--- 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-4 results: [] --- # hubert-classifier-aug-fold-4 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: 0.6864 - Accuracy: 0.8612 - Precision: 0.8735 - Recall: 0.8612 - F1: 0.8606 - Binary: 0.9026 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| | No log | 0.24 | 50 | 4.4183 | 0.0135 | 0.0052 | 0.0135 | 0.0050 | 0.1206 | | No log | 0.48 | 100 | 4.2895 | 0.0412 | 0.0103 | 0.0412 | 0.0108 | 0.2549 | | No log | 0.72 | 150 | 3.9961 | 0.0592 | 0.0278 | 0.0592 | 0.0286 | 0.3335 | | No log | 0.96 | 200 | 3.7039 | 0.0742 | 0.0345 | 0.0742 | 0.0283 | 0.3461 | | 4.2414 | 1.2 | 250 | 3.4082 | 0.1394 | 0.0770 | 0.1394 | 0.0791 | 0.3938 | | 4.2414 | 1.44 | 300 | 3.1852 | 0.1979 | 0.1003 | 0.1979 | 0.1107 | 0.4377 | | 4.2414 | 1.68 | 350 | 2.9114 | 0.2976 | 0.2089 | 0.2976 | 0.2077 | 0.5074 | | 4.2414 | 1.92 | 400 | 2.5996 | 0.3366 | 0.2524 | 0.3366 | 0.2461 | 0.5348 | | 3.2619 | 2.16 | 450 | 2.3085 | 0.4100 | 0.3088 | 0.4100 | 0.3208 | 0.5857 | | 3.2619 | 2.4 | 500 | 2.0470 | 0.4873 | 0.4303 | 0.4873 | 0.4151 | 0.6394 | | 3.2619 | 2.63 | 550 | 1.7979 | 0.5307 | 0.4934 | 0.5307 | 0.4713 | 0.6713 | | 3.2619 | 2.87 | 600 | 1.5495 | 0.6012 | 0.5736 | 0.6012 | 0.5519 | 0.7199 | | 2.2861 | 3.11 | 650 | 1.4334 | 0.6124 | 0.6399 | 0.6124 | 0.5690 | 0.7302 | | 2.2861 | 3.35 | 700 | 1.3103 | 0.6544 | 0.6847 | 0.6544 | 0.6231 | 0.7576 | | 2.2861 | 3.59 | 750 | 1.1635 | 0.6927 | 0.7090 | 0.6927 | 0.6698 | 0.7849 | | 2.2861 | 3.83 | 800 | 1.0149 | 0.7549 | 0.7708 | 0.7549 | 0.7481 | 0.8289 | | 1.5996 | 4.07 | 850 | 1.0088 | 0.7286 | 0.7489 | 0.7286 | 0.7069 | 0.8106 | | 1.5996 | 4.31 | 900 | 0.8567 | 0.7699 | 0.7719 | 0.7699 | 0.7540 | 0.8398 | | 1.5996 | 4.55 | 950 | 0.8231 | 0.7916 | 0.8071 | 0.7916 | 0.7830 | 0.8552 | | 1.5996 | 4.79 | 1000 | 0.8333 | 0.7841 | 0.8041 | 0.7841 | 0.7754 | 0.8489 | | 1.2436 | 5.03 | 1050 | 0.7799 | 0.7946 | 0.8084 | 0.7946 | 0.7884 | 0.8562 | | 1.2436 | 5.27 | 1100 | 0.7429 | 0.7946 | 0.8097 | 0.7946 | 0.7917 | 0.8568 | | 1.2436 | 5.51 | 1150 | 0.7050 | 0.8238 | 0.8384 | 0.8238 | 0.8202 | 0.8772 | | 1.2436 | 5.75 | 1200 | 0.6639 | 0.8193 | 0.8251 | 0.8193 | 0.8139 | 0.8738 | | 1.2436 | 5.99 | 1250 | 0.6918 | 0.8186 | 0.8387 | 0.8186 | 0.8141 | 0.8740 | | 1.0055 | 6.23 | 1300 | 0.6296 | 0.8246 | 0.8383 | 0.8246 | 0.8186 | 0.8768 | | 1.0055 | 6.47 | 1350 | 0.6581 | 0.8253 | 0.8412 | 0.8253 | 0.8215 | 0.8779 | | 1.0055 | 6.71 | 1400 | 0.6302 | 0.8313 | 0.8469 | 0.8313 | 0.8292 | 0.8821 | | 1.0055 | 6.95 | 1450 | 0.5998 | 0.8448 | 0.8593 | 0.8448 | 0.8420 | 0.8906 | | 0.8805 | 7.19 | 1500 | 0.6230 | 0.8456 | 0.8531 | 0.8456 | 0.8443 | 0.8926 | | 0.8805 | 7.43 | 1550 | 0.6308 | 0.8396 | 0.8528 | 0.8396 | 0.8377 | 0.8887 | | 0.8805 | 7.66 | 1600 | 0.5979 | 0.8418 | 0.8531 | 0.8418 | 0.8413 | 0.8897 | | 0.8805 | 7.9 | 1650 | 0.6022 | 0.8478 | 0.8616 | 0.8478 | 0.8469 | 0.8946 | | 0.7681 | 8.14 | 1700 | 0.5872 | 0.8471 | 0.8573 | 0.8471 | 0.8453 | 0.8927 | | 0.7681 | 8.38 | 1750 | 0.5744 | 0.8433 | 0.8570 | 0.8433 | 0.8420 | 0.8909 | | 0.7681 | 8.62 | 1800 | 0.5351 | 0.8643 | 0.8707 | 0.8643 | 0.8627 | 0.9061 | | 0.7681 | 8.86 | 1850 | 0.5688 | 0.8561 | 0.8680 | 0.8561 | 0.8559 | 0.8990 | | 0.7001 | 9.1 | 1900 | 0.6618 | 0.8298 | 0.8457 | 0.8298 | 0.8269 | 0.8810 | | 0.7001 | 9.34 | 1950 | 0.6244 | 0.8426 | 0.8571 | 0.8426 | 0.8422 | 0.8900 | | 0.7001 | 9.58 | 2000 | 0.5802 | 0.8576 | 0.8681 | 0.8576 | 0.8569 | 0.8996 | | 0.7001 | 9.82 | 2050 | 0.5352 | 0.8688 | 0.8761 | 0.8688 | 0.8687 | 0.9072 | | 0.6288 | 10.06 | 2100 | 0.5347 | 0.8651 | 0.8773 | 0.8651 | 0.8637 | 0.9049 | | 0.6288 | 10.3 | 2150 | 0.6019 | 0.8546 | 0.8665 | 0.8546 | 0.8535 | 0.8986 | | 0.6288 | 10.54 | 2200 | 0.5699 | 0.8598 | 0.8670 | 0.8598 | 0.8571 | 0.9005 | | 0.6288 | 10.78 | 2250 | 0.5494 | 0.8748 | 0.8838 | 0.8748 | 0.8730 | 0.9118 | | 0.5959 | 11.02 | 2300 | 0.5471 | 0.8718 | 0.8804 | 0.8718 | 0.8714 | 0.9103 | | 0.5959 | 11.26 | 2350 | 0.5570 | 0.8628 | 0.8738 | 0.8628 | 0.8605 | 0.9042 | | 0.5959 | 11.5 | 2400 | 0.5300 | 0.8801 | 0.8875 | 0.8801 | 0.8791 | 0.9163 | | 0.5959 | 11.74 | 2450 | 0.5418 | 0.8643 | 0.8725 | 0.8643 | 0.8630 | 0.9039 | | 0.5959 | 11.98 | 2500 | 0.5418 | 0.8726 | 0.8822 | 0.8726 | 0.8715 | 0.9108 | | 0.5407 | 12.22 | 2550 | 0.5718 | 0.8658 | 0.8755 | 0.8658 | 0.8652 | 0.9058 | | 0.5407 | 12.46 | 2600 | 0.5686 | 0.8658 | 0.8725 | 0.8658 | 0.8643 | 0.9058 | | 0.5407 | 12.69 | 2650 | 0.6045 | 0.8658 | 0.8768 | 0.8658 | 0.8656 | 0.9053 | | 0.5407 | 12.93 | 2700 | 0.5571 | 0.8621 | 0.8715 | 0.8621 | 0.8607 | 0.9027 | | 0.5175 | 13.17 | 2750 | 0.5367 | 0.8756 | 0.8809 | 0.8756 | 0.8745 | 0.9131 | | 0.5175 | 13.41 | 2800 | 0.5241 | 0.8771 | 0.8827 | 0.8771 | 0.8755 | 0.9142 | | 0.5175 | 13.65 | 2850 | 0.5793 | 0.8703 | 0.8792 | 0.8703 | 0.8691 | 0.9095 | | 0.5175 | 13.89 | 2900 | 