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hubert-classifier-aug-fold-1

Audio Classification
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/hubert-base-ls960
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: hubert-classifier-aug-fold-1
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # hubert-classifier-aug-fold-1
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+
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+ This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5322
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+ - Accuracy: 0.8693
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+ - Precision: 0.8831
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+ - Recall: 0.8693
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+ - F1: 0.8678
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+ - Binary: 0.9088
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 30
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.13 | 50 | 3.9624 | 0.0351 | 0.0015 | 0.0351 | 0.0028 | 0.3143 |
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+ | No log | 0.27 | 100 | 3.4385 | 0.0634 | 0.0224 | 0.0634 | 0.0195 | 0.3432 |
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+ | No log | 0.4 | 150 | 3.2477 | 0.0864 | 0.0446 | 0.0864 | 0.0302 | 0.3568 |
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+ | No log | 0.54 | 200 | 2.9937 | 0.1282 | 0.0531 | 0.1282 | 0.0622 | 0.3873 |
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+ | No log | 0.67 | 250 | 2.7511 | 0.2456 | 0.1513 | 0.2456 | 0.1564 | 0.4709 |
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+ | No log | 0.81 | 300 | 2.4666 | 0.3077 | 0.2328 | 0.3077 | 0.2207 | 0.5151 |
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+ | No log | 0.94 | 350 | 2.1657 | 0.3954 | 0.3244 | 0.3954 | 0.3091 | 0.5765 |
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+ | 3.2407 | 1.08 | 400 | 1.8980 | 0.4453 | 0.3778 | 0.4453 | 0.3735 | 0.6128 |
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+ | 3.2407 | 1.21 | 450 | 1.6861 | 0.5371 | 0.4729 | 0.5371 | 0.4709 | 0.6744 |
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+ | 3.2407 | 1.35 | 500 | 1.5060 | 0.5897 | 0.5801 | 0.5897 | 0.5439 | 0.7140 |
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+ | 3.2407 | 1.48 | 550 | 1.3841 | 0.6019 | 0.5868 | 0.6019 | 0.5615 | 0.7189 |
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+ | 3.2407 | 1.62 | 600 | 1.2200 | 0.6640 | 0.6817 | 0.6640 | 0.6403 | 0.7637 |
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+ | 3.2407 | 1.75 | 650 | 1.1366 | 0.6869 | 0.7098 | 0.6869 | 0.6628 | 0.7799 |
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+ | 3.2407 | 1.89 | 700 | 0.9917 | 0.7382 | 0.7498 | 0.7382 | 0.7264 | 0.8174 |
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+ | 1.6581 | 2.02 | 750 | 0.9604 | 0.7355 | 0.7688 | 0.7355 | 0.7248 | 0.8144 |
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+ | 1.6581 | 2.16 | 800 | 0.8836 | 0.7679 | 0.7816 | 0.7679 | 0.7596 | 0.8391 |
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+ | 1.6581 | 2.29 | 850 | 0.8232 | 0.7787 | 0.8122 | 0.7787 | 0.7725 | 0.8463 |
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+ | 1.6581 | 2.43 | 900 | 0.7861 | 0.7800 | 0.8017 | 0.7800 | 0.7711 | 0.8467 |
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+ | 1.6581 | 2.56 | 950 | 0.7454 | 0.8030 | 0.8288 | 0.8030 | 0.7955 | 0.8632 |
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+ | 1.6581 | 2.7 | 1000 | 0.6824 | 0.8178 | 0.8450 | 0.8178 | 0.8135 | 0.8726 |
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+ | 1.6581 | 2.83 | 1050 | 0.7187 | 0.8138 | 0.8429 | 0.8138 | 0.8116 | 0.8714 |
