--- 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: 0.6680 - Accuracy: 0.8787 - Precision: 0.8938 - Recall: 0.8787 - F1: 0.8770 - Binary: 0.9158 ## 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 | 0.13 | 50 | 4.4184 | 0.0243 | 0.0045 | 0.0243 | 0.0062 | 0.1841 | | No log | 0.27 | 100 | 4.2655 | 0.0418 | 0.0031 | 0.0418 | 0.0053 | 0.2968 | | No log | 0.4 | 150 | 3.9561 | 0.0472 | 0.0055 | 0.0472 | 0.0083 | 0.3246 | | No log | 0.54 | 200 | 3.6420 | 0.0823 | 0.0299 | 0.0823 | 0.0317 | 0.3511 | | No log | 0.67 | 250 | 3.3980 | 0.1201 | 0.0645 | 0.1201 | 0.0588 | 0.3811 | | No log | 0.81 | 300 | 3.1749 | 0.1957 | 0.1344 | 0.1957 | 0.1165 | 0.4321 | | No log | 0.94 | 350 | 2.9362 | 0.2618 | 0.1776 | 0.2618 | 0.1713 | 0.4819 | | 3.8313 | 1.08 | 400 | 2.6613 | 0.3347 | 0.2471 | 0.3347 | 0.2417 | 0.5328 | | 3.8313 | 1.21 | 450 | 2.3242 | 0.4130 | 0.3415 | 0.4130 | 0.3296 | 0.5868 | | 3.8313 | 1.35 | 500 | 2.0383 | 0.4858 | 0.4259 | 0.4858 | 0.4186 | 0.6387 | | 3.8313 | 1.48 | 550 | 1.7209 | 0.5776 | 0.5077 | 0.5776 | 0.5093 | 0.7053 | | 3.8313 | 1.62 | 600 | 1.4932 | 0.6019 | 0.5524 | 0.6019 | 0.5396 | 0.7238 | | 3.8313 | 1.75 | 650 | 1.3401 | 0.6667 | 0.6714 | 0.6667 | 0.6328 | 0.7664 | | 3.8313 | 1.89 | 700 | 1.1896 | 0.7139 | 0.7150 | 0.7139 | 0.6812 | 0.7992 | | 2.0747 | 2.02 | 750 | 1.0789 | 0.7099 | 0.7139 | 0.7099 | 0.6800 | 0.7953 | | 2.0747 | 2.16 | 800 | 1.0023 | 0.7463 | 0.7432 | 0.7463 | 0.7197 | 0.8238 | | 2.0747 | 2.29 | 850 | 0.9637 | 0.7503 | 0.7619 | 0.7503 | 0.7348 | 0.8262 | | 2.0747 | 2.43 | 900 | 0.9403 | 0.7544 | 0.7749 | 0.7544 | 0.7398 | 0.8279 | | 2.0747 | 2.56 | 950 | 0.8283 | 0.7773 | 0.7827 | 0.7773 | 0.7665 | 0.8439 | | 2.0747 | 2.7 | 1000 | 0.8475 | 0.7908 | 0.8127 | 0.7908 | 0.7808 | 0.8521 | | 2.0747 | 2.83 | 1050 | 0.8050 | 0.7611 | 0.7921 | 0.7611 | 0.7570 | 0.8328 | | 2.0747 | 2.96 | 1100 | 0.7450 | 0.8111 | 0.8226 | 0.8111 | 0.8019 | 0.8686 | | 1.1049 | 3.1 | 1150 | 0.7468 | 0.8165 | 0.8359 | 0.8165 | 0.8145 | 0.8709 | | 1.1049 | 3.23 | 1200 | 0.7577 | 0.8057 | 0.8333 | 0.8057 | 0.7980 | 0.8648 | | 1.1049 | 3.37 | 1250 | 0.7135 | 0.8273 | 0.8392 | 0.8273 | 0.8234 | 0.8799 | | 1.1049 | 3.5 | 1300 | 0.7512 | 0.8124 | 0.8269 | 0.8124 | 0.8084 | 0.8690 | | 1.1049 | 3.64 | 1350 | 0.7234 | 0.8192 | 0.8377 | 0.8192 | 0.8128 | 0.8740 | | 1.1049 | 3.77 | 1400 | 0.6902 | 0.8219 | 0.8394 | 0.8219 | 0.8181 | 0.8752 | | 1.1049 | 3.91 | 1450 | 0.7227 | 0.8111 | 0.8208 | 0.8111 | 0.8045 | 0.8676 | | 0.755 | 4.04 | 1500 | 0.6752 | 0.8273 | 0.8458 | 0.8273 | 0.8227 | 0.8788 | | 0.755 | 4.18 | 1550 | 0.6767 | 0.8300 | 0.8440 | 0.8300 | 0.8248 | 0.8807 | | 0.755 | 4.31 | 1600 | 0.7044 | 0.8192 | 0.8352 | 0.8192 | 0.8132 | 0.8737 | | 0.755 | 