0.5608 | 0.8756 | 0.8843 | 0.8756 | 0.8751 | 0.9123 | | 0.4913 | 14.13 | 2950 | 0.5734 | 0.8711 | 0.8781 | 0.8711 | 0.8694 | 0.9100 | | 0.4913 | 14.37 | 3000 | 0.5916 | 0.8771 | 0.8821 | 0.8771 | 0.8758 | 0.9134 | | 0.4913 | 14.61 | 3050 | 0.5651 | 0.8696 | 0.8761 | 0.8696 | 0.8680 | 0.9082 | | 0.4913 | 14.85 | 3100 | 0.5535 | 0.8786 | 0.8831 | 0.8786 | 0.8771 | 0.9152 | | 0.4747 | 15.09 | 3150 | 0.5694 | 0.8741 | 0.8819 | 0.8741 | 0.8737 | 0.9118 | | 0.4747 | 15.33 | 3200 | 0.5759 | 0.8726 | 0.8794 | 0.8726 | 0.8720 | 0.9103 | | 0.4747 | 15.57 | 3250 | 0.5827 | 0.8666 | 0.8718 | 0.8666 | 0.8642 | 0.9070 | | 0.4747 | 15.81 | 3300 | 0.5497 | 0.8763 | 0.8838 | 0.8763 | 0.8758 | 0.9139 | | 0.4456 | 16.05 | 3350 | 0.5757 | 0.8838 | 0.8896 | 0.8838 | 0.8835 | 0.9192 | | 0.4456 | 16.29 | 3400 | 0.5547 | 0.8756 | 0.8830 | 0.8756 | 0.8731 | 0.9123 | | 0.4456 | 16.53 | 3450 | 0.5431 | 0.8808 | 0.8883 | 0.8808 | 0.8801 | 0.9168 | | 0.4456 | 16.77 | 3500 | 0.5459 | 0.8823 | 0.8883 | 0.8823 | 0.8815 | 0.9175 | | 0.4248 | 17.01 | 3550 | 0.5111 | 0.8891 | 0.8947 | 0.8891 | 0.8878 | 0.9220 | | 0.4248 | 17.25 | 3600 | 0.5371 | 0.8868 | 0.8922 | 0.8868 | 0.8860 | 0.9207 | | 0.4248 | 17.49 | 3650 | 0.5757 | 0.8748 | 0.8843 | 0.8748 | 0.8745 | 0.9131 | | 0.4248 | 17.72 | 3700 | 0.5509 | 0.8816 | 0.8880 | 0.8816 | 0.8804 | 0.9168 | | 0.4248 | 17.96 | 3750 | 0.5166 | 0.8853 | 0.8911 | 0.8853 | 0.8845 | 0.9197 | | 0.405 | 18.2 | 3800 | 0.5392 | 0.8823 | 0.8881 | 0.8823 | 0.8814 | 0.9173 | | 0.405 | 18.44 | 3850 | 0.5357 | 0.8793 | 0.8857 | 0.8793 | 0.8784 | 0.9155 | | 0.405 | 18.68 | 3900 | 0.5564 | 0.8748 | 0.8808 | 0.8748 | 0.8739 | 0.9120 | | 0.405 | 18.92 | 3950 | 0.5377 | 0.8853 | 0.8898 | 0.8853 | 0.8842 | 0.9202 | | 0.3925 | 19.16 | 4000 | 0.5489 | 0.8846 | 0.8902 | 0.8846 | 0.8832 | 0.9194 | | 0.3925 | 19.4 | 4050 | 0.5953 | 0.8726 | 0.8800 | 0.8726 | 0.8713 | 0.9115 | | 0.3925 | 19.64 | 4100 | 0.5802 | 0.8756 | 0.8812 | 0.8756 | 0.8738 | 0.9131 | | 0.3925 | 19.88 | 4150 | 0.6130 | 0.8756 | 0.8827 | 0.8756 | 0.8743 | 0.9121 | | 0.3707 | 20.12 | 4200 | 0.6210 | 0.8771 | 0.8828 | 0.8771 | 0.8760 | 0.9137 | | 0.3707 | 20.36 | 4250 | 0.6460 | 0.8786 | 0.8849 | 0.8786 | 0.8774 | 0.9154 | | 0.3707 | 20.6 | 4300 | 0.6255 | 0.8703 | 0.8780 | 0.8703 | 0.8694 | 0.9085 | | 0.3707 | 20.84 | 4350 | 0.6773 | 0.8658 | 0.8739 | 0.8658 | 0.8653 | 0.9056 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1