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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-ref
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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-ref
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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: 3.1461
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- Accuracy: 0.1671
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- Precision: 0.0661
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- Recall: 0.1671
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- F1: 0.0830
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- Binary: 0.4137
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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: 1e-05
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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: 10
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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 | 0.19 | 50 | 4.4123 | 0.0377 | 0.0239 | 0.0377 | 0.0213 | 0.2075 |
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| No log | 0.38 | 100 | 4.3574 | 0.0674 | 0.0177 | 0.0674 | 0.0253 | 0.2741 |
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| No log | 0.58 | 150 | 4.2332 | 0.0323 | 0.0017 | 0.0323 | 0.0032 | 0.2884 |
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| No log | 0.77 | 200 | 4.1388 | 0.0647 | 0.0160 | 0.0647 | 0.0182 | 0.3380 |
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| No log | 0.96 | 250 | 4.0567 | 0.0674 | 0.0350 | 0.0674 | 0.0222 | 0.3407 |
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| No log | 1.15 | 300 | 4.0043 | 0.0566 | 0.0114 | 0.0566 | 0.0143 | 0.3221 |
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| No log | 1.34 | 350 | 3.9470 | 0.0485 | 0.0049 | 0.0485 | 0.0080 | 0.3221 |
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| No log | 1.53 | 400 | 3.8803 | 0.0593 | 0.0124 | 0.0593 | 0.0135 | 0.3353 |
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| No log | 1.73 | 450 | 3.8326 | 0.0566 | 0.0057 | 0.0566 | 0.0097 | 0.3323 |
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| 4.1711 | 1.92 | 500 | 3.7760 | 0.0566 | 0.0061 | 0.0566 | 0.0103 | 0.3356 |
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| 4.1711 | 2.11 | 550 | 3.7454 | 0.0647 | 0.0066 | 0.0647 | 0.0118 | 0.3372 |
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| 4.1711 | 2.3 | 600 | 3.7036 | 0.0701 | 0.0075 | 0.0701 | 0.0132 | 0.3429 |
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| 4.1711 | 2.49 | 650 | 3.6729 | 0.0728 | 0.0094 | 0.0728 | 0.0161 | 0.3431 |
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| 4.1711 | 2.68 | 700 | 3.6306 | 0.0728 | 0.0117 | 0.0728 | 0.0177 | 0.3461 |
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| 4.1711 | 2.88 | 750 | 3.6075 | 0.0836 | 0.0155 | 0.0836 | 0.0237 | 0.3536 |
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| 4.1711 | 3.07 | 800 | 3.5817 | 0.0943 | 0.0284 | 0.0943 | 0.0285 | 0.3604 |
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| 4.1711 | 3.26 | 850 | 3.5607 | 0.0916 | 0.0179 | 0.0916 | 0.0272 | 0.3577 |
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| 4.1711 | 3.45 | 900 | 3.5373 | 0.0943 | 0.0214 | 0.0943 | 0.0304 | 0.3588 |
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| 4.1711 | 3.64 | 950 | 3.5083 | 0.1078 | 0.0357 | 0.1078 | 0.0464 | 0.3714 |
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| 3.7424 | 3.84 | 1000 | 3.4717 | 0.1105 | 0.0512 | 0.1105 | 0.0520 | 0.3765 |
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| 3.7424 | 4.03 | 1050 | 3.4619 | 0.1213 | 0.0361 | 0.1213 | 0.0489 | 0.3825 |
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| 3.7424 | 4.22 | 1100 | 3.4375 | 0.1240 | 0.0453 | 0.1240 | 0.0554 | 0.3844 |
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| 3.7424 | 4.41 | 1150 | 3.4282 | 0.1267 | 0.0390 | 0.1267 | 0.0547 | 0.3849 |