4.45 | 1650 | 0.7419 | 0.8246 | 0.8502 | 0.8246 | 0.8215 | 0.8776 | | 0.755 | 4.58 | 1700 | 0.7255 | 0.8192 | 0.8429 | 0.8192 | 0.8153 | 0.8742 | | 0.755 | 4.72 | 1750 | 0.7030 | 0.8354 | 0.8586 | 0.8354 | 0.8332 | 0.8860 | | 0.755 | 4.85 | 1800 | 0.6936 | 0.8421 | 0.8640 | 0.8421 | 0.8394 | 0.8901 | | 0.755 | 4.99 | 1850 | 0.6561 | 0.8394 | 0.8586 | 0.8394 | 0.8351 | 0.8883 | | 0.5857 | 5.12 | 1900 | 0.7286 | 0.8381 | 0.8604 | 0.8381 | 0.8344 | 0.8874 | | 0.5857 | 5.26 | 1950 | 0.6338 | 0.8462 | 0.8645 | 0.8462 | 0.8444 | 0.8939 | | 0.5857 | 5.39 | 2000 | 0.6636 | 0.8408 | 0.8599 | 0.8408 | 0.8392 | 0.8888 | | 0.5857 | 5.53 | 2050 | 0.7965 | 0.8246 | 0.8483 | 0.8246 | 0.8223 | 0.8789 | | 0.5857 | 5.66 | 2100 | 0.6798 | 0.8475 | 0.8655 | 0.8475 | 0.8439 | 0.8949 | | 0.5857 | 5.8 | 2150 | 0.6231 | 0.8529 | 0.8687 | 0.8529 | 0.8508 | 0.8973 | | 0.5857 | 5.93 | 2200 | 0.6443 | 0.8435 | 0.8587 | 0.8435 | 0.8397 | 0.8916 | | 0.4922 | 6.06 | 2250 | 0.6697 | 0.8394 | 0.8607 | 0.8394 | 0.8378 | 0.8883 | | 0.4922 | 6.2 | 2300 | 0.7305 | 0.8259 | 0.8553 | 0.8259 | 0.8258 | 0.8779 | | 0.4922 | 6.33 | 2350 | 0.6740 | 0.8502 | 0.8682 | 0.8502 | 0.8495 | 0.8953 | | 0.4922 | 6.47 | 2400 | 0.7172 | 0.8421 | 0.8641 | 0.8421 | 0.8403 | 0.8892 | | 0.4922 | 6.6 | 2450 | 0.7219 | 0.8556 | 0.8727 | 0.8556 | 0.8495 | 0.8989 | | 0.4922 | 6.74 | 2500 | 0.6512 | 0.8502 | 0.8657 | 0.8502 | 0.8479 | 0.8954 | | 0.4922 | 6.87 | 2550 | 0.6893 | 0.8408 | 0.8594 | 0.8408 | 0.8372 | 0.8901 | | 0.4266 | 7.01 | 2600 | 0.6625 | 0.8475 | 0.8630 | 0.8475 | 0.8458 | 0.8943 | | 0.4266 | 7.14 | 2650 | 0.8103 | 0.8246 | 0.8526 | 0.8246 | 0.8239 | 0.8771 | | 0.4266 | 7.28 | 2700 | 0.7695 | 0.8556 | 0.8755 | 0.8556 | 0.8534 | 0.8997 | | 0.4266 | 7.41 | 2750 | 0.7239 | 0.8340 | 0.8581 | 0.8340 | 0.8332 | 0.8846 | | 0.4266 | 7.55 | 2800 | 0.7330 | 0.8340 | 0.8553 | 0.8340 | 0.8315 | 0.8850 | | 0.4266 | 7.68 | 2850 | 0.7010 | 0.8516 | 0.8746 | 0.8516 | 0.8504 | 0.8973 | | 0.4266 | 7.82 | 2900 | 0.7827 | 0.8421 | 0.8627 | 0.8421 | 0.8400 | 0.8912 | | 0.4266 | 7.95 | 2950 | 0.6885 | 0.8502 | 0.8659 | 0.8502 | 0.8495 | 0.8964 | | 0.3842 | 8.09 | 3000 | 0.7856 | 0.8475 | 0.8690 | 0.8475 | 0.8454 | 0.8939 | | 0.3842 | 8.22 | 3050 | 0.8063 | 0.8354 | 0.8597 | 0.8354 | 0.8331 | 0.8853 | | 0.3842 | 8.36 | 3100 | 0.6893 | 0.8610 | 0.8726 | 0.8610 | 0.8581 | 0.9028 | | 0.3842 | 8.49 | 3150 | 0.7546 | 0.8462 | 0.8643 | 0.8462 | 0.8444 | 0.8934 | | 0.3842 | 8.63 | 3200 | 0.7635 | 0.8489 | 0.8712 | 0.8489 | 0.8474 | 0.8958 | | 0.3842 | 8.76 | 3250 | 0.7346 | 0.8462 | 0.8626 | 0.8462 | 0.8441 | 0.8930 | | 0.3842 | 8.89 | 3300 | 0.8108 | 0.8529 | 0.8629 | 0.8529 | 0.8474 | 0.8978 | | 0.342 | 9.03 | 3350 | 0.6884 | 0.8637 | 