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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
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