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| 3.7424 | 4.6 | 1200 | 3.4076 | 0.1267 | 0.0334 | 0.1267 | 0.0493 | 0.3838 |
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| 3.7424 | 4.79 | 1250 | 3.3875 | 0.1078 | 0.0263 | 0.1078 | 0.0388 | 0.3730 |
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| 3.7424 | 4.99 | 1300 | 3.3746 | 0.1240 | 0.0547 | 0.1240 | 0.0496 | 0.3822 |
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| 3.7424 | 5.18 | 1350 | 3.3459 | 0.1375 | 0.0621 | 0.1375 | 0.0618 | 0.3946 |
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| 3.7424 | 5.37 | 1400 | 3.3313 | 0.1375 | 0.0598 | 0.1375 | 0.0650 | 0.3946 |
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| 3.7424 | 5.56 | 1450 | 3.3263 | 0.1429 | 0.0556 | 0.1429 | 0.0623 | 0.3951 |
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| 3.5358 | 5.75 | 1500 | 3.3100 | 0.1348 | 0.0629 | 0.1348 | 0.0640 | 0.3895 |
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| 3.5358 | 5.94 | 1550 | 3.2880 | 0.1402 | 0.0637 | 0.1402 | 0.0641 | 0.3957 |
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| 3.5358 | 6.14 | 1600 | 3.2742 | 0.1402 | 0.0628 | 0.1402 | 0.0640 | 0.3965 |
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| 3.5358 | 6.33 | 1650 | 3.2605 | 0.1509 | 0.0861 | 0.1509 | 0.0786 | 0.4049 |
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| 3.5358 | 6.52 | 1700 | 3.2480 | 0.1429 | 0.0626 | 0.1429 | 0.0663 | 0.3976 |
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| 3.5358 | 6.71 | 1750 | 3.2435 | 0.1482 | 0.0575 | 0.1482 | 0.0665 | 0.4030 |
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| 3.5358 | 6.9 | 1800 | 3.2324 | 0.1482 | 0.0619 | 0.1482 | 0.0670 | 0.4022 |
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| 3.5358 | 7.09 | 1850 | 3.2193 | 0.1563 | 0.0806 | 0.1563 | 0.0799 | 0.4070 |
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| 3.5358 | 7.29 | 1900 | 3.2122 | 0.1644 | 0.0825 | 0.1644 | 0.0865 | 0.4119 |
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| 3.5358 | 7.48 | 1950 | 3.1995 | 0.1617 | 0.0776 | 0.1617 | 0.0836 | 0.4108 |
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| 3.4065 | 7.67 | 2000 | 3.1945 | 0.1617 | 0.0771 | 0.1617 | 0.0837 | 0.4116 |
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| 3.4065 | 7.86 | 2050 | 3.1851 | 0.1725 | 0.0832 | 0.1725 | 0.0919 | 0.4191 |
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| 3.4065 | 8.05 | 2100 | 3.1805 | 0.1617 | 0.0592 | 0.1617 | 0.0776 | 0.4100 |
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| 3.4065 | 8.25 | 2150 | 3.1729 | 0.1617 | 0.0573 | 0.1617 | 0.0762 | 0.4100 |
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| 3.4065 | 8.44 | 2200 | 3.1696 | 0.1617 | 0.0571 | 0.1617 | 0.0750 | 0.4100 |
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| 3.4065 | 8.63 | 2250 | 3.1638 | 0.1644 | 0.0651 | 0.1644 | 0.0781 | 0.4119 |
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| 3.4065 | 8.82 | 2300 | 3.1597 | 0.1590 | 0.0540 | 0.1590 | 0.0735 | 0.4089 |
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| 3.4065 | 9.01 | 2350 | 3.1548 | 0.1671 | 0.0688 | 0.1671 | 0.0860 | 0.4137 |
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| 3.4065 | 9.2 | 2400 | 3.1540 | 0.1617 | 0.0623 | 0.1617 | 0.0798 | 0.4100 |
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| 3.4065 | 9.4 | 2450 | 3.1489 | 0.1644 | 0.0661 | 0.1644 | 0.0820 | 0.4119 |
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| 3.3382 | 9.59 | 2500 | 3.1493 | 0.1644 | 0.0706 | 0.1644 | 0.0820 | 0.4119 |
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| 3.3382 | 9.78 | 2550 | 3.1464 | 0.1671 | 0.0661 | 0.1671 | 0.0831 | 0.4137 |
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| 3.3382 | 9.97 | 2600 | 3.1461 | 0.1671 | 0.0661 | 0.1671 | 0.0830 | 0.4137 |
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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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