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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: 0.5129
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- Accuracy: 0.8706
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- Precision: 0.8830
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- Recall: 0.8706
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- F1: 0.8687
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- Binary: 0.9084
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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 | 0.22 | 50 | 3.8853 | 0.0499 | 0.0125 | 0.0499 | 0.0132 | 0.3132 |
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| No log | 0.43 | 100 | 3.4193 | 0.0702 | 0.0103 | 0.0702 | 0.0167 | 0.3417 |
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| No log | 0.65 | 150 | 3.1606 | 0.1161 | 0.0528 | 0.1161 | 0.0512 | 0.3783 |
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| No log | 0.86 | 200 | 2.9650 | 0.1714 | 0.1093 | 0.1714 | 0.1008 | 0.4148 |
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| 3.6314 | 1.08 | 250 | 2.7976 | 0.1714 | 0.0947 | 0.1714 | 0.0927 | 0.4148 |
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| 3.6314 | 1.29 | 300 | 2.4874 | 0.2740 | 0.1723 | 0.2740 | 0.1770 | 0.4870 |
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| 3.6314 | 1.51 | 350 | 2.3192 | 0.3414 | 0.2741 | 0.3414 | 0.2588 | 0.5379 |
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| 3.6314 | 1.72 | 400 | 2.1116 | 0.4278 | 0.3772 | 0.4278 | 0.3514 | 0.5988 |
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| 3.6314 | 1.94 | 450 | 1.9495 | 0.4683 | 0.4712 | 0.4683 | 0.4075 | 0.6273 |
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| 2.6994 | 2.16 | 500 | 1.8632 | 0.4764 | 0.4337 | 0.4764 | 0.4098 | 0.6313 |
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| 2.6994 | 2.37 | 550 | 1.6822 | 0.5250 | 0.4922 | 0.5250 | 0.4621 | 0.6664 |
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| 2.6994 | 2.59 | 600 | 1.5299 | 0.5938 | 0.5590 | 0.5938 | 0.5296 | 0.7165 |
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| 2.6994 | 2.8 | 650 | 1.4177 | 0.6140 | 0.5885 | 0.6140 | 0.5664 | 0.7298 |
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| 2.1575 | 3.02 | 700 | 1.3614 | 0.6100 | 0.5558 | 0.6100 | 0.5498 | 0.7283 |
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| 2.1575 | 3.23 | 750 | 1.2209 | 0.6491 | 0.6650 | 0.6491 | 0.6086 | 0.7528 |
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| 2.1575 | 3.45 | 800 | 1.2059 | 0.6829 | 0.7281 | 0.6829 | 0.6553 | 0.7748 |
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| 2.1575 | 3.66 | 850 | 1.1207 | 0.7031 | 0.7301 | 0.7031 | 0.6741 | 0.7908 |
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| 2.1575 | 3.88 | 900 | 1.0553 | 0.7139 | 0.7368 | 0.7139 | 0.6967 | 0.7993 |
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| 1.8321 | 4.09 | 950 | 0.9864 | 0.7490 | 0.7412 | 0.7490 | 0.7291 | 0.8250 |
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| 1.8321 | 4.31 | 1000 | 0.9365 | 0.7544 | 0.7653 | 0.7544 | 0.7360 | 0.8278 |
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| 1.8321 | 4.53 | 1050 | 0.9050 | 0.7584 | 0.7617 | 0.7584 | 0.7438 | 0.8323 |
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| 1.8321 | 4.74 | 1100 | 0.8241 | 0.7760 | 0.7896 | 0.7760 | 0.7638 | 0.8424 |
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| 1.8321 | 4.96 | 1150 | 0.8260 | 0.7611 | 0.7712 | 0.7611 | 0.7455 | 0.8335 |
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| 1.6136 | 5.17 | 1200 | 0.8008 | 0.7814 | 0.8013 | 0.7814 | 0.7738 | 0.8474 |
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| 1.6136 | 5.39 | 1250 | 0.7588 | 0.7787 | 0.7981 | 0.7787 | 0.7694 | 0.8453 |
