529 | 13.89 | 2900 | 0.5858 | 0.8674 | 0.8779 | 0.8674 | 0.8671 | 0.9072 |
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| 0.4938 | 14.13 | 2950 | 0.6406 | 0.8674 | 0.8770 | 0.8674 | 0.8669 | 0.9068 |
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| 0.4938 | 14.37 | 3000 | 0.6754 | 0.8599 | 0.8696 | 0.8599 | 0.8583 | 0.9027 |
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| 0.4938 | 14.61 | 3050 | 0.6439 | 0.8637 | 0.8734 | 0.8637 | 0.8623 | 0.9053 |
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| 0.4938 | 14.85 | 3100 | 0.6580 | 0.8674 | 0.8781 | 0.8674 | 0.8661 | 0.9075 |
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| 0.4645 | 15.09 | 3150 | 0.6317 | 0.8697 | 0.8783 | 0.8697 | 0.8690 | 0.9096 |
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| 0.4645 | 15.33 | 3200 | 0.5905 | 0.8772 | 0.8865 | 0.8772 | 0.8764 | 0.9157 |
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| 0.4645 | 15.57 | 3250 | 0.6268 | 0.8764 | 0.8847 | 0.8764 | 0.8751 | 0.9145 |
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| 0.4645 | 15.81 | 3300 | 0.6298 | 0.8727 | 0.8808 | 0.8727 | 0.8719 | 0.9112 |
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| 0.4467 | 16.05 | 3350 | 0.6039 | 0.8749 | 0.8828 | 0.8749 | 0.8736 | 0.9127 |
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| 0.4467 | 16.29 | 3400 | 0.5955 | 0.8831 | 0.8890 | 0.8831 | 0.8823 | 0.9194 |
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| 0.4467 | 16.53 | 3450 | 0.5954 | 0.8772 | 0.8865 | 0.8772 | 0.8761 | 0.9146 |
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| 0.4467 | 16.77 | 3500 | 0.6088 | 0.8779 | 0.8857 | 0.8779 | 0.8773 | 0.9152 |
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| 0.4269 | 17.01 | 3550 | 0.6572 | 0.8757 | 0.8830 | 0.8757 | 0.8748 | 0.9139 |
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| 0.4269 | 17.25 | 3600 | 0.6490 | 0.8644 | 0.8730 | 0.8644 | 0.8626 | 0.9058 |
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| 0.4269 | 17.49 | 3650 | 0.6591 | 0.8712 | 0.8794 | 0.8712 | 0.8696 | 0.9104 |
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| 0.4269 | 17.72 | 3700 | 0.6369 | 0.8742 | 0.8854 | 0.8742 | 0.8726 | 0.9125 |
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| 0.4269 | 17.96 | 3750 | 0.6000 | 0.8869 | 0.8922 | 0.8869 | 0.8863 | 0.9211 |
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| 0.4099 | 18.2 | 3800 | 0.6395 | 0.8682 | 0.8763 | 0.8682 | 0.8665 | 0.9076 |
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| 0.4099 | 18.44 | 3850 | 0.6416 | 0.8734 | 0.8795 | 0.8734 | 0.8727 | 0.9118 |
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| 0.4099 | 18.68 | 3900 | 0.5822 | 0.8794 | 0.8855 | 0.8794 | 0.8785 | 0.9165 |
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| 0.4099 | 18.92 | 3950 | 0.6365 | 0.8816 | 0.8885 | 0.8816 | 0.8806 | 0.9178 |
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| 0.3945 | 19.16 | 4000 | 0.6389 | 0.8801 | 0.8864 | 0.8801 | 0.8791 | 0.9169 |
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| 0.3945 | 19.4 | 4050 | 0.5901 | 0.8846 | 0.8908 | 0.8846 | 0.8843 | 0.9203 |
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| 0.3945 | 19.64 | 4100 | 0.6197 | 0.8824 | 0.8879 | 0.8824 | 0.8816 | 0.9193 |
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| 0.3945 | 19.88 | 4150 | 0.6365 | 0.8809 | 0.8875 | 0.8809 | 0.8798 | 0.9177 |
