--- 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: 0.6062 - Accuracy: 0.8491 - Precision: 0.8645 - Recall: 0.8491 - F1: 0.8497 - Binary: 0.8941 ## 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 | 4.3309 | 0.0297 | 0.0020 | 0.0297 | 0.0036 | 0.2159 | | No log | 0.43 | 100 | 3.6722 | 0.0553 | 0.0262 | 0.0553 | 0.0174 | 0.3324 | | No log | 0.65 | 150 | 3.3950 | 0.0621 | 0.0161 | 0.0621 | 0.0170 | 0.3379 | | No log | 0.86 | 200 | 3.2145 | 0.1134 | 0.0428 | 0.1134 | 0.0506 | 0.3713 | | 3.8422 | 1.08 | 250 | 3.0635 | 0.1525 | 0.0902 | 0.1525 | 0.0873 | 0.4015 | | 3.8422 | 1.29 | 300 | 2.8389 | 0.2119 | 0.1084 | 0.2119 | 0.1229 | 0.4354 | | 3.8422 | 1.51 | 350 | 2.5852 | 0.2348 | 0.1770 | 0.2348 | 0.1484 | 0.4611 | | 3.8422 | 1.72 | 400 | 2.3922 | 0.2888 | 0.2381 | 0.2888 | 0.2034 | 0.4977 | | 3.8422 | 1.94 | 450 | 2.2068 | 0.3185 | 0.2658 | 0.3185 | 0.2420 | 0.5202 | | 2.9357 | 2.16 | 500 | 2.0744 | 0.4103 | 0.4153 | 0.4103 | 0.3548 | 0.5827 | | 2.9357 | 2.37 | 550 | 1.8778 | 0.4588 | 0.3960 | 0.4588 | 0.3847 | 0.6197 | | 2.9357 | 2.59 | 600 | 1.8338 | 0.4440 | 0.4075 | 0.4440 | 0.3748 | 0.6085 | | 2.9357 | 2.8 | 650 | 1.6534 | 0.4777 | 0.4265 | 0.4777 | 0.4116 | 0.6337 | | 2.3301 | 3.02 | 700 | 1.5716 | 0.5344 | 0.5094 | 0.5344 | 0.4768 | 0.6733 | | 2.3301 | 3.23 | 750 | 1.4864 | 0.5655 | 0.5298 | 0.5655 | 0.5208 | 0.6926 | | 2.3301 | 3.45 | 800 | 1.4863 | 0.5452 | 0.5615 | 0.5452 | 0.5023 | 0.6803 | | 2.3301 | 3.66 | 850 | 1.3882 | 0.5655 | 0.5432 | 0.5655 | 0.5064 | 0.6949 | | 2.3301 | 3.88 | 900 | 1.3190 | 0.5897 | 0.5922 | 0.5897 | 0.5473 | 0.7117 | | 1.9679 | 4.09 | 950 | 1.1769 | 0.6559 | 0.6830 | 0.6559 | 0.6272 | 0.7580 | | 1.9679 | 4.31 | 1000 | 1.2232 | 0.6289 | 0.6469 | 0.6289 | 0.6012 | 0.7402 | | 1.9679 | 4.53 | 1050 | 1.1078 | 0.6694 | 0.6994 | 0.6694 | 0.6583 | 0.7675 | | 1.9679 | 4.74 | 1100 | 1.0104 | 0.7072 | 0.7350 | 0.7072 | 0.6881 | 0.7949 | | 1.9679 | 4.96 | 1150 | 0.9920 | 0.7152 | 0.7264 | 0.7152 | 0.6941 | 0.8016 | | 1.7302 | 5.17 | 1200 | 0.9389 | 0.7449 | 0.7648 | 0.7449 | 0.7335 | 0.8208 | | 1.7302 | 5.39 | 1250 | 0.8678 | 0.7611 | 0.7735 | 0.7611 | 0.7461 | 0.8344 | | 1.7302 | 5.6 | 1300 | 0.8991 | 0.7449 | 0.7474 | 0.7449 | 0.7261 | 0.8217 | | 1.7302 | 5.82 | 1350 | 0.7903 | 0.7665 | 0.7756 | 0.7665 | 0.7544 | 0.8368 | | 1.531 | 6.03 | 1400 | 0.8221 | 0.7665 | 0.7852 | 0.7665 | 0.7528 | 0.8355 | | 1.531 | 6.25 | 1450 | 0.7516 | 0.7773 | 0.8021 | 0.7773 | 0.7606 | 0.8436 | | 1.531 | 6.47 | 1500 | 0.7573 | 0.7787 | 0.7870 | 0.7787 | 0.7655 | 0.8452 | | 1.531 | 6.68 | 1550 | 0.7561 | 0.7719 | 0.7814 | 0.7719 | 0.7595 | 0.8401 | | 1.531 | 6.9 | 1600 | 0.6898 | 0.8030 | 0.8144 | 