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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-large
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results: []
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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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# hubert-classifier-aug-large
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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: 0.5737
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- Accuracy: 0.8518
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- Precision: 0.8710
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- Recall: 0.8518
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- F1: 0.8459
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- Binary: 0.8946
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 10
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- mixed_precision_training: Native AMP
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### Training results
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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 | 0.1 | 50 | 4.2254 | 0.0296 | 0.0051 | 0.0296 | 0.0050 | 0.2472 |
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| No log | 0.2 | 100 | 3.6044 | 0.0499 | 0.0034 | 0.0499 | 0.0062 | 0.3265 |
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| No log | 0.29 | 150 | 3.4158 | 0.0620 | 0.0137 | 0.0620 | 0.0182 | 0.3314 |
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| No log | 0.39 | 200 | 3.1970 | 0.0943 | 0.0301 | 0.0943 | 0.0402 | 0.3632 |
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| No log | 0.49 | 250 | 3.1359 | 0.1253 | 0.0438 | 0.1253 | 0.0550 | 0.3786 |
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| No log | 0.59 | 300 | 2.8477 | 0.1941 | 0.0877 | 0.1941 | 0.1046 | 0.4311 |
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| No log | 0.69 | 350 | 2.5968 | 0.2291 | 0.1477 | 0.2291 | 0.1464 | 0.4569 |
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| No log | 0.78 | 400 | 2.3869 | 0.3019 | 0.2233 | 0.3019 | 0.1981 | 0.5092 |
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| No log | 0.88 | 450 | 2.2318 | 0.3342 | 0.2618 | 0.3342 | 0.2491 | 0.5337 |
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| 3.3246 | 0.98 | 500 | 2.0726 | 0.4057 | 0.3384 | 0.4057 | 0.3220 | 0.5819 |
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| 3.3246 | 1.08 | 550 | 1.9390 | 0.4043 | 0.3492 | 0.4043 | 0.3267 | 0.5813 |
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| 3.3246 | 1.18 | 600 | 1.8723 | 0.4394 | 0.3484 | 0.4394 | 0.3548 | 0.6063 |
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| 3.3246 | 1.27 | 650 | 1.7220 | 0.5081 | 0.4868 | 0.5081 | 0.4462 | 0.6571 |
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| 3.3246 | 1.37 | 700 | 1.5947 | 0.5283 | 0.4654 | 0.5283 | 0.4610 | 0.6691 |
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| 3.3246 | 1.47 | 750 | 1.5081 | 0.5512 | 0.5536 | 0.5512 | 0.5010 | 0.6863 |
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| 3.3246 | 1.57 | 800 | 1.3927 | 0.6078 | 0.6098 | 0.6078 | 0.5698 | 0.7252 |
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| 3.3246 | 1.67 | 850 | 1.2970 | 0.6361 | 0.6405 | 0.6361 | 0.6036 | 0.7445 |
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| 3.3246 | 1.76 | 900 | 1.2218 | 0.6658 | 0.6832 | 0.6658 | 0.6470 | 0.7663 |
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| 3.3246 | 1.86 | 950 | 1.2574 | 0.6725 | 0.7113 | 0.6725 | 0.6538 | 0.7698 |
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| 2.1116 | 1.96 | 1000 | 1.0768 | 0.7224 | 0.7288 | 0.7224 | 0.7073 | 0.8058 |
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| 2.1116 | 2.06 | 1050 | 1.0574 | 0.7318 | 0.7445 | 0.7318 | 0.7160 | 0.8113 |
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| 2.1116 | 2.16 | 1100 | 0.9994 | 0.7332 | 0.7526 | 0.7332 | 0.7171 | 0.8139 |
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| 2.1116 | 2.25 | 1150 | 0.9494 | 0.7358 | 0.7497 | 0.7358 | 0.7196 | 0.8159 |
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| 2.1116 | 2.35 | 1200 | 0.8719 | 0.7588 | 0.7743 | 0.7588 | 0.7456 | 0.8306 |
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| 2.1116 | 2.45 | 1250 | 0.8674 | 0.7642 | 0.7862 | 0.7642 | 0.7530 | 0.8345 |
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| 2.1116 | 2.55 | 1300 | 0.8805 | 0.7857 | 0.8075 | 0.7857 | 0.7754 | 0.8487 |
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| 2.1116 | 2.65 | 1350 | 0.8389 | 0.7682 | 0.7955 | 0.7682 | 0.7601 | 0.8377 |
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| 2.1116 | 2.75 | 1400 | 0.8189 | 0.7763 | 0.7913 | 0.7763 | 0.7640 | 0.8411 |
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| 2.1116 | 2.84 | 1450 | 0.7739 | 0.7871 | 0.7881 | 0.7871 | 0.7737 | 0.8491 |
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| 1.5744 | 2.94 | 1500 | 0.7971 | 0.7668 | 0.7887 | 0.7668 | 0.7567 | 0.8360 |
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| 1.5744 | 3.04 | 1550 | 0.7348 | 0.7844 | 0.7998 | 0.7844 | 0.7781 | 0.8478 |