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| 1.6136 | 5.6 | 1300 | 0.7619 | 0.7868 | 0.7963 | 0.7868 | 0.7755 | 0.8520 |
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| 1.6136 | 5.82 | 1350 | 0.7611 | 0.7868 | 0.7972 | 0.7868 | 0.7755 | 0.8514 |
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| 1.4406 | 6.03 | 1400 | 0.7094 | 0.7976 | 0.8090 | 0.7976 | 0.7923 | 0.8591 |
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| 1.4406 | 6.25 | 1450 | 0.7133 | 0.7976 | 0.7990 | 0.7976 | 0.7873 | 0.8595 |
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| 1.4406 | 6.47 | 1500 | 0.7126 | 0.8016 | 0.8151 | 0.8016 | 0.7921 | 0.8618 |
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| 1.4406 | 6.68 | 1550 | 0.6381 | 0.8043 | 0.8242 | 0.8043 | 0.7979 | 0.8642 |
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| 1.4406 | 6.9 | 1600 | 0.6248 | 0.8111 | 0.8253 | 0.8111 | 0.8079 | 0.8683 |
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| 1.3033 | 7.11 | 1650 | 0.6333 | 0.8138 | 0.8391 | 0.8138 | 0.8125 | 0.8699 |
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| 1.3033 | 7.33 | 1700 | 0.6087 | 0.8232 | 0.8450 | 0.8232 | 0.8188 | 0.8775 |
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| 1.3033 | 7.54 | 1750 | 0.5919 | 0.8273 | 0.8347 | 0.8273 | 0.8199 | 0.8796 |
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| 1.3033 | 7.76 | 1800 | 0.5835 | 0.8232 | 0.8398 | 0.8232 | 0.8198 | 0.8779 |
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| 1.3033 | 7.97 | 1850 | 0.5697 | 0.8232 | 0.8397 | 0.8232 | 0.8192 | 0.8780 |
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| 1.2408 | 8.19 | 1900 | 0.6186 | 0.8111 | 0.8354 | 0.8111 | 0.8050 | 0.8694 |
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| 1.2408 | 8.41 | 1950 | 0.5622 | 0.8327 | 0.8445 | 0.8327 | 0.8306 | 0.8830 |
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| 1.2408 | 8.62 | 2000 | 0.5740 | 0.8205 | 0.8407 | 0.8205 | 0.8184 | 0.8756 |
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| 1.2408 | 8.84 | 2050 | 0.5627 | 0.8300 | 0.8460 | 0.8300 | 0.8286 | 0.8816 |
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| 1.1272 | 9.05 | 2100 | 0.5340 | 0.8381 | 0.8538 | 0.8381 | 0.8354 | 0.8873 |
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| 1.1272 | 9.27 | 2150 | 0.5927 | 0.8246 | 0.8475 | 0.8246 | 0.8205 | 0.8795 |
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| 1.1272 | 9.48 | 2200 | 0.5429 | 0.8300 | 0.8511 | 0.8300 | 0.8292 | 0.8812 |
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| 1.1272 | 9.7 | 2250 | 0.4781 | 0.8529 | 0.8659 | 0.8529 | 0.8518 | 0.8977 |
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| 1.1272 | 9.91 | 2300 | 0.5081 | 0.8448 | 0.8621 | 0.8448 | 0.8442 | 0.8930 |
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| 1.0783 | 10.13 | 2350 | 0.5015 | 0.8489 | 0.8680 | 0.8489 | 0.8473 | 0.8947 |
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| 1.0783 | 10.34 | 2400 | 0.5225 | 0.8421 | 0.8627 | 0.8421 | 0.8389 | 0.8906 |
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| 1.0783 | 10.56 | 2450 | 0.5113 | 0.8435 | 0.8602 | 0.8435 | 0.8426 | 0.8911 |
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| 1.0783 | 10.78 | 2500 | 0.5009 | 0.8556 | 0.8712 | 0.8556 | 0.8526 | 0.9005 |
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| 1.0783 | 10.99 | 2550 | 0.5112 | 0.8421 | 0.8571 | 0.8421 | 0.8416 | 0.8922 |
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| 1.0384 | 11.21 | 2600 | 0.4812 | 0.8543 | 0.8663 | 0.8543 | 0.8516 | 0.8996 |
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| 1.0384 | 11.42 | 2650 | 0.5017 | 0.8583 | 0.8731 | 0.8583 | 0.8561 | 0.9020 |