0.8030 | 0.7957 | 0.8609 | | 1.3639 | 7.11 | 1650 | 0.6912 | 0.7989 | 0.8155 | 0.7989 | 0.7916 | 0.8603 | | 1.3639 | 7.33 | 1700 | 0.6771 | 0.7989 | 0.8201 | 0.7989 | 0.7915 | 0.8590 | | 1.3639 | 7.54 | 1750 | 0.6652 | 0.8016 | 0.8207 | 0.8016 | 0.7952 | 0.8617 | | 1.3639 | 7.76 | 1800 | 0.7130 | 0.8016 | 0.8227 | 0.8016 | 0.7972 | 0.8613 | | 1.3639 | 7.97 | 1850 | 0.6661 | 0.7962 | 0.8173 | 0.7962 | 0.7878 | 0.8575 | | 1.2774 | 8.19 | 1900 | 0.6532 | 0.8178 | 0.8313 | 0.8178 | 0.8130 | 0.8722 | | 1.2774 | 8.41 | 1950 | 0.6291 | 0.8178 | 0.8307 | 0.8178 | 0.8144 | 0.8729 | | 1.2774 | 8.62 | 2000 | 0.6585 | 0.8030 | 0.8183 | 0.8030 | 0.7958 | 0.8618 | | 1.2774 | 8.84 | 2050 | 0.6427 | 0.8084 | 0.8216 | 0.8084 | 0.8038 | 0.8656 | | 1.1807 | 9.05 | 2100 | 0.7025 | 0.8124 | 0.8246 | 0.8124 | 0.8081 | 0.8686 | | 1.1807 | 9.27 | 2150 | 0.6977 | 0.8016 | 0.8211 | 0.8016 | 0.7980 | 0.8599 | | 1.1807 | 9.48 | 2200 | 0.6439 | 0.8246 | 0.8404 | 0.8246 | 0.8223 | 0.8760 | | 1.1807 | 9.7 | 2250 | 0.5890 | 0.8394 | 0.8503 | 0.8394 | 0.8374 | 0.8860 | | 1.1807 | 9.91 | 2300 | 0.5406 | 0.8313 | 0.8429 | 0.8313 | 0.8281 | 0.8816 | | 1.105 | 10.13 | 2350 | 0.6131 | 0.8232 | 0.8371 | 0.8232 | 0.8203 | 0.8742 | | 1.105 | 10.34 | 2400 | 0.6241 | 0.8232 | 0.8421 | 0.8232 | 0.8206 | 0.8738 | | 1.105 | 10.56 | 2450 | 0.6349 | 0.8354 | 0.8498 | 0.8354 | 0.8328 | 0.8837 | | 1.105 | 10.78 | 2500 | 0.7053 | 0.8165 | 0.8290 | 0.8165 | 0.8118 | 0.8713 | | 1.105 | 10.99 | 2550 | 0.5652 | 0.8381 | 0.8504 | 0.8381 | 0.8353 | 0.8866 | | 1.0741 | 11.21 | 2600 | 0.5764 | 0.8408 | 0.8533 | 0.8408 | 0.8378 | 0.8887 | | 1.0741 | 11.42 | 2650 | 0.5663 | 0.8448 | 0.8612 | 0.8448 | 0.8441 | 0.8915 | | 1.0741 | 11.64 | 2700 | 0.6290 | 0.8219 | 0.8361 | 0.8219 | 0.8194 | 0.8741 | | 1.0741 | 11.85 | 2750 | 0.5994 | 0.8381 | 0.8546 | 0.8381 | 0.8355 | 0.8873 | | 1.0208 | 12.07 | 2800 | 0.5851 | 0.8327 | 0.8434 | 0.8327 | 0.8289 | 0.8826 | | 1.0208 | 12.28 | 2850 | 0.6522 | 0.8219 | 0.8411 | 0.8219 | 0.8204 | 0.8746 | | 1.0208 | 12.5 | 2900 | 0.6401 | 0.8273 | 0.8392 | 0.8273 | 0.8241 | 0.8779 | | 1.0208 | 12.72 | 2950 | 0.5764 | 0.8475 | 0.8607 | 0.8475 | 0.8448 | 0.8930 | | 1.0208 | 12.93 | 3000 | 0.5834 | 0.8354 | 0.8432 | 0.8354 | 0.8315 | 0.8839 | | 0.9784 | 13.15 | 3050 | 0.6171 | 0.8394 | 0.8562 | 0.8394 | 0.8367 | 0.8872 | | 0.9784 | 13.36 | 3100 | 0.6362 | 0.8300 | 0.8428 | 0.8300 | 0.8258 | 0.8798 | | 0.9784 | 13.58 | 3150 | 0.6154 | 0.8313 | 0.8466 | 0.8313 | 0.8301 | 0.8807 | | 0.9784 | 13.79 | 3200 | 0.5939 | 0.8421 | 0.8561 | 0.8421 | 0.8395 | 0.8887 | | 0.9237 | 14.01 | 3250 | 0.6167 | 0.8435 | 0.8516 | 0.8435 | 0.8412 | 0.8900 | | 0.9237 | 14.22 | 