--- 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-0 results: [] --- # hubert-classifier-aug-fold-0 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: 0.5592 - Accuracy: 0.8464 - Precision: 0.8588 - Recall: 0.8464 - F1: 0.8431 - Binary: 0.8926 ## 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 | 4.4295 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1332 | | No log | 0.43 | 100 | 4.4254 | 0.0148 | 0.0002 | 0.0148 | 0.0004 | 0.1274 | | No log | 0.65 | 150 | 3.8186 | 0.0364 | 0.0121 | 0.0364 | 0.0050 | 0.3090 | | No log | 0.86 | 200 | 3.5321 | 0.0391 | 0.0090 | 0.0391 | 0.0062 | 0.3193 | | 4.1413 | 1.08 | 250 | 3.3337 | 0.0728 | 0.0256 | 0.0728 | 0.0286 | 0.3453 | | 4.1413 | 1.29 | 300 | 3.1664 | 0.0970 | 0.0489 | 0.0970 | 0.0400 | 0.3590 | | 4.1413 | 1.51 | 350 | 2.9961 | 0.1253 | 0.0613 | 0.1253 | 0.0631 | 0.3821 | | 4.1413 | 1.73 | 400 | 2.8225 | 0.1739 | 0.0798 | 0.1739 | 0.0904 | 0.4181 | | 4.1413 | 1.94 | 450 | 2.6439 | 0.2116 | 0.1109 | 0.2116 | 0.1236 | 0.4457 | | 3.2276 | 2.16 | 500 | 2.4578 | 0.2385 | 0.1802 | 0.2385 | 0.1570 | 0.4670 | | 3.2276 | 2.37 | 550 | 2.2801 | 0.3396 | 0.2831 | 0.3396 | 0.2516 | 0.5358 | | 3.2276 | 2.59 | 600 | 2.0684 | 0.4003 | 0.3030 | 0.4003 | 0.3068 | 0.5796 | | 3.2276 | 2.8 | 650 | 1.9308 | 0.4299 | 0.3493 | 0.4299 | 0.3516 | 0.6005 | | 2.5852 | 3.02 | 700 | 1.8448 | 0.4501 | 0.4000 | 0.4501 | 0.3811 | 0.6146 | | 2.5852 | 3.24 | 750 | 1.6568 | 0.5283 | 0.4743 | 0.5283 | 0.4552 | 0.6689 | | 2.5852 | 3.45 | 800 | 1.6974 | 0.4690 | 0.4551 | 0.4690 | 0.4169 | 0.6264 | | 2.5852 | 3.67 | 850 | 1.4828 | 0.5687 | 0.5769 | 0.5687 | 0.5231 | 0.6978 | | 2.5852 | 3.88 | 900 | 1.4420 | 0.5580 | 0.5477 | 0.5580 | 0.5126 | 0.6896 | | 2.1226 | 4.1 | 950 | 1.3306 | 0.6186 | 0.6133 | 0.6186 | 0.5784 | 0.7315 | | 2.1226 | 4.31 | 1000 | 1.2209 | 0.6456 | 0.6561 | 0.6456 | 0.6076 | 0.7500 | | 2.1226 | 4.53 | 1050 | 1.1256 | 0.6698 | 0.6865 | 0.6698 | 0.6404 | 0.7664 | | 2.1226 | 4.75 | 1100 | 1.0700 | 0.6846 | 0.7003 | 0.6846 | 0.6586 | 0.7770 | | 2.1226 | 4.96 | 1150 | 1.0085 | 0.7156 | 0.7415 | 0.7156 | 0.6942 | 0.7993 | | 1.8257 | 5.18 | 1200 | 1.0190 | 0.7224 | 0.7397 | 0.7224 | 0.7028 | 0.8046 | | 1.8257 | 5.39 | 1250 | 0.9742 | 0.7102 | 0.7244 | 0.7102 | 0.6886 | 0.7961 | | 1.8257 | 5.61 | 1300 | 0.8793 | 0.7561 | 0.7680 | 0.7561 | 0.7384 | 0.8284 | | 1.8257 | 5.83 | 1350 | 0.8472 | 0.7547 | 0.7763 | 0.7547 | 0.7426 | 0.8280 | | 1.5842 | 6.04 | 1400 | 0.8424 | 0.7601 | 0.7956 | 0.7601 | 0.7487 | 0.8327 | | 1.5842 | 6.26 | 1450 | 0.7802 | 0.7642 | 0.7846 | 0.7642 | 0.7513 | 0.8348 | | 1.5842 | 6.47 | 1500 | 0.7447 | 0.7965 | 0.8096 | 0.7965 | 0.7914 | 0.8574 | | 1.5842 | 6.69 | 1550 | 0.7081 | 0.7844 | 0.8035 | 0.7844 | 0.7772 | 0.8499 | | 1.5842 | 6.9 | 1600 | 0.7616 | 0.7722 | 0.7995 | 0.7722 | 0.7681 | 0.8399 | | 1.4387 | 7.12 | 1650 | 0.7133 | 0.7709 | 0.7904 | 0.7709 | 0.7607 | 0.8403 | | 1.4387 | 7.34 | 1700 | 0.6570 | 0.8127 | 0.8301 | 0.8127 | 0.8094 | 0.8695 | | 1.4387 | 7.55 | 1750 | 0.6325 | 0.8221 | 0.8461 | 0.8221 | 0.8212 | 0.8761 | | 1.4387 | 7.77 | 1800 | 0.6352 | 0.8032 | 0.8251 | 0.8032 | 0.8004 | 0.8625 | | 1.4387 | 7.98 | 1850 | 0.6313 | 0.8086 | 0.8270 | 0.8086 | 0.8040 | 0.8678 | | 1.3174 | 8.2 | 1900 | 0.6843 | 0.8154 | 0.8372 | 0.8154 | 0.8100 | 0.8710 | | 1.3174 | 8.41 | 1950 | 0.6142 | 0.8194 | 0.8360 | 0.8194 | 0.8153 | 0.8739 | | 1.3174 | 8.63 | 2000 | 0.6324 | 0.8154 | 0.8229 | 0.8154 | 0.8102 | 0.8710 | | 1.3174 | 8.85 | 2050 | 0.5751 | 0.8383 | 0.8566 | 0.8383 | 0.8351 | 0.8852 | | 1.2131 | 9.06 | 2100 | 0.5873 | 0.8275 | 0.8439 | 0.8275 | 0.8250 | 0.8805 | | 1.2131 | 9.28 | 2150 | 0.6016 | 0.8167 | 0.8346 | 0.8167 | 0.8131 | 0.8729 | | 1.2131 | 9.49 | 2200 | 0.5982 | 0.8410 | 0.8617 | 0.8410 | 0.8387 | 0.8879 | | 1.2131 | 9.71 | 2250 | 0.5490 | 0.8437 | 0.8564 | 0.8437 | 0.8410 | 0.8912 | | 1.2131 | 9.92 | 2300 | 0.5587 | 0.8342 | 0.8537 | 0.8342 | 0.8309 | 0.8837 | | 1.1426 | 10.14 | 2350 | 0.5969 | 0.8261 | 0.8446 | 0.8261 | 0.8214 | 0.8790 | | 1.1426 | 10.36 | 2400 | 0.5936 | 0.8410 | 0.8575 | 0.8410 | 0.8382 | 0.8889 | | 1.1426 | 10.57 | 2450 | 0.5656 | 0.8383 | 0.8579 | 0.8383 | 0.8364 | 0.8865 | | 1.1426 | 10.79 | 2500 | 0.5130 | 0.8625 | 0.8756 | 0.8625 | 0.8593 | 0.9054 | | 1.0738 | 11.0 | 2550 | 0.5832 | 0.8396 | 0.8618 | 0.8396 | 0.8389 | 0.8880 | | 1.0738 | 11.22 | 2600 | 0.5554 | 0.8423 | 0.8634 | 0.8423 | 0.8417 | 0.8908 | | 1.0738 | 11.43 | 2650 | 0.5763 | 0.8275 | 0.8490 | 0.8275 | 0.8238 | 0.8801 | | 1.0738 | 11.65 | 2700 | 0.5697 | 0.8329 | 0.8452 | 0.8329 | 0.8281 | 0.8857 | | 1.0738 | 11.87 | 2750 | 0.5413 | 0.8464 | 0.8655 | 0.8464 | 0.8432 | 0.8922 | | 1.0326 | 12.08 | 2800 | 0.5954 | 0.8235 | 0.8443 | 0.8235 | 0.8176 | 0.8761 | | 1.0326 | 12.3 | 2850 | 0.5665 | 0.8410 | 0.8611 | 0.8410 | 0.8354 | 0.8908 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.15.1