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| 1.0384 | 11.64 | 2700 | 0.4837 | 0.8489 | 0.8650 | 0.8489 | 0.8450 | 0.8958 |
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| 1.0384 | 11.85 | 2750 | 0.5084 | 0.8435 | 0.8643 | 0.8435 | 0.8389 | 0.8920 |
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| 1.0115 | 12.07 | 2800 | 0.4843 | 0.8583 | 0.8767 | 0.8583 | 0.8562 | 0.9024 |
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| 1.0115 | 12.28 | 2850 | 0.5396 | 0.8502 | 0.8703 | 0.8502 | 0.8465 | 0.8973 |
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| 1.0115 | 12.5 | 2900 | 0.5440 | 0.8435 | 0.8563 | 0.8435 | 0.8375 | 0.8916 |
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| 1.0115 | 12.72 | 2950 | 0.5076 | 0.8489 | 0.8600 | 0.8489 | 0.8460 | 0.8958 |
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| 1.0115 | 12.93 | 3000 | 0.5253 | 0.8435 | 0.8571 | 0.8435 | 0.8422 | 0.8915 |
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| 0.9461 | 13.15 | 3050 | 0.4841 | 0.8664 | 0.8828 | 0.8664 | 0.8669 | 0.9081 |
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| 0.9461 | 13.36 | 3100 | 0.4890 | 0.8637 | 0.8759 | 0.8637 | 0.8626 | 0.9062 |
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| 0.9461 | 13.58 | 3150 | 0.4948 | 0.8664 | 0.8819 | 0.8664 | 0.8656 | 0.9076 |
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| 0.9461 | 13.79 | 3200 | 0.4851 | 0.8596 | 0.8763 | 0.8596 | 0.8590 | 0.9028 |
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| 0.9001 | 14.01 | 3250 | 0.4966 | 0.8583 | 0.8735 | 0.8583 | 0.8587 | 0.9020 |
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| 0.9001 | 14.22 | 3300 | 0.5335 | 0.8543 | 0.8688 | 0.8543 | 0.8531 | 0.8985 |
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| 0.9001 | 14.44 | 3350 | 0.5072 | 0.8596 | 0.8732 | 0.8596 | 0.8587 | 0.9024 |
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| 0.9001 | 14.66 | 3400 | 0.4968 | 0.8664 | 0.8793 | 0.8664 | 0.8654 | 0.9076 |
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| 0.9001 | 14.87 | 3450 | 0.4763 | 0.8637 | 0.8779 | 0.8637 | 0.8629 | 0.9057 |
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| 0.8663 | 15.09 | 3500 | 0.5134 | 0.8704 | 0.8781 | 0.8704 | 0.8685 | 0.9104 |
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| 0.8663 | 15.3 | 3550 | 0.5010 | 0.8664 | 0.8812 | 0.8664 | 0.8668 | 0.9081 |
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| 0.8663 | 15.52 | 3600 | 0.5221 | 0.8556 | 0.8694 | 0.8556 | 0.8547 | 0.9000 |
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| 0.8663 | 15.73 | 3650 | 0.4990 | 0.8704 | 0.8813 | 0.8704 | 0.8691 | 0.9104 |
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| 0.8663 | 15.95 | 3700 | 0.5046 | 0.8610 | 0.8729 | 0.8610 | 0.8591 | 0.9032 |
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| 0.8499 | 16.16 | 3750 | 0.5099 | 0.8583 | 0.8730 | 0.8583 | 0.8562 | 0.9019 |
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| 0.8499 | 16.38 | 3800 | 0.4859 | 0.8677 | 0.8786 | 0.8677 | 0.8668 | 0.9085 |
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| 0.8499 | 16.59 | 3850 | 0.5005 | 0.8637 | 0.8731 | 0.8637 | 0.8619 | 0.9067 |
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| 0.8499 | 16.81 | 3900 | 0.5169 | 0.8596 | 0.8735 | 0.8596 | 0.8590 | 0.9023 |
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| 0.8149 | 17.03 | 3950 | 0.5035 | 0.8583 | 0.8710 | 0.8583 | 0.8579 | 0.9019 |
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| 0.8149 | 17.24 | 4000 | 0.5211 | 0.8570 | 0.8748 | 0.8570 | 0.8573 | 0.9009 |
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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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