3300 | 0.6338 | 0.8408 | 0.8490 | 0.8408 | 0.8388 | 0.8887 | | 0.9237 | 14.44 | 3350 | 0.6051 | 0.8421 | 0.8558 | 0.8421 | 0.8408 | 0.8900 | | 0.9237 | 14.66 | 3400 | 0.5816 | 0.8367 | 0.8493 | 0.8367 | 0.8343 | 0.8854 | | 0.9237 | 14.87 | 3450 | 0.6617 | 0.8327 | 0.8512 | 0.8327 | 0.8309 | 0.8825 | | 0.8932 | 15.09 | 3500 | 0.6038 | 0.8448 | 0.8590 | 0.8448 | 0.8439 | 0.8915 | | 0.8932 | 15.3 | 3550 | 0.6460 | 0.8408 | 0.8543 | 0.8408 | 0.8389 | 0.8883 | | 0.8932 | 15.52 | 3600 | 0.5571 | 0.8489 | 0.8586 | 0.8489 | 0.8474 | 0.8943 | | 0.8932 | 15.73 | 3650 | 0.6321 | 0.8273 | 0.8420 | 0.8273 | 0.8251 | 0.8792 | | 0.8932 | 15.95 | 3700 | 0.6127 | 0.8448 | 0.8598 | 0.8448 | 0.8422 | 0.8919 | | 0.856 | 16.16 | 3750 | 0.5622 | 0.8502 | 0.8620 | 0.8502 | 0.8492 | 0.8957 | | 0.856 | 16.38 | 3800 | 0.5919 | 0.8529 | 0.8648 | 0.8529 | 0.8508 | 0.8981 | | 0.856 | 16.59 | 3850 | 0.5345 | 0.8489 | 0.8582 | 0.8489 | 0.8468 | 0.8947 | | 0.856 | 16.81 | 3900 | 0.6384 | 0.8408 | 0.8585 | 0.8408 | 0.8399 | 0.8883 | | 0.8435 | 17.03 | 3950 | 0.5643 | 0.8596 | 0.8724 | 0.8596 | 0.8582 | 0.9024 | | 0.8435 | 17.24 | 4000 | 0.5582 | 0.8448 | 0.8573 | 0.8448 | 0.8441 | 0.8911 | | 0.8435 | 17.46 | 4050 | 0.5755 | 0.8489 | 0.8590 | 0.8489 | 0.8467 | 0.8943 | | 0.8435 | 17.67 | 4100 | 0.6104 | 0.8340 | 0.8478 | 0.8340 | 0.8309 | 0.8839 | | 0.8435 | 17.89 | 4150 | 0.6327 | 0.8435 | 0.8568 | 0.8435 | 0.8413 | 0.8906 | | 0.7849 | 18.1 | 4200 | 0.5686 | 0.8583 | 0.8732 | 0.8583 | 0.8566 | 0.9009 | | 0.7849 | 18.32 | 4250 | 0.6191 | 0.8462 | 0.8622 | 0.8462 | 0.8430 | 0.8924 | | 0.7849 | 18.53 | 4300 | 0.5812 | 0.8489 | 0.8641 | 0.8489 | 0.8491 | 0.8934 | | 0.7849 | 18.75 | 4350 | 0.5785 | 0.8529 | 0.8678 | 0.8529 | 0.8519 | 0.8966 | | 0.7849 | 18.97 | 4400 | 0.5474 | 0.8502 | 0.8638 | 0.8502 | 0.8484 | 0.8957 | | 0.779 | 19.18 | 4450 | 0.5515 | 0.8637 | 0.8753 | 0.8637 | 0.8620 | 0.9047 | | 0.779 | 19.4 | 4500 | 0.5591 | 0.8623 | 0.8736 | 0.8623 | 0.8610 | 0.9047 | | 0.779 | 19.61 | 4550 | 0.5287 | 0.8718 | 0.8806 | 0.8718 | 0.8700 | 0.9108 | | 0.779 | 19.83 | 4600 | 0.5561 | 0.8610 | 0.8704 | 0.8610 | 0.8598 | 0.9032 | | 0.7593 | 20.04 | 4650 | 0.5487 | 0.8583 | 0.8686 | 0.8583 | 0.8566 | 0.9013 | | 0.7593 | 20.26 | 4700 | 0.5873 | 0.8502 | 0.8649 | 0.8502 | 0.8470 | 0.8957 | | 0.7593 | 20.47 | 4750 | 0.5470 | 0.8475 | 0.8603 | 0.8475 | 0.8454 | 0.8934 | | 0.7593 | 20.69 | 4800 | 0.5921 | 0.8543 | 0.8680 | 0.8543 | 0.8521 | 0.8977 | | 0.7593 | 20.91 | 4850 | 0.5692 | 0.8543 | 0.8658 | 0.8543 | 0.8523 | 0.8981 | | 0.7392 | 21.12 | 4900 | 0.5863 | 0.8394 | 0.8527 | 0.8394 | 0.8370 | 0.8881 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1