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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-0
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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-0
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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: 0.5873
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- Accuracy: 0.8787
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- Precision: 0.8925
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- Recall: 0.8787
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- F1: 0.8784
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- Binary: 0.9162
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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: 100
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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 | 4.4206 | 0.0195 | 0.0007 | 0.0195 | 0.0014 | 0.1390 |
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| No log | 0.48 | 100 | 4.3006 | 0.0442 | 0.0114 | 0.0442 | 0.0127 | 0.2528 |
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| No log | 0.72 | 150 | 3.9867 | 0.0472 | 0.0033 | 0.0472 | 0.0061 | 0.3276 |
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| No log | 0.96 | 200 | 3.6925 | 0.0712 | 0.0116 | 0.0712 | 0.0180 | 0.3447 |
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| 4.2438 | 1.2 | 250 | 3.4305 | 0.0854 | 0.0508 | 0.0854 | 0.0319 | 0.3580 |
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| 4.2438 | 1.44 | 300 | 3.2405 | 0.1071 | 0.0689 | 0.1071 | 0.0432 | 0.3730 |
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| 4.2438 | 1.68 | 350 | 3.0535 | 0.1491 | 0.1053 | 0.1491 | 0.0823 | 0.3999 |
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| 4.2438 | 1.92 | 400 | 2.7897 | 0.2419 | 0.2020 | 0.2419 | 0.1678 | 0.4667 |
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| 3.3411 | 2.16 | 450 | 2.4987 | 0.3303 | 0.2416 | 0.3303 | 0.2457 | 0.5288 |
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| 3.3411 | 2.4 | 500 | 2.1588 | 0.4779 | 0.3998 | 0.4779 | 0.4078 | 0.6354 |
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| 3.3411 | 2.63 | 550 | 1.8909 | 0.5273 | 0.4768 | 0.5273 | 0.4604 | 0.6688 |
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| 3.3411 | 2.87 | 600 | 1.6458 | 0.5708 | 0.5612 | 0.5708 | 0.5191 | 0.6994 |
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| 2.4102 | 3.11 | 650 | 1.4630 | 0.6187 | 0.6002 | 0.6187 | 0.5757 | 0.7327 |
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| 2.4102 | 3.35 | 700 | 1.2770 | 0.6764 | 0.6582 | 0.6764 | 0.6409 | 0.7730 |
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| 2.4102 | 3.59 | 750 | 1.1875 | 0.6966 | 0.6830 | 0.6966 | 0.6696 | 0.7884 |
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| 2.4102 | 3.83 | 800 | 1.0563 | 0.7228 | 0.7372 | 0.7228 | 0.7012 | 0.8073 |
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| 1.6409 | 4.07 | 850 | 0.9471 | 0.7506 | 0.7688 | 0.7506 | 0.7322 | 0.8260 |
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| 1.6409 | 4.31 | 900 | 0.9012 | 0.7588 | 0.7677 | 0.7588 | 0.7471 | 0.8313 |
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| 1.6409 | 4.55 | 950 | 0.8540 | 0.7768 | 0.8025 | 0.7768 | 0.7685 | 0.8435 |
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| 1.6409 | 4.79 | 1000 | 0.7910 | 0.7828 | 0.7915 | 0.7828 | 0.7723 | 0.8479 |
