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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: 0.5721
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- Accuracy: 0.8706
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- Precision: 0.8813
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- Recall: 0.8706
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- F1: 0.8644
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- Binary: 0.9094
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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 | 4.4259 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1278 |
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| No log | 0.38 | 100 | 3.8225 | 0.0485 | 0.0033 | 0.0485 | 0.0060 | 0.3151 |
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| No log | 0.58 | 150 | 3.3928 | 0.0701 | 0.0104 | 0.0701 | 0.0169 | 0.3450 |
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| No log | 0.77 | 200 | 3.2088 | 0.0836 | 0.0166 | 0.0836 | 0.0258 | 0.3553 |
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| No log | 0.96 | 250 | 3.0752 | 0.1078 | 0.0279 | 0.1078 | 0.0388 | 0.3714 |
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| 3.8204 | 1.15 | 300 | 2.9617 | 0.1186 | 0.0338 | 0.1186 | 0.0443 | 0.3776 |
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| 3.8204 | 1.34 | 350 | 2.7928 | 0.1617 | 0.0704 | 0.1617 | 0.0848 | 0.4100 |
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| 3.8204 | 1.53 | 400 | 2.5904 | 0.2237 | 0.1640 | 0.2237 | 0.1427 | 0.4542 |
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| 3.8204 | 1.73 | 450 | 2.3895 | 0.2615 | 0.1634 | 0.2615 | 0.1771 | 0.4814 |
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| 3.8204 | 1.92 | 500 | 2.2567 | 0.2992 | 0.2170 | 0.2992 | 0.2160 | 0.5097 |
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| 2.9288 | 2.11 | 550 | 2.0903 | 0.3801 | 0.2993 | 0.3801 | 0.3008 | 0.5668 |
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| 2.9288 | 2.3 | 600 | 1.9624 | 0.4151 | 0.3638 | 0.4151 | 0.3389 | 0.5900 |
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| 2.9288 | 2.49 | 650 | 1.7353 | 0.5148 | 0.4641 | 0.5148 | 0.4447 | 0.6625 |
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| 2.9288 | 2.68 | 700 | 1.6687 | 0.5013 | 0.4658 | 0.5013 | 0.4433 | 0.6526 |
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| 2.9288 | 2.88 | 750 | 1.5726 | 0.5391 | 0.5299 | 0.5391 | 0.4914 | 0.6817 |
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| 2.2908 | 3.07 | 800 | 1.4471 | 0.5714 | 0.5785 | 0.5714 | 0.5269 | 0.7003 |
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| 2.2908 | 3.26 | 850 | 1.3350 | 0.6334 | 0.6432 | 0.6334 | 0.6040 | 0.7445 |
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| 2.2908 | 3.45 | 900 | 1.1787 | 0.6685 | 0.6811 | 0.6685 | 0.6349 | 0.7690 |
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| 2.2908 | 3.64 | 950 | 1.1315 | 0.6846 | 0.6962 | 0.6846 | 0.6524 | 0.7803 |
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| 2.2908 | 3.84 | 1000 | 1.0283 | 0.7493 | 0.7751 | 0.7493 | 0.7298 | 0.8248 |
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| 1.8601 | 4.03 | 1050 | 1.0553 | 0.7089 | 0.7311 | 0.7089 | 0.6824 | 0.7984 |
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| 1.8601 | 4.22 | 1100 | 0.9262 | 0.7385 | 0.7627 | 0.7385 | 0.7190 | 0.8170 |
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| 1.8601 | 4.41 | 1150 | 0.9209 | 0.7385 | 0.7586 | 0.7385 | 0.7221 | 0.8170 |
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| 1.8601 | 4.6 | 1200 | 0.9163 | 0.7520 | 0.8027 | 0.7520 | 0.7416 | 0.8280 |
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| 1.8601 | 4.79 | 1250 | 0.7954 | 0.8140 | 0.8394 | 0.8140 | 0.8028 | 0.8682 |
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| 1.8601 | 4.99 | 1300 | 0.8244 | 0.7709 | 0.7999 | 0.7709 | 0.7651 | 0.8388 |
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| 1.624 | 5.18 | 1350 | 0.8025 | 0.7655 | 0.8100 | 0.7655 | 0.7599 | 0.8364 |
