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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-1
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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-1
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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: 0.5653
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- Accuracy: 0.8814
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- Precision: 0.8996
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- Recall: 0.8814
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- F1: 0.8797
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- Binary: 0.9213
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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 | 0.19 | 50 | 4.4122 | 0.0270 | 0.0009 | 0.0270 | 0.0018 | 0.1908 |
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| No log | 0.38 | 100 | 3.8703 | 0.0485 | 0.0031 | 0.0485 | 0.0057 | 0.3057 |
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| No log | 0.58 | 150 | 3.4529 | 0.0728 | 0.0261 | 0.0728 | 0.0208 | 0.3396 |
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| No log | 0.77 | 200 | 3.3007 | 0.0701 | 0.0092 | 0.0701 | 0.0156 | 0.3385 |
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| No log | 0.96 | 250 | 3.2150 | 0.0809 | 0.0227 | 0.0809 | 0.0284 | 0.3477 |
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| 3.8633 | 1.15 | 300 | 3.1101 | 0.0997 | 0.0221 | 0.0997 | 0.0330 | 0.3588 |
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| 3.8633 | 1.34 | 350 | 3.0269 | 0.1132 | 0.0298 | 0.1132 | 0.0427 | 0.3704 |
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| 3.8633 | 1.53 | 400 | 2.9362 | 0.1509 | 0.0640 | 0.1509 | 0.0799 | 0.3943 |
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| 3.8633 | 1.73 | 450 | 2.7990 | 0.2183 | 0.1378 | 0.2183 | 0.1356 | 0.4488 |
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| 3.8633 | 1.92 | 500 | 2.6316 | 0.2345 | 0.1408 | 0.2345 | 0.1447 | 0.4609 |
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| 3.1477 | 2.11 | 550 | 2.4207 | 0.2911 | 0.1703 | 0.2911 | 0.1919 | 0.5027 |
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| 3.1477 | 2.3 | 600 | 2.2241 | 0.3315 | 0.2375 | 0.3315 | 0.2437 | 0.5329 |
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| 3.1477 | 2.49 | 650 | 2.0490 | 0.4016 | 0.3209 | 0.4016 | 0.3144 | 0.5782 |
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| 3.1477 | 2.68 | 700 | 1.9507 | 0.4474 | 0.3860 | 0.4474 | 0.3751 | 0.6111 |
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| 3.1477 | 2.88 | 750 | 1.7289 | 0.4987 | 0.4422 | 0.4987 | 0.4249 | 0.6469 |
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| 2.5128 | 3.07 | 800 | 1.6980 | 0.5229 | 0.4922 | 0.5229 | 0.4716 | 0.6650 |
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| 2.5128 | 3.26 | 850 | 1.5772 | 0.5337 | 0.4984 | 0.5337 | 0.4794 | 0.6752 |
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| 2.5128 | 3.45 | 900 | 1.4601 | 0.5526 | 0.5254 | 0.5526 | 0.4960 | 0.6857 |
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| 2.5128 | 3.64 | 950 | 1.3596 | 0.5984 | 0.5985 | 0.5984 | 0.5666 | 0.7175 |
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| 2.5128 | 3.84 | 1000 | 1.2260 | 0.6550 | 0.7094 | 0.6550 | 0.6282 | 0.7580 |
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| 2.0178 | 4.03 | 1050 | 1.2179 | 0.6334 | 0.6586 | 0.6334 | 0.5903 | 0.7429 |