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| 1.2621 | 5.03 | 1050 | 0.7229 | 0.7918 | 0.7952 | 0.7918 | 0.7804 | 0.8542 |
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| 1.2621 | 5.27 | 1100 | 0.7388 | 0.8067 | 0.8250 | 0.8067 | 0.8031 | 0.8650 |
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| 1.2621 | 5.51 | 1150 | 0.7315 | 0.8090 | 0.8298 | 0.8090 | 0.8029 | 0.8672 |
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| 1.2621 | 5.75 | 1200 | 0.7357 | 0.7903 | 0.8053 | 0.7903 | 0.7856 | 0.8533 |
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| 1.2621 | 5.99 | 1250 | 0.7088 | 0.8090 | 0.8240 | 0.8090 | 0.8037 | 0.8672 |
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| 1.0138 | 6.23 | 1300 | 0.6828 | 0.8112 | 0.8209 | 0.8112 | 0.8077 | 0.8684 |
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| 1.0138 | 6.47 | 1350 | 0.7561 | 0.8082 | 0.8229 | 0.8082 | 0.8032 | 0.8678 |
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| 1.0138 | 6.71 | 1400 | 0.6640 | 0.8292 | 0.8415 | 0.8292 | 0.8250 | 0.8812 |
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| 1.0138 | 6.95 | 1450 | 0.6330 | 0.8315 | 0.8453 | 0.8315 | 0.8282 | 0.8828 |
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| 0.9058 | 7.19 | 1500 | 0.6482 | 0.8217 | 0.8331 | 0.8217 | 0.8189 | 0.8764 |
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| 0.9058 | 7.43 | 1550 | 0.7005 | 0.8187 | 0.8330 | 0.8187 | 0.8135 | 0.8736 |
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| 0.9058 | 7.66 | 1600 | 0.5902 | 0.8562 | 0.8645 | 0.8562 | 0.8533 | 0.8998 |
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| 0.9058 | 7.9 | 1650 | 0.5481 | 0.8607 | 0.8723 | 0.8607 | 0.8594 | 0.9019 |
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| 0.7905 | 8.14 | 1700 | 0.6131 | 0.8427 | 0.8534 | 0.8427 | 0.8394 | 0.8899 |
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| 0.7905 | 8.38 | 1750 | 0.6664 | 0.8419 | 0.8541 | 0.8419 | 0.8394 | 0.8897 |
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| 0.7905 | 8.62 | 1800 | 0.6453 | 0.8330 | 0.8473 | 0.8330 | 0.8293 | 0.8842 |
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| 0.7905 | 8.86 | 1850 | 0.6178 | 0.8390 | 0.8553 | 0.8390 | 0.8362 | 0.8873 |
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| 0.7208 | 9.1 | 1900 | 0.6779 | 0.8412 | 0.8540 | 0.8412 | 0.8379 | 0.8895 |
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| 0.7208 | 9.34 | 1950 | 0.5752 | 0.8607 | 0.8690 | 0.8607 | 0.8581 | 0.9031 |
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| 0.7208 | 9.58 | 2000 | 0.6717 | 0.8434 | 0.8544 | 0.8434 | 0.8408 | 0.8909 |
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| 0.7208 | 9.82 | 2050 | 0.6790 | 0.8345 | 0.8500 | 0.8345 | 0.8321 | 0.8848 |
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| 0.6476 | 10.06 | 2100 | 0.6429 | 0.8494 | 0.8631 | 0.8494 | 0.8472 | 0.8954 |
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| 0.6476 | 10.3 | 2150 | 0.6006 | 0.8577 | 0.8668 | 0.8577 | 0.8558 | 0.9007 |
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| 0.6476 | 10.54 | 2200 | 0.5987 | 0.8532 | 0.8634 | 0.8532 | 0.8519 | 0.8974 |
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| 0.6476 | 10.78 | 2250 | 0.6524 | 0.8472 | 0.8594 | 0.8472 | 0.8443 | 0.8934 |
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| 0.6156 | 11.02 | 2300 | 0.6748 | 0.8412 | 0.8529 | 0.8412 | 0.8386 | 0.8904 |