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| 1.624 | 5.37 | 1400 | 0.7521 | 0.7925 | 0.8238 | 0.7925 | 0.7827 | 0.8558 |
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| 1.624 | 5.56 | 1450 | 0.7821 | 0.7898 | 0.8231 | 0.7898 | 0.7824 | 0.8528 |
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| 1.624 | 5.75 | 1500 | 0.6841 | 0.7951 | 0.8237 | 0.7951 | 0.7887 | 0.8577 |
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| 1.624 | 5.94 | 1550 | 0.6757 | 0.7978 | 0.8276 | 0.7978 | 0.7935 | 0.8596 |
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| 1.4343 | 6.14 | 1600 | 0.6709 | 0.8140 | 0.8415 | 0.8140 | 0.8094 | 0.8709 |
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| 1.4343 | 6.33 | 1650 | 0.6361 | 0.8113 | 0.8364 | 0.8113 | 0.8045 | 0.8690 |
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| 1.4343 | 6.52 | 1700 | 0.6413 | 0.8275 | 0.8479 | 0.8275 | 0.8231 | 0.8814 |
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| 1.4343 | 6.71 | 1750 | 0.6074 | 0.8302 | 0.8484 | 0.8302 | 0.8244 | 0.8803 |
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| 1.4343 | 6.9 | 1800 | 0.6286 | 0.8005 | 0.8321 | 0.8005 | 0.7964 | 0.8606 |
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| 1.3175 | 7.09 | 1850 | 0.5431 | 0.8356 | 0.8558 | 0.8356 | 0.8312 | 0.8860 |
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| 1.3175 | 7.29 | 1900 | 0.5612 | 0.8491 | 0.8828 | 0.8491 | 0.8499 | 0.8927 |
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| 1.3175 | 7.48 | 1950 | 0.5324 | 0.8491 | 0.8795 | 0.8491 | 0.8492 | 0.8954 |
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| 1.3175 | 7.67 | 2000 | 0.5793 | 0.8383 | 0.8589 | 0.8383 | 0.8345 | 0.8871 |
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| 1.3175 | 7.86 | 2050 | 0.5722 | 0.8248 | 0.8575 | 0.8248 | 0.8258 | 0.8757 |
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| 1.2154 | 8.05 | 2100 | 0.6362 | 0.8167 | 0.8511 | 0.8167 | 0.8149 | 0.8701 |
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| 1.2154 | 8.25 | 2150 | 0.5846 | 0.8302 | 0.8588 | 0.8302 | 0.8293 | 0.8814 |
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| 1.2154 | 8.44 | 2200 | 0.6121 | 0.8113 | 0.8477 | 0.8113 | 0.8065 | 0.8704 |
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| 1.2154 | 8.63 | 2250 | 0.5895 | 0.8356 | 0.8671 | 0.8356 | 0.8343 | 0.8871 |
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| 1.2154 | 8.82 | 2300 | 0.5404 | 0.8437 | 0.8756 | 0.8437 | 0.8407 | 0.8927 |
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| 1.1306 | 9.01 | 2350 | 0.5433 | 0.8410 | 0.8657 | 0.8410 | 0.8394 | 0.8900 |
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| 1.1306 | 9.2 | 2400 | 0.5535 | 0.8383 | 0.8645 | 0.8383 | 0.8380 | 0.8881 |
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| 1.1306 | 9.4 | 2450 | 0.5201 | 0.8518 | 0.8850 | 0.8518 | 0.8518 | 0.8957 |
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| 1.1306 | 9.59 | 2500 | 0.5464 | 0.8383 | 0.8680 | 0.8383 | 0.8373 | 0.8881 |
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| 1.1306 | 9.78 | 2550 | 0.5960 | 0.8329 | 0.8586 | 0.8329 | 0.8304 | 0.8841 |
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| 1.1306 | 9.97 | 2600 | 0.5304 | 0.8518 | 0.8790 | 0.8518 | 0.8506 | 0.8957 |
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| 1.0743 | 10.16 | 2650 | 0.4804 | 0.8706 | 0.8937 | 0.8706 | 0.8703 | 0.9086 |
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| 1.0743 | 10.35 | 2700 | 0.5004 | 0.8652 | 0.8908 | 0.8652 | 0.8640 | 0.9059 |
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| 1.0743 | 10.55 | 2750 | 0.4730 | 0.8652 | 0.8921 | 0.8652 | 0.8643 | 0.9070 |
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| 1.0743 | 10.74 | 2800 | 0.4958 | 0.8383 | 0.8663 | 0.8383 | 0.8372 | 0.8879 |
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| 1.0743 | 10.93 | 2850 | 0.4672 | 0.8544 | 0.8814 | 0.8544 | 0.8538 | 0.8973 |
