--- 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-2 results: [] --- # hubert-classifier-aug-fold-2 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: 0.7308 - Accuracy: 0.8585 - Precision: 0.8738 - Recall: 0.8585 - F1: 0.8578 - Binary: 0.8997 ## 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 | 0.13 | 50 | 3.8576 | 0.0621 | 0.0074 | 0.0621 | 0.0112 | 0.3362 | | No log | 0.27 | 100 | 3.4091 | 0.0931 | 0.0349 | 0.0931 | 0.0334 | 0.3617 | | No log | 0.4 | 150 | 3.2224 | 0.1053 | 0.0231 | 0.1053 | 0.0330 | 0.3698 | | No log | 0.54 | 200 | 3.0715 | 0.1215 | 0.0509 | 0.1215 | 0.0558 | 0.3800 | | No log | 0.67 | 250 | 2.7013 | 0.2416 | 0.1533 | 0.2416 | 0.1549 | 0.4665 | | No log | 0.81 | 300 | 2.4186 | 0.3077 | 0.2216 | 0.3077 | 0.2178 | 0.5124 | | No log | 0.94 | 350 | 2.2086 | 0.3725 | 0.2827 | 0.3725 | 0.2882 | 0.5592 | | 3.209 | 1.08 | 400 | 1.9624 | 0.4534 | 0.4314 | 0.4534 | 0.3907 | 0.6155 | | 3.209 | 1.21 | 450 | 1.7552 | 0.5344 | 0.4957 | 0.5344 | 0.4788 | 0.6735 | | 3.209 | 1.35 | 500 | 1.5887 | 0.5668 | 0.5359 | 0.5668 | 0.5127 | 0.6957 | | 3.209 | 1.48 | 550 | 1.4249 | 0.6073 | 0.5999 | 0.6073 | 0.5671 | 0.7248 | | 3.209 | 1.62 | 600 | 1.2562 | 0.6397 | 0.6424 | 0.6397 | 0.6007 | 0.7486 | | 3.209 | 1.75 | 650 | 1.1504 | 0.6680 | 0.6724 | 0.6680 | 0.6379 | 0.7683 | | 3.209 | 1.89 | 700 | 1.0797 | 0.6991 | 0.7111 | 0.6991 | 0.6753 | 0.7896 | | 1.7186 | 2.02 | 750 | 1.0473 | 0.7314 | 0.7634 | 0.7314 | 0.7173 | 0.8139 | | 1.7186 | 2.16 | 800 | 0.9383 | 0.7503 | 0.7777 | 0.7503 | 0.7343 | 0.8271 | | 1.7186 | 2.29 | 850 | 0.9288 | 0.7679 | 0.7842 | 0.7679 | 0.7567 | 0.8393 | | 1.7186 | 2.43 | 900 | 0.8284 | 0.7746 | 0.7897 | 0.7746 | 0.7679 | 0.8432 | | 1.7186 | 2.56 | 950 | 0.8246 | 0.7800 | 0.8108 | 0.7800 | 0.7729 | 0.8474 | | 1.7186 | 2.7 | 1000 | 0.8581 | 0.7665 | 0.7893 | 0.7665 | 0.7586 | 0.8370 | | 1.7186 | 2.83 | 1050 | 0.7753 | 0.7962 | 0.8151 | 0.7962 | 0.7919 | 0.8583 | | 1.7186 | 2.96 | 1100 | 0.7556 | 0.8043 | 0.8205 | 0.8043 | 0.7965 | 0.8641 | | 1.0562 | 3.1 | 1150 | 0.8130 | 0.7962 | 0.8149 | 0.7962 | 0.7913 | 0.8567 | | 1.0562 | 3.23 | 1200 | 0.7633 | 0.7895 | 0.8189 | 0.7895 | 0.7880 | 0.8528 | | 1.0562 | 3.37 | 1250 | 0.6852 | 0.8178 | 0.8352 | 0.8178 | 0.8135 | 0.8742 | | 1.0562 | 3.5 | 1300 | 0.8465 | 0.7638 | 0.8001 | 0.7638 | 0.7588 | 0.8347 | | 1.0562 | 3.64 | 1350 | 0.7130 | 0.8111 | 0.8215 | 0.8111 | 0.8026 | 0.8686 | | 1.0562 | 3.77 | 1400 | 0.7425 | 0.8057 | 0.8242 | 0.8057 | 0.8040 | 0.8657 | | 1.0562 | 3.91 | 1450 | 0.7719 | 0.8057 | 0.8220 | 0.8057 | 0.8030 | 0.8634 | | 0.782 | 4.04 | 1500 | 0.6581 | 0.8354 | 0.8497 | 0.8354 | 0.8332 | 0.8850 | | 0.782 | 4.18 | 1550 | 0.7134 | 0.8124 | 0.8281 | 0.8124 | 0.8095 | 0.8682 | | 0.782 | 4.31 | 1600 | 0.6923 | 0.8259 | 0.8455 | 0.8259 | 0.8234 | 0.8795 | | 0.782 | 4.45 | 1650 | 0.6699 | 0.8367 | 0.8539 | 0.8367 | 0.8347 | 0.8868 | | 0.782 | 4.58 | 1700 | 0.6169 | 0.8475 | 0.8597 | 0.8475 | 0.8456 | 0.8926 | | 0.782 | 4.72 | 1750 | 0.6198 | 0.8381 | 0.8491 | 0.8381 | 0.8349 | 0.8870 | | 0.782 | 4.85 | 1800 | 0.6939 | 0.8313 | 0.8502 | 0.8313 | 0.8318 | 0.8823 | | 0.782 | 4.99 | 1850 | 0.7710 | 0.8205 | 0.8408 | 0.8205 | 0.8147 | 0.8729 | | 0.6114 | 5.12 | 1900 | 0.6556 | 0.8381 | 0.8495 | 0.8381 | 0.8362 | 0.8880 | | 0.6114 | 5.26 | 1950 | 0.7667 | 0.8151 | 0.8332 | 0.8151 | 0.8129 | 0.8698 | | 0.6114 | 5.39 | 2000 | 0.7299 | 0.8232 | 0.8413 | 0.8232 | 0.8200 | 0.8748 | | 0.6114 | 5.53 | 2050 | 0.7309 | 0.8327 | 0.8419 | 0.8327 | 0.8266 | 0.8835 | | 0.6114 | 5.66 | 2100 | 0.7464 | 0.8192 | 0.8344 | 0.8192 | 0.8165 | 0.8750 | | 0.6114 | 5.8 | 2150 | 0.7440 | 0.8340 | 0.8484 | 0.8340 | 0.8323 | 0.8849 | | 0.6114 | 5.93 | 2200 | 0.7002 | 0.8475 | 0.8594 | 0.8475 | 0.8455 | 0.8935 | | 0.5068 | 6.06 | 2250 | 0.7030 | 0.8448 | 0.8599 | 0.8448 | 0.8421 | 0.8912 | | 0.5068 | 6.2 | 2300 | 0.7355 | 0.8421 | 0.8560 | 0.8421 | 0.8398 | 0.8889 | | 0.5068 | 6.33 | 2350 | 0.7511 | 0.8246 | 0.8339 | 0.8246 | 0.8209 | 0.8771 | | 0.5068 | 6.47 | 2400 | 0.6739 | 0.8421 | 0.8534 | 0.8421 | 0.8408 | 0.8903 | | 0.5068 | 6.6 | 2450 | 0.6982 | 0.8475 | 0.8654 | 0.8475 | 0.8441 | 0.8931 | | 0.5068 | 6.74 | 2500 | 0.7522 | 0.8408 | 0.8531 | 0.8408 | 0.8394 | 0.8880 | | 0.5068 | 6.87 | 2550 | 0.7261 | 0.8354 | 0.8479 | 0.8354 | 0.8323 | 0.8856 | | 0.4429 | 7.01 | 2600 | 0.7445 | 0.8421 | 0.8557 | 0.8421 | 0.8402 | 0.8896 | | 0.4429 | 7.14 | 2650 | 0.7698 | 0.8327 | 0.8406 | 0.8327 | 0.8290 | 0.8823 | | 0.4429 | 7.28 | 2700 | 0.8428 | 0.8273 | 0.8433 | 0.8273 | 0.8254 | 0.8806 | | 0.4429 | 7.41 | 2750 | 0.8403 | 0.8259 | 0.8443 | 0.8259 | 0.8242 | 0.8789 | | 0.4429 | 7.55 | 2800 | 0.7045 | 0.8381 | 0.8527 | 0.8381 | 0.8372 | 0.8866 | | 0.4429 | 7.68 | 2850 | 0.8546 | 0.8327 | 0.8441 | 0.8327 | 0.8309 | 0.8825 | | 0.4429 | 7.82 | 2900 | 0.7722 | 0.8340 | 0.8511 | 0.8340 | 0.8350 | 0.8830 | | 0.4429 | 7.95 | 2950 | 0.7198 | 0.8435 | 0.8590 | 0.8435 | 0.8432 | 0.8927 | | 0.3872 | 8.09 | 3000 | 0.7239 | 0.8408 | 0.8510 | 0.8408 | 0.8382 | 0.8914 | | 0.3872 | 8.22 | 3050 | 0.7778 | 0.8475 | 0.8572 | 0.8475 | 0.8452 | 0.8950 | | 0.3872 | 8.36 | 3100 | 0.8277 | 0.8394 | 0.8513 | 0.8394 | 0.8378 | 0.8892 | | 0.3872 | 8.49 | 3150 | 0.7813 | 0.8462 | 0.8587 | 0.8462 | 