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| 2.0178 | 4.22 | 1100 | 1.1750 | 0.6523 | 0.6673 | 0.6523 | 0.6209 | 0.7569 |
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| 2.0178 | 4.41 | 1150 | 1.1022 | 0.6927 | 0.7149 | 0.6927 | 0.6702 | 0.7852 |
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| 2.0178 | 4.6 | 1200 | 1.0216 | 0.7143 | 0.7344 | 0.7143 | 0.6957 | 0.8005 |
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| 2.0178 | 4.79 | 1250 | 1.0195 | 0.7008 | 0.7571 | 0.7008 | 0.6843 | 0.7908 |
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| 2.0178 | 4.99 | 1300 | 1.0202 | 0.6765 | 0.6885 | 0.6765 | 0.6532 | 0.7739 |
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| 1.715 | 5.18 | 1350 | 0.9848 | 0.7547 | 0.7772 | 0.7547 | 0.7378 | 0.8264 |
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| 1.715 | 5.37 | 1400 | 1.0069 | 0.7385 | 0.7797 | 0.7385 | 0.7229 | 0.8146 |
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| 1.715 | 5.56 | 1450 | 0.9229 | 0.7547 | 0.7937 | 0.7547 | 0.7420 | 0.8267 |
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| 1.715 | 5.75 | 1500 | 0.8889 | 0.7385 | 0.7600 | 0.7385 | 0.7170 | 0.8191 |
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| 1.715 | 5.94 | 1550 | 0.8469 | 0.7736 | 0.8162 | 0.7736 | 0.7670 | 0.8410 |
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| 1.5024 | 6.14 | 1600 | 0.8843 | 0.7844 | 0.8278 | 0.7844 | 0.7767 | 0.8485 |
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| 1.5024 | 6.33 | 1650 | 0.8125 | 0.7898 | 0.8149 | 0.7898 | 0.7797 | 0.8512 |
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| 1.5024 | 6.52 | 1700 | 0.9072 | 0.7520 | 0.7806 | 0.7520 | 0.7365 | 0.8240 |
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| 1.5024 | 6.71 | 1750 | 0.7896 | 0.7547 | 0.7818 | 0.7547 | 0.7398 | 0.8267 |
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| 1.5024 | 6.9 | 1800 | 0.7102 | 0.8113 | 0.8396 | 0.8113 | 0.8045 | 0.8663 |
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| 1.3605 | 7.09 | 1850 | 0.7241 | 0.8032 | 0.8298 | 0.8032 | 0.7930 | 0.8606 |
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| 1.3605 | 7.29 | 1900 | 0.7586 | 0.8005 | 0.8323 | 0.8005 | 0.7892 | 0.8588 |
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| 1.3605 | 7.48 | 1950 | 0.7546 | 0.7898 | 0.8105 | 0.7898 | 0.7759 | 0.8512 |
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| 1.3605 | 7.67 | 2000 | 0.7103 | 0.8032 | 0.8242 | 0.8032 | 0.7893 | 0.8606 |
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| 1.3605 | 7.86 | 2050 | 0.7397 | 0.8140 | 0.8239 | 0.8140 | 0.7982 | 0.8682 |
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| 1.2475 | 8.05 | 2100 | 0.7723 | 0.8059 | 0.8207 | 0.8059 | 0.7894 | 0.8636 |
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| 1.2475 | 8.25 | 2150 | 0.7099 | 0.7925 | 0.8088 | 0.7925 | 0.7790 | 0.8571 |
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| 1.2475 | 8.44 | 2200 | 0.6816 | 0.8167 | 0.8316 | 0.8167 | 0.7998 | 0.8712 |
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| 1.2475 | 8.63 | 2250 | 0.6676 | 0.8113 | 0.8264 | 0.8113 | 0.8025 | 0.8663 |
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| 1.2475 | 8.82 | 2300 | 0.7176 | 0.8113 | 0.8326 | 0.8113 | 0.7973 | 0.8663 |
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| 1.1791 | 9.01 | 2350 | 0.6161 | 0.8356 | 0.8500 | 0.8356 | 0.8238 | 0.8833 |
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| 1.1791 | 9.2 | 2400 | 0.6973 | 0.8032 | 0.8165 | 0.8032 | 0.7873 | 0.8606 |