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| 1.0274 | 11.12 | 2900 | 0.5339 | 0.8571 | 0.8807 | 0.8571 | 0.8565 | 0.9003 |
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| 1.0274 | 11.31 | 2950 | 0.5013 | 0.8491 | 0.8698 | 0.8491 | 0.8462 | 0.8954 |
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| 1.0274 | 11.51 | 3000 | 0.4882 | 0.8679 | 0.8904 | 0.8679 | 0.8677 | 0.9078 |
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| 1.0274 | 11.7 | 3050 | 0.5059 | 0.8518 | 0.8837 | 0.8518 | 0.8519 | 0.8965 |
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| 1.0274 | 11.89 | 3100 | 0.4636 | 0.8679 | 0.8864 | 0.8679 | 0.8666 | 0.9075 |
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| 0.9585 | 12.08 | 3150 | 0.4667 | 0.8787 | 0.8955 | 0.8787 | 0.8776 | 0.9143 |
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| 0.9585 | 12.27 | 3200 | 0.5159 | 0.8544 | 0.8734 | 0.8544 | 0.8534 | 0.8976 |
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| 0.9585 | 12.46 | 3250 | 0.5177 | 0.8518 | 0.8748 | 0.8518 | 0.8528 | 0.8987 |
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| 0.9585 | 12.66 | 3300 | 0.4435 | 0.8841 | 0.9040 | 0.8841 | 0.8835 | 0.9189 |
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| 0.9585 | 12.85 | 3350 | 0.5116 | 0.8544 | 0.8851 | 0.8544 | 0.8558 | 0.8992 |
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| 0.9352 | 13.04 | 3400 | 0.4538 | 0.8706 | 0.8888 | 0.8706 | 0.8697 | 0.9105 |
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| 0.9352 | 13.23 | 3450 | 0.4973 | 0.8706 | 0.8944 | 0.8706 | 0.8684 | 0.9086 |
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| 0.9352 | 13.42 | 3500 | 0.4465 | 0.8760 | 0.8937 | 0.8760 | 0.8741 | 0.9135 |
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| 0.9352 | 13.61 | 3550 | 0.4691 | 0.8814 | 0.9042 | 0.8814 | 0.8806 | 0.9154 |
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| 0.9352 | 13.81 | 3600 | 0.5010 | 0.8652 | 0.8916 | 0.8652 | 0.8641 | 0.9051 |
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| 0.9352 | 14.0 | 3650 | 0.5133 | 0.8410 | 0.8728 | 0.8410 | 0.8396 | 0.8879 |
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| 0.8941 | 14.19 | 3700 | 0.4476 | 0.8706 | 0.8961 | 0.8706 | 0.8729 | 0.9086 |
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| 0.8941 | 14.38 | 3750 | 0.4321 | 0.8679 | 0.8915 | 0.8679 | 0.8681 | 0.9067 |
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| 0.8941 | 14.57 | 3800 | 0.4033 | 0.8841 | 0.8991 | 0.8841 | 0.8835 | 0.9181 |
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| 0.8941 | 14.77 | 3850 | 0.4599 | 0.8841 | 0.9052 | 0.8841 | 0.8827 | 0.9181 |
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| 0.8941 | 14.96 | 3900 | 0.4673 | 0.8625 | 0.8883 | 0.8625 | 0.8631 | 0.9040 |
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| 0.8574 | 15.15 | 3950 | 0.4906 | 0.8760 | 0.8993 | 0.8760 | 0.8749 | 0.9135 |
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| 0.8574 | 15.34 | 4000 | 0.5055 | 0.8544 | 0.8836 | 0.8544 | 0.8518 | 0.8984 |
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| 0.8574 | 15.53 | 4050 | 0.4119 | 0.8841 | 0.8985 | 0.8841 | 0.8831 | 0.9191 |
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| 0.8574 | 15.72 | 4100 | 0.4684 | 0.8760 | 0.8989 | 0.8760 | 0.8752 | 0.9135 |
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| 0.8574 | 15.92 | 4150 | 0.4453 | 0.8787 | 0.8999 | 0.8787 | 0.8776 | 0.9151 |
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| 0.8344 | 16.11 | 4200 | 0.4928 | 0.8787 | 0.9000 | 0.8787 | 0.8783 | 0.9143 |
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| 0.8344 | 16.3 | 4250 | 0.4535 | 0.8868 | 0.9067 | 0.8868 | 0.8863 | 0.9191 |
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| 0.8344 | 16.49 | 4300 | 0.4259 | 0.8787 | 0.8986 | 0.8787 | 0.8781 | 0.9154 |
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| 0.8344 | 16.68 | 4350 | 0.4289 | 0.8787 | 0.8970 | 0.8787 | 0.8784 | 0.9154 |
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| 0.8344 | 16.87 | 4400 | 0.4828 | 0.8814 | 0.9013 | 0.8814 | 0.8813 | 0.9154 |