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| 0.6156 | 11.26 | 2350 | 0.5571 | 0.8577 | 0.8644 | 0.8577 | 0.8547 | 0.9011 |
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| 0.6156 | 11.5 | 2400 | 0.6081 | 0.8502 | 0.8607 | 0.8502 | 0.8468 | 0.8959 |
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| 0.6156 | 11.74 | 2450 | 0.5866 | 0.8592 | 0.8692 | 0.8592 | 0.8575 | 0.9022 |
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| 0.6156 | 11.98 | 2500 | 0.6205 | 0.8517 | 0.8630 | 0.8517 | 0.8501 | 0.8966 |
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| 0.5738 | 12.22 | 2550 | 0.6544 | 0.8562 | 0.8704 | 0.8562 | 0.8549 | 0.8996 |
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| 0.5738 | 12.46 | 2600 | 0.6792 | 0.8427 | 0.8545 | 0.8427 | 0.8385 | 0.8906 |
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| 0.5738 | 12.69 | 2650 | 0.6009 | 0.8569 | 0.8676 | 0.8569 | 0.8557 | 0.9008 |
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| 0.5738 | 12.93 | 2700 | 0.6580 | 0.8524 | 0.8621 | 0.8524 | 0.8490 | 0.8972 |
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| 0.5416 | 13.17 | 2750 | 0.6781 | 0.8532 | 0.8639 | 0.8532 | 0.8504 | 0.8977 |
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| 0.5416 | 13.41 | 2800 | 0.5903 | 0.8659 | 0.8749 | 0.8659 | 0.8646 | 0.9084 |
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| 0.5416 | 13.65 | 2850 | 0.5766 | 0.8644 | 0.8728 | 0.8644 | 0.8620 | 0.9064 |
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| 0.5416 | 13.89 | 2900 | 0.6674 | 0.8592 | 0.8688 | 0.8592 | 0.8565 | 0.9027 |
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| 0.5213 | 14.13 | 2950 | 0.6256 | 0.8652 | 0.8751 | 0.8652 | 0.8635 | 0.9067 |
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| 0.5213 | 14.37 | 3000 | 0.6518 | 0.8622 | 0.8704 | 0.8622 | 0.8602 | 0.9051 |
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| 0.5213 | 14.61 | 3050 | 0.6694 | 0.8547 | 0.8661 | 0.8547 | 0.8531 | 0.8999 |
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| 0.5213 | 14.85 | 3100 | 0.6153 | 0.8719 | 0.8799 | 0.8719 | 0.8710 | 0.9125 |
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| 0.4856 | 15.09 | 3150 | 0.6067 | 0.8727 | 0.8821 | 0.8727 | 0.8715 | 0.9106 |
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| 0.4856 | 15.33 | 3200 | 0.6354 | 0.8592 | 0.8712 | 0.8592 | 0.8581 | 0.9019 |
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| 0.4856 | 15.57 | 3250 | 0.6773 | 0.8532 | 0.8623 | 0.8532 | 0.8507 | 0.8988 |
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| 0.4856 | 15.81 | 3300 | 0.6356 | 0.8682 | 0.8759 | 0.8682 | 0.8660 | 0.9088 |
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| 0.4631 | 16.05 | 3350 | 0.6139 | 0.8712 | 0.8783 | 0.8712 | 0.8700 | 0.9102 |
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| 0.4631 | 16.29 | 3400 | 0.6589 | 0.8622 | 0.8730 | 0.8622 | 0.8612 | 0.9049 |
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| 0.4631 | 16.53 | 3450 | 0.6439 | 0.8539 | 0.8660 | 0.8539 | 0.8516 | 0.8982 |
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| 0.4631 | 16.77 | 3500 | 0.6727 | 0.8689 | 0.8757 | 0.8689 | 0.8673 | 0.9091 |
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| 0.4605 | 17.01 | 3550 | 0.6359 | 0.8712 | 0.8793 | 0.8712 | 0.8703 | 0.9103 |
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| 0.4605 | 17.25 | 3600 | 0.6926 | 0.8547 | 0.8647 | 0.8547 | 0.8534 | 0.8999 |