0.8767 | 0.8637 | 0.8621 | 0.9062 | | 0.342 | 9.16 | 3400 | 0.7026 | 0.8704 | 0.8856 | 0.8704 | 0.8698 | 0.9100 | | 0.342 | 9.3 | 3450 | 0.7660 | 0.8489 | 0.8674 | 0.8489 | 0.8464 | 0.8943 | | 0.342 | 9.43 | 3500 | 0.7238 | 0.8570 | 0.8738 | 0.8570 | 0.8558 | 0.9016 | | 0.342 | 9.57 | 3550 | 0.7542 | 0.8677 | 0.8820 | 0.8677 | 0.8666 | 0.9090 | | 0.342 | 9.7 | 3600 | 0.7187 | 0.8489 | 0.8668 | 0.8489 | 0.8483 | 0.8949 | | 0.342 | 9.84 | 3650 | 0.6752 | 0.8664 | 0.8794 | 0.8664 | 0.8645 | 0.9067 | | 0.342 | 9.97 | 3700 | 0.7404 | 0.8637 | 0.8809 | 0.8637 | 0.8617 | 0.9053 | | 0.3175 | 10.11 | 3750 | 0.7444 | 0.8623 | 0.8831 | 0.8623 | 0.8619 | 0.9035 | | 0.3175 | 10.24 | 3800 | 0.7315 | 0.8543 | 0.8744 | 0.8543 | 0.8528 | 0.8987 | | 0.3175 | 10.38 | 3850 | 0.7424 | 0.8529 | 0.8730 | 0.8529 | 0.8519 | 0.8969 | | 0.3175 | 10.51 | 3900 | 0.6655 | 0.8664 | 0.8820 | 0.8664 | 0.8649 | 0.9076 | | 0.3175 | 10.65 | 3950 | 0.7943 | 0.8570 | 0.8775 | 0.8570 | 0.8553 | 0.9005 | | 0.3175 | 10.78 | 4000 | 0.7559 | 0.8583 | 0.8790 | 0.8583 | 0.8579 | 0.9019 | | 0.3175 | 10.92 | 4050 | 0.7496 | 0.8489 | 0.8650 | 0.8489 | 0.8475 | 0.8964 | | 0.2912 | 11.05 | 4100 | 0.7507 | 0.8570 | 0.8731 | 0.8570 | 0.8552 | 0.9005 | | 0.2912 | 11.19 | 4150 | 0.7952 | 0.8596 | 0.8776 | 0.8596 | 0.8588 | 0.9034 | | 0.2912 | 11.32 | 4200 | 0.7547 | 0.8516 | 0.8668 | 0.8516 | 0.8499 | 0.8977 | | 0.2912 | 11.46 | 4250 | 0.8149 | 0.8475 | 0.8677 | 0.8475 | 0.8442 | 0.8954 | | 0.2912 | 11.59 | 4300 | 0.7429 | 0.8596 | 0.8741 | 0.8596 | 0.8577 | 0.9023 | | 0.2912 | 11.73 | 4350 | 0.7403 | 0.8650 | 0.8810 | 0.8650 | 0.8635 | 0.9062 | | 0.2912 | 11.86 | 4400 | 0.7918 | 0.8570 | 0.8705 | 0.8570 | 0.8538 | 0.9009 | | 0.2912 | 11.99 | 4450 | 0.7712 | 0.8610 | 0.8773 | 0.8610 | 0.8586 | 0.9043 | | 0.2795 | 12.13 | 4500 | 0.7388 | 0.8677 | 0.8814 | 0.8677 | 0.8650 | 0.9096 | | 0.2795 | 12.26 | 4550 | 0.7508 | 0.8677 | 0.8814 | 0.8677 | 0.8670 | 0.9090 | | 0.2795 | 12.4 | 4600 | 0.8635 | 0.8543 | 0.8730 | 0.8543 | 0.8513 | 0.8987 | | 0.2795 | 12.53 | 4650 | 0.7977 | 0.8677 | 0.8831 | 0.8677 | 0.8656 | 0.9090 | | 0.2795 | 12.67 | 4700 | 0.7686 | 0.8556 | 0.8750 | 0.8556 | 0.8562 | 0.8996 | | 0.2795 | 12.8 | 4750 | 0.7998 | 0.8570 | 0.8719 | 0.8570 | 0.8557 | 0.9015 | | 0.2795 | 12.94 | 4800 | 0.8172 | 0.8637 | 0.8762 | 0.8637 | 0.8617 | 0.9057 | | 0.2638 | 13.07 | 4850 | 0.8317 | 0.8502 | 0.8670 | 0.8502 | 0.8481 | 0.8968 | | 0.2638 | 13.21 | 4900 | 0.8888 | 0.8529 | 0.8664 | 0.8529 | 0.8516 | 0.8977 | | 0.2638 | 13.34 | 4950 | 0.8767 | 0.8583 | 0.8763 | 0.8583 | 0.8570 | 0.9011 | | 0.2638 | 13.48 | 5000 | 0.8237 | 0.8610 | 0.8762 | 0.8610 | 0.8607 | 0.9028 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1