--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: hubert-classifier-aug-fold-3 results: [] --- # hubert-classifier-aug-fold-3 This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5105 - Accuracy: 0.8639 - Precision: 0.8778 - Recall: 0.8639 - F1: 0.8624 - Binary: 0.9049 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| | No log | 0.22 | 50 | 3.8852 | 0.0445 | 0.0072 | 0.0445 | 0.0108 | 0.3195 | | No log | 0.43 | 100 | 3.4756 | 0.0499 | 0.0263 | 0.0499 | 0.0140 | 0.3247 | | No log | 0.65 | 150 | 3.2680 | 0.0916 | 0.0193 | 0.0916 | 0.0272 | 0.3611 | | No log | 0.86 | 200 | 3.1133 | 0.1038 | 0.0266 | 0.1038 | 0.0369 | 0.3695 | | 3.7026 | 1.08 | 250 | 2.9266 | 0.1765 | 0.0816 | 0.1765 | 0.0972 | 0.4212 | | 3.7026 | 1.29 | 300 | 2.7455 | 0.2412 | 0.1540 | 0.2412 | 0.1516 | 0.4644 | | 3.7026 | 1.51 | 350 | 2.4869 | 0.2898 | 0.1860 | 0.2898 | 0.1860 | 0.5008 | | 3.7026 | 1.73 | 400 | 2.2598 | 0.3275 | 0.2377 | 0.3275 | 0.2426 | 0.5284 | | 3.7026 | 1.94 | 450 | 2.0416 | 0.4232 | 0.3176 | 0.4232 | 0.3356 | 0.5947 | | 2.8767 | 2.16 | 500 | 1.8498 | 0.4461 | 0.3751 | 0.4461 | 0.3752 | 0.6124 | | 2.8767 | 2.37 | 550 | 1.6887 | 0.5202 | 0.4826 | 0.5202 | 0.4522 | 0.6639 | | 2.8767 | 2.59 | 600 | 1.5765 | 0.5256 | 0.4880 | 0.5256 | 0.4630 | 0.6677 | | 2.8767 | 2.8 | 650 | 1.4405 | 0.5930 | 0.5650 | 0.5930 | 0.5478 | 0.7152 | | 2.2257 | 3.02 | 700 | 1.3663 | 0.6253 | 0.6037 | 0.6253 | 0.5834 | 0.7371 | | 2.2257 | 3.24 | 750 | 1.2404 | 0.6577 | 0.6668 | 0.6577 | 0.6159 | 0.7602 | | 2.2257 | 3.45 | 800 | 1.1946 | 0.6887 | 0.7128 | 0.6887 | 0.6654 | 0.7807 | | 2.2257 | 3.67 | 850 | 1.0658 | 0.7251 | 0.7319 | 0.7251 | 0.7025 | 0.8073 | | 2.2257 | 3.88 | 900 | 1.0567 | 0.7129 | 0.7529 | 0.7129 | 0.6874 | 0.7982 | | 1.8296 | 4.1 | 950 | 1.0013 | 0.7372 | 0.7537 | 0.7372 | 0.7209 | 0.8151 | | 1.8296 | 4.31 | 1000 | 0.9092 | 0.7534 | 0.7863 | 0.7534 | 0.7388 | 0.8284 | | 1.8296 | 4.53 | 1050 | 0.8869 | 0.7574 | 0.7865 | 0.7574 | 0.7467 | 0.8313 | | 1.8296 | 4.75 | 1100 | 0.8033 | 0.7763 | 0.8084 | 0.7763 | 0.7677 | 0.8446 | | 1.8296 | 4.96 | 1150 | 0.7981 | 0.7790 | 0.8161 | 0.7790 | 0.7664 | 0.8451 | | 1.6084 | 5.18 | 1200 | 0.7499 | 0.7978 | 0.8090 | 0.7978 | 0.7889 | 0.8588 | | 1.6084 | 5.39 | 1250 | 0.7066 | 0.7938 | 0.8039 | 0.7938 | 0.7807 | 0.8550 | | 1.6084 | 5.61 | 1300 | 0.7537 | 0.7938 | 0.8163 | 0.7938 | 0.7871 | 0.8555 | | 