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| 0.4605 | 17.49 | 3650 | 0.6937 | 0.8562 | 0.8687 | 0.8562 | 0.8544 | 0.9008 |
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| 0.4605 | 17.72 | 3700 | 0.6625 | 0.8659 | 0.8777 | 0.8659 | 0.8649 | 0.9068 |
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| 0.4605 | 17.96 | 3750 | 0.6542 | 0.8674 | 0.8784 | 0.8674 | 0.8655 | 0.9090 |
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| 0.4371 | 18.2 | 3800 | 0.5719 | 0.8742 | 0.8831 | 0.8742 | 0.8727 | 0.9121 |
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| 0.4371 | 18.44 | 3850 | 0.6245 | 0.8734 | 0.8811 | 0.8734 | 0.8727 | 0.9124 |
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| 0.4371 | 18.68 | 3900 | 0.6993 | 0.8577 | 0.8680 | 0.8577 | 0.8559 | 0.9018 |
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| 0.4371 | 18.92 | 3950 | 0.6896 | 0.8592 | 0.8681 | 0.8592 | 0.8573 | 0.9028 |
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| 0.4277 | 19.16 | 4000 | 0.6869 | 0.8517 | 0.8640 | 0.8517 | 0.8507 | 0.8973 |
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| 0.4277 | 19.4 | 4050 | 0.6963 | 0.8599 | 0.8692 | 0.8599 | 0.8587 | 0.9021 |
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| 0.4277 | 19.64 | 4100 | 0.5527 | 0.8831 | 0.8898 | 0.8831 | 0.8819 | 0.9184 |
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| 0.4277 | 19.88 | 4150 | 0.6925 | 0.8592 | 0.8699 | 0.8592 | 0.8580 | 0.9025 |
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| 0.401 | 20.12 | 4200 | 0.6998 | 0.8592 | 0.8719 | 0.8592 | 0.8582 | 0.9040 |
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| 0.401 | 20.36 | 4250 | 0.6390 | 0.8757 | 0.8849 | 0.8757 | 0.8743 | 0.9139 |
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| 0.401 | 20.6 | 4300 | 0.6792 | 0.8659 | 0.8762 | 0.8659 | 0.8641 | 0.9075 |
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| 0.401 | 20.84 | 4350 | 0.6946 | 0.8554 | 0.8662 | 0.8554 | 0.8529 | 0.8990 |
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| 0.3945 | 21.08 | 4400 | 0.8223 | 0.8427 | 0.8559 | 0.8427 | 0.8409 | 0.8903 |
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| 0.3945 | 21.32 | 4450 | 0.7841 | 0.8622 | 0.8710 | 0.8622 | 0.8599 | 0.9040 |
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| 0.3945 | 21.56 | 4500 | 0.6545 | 0.8697 | 0.8766 | 0.8697 | 0.8687 | 0.9093 |
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| 0.3945 | 21.8 | 4550 | 0.7135 | 0.8652 | 0.8710 | 0.8652 | 0.8630 | 0.9072 |
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| 0.3829 | 22.04 | 4600 | 0.6901 | 0.8622 | 0.8705 | 0.8622 | 0.8610 | 0.9046 |
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| 0.3829 | 22.28 | 4650 | 0.6960 | 0.8599 | 0.8688 | 0.8599 | 0.8579 | 0.9035 |
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| 0.3829 | 22.51 | 4700 | 0.7047 | 0.8644 | 0.8752 | 0.8644 | 0.8630 | 0.9061 |
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| 0.3829 | 22.75 | 4750 | 0.6855 | 0.8674 | 0.8784 | 0.8674 | 0.8662 | 0.9094 |
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| 0.3829 | 22.99 | 4800 | 0.7315 | 0.8539 | 0.8652 | 0.8539 | 0.8516 | 0.8993 |
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| 0.3695 | 23.23 | 4850 | 0.7299 | 0.8569 | 0.8663 | 0.8569 | 0.8545 | 0.9005 |
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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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