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| 1.5744 | 3.14 | 1600 | 0.7241 | 0.7925 | 0.8115 | 0.7925 | 0.7838 | 0.8534 |
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| 1.5744 | 3.24 | 1650 | 0.7763 | 0.7749 | 0.8010 | 0.7749 | 0.7709 | 0.8411 |
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| 1.5744 | 3.33 | 1700 | 0.6638 | 0.8073 | 0.8278 | 0.8073 | 0.8036 | 0.8646 |
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| 1.5744 | 3.43 | 1750 | 0.7065 | 0.8100 | 0.8339 | 0.8100 | 0.8021 | 0.8656 |
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| 1.5744 | 3.53 | 1800 | 0.7391 | 0.7951 | 0.8212 | 0.7951 | 0.7878 | 0.8561 |
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| 1.5744 | 3.63 | 1850 | 0.6450 | 0.8181 | 0.8385 | 0.8181 | 0.8125 | 0.8721 |
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| 1.5744 | 3.73 | 1900 | 0.6834 | 0.8113 | 0.8342 | 0.8113 | 0.8076 | 0.8670 |
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| 1.5744 | 3.82 | 1950 | 0.6616 | 0.8113 | 0.8311 | 0.8113 | 0.8025 | 0.8668 |
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| 1.312 | 3.92 | 2000 | 0.6177 | 0.8194 | 0.8388 | 0.8194 | 0.8149 | 0.8718 |
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| 1.312 | 4.02 | 2050 | 0.6550 | 0.8059 | 0.8338 | 0.8059 | 0.7979 | 0.8637 |
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| 1.312 | 4.12 | 2100 | 0.5995 | 0.8261 | 0.8438 | 0.8261 | 0.8206 | 0.8772 |
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| 1.312 | 4.22 | 2150 | 0.6833 | 0.8127 | 0.8340 | 0.8127 | 0.8069 | 0.8678 |
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| 1.312 | 4.31 | 2200 | 0.6185 | 0.8275 | 0.8438 | 0.8275 | 0.8232 | 0.8787 |
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| 1.312 | 4.41 | 2250 | 0.6334 | 0.8167 | 0.8364 | 0.8167 | 0.8111 | 0.8708 |
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| 1.312 | 4.51 | 2300 | 0.6100 | 0.8208 | 0.8372 | 0.8208 | 0.8171 | 0.8725 |
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| 1.312 | 4.61 | 2350 | 0.5953 | 0.8302 | 0.8477 | 0.8302 | 0.8268 | 0.8805 |
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| 1.312 | 4.71 | 2400 | 0.5847 | 0.8194 | 0.8278 | 0.8194 | 0.8125 | 0.8735 |
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| 1.312 | 4.8 | 2450 | 0.5932 | 0.8329 | 0.8475 | 0.8329 | 0.8295 | 0.8814 |
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| 1.1711 | 4.9 | 2500 | 0.5890 | 0.8329 | 0.8511 | 0.8329 | 0.8285 | 0.8811 |
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| 1.1711 | 5.0 | 2550 | 0.5662 | 0.8437 | 0.8574 | 0.8437 | 0.8374 | 0.8885 |
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| 1.1711 | 5.1 | 2600 | 0.5648 | 0.8531 | 0.8657 | 0.8531 | 0.8494 | 0.8953 |
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| 1.1711 | 5.2 | 2650 | 0.5805 | 0.8410 | 0.8555 | 0.8410 | 0.8385 | 0.8858 |
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| 1.1711 | 5.29 | 2700 | 0.5372 | 0.8477 | 0.8631 | 0.8477 | 0.8426 | 0.8919 |
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| 1.1711 | 5.39 | 2750 | 0.5698 | 0.8491 | 0.8661 | 0.8491 | 0.8481 | 0.8919 |
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| 1.1711 | 5.49 | 2800 | 0.5499 | 0.8571 | 0.8756 | 0.8571 | 0.8525 | 0.8993 |
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| 1.1711 | 5.59 | 2850 | 0.5643 | 0.8504 | 0.8671 | 0.8504 | 0.8477 | 0.8941 |
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| 1.1711 | 5.69 | 2900 | 0.5834 | 0.8491 | 0.8649 | 0.8491 | 0.8461 | 0.8923 |
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| 1.1711 | 5.78 | 2950 | 0.5306 | 0.8612 | 0.8760 | 0.8612 | 0.8580 | 0.9004 |
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| 1.078 | 5.88 | 3000 | 0.5276 | 0.8571 | 0.8738 | 0.8571 | 0.8532 | 0.8980 |
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| 1.078 | 5.98 | 3050 | 0.5294 | 0.8531 | 0.8792 | 0.8531 | 0.8507 | 0.8960 |
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| 1.078 | 6.08 | 3100 | 0.5305 | 0.8558 | 0.8740 | 0.8558 | 0.8525 | 0.8974 |
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| 1.078 | 6.18 | 3150 | 0.5546 | 0.8518 | 0.8700 | 0.8518 | 0.8488 | 0.8946 |
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| 1.078 | 6.27 | 3200 | 0.5292 | 0.8652 | 0.8844 | 0.8652 | 0.8613 | 0.9049 |
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| 1.078 | 6.37 | 3250 | 0.5871 | 0.8288 | 0.8527 | 0.8288 | 0.8270 | 0.8796 |
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| 1.078 | 6.47 | 3300 | 0.5549 | 0.8477 | 0.8656 | 0.8477 | 0.8438 | 0.8914 |
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| 1.078 | 6.57 | 3350 | 0.5559 | 0.8410 | 0.8544 | 0.8410 | 0.8349 | 0.8876 |
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| 1.078 | 6.67 | 3400 | 0.5496 | 0.8531 | 0.8688 | 0.8531 | 0.8496 | 0.8961 |
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| 1.078 | 6.76 | 3450 | 0.5737 | 0.8518 | 0.8710 | 0.8518 | 0.8459 | 0.8946 |
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### Framework versions
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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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