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| 1.1791 | 9.4 | 2450 | 0.6981 | 0.8248 | 0.8531 | 0.8248 | 0.8138 | 0.8757 |
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| 1.1791 | 9.59 | 2500 | 0.6134 | 0.8356 | 0.8417 | 0.8356 | 0.8231 | 0.8833 |
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| 1.1791 | 9.78 | 2550 | 0.5840 | 0.8356 | 0.8498 | 0.8356 | 0.8220 | 0.8833 |
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| 1.1791 | 9.97 | 2600 | 0.5940 | 0.8329 | 0.8479 | 0.8329 | 0.8170 | 0.8814 |
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| 1.1047 | 10.16 | 2650 | 0.5539 | 0.8491 | 0.8641 | 0.8491 | 0.8435 | 0.8935 |
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| 1.1047 | 10.35 | 2700 | 0.6522 | 0.8410 | 0.8582 | 0.8410 | 0.8269 | 0.8871 |
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| 1.1047 | 10.55 | 2750 | 0.5940 | 0.8356 | 0.8631 | 0.8356 | 0.8263 | 0.8852 |
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| 1.1047 | 10.74 | 2800 | 0.5980 | 0.8464 | 0.8558 | 0.8464 | 0.8376 | 0.8927 |
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| 1.1047 | 10.93 | 2850 | 0.6173 | 0.8302 | 0.8518 | 0.8302 | 0.8222 | 0.8795 |
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| 1.0458 | 11.12 | 2900 | 0.5973 | 0.8356 | 0.8580 | 0.8356 | 0.8257 | 0.8833 |
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| 1.0458 | 11.31 | 2950 | 0.5135 | 0.8706 | 0.8853 | 0.8706 | 0.8660 | 0.9078 |
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| 1.0458 | 11.51 | 3000 | 0.5858 | 0.8410 | 0.8551 | 0.8410 | 0.8290 | 0.8871 |
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| 1.0458 | 11.7 | 3050 | 0.6788 | 0.8248 | 0.8479 | 0.8248 | 0.8113 | 0.8757 |
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| 1.0458 | 11.89 | 3100 | 0.5917 | 0.8437 | 0.8565 | 0.8437 | 0.8306 | 0.8889 |
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| 0.9866 | 12.08 | 3150 | 0.6466 | 0.8194 | 0.8328 | 0.8194 | 0.8078 | 0.8720 |
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| 0.9866 | 12.27 | 3200 | 0.6311 | 0.8194 | 0.8357 | 0.8194 | 0.8102 | 0.8720 |
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| 0.9866 | 12.46 | 3250 | 0.6292 | 0.8383 | 0.8589 | 0.8383 | 0.8276 | 0.8852 |
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| 0.9866 | 12.66 | 3300 | 0.5887 | 0.8437 | 0.8609 | 0.8437 | 0.8360 | 0.8908 |
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| 0.9866 | 12.85 | 3350 | 0.6003 | 0.8302 | 0.8490 | 0.8302 | 0.8221 | 0.8795 |
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| 0.9574 | 13.04 | 3400 | 0.5590 | 0.8625 | 0.8881 | 0.8625 | 0.8580 | 0.9022 |
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| 0.9574 | 13.23 | 3450 | 0.6750 | 0.8086 | 0.8223 | 0.8086 | 0.7958 | 0.8644 |
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| 0.9574 | 13.42 | 3500 | 0.6180 | 0.8302 | 0.8556 | 0.8302 | 0.8245 | 0.8795 |
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| 0.9574 | 13.61 | 3550 | 0.5702 | 0.8625 | 0.8812 | 0.8625 | 0.8572 | 0.9022 |
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| 0.9574 | 13.81 | 3600 | 0.5661 | 0.8625 | 0.8747 | 0.8625 | 0.8536 | 0.9022 |
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| 0.9574 | 14.0 | 3650 | 0.6820 | 0.8302 | 0.8449 | 0.8302 | 0.8230 | 0.8825 |
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| 0.9173 | 14.19 | 3700 | 0.5872 | 0.8544 | 0.8772 | 0.8544 | 0.8474 | 0.8984 |
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| 0.9173 | 14.38 | 3750 | 0.5503 | 0.8571 | 0.8758 | 0.8571 | 0.8506 | 0.8984 |