0.8459 | 0.8912 | | 0.3872 | 8.63 | 3200 | 0.7736 | 0.8394 | 0.8503 | 0.8394 | 0.8373 | 0.8880 | | 0.3872 | 8.76 | 3250 | 0.7917 | 0.8394 | 0.8530 | 0.8394 | 0.8371 | 0.8883 | | 0.3872 | 8.89 | 3300 | 0.7909 | 0.8475 | 0.8565 | 0.8475 | 0.8456 | 0.8930 | | 0.3527 | 9.03 | 3350 | 0.7729 | 0.8529 | 0.8659 | 0.8529 | 0.8506 | 0.8977 | | 0.3527 | 9.16 | 3400 | 0.8406 | 0.8475 | 0.8635 | 0.8475 | 0.8443 | 0.8937 | | 0.3527 | 9.3 | 3450 | 0.7908 | 0.8435 | 0.8561 | 0.8435 | 0.8406 | 0.8910 | | 0.3527 | 9.43 | 3500 | 0.8294 | 0.8300 | 0.8432 | 0.8300 | 0.8279 | 0.8808 | | 0.3527 | 9.57 | 3550 | 0.8214 | 0.8421 | 0.8559 | 0.8421 | 0.8418 | 0.8897 | | 0.3527 | 9.7 | 3600 | 0.8200 | 0.8435 | 0.8571 | 0.8435 | 0.8426 | 0.8918 | | 0.3527 | 9.84 | 3650 | 0.7658 | 0.8502 | 0.8594 | 0.8502 | 0.8487 | 0.8969 | | 0.3527 | 9.97 | 3700 | 0.8817 | 0.8354 | 0.8505 | 0.8354 | 0.8350 | 0.8842 | | 0.3278 | 10.11 | 3750 | 0.8310 | 0.8489 | 0.8660 | 0.8489 | 0.8493 | 0.8950 | | 0.3278 | 10.24 | 3800 | 0.6998 | 0.8664 | 0.8765 | 0.8664 | 0.8654 | 0.9072 | | 0.3278 | 10.38 | 3850 | 0.7762 | 0.8435 | 0.8588 | 0.8435 | 0.8439 | 0.8904 | | 0.3278 | 10.51 | 3900 | 0.9134 | 0.8435 | 0.8563 | 0.8435 | 0.8427 | 0.8920 | | 0.3278 | 10.65 | 3950 | 0.7972 | 0.8556 | 0.8722 | 0.8556 | 0.8542 | 0.8988 | | 0.3278 | 10.78 | 4000 | 0.8311 | 0.8610 | 0.8727 | 0.8610 | 0.8598 | 0.9043 | | 0.3278 | 10.92 | 4050 | 0.8660 | 0.8543 | 0.8647 | 0.8543 | 0.8540 | 0.8993 | | 0.2997 | 11.05 | 4100 | 0.8472 | 0.8556 | 0.8679 | 0.8556 | 0.8544 | 0.8988 | | 0.2997 | 11.19 | 4150 | 0.7564 | 0.8556 | 0.8672 | 0.8556 | 0.8544 | 0.9001 | | 0.2997 | 11.32 | 4200 | 0.7832 | 0.8529 | 0.8684 | 0.8529 | 0.8527 | 0.9005 | | 0.2997 | 11.46 | 4250 | 0.8058 | 0.8623 | 0.8735 | 0.8623 | 0.8591 | 0.9049 | | 0.2997 | 11.59 | 4300 | 0.7588 | 0.8623 | 0.8788 | 0.8623 | 0.8613 | 0.9054 | | 0.2997 | 11.73 | 4350 | 0.8209 | 0.8462 | 0.8658 | 0.8462 | 0.8472 | 0.8926 | | 0.2997 | 11.86 | 4400 | 0.7649 | 0.8650 | 0.8815 | 0.8650 | 0.8660 | 0.9043 | | 0.2997 | 11.99 | 4450 | 0.7985 | 0.8529 | 0.8671 | 0.8529 | 0.8514 | 0.8966 | | 0.283 | 12.13 | 4500 | 0.7353 | 0.8543 | 0.8640 | 0.8543 | 0.8529 | 0.8977 | | 0.283 | 12.26 | 4550 | 0.7714 | 0.8543 | 0.8658 | 0.8543 | 0.8529 | 0.8992 | | 0.283 | 12.4 | 4600 | 0.8393 | 0.8502 | 0.8629 | 0.8502 | 0.8476 | 0.8945 | | 0.283 | 12.53 | 4650 | 0.7847 | 0.8556 | 0.8652 | 0.8556 | 0.8534 | 0.9001 | | 0.283 | 12.67 | 4700 | 0.8041 | 0.8623 | 0.8722 | 0.8623 | 0.8590 | 0.9043 | | 0.283 | 12.8 | 4750 | 0.8478 | 0.8475 | 0.8592 | 0.8475 | 0.8458 | 0.8939 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1