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+ | 1.6581 | 2.96 | 1100 | 0.7004 | 0.8084 | 0.8280 | 0.8084 | 0.8041 | 0.8675 |
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+ | 0.9905 | 3.1 | 1150 | 0.6490 | 0.8219 | 0.8390 | 0.8219 | 0.8176 | 0.8771 |
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+ | 0.9905 | 3.23 | 1200 | 0.6872 | 0.8178 | 0.8328 | 0.8178 | 0.8154 | 0.8737 |
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+ | 0.9905 | 3.37 | 1250 | 0.6676 | 0.8340 | 0.8548 | 0.8340 | 0.8270 | 0.8850 |
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+ | 0.9905 | 3.5 | 1300 | 0.6439 | 0.8205 | 0.8480 | 0.8205 | 0.8212 | 0.8750 |
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+ | 0.9905 | 3.64 | 1350 | 0.5648 | 0.8354 | 0.8527 | 0.8354 | 0.8319 | 0.8854 |
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+ | 0.9905 | 3.77 | 1400 | 0.6231 | 0.8340 | 0.8537 | 0.8340 | 0.8310 | 0.8854 |
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+ | 0.9905 | 3.91 | 1450 | 0.6813 | 0.8232 | 0.8532 | 0.8232 | 0.8219 | 0.8779 |
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+ | 0.7084 | 4.04 | 1500 | 0.6047 | 0.8475 | 0.8680 | 0.8475 | 0.8459 | 0.8941 |
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+ | 0.7084 | 4.18 | 1550 | 0.6319 | 0.8354 | 0.8489 | 0.8354 | 0.8323 | 0.8869 |
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+ | 0.7084 | 4.31 | 1600 | 0.5892 | 0.8650 | 0.8814 | 0.8650 | 0.8625 | 0.9066 |
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+ | 0.7084 | 4.45 | 1650 | 0.5572 | 0.8650 | 0.8825 | 0.8650 | 0.8649 | 0.9066 |
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+ | 0.7084 | 4.58 | 1700 | 0.6023 | 0.8556 | 0.8705 | 0.8556 | 0.8530 | 0.9005 |
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+ | 0.7084 | 4.72 | 1750 | 0.5772 | 0.8489 | 0.8729 | 0.8489 | 0.8456 | 0.8953 |
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+ | 0.7084 | 4.85 | 1800 | 0.6242 | 0.8435 | 0.8643 | 0.8435 | 0.8416 | 0.8920 |
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+ | 0.7084 | 4.99 | 1850 | 0.5903 | 0.8421 | 0.8574 | 0.8421 | 0.8392 | 0.8906 |
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+ | 0.5634 | 5.12 | 1900 | 0.6256 | 0.8475 | 0.8598 | 0.8475 | 0.8438 | 0.8949 |
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+ | 0.5634 | 5.26 | 1950 | 0.5891 | 0.8556 | 0.8728 | 0.8556 | 0.8553 | 0.9005 |
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+ | 0.5634 | 5.39 | 2000 | 0.5435 | 0.8704 | 0.8843 | 0.8704 | 0.8681 | 0.9100 |
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+ | 0.5634 | 5.53 | 2050 | 0.5093 | 0.8853 | 0.8952 | 0.8853 | 0.8840 | 0.9204 |
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+ | 0.5634 | 5.66 | 2100 | 0.6058 | 0.8677 | 0.8784 | 0.8677 | 0.8676 | 0.9086 |
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+ | 0.5634 | 5.8 | 2150 | 0.5635 | 0.8610 | 0.8728 | 0.8610 | 0.8607 | 0.9040 |
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+ | 0.5634 | 5.93 | 2200 | 0.5897 | 0.8664 | 0.8828 | 0.8664 | 0.8661 | 0.9077 |
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+ | 0.466 | 6.06 | 2250 | 0.6280 | 0.8623 | 0.8830 | 0.8623 | 0.8612 | 0.9058 |
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+ | 0.466 | 6.2 | 2300 | 0.7129 | 0.8394 | 0.8548 | 0.8394 | 0.8371 | 0.8888 |
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+ | 0.466 | 6.33 | 2350 | 0.6993 | 0.8435 | 0.8606 | 0.8435 | 0.8408 | 0.8926 |
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+ | 0.466 | 6.47 | 2400 | 0.6314 | 0.8462 | 0.8630 | 0.8462 | 0.8440 | 0.8934 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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