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| 0.9173 | 14.57 | 3800 | 0.5711 | 0.8652 | 0.8889 | 0.8652 | 0.8594 | 0.9040 |
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| 0.9173 | 14.77 | 3850 | 0.5832 | 0.8491 | 0.8703 | 0.8491 | 0.8431 | 0.8927 |
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| 0.9173 | 14.96 | 3900 | 0.5457 | 0.8706 | 0.8929 | 0.8706 | 0.8658 | 0.9089 |
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| 0.8859 | 15.15 | 3950 | 0.6410 | 0.8491 | 0.8667 | 0.8491 | 0.8406 | 0.8927 |
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| 0.8859 | 15.34 | 4000 | 0.5822 | 0.8410 | 0.8661 | 0.8410 | 0.8340 | 0.8871 |
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| 0.8859 | 15.53 | 4050 | 0.6173 | 0.8464 | 0.8720 | 0.8464 | 0.8406 | 0.8919 |
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| 0.8859 | 15.72 | 4100 | 0.6509 | 0.8356 | 0.8535 | 0.8356 | 0.8267 | 0.8833 |
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| 0.8859 | 15.92 | 4150 | 0.7177 | 0.8275 | 0.8419 | 0.8275 | 0.8156 | 0.8776 |
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| 0.8447 | 16.11 | 4200 | 0.5898 | 0.8437 | 0.8531 | 0.8437 | 0.8347 | 0.8889 |
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| 0.8447 | 16.3 | 4250 | 0.6429 | 0.8383 | 0.8513 | 0.8383 | 0.8296 | 0.8852 |
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| 0.8447 | 16.49 | 4300 | 0.5914 | 0.8625 | 0.8707 | 0.8625 | 0.8553 | 0.9022 |
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| 0.8447 | 16.68 | 4350 | 0.5698 | 0.8518 | 0.8714 | 0.8518 | 0.8460 | 0.8946 |
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| 0.8447 | 16.87 | 4400 | 0.5938 | 0.8491 | 0.8695 | 0.8491 | 0.8439 | 0.8946 |
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| 0.8181 | 17.07 | 4450 | 0.6076 | 0.8356 | 0.8441 | 0.8356 | 0.8263 | 0.8852 |
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| 0.8181 | 17.26 | 4500 | 0.5691 | 0.8383 | 0.8518 | 0.8383 | 0.8298 | 0.8852 |
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| 0.8181 | 17.45 | 4550 | 0.5490 | 0.8625 | 0.8737 | 0.8625 | 0.8551 | 0.9040 |
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| 0.8181 | 17.64 | 4600 | 0.5963 | 0.8598 | 0.8791 | 0.8598 | 0.8538 | 0.9003 |
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| 0.8181 | 17.83 | 4650 | 0.6371 | 0.8464 | 0.8699 | 0.8464 | 0.8406 | 0.8908 |
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| 0.8015 | 18.02 | 4700 | 0.6348 | 0.8491 | 0.8675 | 0.8491 | 0.8449 | 0.8927 |
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| 0.8015 | 18.22 | 4750 | 0.6207 | 0.8571 | 0.8711 | 0.8571 | 0.8487 | 0.8984 |
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| 0.8015 | 18.41 | 4800 | 0.6759 | 0.8518 | 0.8709 | 0.8518 | 0.8479 | 0.8946 |
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| 0.8015 | 18.6 | 4850 | 0.7267 | 0.8248 | 0.8346 | 0.8248 | 0.8136 | 0.8757 |
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| 0.8015 | 18.79 | 4900 | 0.6420 | 0.8410 | 0.8629 | 0.8410 | 0.8361 | 0.8871 |
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| 0.8015 | 18.98 | 4950 | 0.6260 | 0.8464 | 0.8581 | 0.8464 | 0.8375 | 0.8908 |
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| 0.7757 | 19.18 | 5000 | 0.6714 | 0.8410 | 0.8666 | 0.8410 | 0.8361 | 0.8889 |
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| 0.7757 | 19.37 | 5050 | 0.6414 | 0.8383 | 0.8485 | 0.8383 | 0.8285 | 0.8852 |
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| 0.7757 | 19.56 | 5100 | 0.6348 | 0.8356 | 0.8547 | 0.8356 | 0.8261 | 0.8833 |