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| 0.8066 | 17.07 | 4450 | 0.4866 | 0.8787 | 0.8969 | 0.8787 | 0.8792 | 0.9135 |
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| 0.8066 | 17.26 | 4500 | 0.4388 | 0.8760 | 0.8934 | 0.8760 | 0.8768 | 0.9116 |
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| 0.8066 | 17.45 | 4550 | 0.5018 | 0.8787 | 0.8993 | 0.8787 | 0.8775 | 0.9143 |
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| 0.8066 | 17.64 | 4600 | 0.4838 | 0.8814 | 0.8981 | 0.8814 | 0.8808 | 0.9154 |
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| 0.8066 | 17.83 | 4650 | 0.5394 | 0.8679 | 0.8893 | 0.8679 | 0.8662 | 0.9059 |
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| 0.7757 | 18.02 | 4700 | 0.4628 | 0.8814 | 0.8964 | 0.8814 | 0.8800 | 0.9162 |
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| 0.7757 | 18.22 | 4750 | 0.5456 | 0.8733 | 0.8907 | 0.8733 | 0.8719 | 0.9097 |
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| 0.7757 | 18.41 | 4800 | 0.4858 | 0.8814 | 0.8970 | 0.8814 | 0.8804 | 0.9154 |
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| 0.7757 | 18.6 | 4850 | 0.5836 | 0.8571 | 0.8776 | 0.8571 | 0.8568 | 0.8984 |
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| 0.7757 | 18.79 | 4900 | 0.5008 | 0.8787 | 0.8985 | 0.8787 | 0.8781 | 0.9143 |
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| 0.7757 | 18.98 | 4950 | 0.5259 | 0.8760 | 0.8950 | 0.8760 | 0.8749 | 0.9116 |
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| 0.7595 | 19.18 | 5000 | 0.5906 | 0.8652 | 0.8869 | 0.8652 | 0.8657 | 0.9040 |
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| 0.7595 | 19.37 | 5050 | 0.4905 | 0.8841 | 0.8993 | 0.8841 | 0.8839 | 0.9173 |
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| 0.7595 | 19.56 | 5100 | 0.5958 | 0.8598 | 0.8804 | 0.8598 | 0.8596 | 0.9003 |
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| 0.7595 | 19.75 | 5150 | 0.5466 | 0.8679 | 0.8924 | 0.8679 | 0.8666 | 0.9059 |
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| 0.7595 | 19.94 | 5200 | 0.4639 | 0.8841 | 0.9008 | 0.8841 | 0.8834 | 0.9173 |
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| 0.7257 | 20.13 | 5250 | 0.5094 | 0.8787 | 0.9015 | 0.8787 | 0.8795 | 0.9135 |
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| 0.7257 | 20.33 | 5300 | 0.5310 | 0.8733 | 0.8973 | 0.8733 | 0.8737 | 0.9097 |
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| 0.7257 | 20.52 | 5350 | 0.5118 | 0.8733 | 0.8925 | 0.8733 | 0.8734 | 0.9097 |
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| 0.7257 | 20.71 | 5400 | 0.5166 | 0.8814 | 0.9017 | 0.8814 | 0.8814 | 0.9154 |
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| 0.7257 | 20.9 | 5450 | 0.4850 | 0.8814 | 0.8984 | 0.8814 | 0.8807 | 0.9164 |
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| 0.7185 | 21.09 | 5500 | 0.5161 | 0.8841 | 0.9018 | 0.8841 | 0.8842 | 0.9183 |
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| 0.7185 | 21.28 | 5550 | 0.5197 | 0.8706 | 0.8904 | 0.8706 | 0.8694 | 0.9086 |
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| 0.7185 | 21.48 | 5600 | 0.5297 | 0.8733 | 0.8921 | 0.8733 | 0.8728 | 0.9097 |
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| 0.7185 | 21.67 | 5650 | 0.5317 | 0.8706 | 0.8913 | 0.8706 | 0.8694 | 0.9078 |
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| 0.7185 | 21.86 | 5700 | 0.5120 | 0.8625 | 0.8809 | 0.8625 | 0.8610 | 0.9022 |
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| 0.6968 | 22.05 | 5750 | 0.5144 | 0.8760 | 0.8960 | 0.8760 | 0.8753 | 0.9116 |
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| 0.6968 | 22.24 | 5800 | 0.5688 | 0.8733 | 0.8911 | 0.8733 | 0.8721 | 0.9097 |
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| 0.6968 | 22.44 | 5850 | 0.5430 | 0.8733 | 0.8916 | 0.8733 | 0.8724 | 0.9097 |
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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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