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| 0.371 | 20.12 | 4200 | 0.6293 | 0.8831 | 0.8898 | 0.8831 | 0.8822 | 0.9190 |
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| 0.371 | 20.36 | 4250 | 0.6753 | 0.8742 | 0.8815 | 0.8742 | 0.8732 | 0.9127 |
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| 0.371 | 20.6 | 4300 | 0.6299 | 0.8757 | 0.8831 | 0.8757 | 0.8754 | 0.9140 |
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| 0.371 | 20.84 | 4350 | 0.6034 | 0.8869 | 0.8950 | 0.8869 | 0.8863 | 0.9216 |
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| 0.3772 | 21.08 | 4400 | 0.5806 | 0.8959 | 0.9025 | 0.8959 | 0.8955 | 0.9276 |
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| 0.3772 | 21.32 | 4450 | 0.5994 | 0.8899 | 0.8952 | 0.8899 | 0.8890 | 0.9237 |
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| 0.3772 | 21.56 | 4500 | 0.6378 | 0.8824 | 0.8895 | 0.8824 | 0.8819 | 0.9181 |
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| 0.3772 | 21.8 | 4550 | 0.6267 | 0.8816 | 0.8869 | 0.8816 | 0.8808 | 0.9182 |
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| 0.3512 | 22.04 | 4600 | 0.6104 | 0.8816 | 0.8878 | 0.8816 | 0.8804 | 0.9185 |
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| 0.3512 | 22.28 | 4650 | 0.6146 | 0.8854 | 0.8907 | 0.8854 | 0.8845 | 0.9209 |
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| 0.3512 | 22.51 | 4700 | 0.6408 | 0.8869 | 0.8944 | 0.8869 | 0.8866 | 0.9213 |
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| 0.3512 | 22.75 | 4750 | 0.6246 | 0.8839 | 0.8906 | 0.8839 | 0.8833 | 0.9196 |
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| 0.3512 | 22.99 | 4800 | 0.5967 | 0.8846 | 0.8911 | 0.8846 | 0.8837 | 0.9199 |
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| 0.3339 | 23.23 | 4850 | 0.6727 | 0.8801 | 0.8867 | 0.8801 | 0.8795 | 0.9165 |
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| 0.3339 | 23.47 | 4900 | 0.6122 | 0.8839 | 0.8905 | 0.8839 | 0.8833 | 0.9187 |
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| 0.3339 | 23.71 | 4950 | 0.6191 | 0.8869 | 0.8932 | 0.8869 | 0.8860 | 0.9211 |
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| 0.3339 | 23.95 | 5000 | 0.6397 | 0.8906 | 0.8974 | 0.8906 | 0.8898 | 0.9240 |
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| 0.3172 | 24.19 | 5050 | 0.6519 | 0.8824 | 0.8905 | 0.8824 | 0.8821 | 0.9177 |
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| 0.3172 | 24.43 | 5100 | 0.6141 | 0.8884 | 0.8948 | 0.8884 | 0.8877 | 0.9224 |
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| 0.3172 | 24.67 | 5150 | 0.6530 | 0.8846 | 0.8912 | 0.8846 | 0.8836 | 0.9200 |
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| 0.3172 | 24.91 | 5200 | 0.6152 | 0.8884 | 0.8938 | 0.8884 | 0.8875 | 0.9226 |
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| 0.3049 | 25.15 | 5250 | 0.6550 | 0.8846 | 0.8920 | 0.8846 | 0.8833 | 0.9200 |
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| 0.3049 | 25.39 | 5300 | 0.6510 | 0.8876 | 0.8938 | 0.8876 | 0.8867 | 0.9221 |
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| 0.3049 | 25.63 | 5350 | 0.6402 | 0.8816 | 0.8883 | 0.8816 | 0.8812 | 0.9182 |
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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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