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| 0.7757 | 19.75 | 5150 | 0.6811 | 0.8464 | 0.8625 | 0.8464 | 0.8377 | 0.8908 |
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| 0.7757 | 19.94 | 5200 | 0.6294 | 0.8383 | 0.8511 | 0.8383 | 0.8286 | 0.8852 |
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| 0.7456 | 20.13 | 5250 | 0.6511 | 0.8679 | 0.8785 | 0.8679 | 0.8589 | 0.9078 |
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| 0.7456 | 20.33 | 5300 | 0.6374 | 0.8437 | 0.8543 | 0.8437 | 0.8344 | 0.8889 |
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| 0.7456 | 20.52 | 5350 | 0.6019 | 0.8544 | 0.8648 | 0.8544 | 0.8457 | 0.8965 |
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| 0.7456 | 20.71 | 5400 | 0.6060 | 0.8571 | 0.8632 | 0.8571 | 0.8469 | 0.8984 |
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| 0.7456 | 20.9 | 5450 | 0.6730 | 0.8518 | 0.8626 | 0.8518 | 0.8453 | 0.8946 |
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| 0.7406 | 21.09 | 5500 | 0.6091 | 0.8544 | 0.8633 | 0.8544 | 0.8450 | 0.8965 |
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| 0.7406 | 21.28 | 5550 | 0.6378 | 0.8598 | 0.8691 | 0.8598 | 0.8511 | 0.9003 |
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| 0.7406 | 21.48 | 5600 | 0.5868 | 0.8464 | 0.8543 | 0.8464 | 0.8388 | 0.8908 |
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| 0.7406 | 21.67 | 5650 | 0.5930 | 0.8706 | 0.8864 | 0.8706 | 0.8658 | 0.9078 |
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| 0.7406 | 21.86 | 5700 | 0.6086 | 0.8544 | 0.8711 | 0.8544 | 0.8497 | 0.8965 |
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| 0.7057 | 22.05 | 5750 | 0.6130 | 0.8518 | 0.8751 | 0.8518 | 0.8471 | 0.8946 |
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| 0.7057 | 22.24 | 5800 | 0.6477 | 0.8464 | 0.8728 | 0.8464 | 0.8393 | 0.8908 |
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| 0.7057 | 22.44 | 5850 | 0.6165 | 0.8518 | 0.8595 | 0.8518 | 0.8434 | 0.8946 |
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| 0.7057 | 22.63 | 5900 | 0.6288 | 0.8571 | 0.8693 | 0.8571 | 0.8491 | 0.8984 |
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| 0.7057 | 22.82 | 5950 | 0.6246 | 0.8544 | 0.8749 | 0.8544 | 0.8490 | 0.8965 |
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| 0.695 | 23.01 | 6000 | 0.5991 | 0.8679 | 0.8874 | 0.8679 | 0.8645 | 0.9059 |
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| 0.695 | 23.2 | 6050 | 0.6234 | 0.8598 | 0.8816 | 0.8598 | 0.8556 | 0.9003 |
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| 0.695 | 23.39 | 6100 | 0.5764 | 0.8679 | 0.8885 | 0.8679 | 0.8641 | 0.9059 |
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| 0.695 | 23.59 | 6150 | 0.6290 | 0.8518 | 0.8641 | 0.8518 | 0.8453 | 0.8946 |
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| 0.695 | 23.78 | 6200 | 0.6267 | 0.8518 | 0.8634 | 0.8518 | 0.8433 | 0.8946 |
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| 0.695 | 23.97 | 6250 | 0.6294 | 0.8491 | 0.8582 | 0.8491 | 0.8404 | 0.8927 |
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| 0.6782 | 24.16 | 6300 | 0.6001 | 0.8491 | 0.8618 | 0.8491 | 0.8421 | 0.8927 |
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| 0.6782 | 24.35 | 6350 | 0.6042 | 0.8598 | 0.8687 | 0.8598 | 0.8530 | 0.9003 |
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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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