1.6084 | 5.83 | 1350 | 0.7293 | 0.8113 | 0.8328 | 0.8113 | 0.8065 | 0.8687 | | 1.4253 | 6.04 | 1400 | 0.7055 | 0.7992 | 0.8276 | 0.7992 | 0.7905 | 0.8593 | | 1.4253 | 6.26 | 1450 | 0.6732 | 0.8059 | 0.8226 | 0.8059 | 0.8003 | 0.8648 | | 1.4253 | 6.47 | 1500 | 0.6510 | 0.8315 | 0.8461 | 0.8315 | 0.8272 | 0.8838 | | 1.4253 | 6.69 | 1550 | 0.6113 | 0.8329 | 0.8565 | 0.8329 | 0.8314 | 0.8814 | | 1.4253 | 6.9 | 1600 | 0.6299 | 0.8248 | 0.8435 | 0.8248 | 0.8215 | 0.8787 | | 1.2904 | 7.12 | 1650 | 0.6132 | 0.8410 | 0.8553 | 0.8410 | 0.8377 | 0.8900 | | 1.2904 | 7.34 | 1700 | 0.5883 | 0.8464 | 0.8592 | 0.8464 | 0.8437 | 0.8938 | | 1.2904 | 7.55 | 1750 | 0.5995 | 0.8437 | 0.8565 | 0.8437 | 0.8363 | 0.8908 | | 1.2904 | 7.77 | 1800 | 0.5861 | 0.8450 | 0.8612 | 0.8450 | 0.8400 | 0.8923 | | 1.2904 | 7.98 | 1850 | 0.6007 | 0.8410 | 0.8613 | 0.8410 | 0.8370 | 0.8889 | | 1.2105 | 8.2 | 1900 | 0.5518 | 0.8437 | 0.8594 | 0.8437 | 0.8407 | 0.8904 | | 1.2105 | 8.41 | 1950 | 0.5739 | 0.8342 | 0.8522 | 0.8342 | 0.8294 | 0.8846 | | 1.2105 | 8.63 | 2000 | 0.5998 | 0.8275 | 0.8475 | 0.8275 | 0.8222 | 0.8806 | | 1.2105 | 8.85 | 2050 | 0.5662 | 0.8531 | 0.8716 | 0.8531 | 0.8471 | 0.8978 | | 1.1291 | 9.06 | 2100 | 0.5144 | 0.8639 | 0.8818 | 0.8639 | 0.8607 | 0.9040 | | 1.1291 | 9.28 | 2150 | 0.4782 | 0.8625 | 0.8843 | 0.8625 | 0.8584 | 0.9036 | | 1.1291 | 9.49 | 2200 | 0.4787 | 0.8612 | 0.8736 | 0.8612 | 0.8571 | 0.9026 | | 1.1291 | 9.71 | 2250 | 0.4866 | 0.8666 | 0.8800 | 0.8666 | 0.8629 | 0.9050 | | 1.1291 | 9.92 | 2300 | 0.5999 | 0.8342 | 0.8468 | 0.8342 | 0.8279 | 0.8846 | | 1.0646 | 10.14 | 2350 | 0.5397 | 0.8518 | 0.8723 | 0.8518 | 0.8496 | 0.8962 | | 1.0646 | 10.36 | 2400 | 0.4718 | 0.8585 | 0.8761 | 0.8585 | 0.8541 | 0.9005 | | 1.0646 | 10.57 | 2450 | 0.4909 | 0.8625 | 0.8772 | 0.8625 | 0.8603 | 0.9035 | | 1.0646 | 10.79 | 2500 | 0.4706 | 0.8544 | 0.8729 | 0.8544 | 0.8510 | 0.8988 | | 1.0145 | 11.0 | 2550 | 0.4830 | 0.8558 | 0.8692 | 0.8558 | 0.8522 | 0.8993 | | 1.0145 | 11.22 | 2600 | 0.4926 | 0.8652 | 0.8774 | 0.8652 | 0.8622 | 0.9055 | | 1.0145 | 11.43 | 2650 | 0.4879 | 0.8693 | 0.8827 | 0.8693 | 0.8672 | 0.9086 | | 1.0145 | 11.65 | 2700 | 0.5248 | 0.8666 | 0.8811 | 0.8666 | 0.8634 | 0.9063 | | 1.0145 | 11.87 | 2750 | 0.4835 | 0.8585 | 0.8733 | 0.8585 | 0.8550 | 0.9012 | | 0.9883 | 12.08 | 2800 | 0.4525 | 0.8814 | 0.8929 | 0.8814 | 0.8807 | 0.9177 | | 0.9883 | 12.3 | 2850 | 0.4808 | 0.8706 | 0.8856 | 0.8706 | 0.8667 | 0.9101 | | 0.9883 | 12.51 | 2900 | 0.4736 | 0.8720 | 0.8848 | 0.8720 | 0.8699 | 0.9111 | | 0.9883 | 12.73 | 2950 | 0.4256 | 0.8774 | 0.8945 | 0.8774 | 0.8762 | 0.9146 | | 0.9883 | 12.94 | 3000 | 0.4400 | 0.8841 | 0.8953 | 0.8841 | 0.8816 | 0.9182 | | 0.9479 | 13.16 | 3050 | 0.4743 | 0.8666 | 0.8836 | 0.8666 | 0.8660 | 0.9067 | | 0.9479 | 13.38 | 3100 | 0.4729 | 0.8760 | 0.8926 | 0.8760 | 0.8749 | 0.9133 | | 0.9479 | 13.59 | 3150 | 0.4608 | 0.8733 | 0.8857 | 0.8733 | 0.8704 | 0.9105 | | 0.9479 | 13.81 | 3200 | 0.4206 | 0.8868 | 0.8948 | 0.8868 | 0.8841 | 0.9216 | | 0.8867 | 14.02 | 3250 | 0.4332 | 0.8881 | 0.8992 | 0.8881 | 0.8871 | 0.9201 | | 0.8867 | 14.24 | 3300 | 0.4635 | 0.8733 | 0.8860 | 0.8733 | 0.8718 | 0.9108 | | 0.8867 | 14.46 | 3350 | 0.4691 | 0.8827 | 0.8960 | 0.8827 | 0.8820 | 0.9170 | | 0.8867 | 14.67 | 3400 | 0.4282 | 0.8774 | 0.8882 | 0.8774 | 0.8754 | 0.9140 | | 0.8867 | 14.89 | 3450 | 0.4504 | 0.8801 | 0.8917 | 0.8801 | 0.8782 | 0.9162 | | 0.8593 | 15.1 | 3500 | 0.4781 | 0.8760 | 0.8884 | 0.8760 | 0.8736 | 0.9125 | | 0.8593 | 15.32 | 3550 | 0.4614 | 0.8895 | 0.8993 | 0.8895 | 0.8878 | 0.9230 | | 0.8593 | 15.53 | 3600 | 0.4659 | 0.8774 | 0.8918 | 0.8774 | 0.8766 | 0.9151 | | 0.8593 | 15.75 | 3650 | 0.4496 | 0.8814 | 0.8916 | 0.8814 | 0.8797 | 0.9164 | | 0.8593 | 15.97 | 3700 | 0.4648 | 0.8827 | 0.8941 | 0.8827 | 0.8817 | 0.9186 | | 0.8329 | 16.18 | 3750 | 0.4735 | 0.8827 | 0.8962 | 0.8827 | 0.8815 | 0.9182 | | 0.8329 | 16.4 | 3800 | 0.4255 | 0.8935 | 0.9057 | 0.8935 | 0.8928 | 0.9252 | | 0.8329 | 16.61 | 3850 | 0.4689 | 0.8747 | 0.8878 | 0.8747 | 0.8739 | 0.9117 | | 0.8329 | 16.83 | 3900 | 0.4596 | 0.8841 | 0.8948 | 0.8841 | 0.8834 | 0.9183 | | 0.8168 | 17.04 | 3950 | 0.4942 | 0.8760 | 0.8914 | 0.8760 | 0.8714 | 0.9123 | | 0.8168 | 17.26 | 4000 | 0.5265 | 0.8747 | 0.8899 | 0.8747 | 0.8733 | 0.9117 | | 0.8168 | 17.48 | 4050 | 0.4592 | 0.8787 | 0.8910 | 0.8787 | 0.8767 | 0.9150 | | 0.8168 | 17.69 | 4100 | 0.4839 | 0.8693 | 0.8824 | 0.8693 | 0.8678 | 0.9080 | | 0.8168 | 17.91 | 4150 | 0.4688 | 0.8827 | 0.8923 | 0.8827 | 0.8804 | 0.9173 | | 0.7604 | 18.12 | 4200 | 0.4593 | 0.8733 | 0.8834 | 0.8733 | 0.8708 | 0.9098 | | 0.7604 | 18.34 | 4250 | 0.4871 | 0.8760 | 0.8876 | 0.8760 | 0.8741 | 0.9117 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1