---
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-4
  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-4

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: 0.6366
- Accuracy: 0.8369
- Precision: 0.8588
- Recall: 0.8369
- F1: 0.8354
- Binary: 0.8865

## 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 0.24  | 50   | 4.4216          | 0.0247   | 0.0070    | 0.0247 | 0.0085 | 0.1557 |
| No log        | 0.48  | 100  | 4.3485          | 0.0652   | 0.0204    | 0.0652 | 0.0249 | 0.2965 |
| No log        | 0.72  | 150  | 4.0225          | 0.0577   | 0.0118    | 0.0577 | 0.0151 | 0.3349 |
| No log        | 0.96  | 200  | 3.7163          | 0.0780   | 0.0180    | 0.0780 | 0.0245 | 0.3495 |
| 4.2635        | 1.2   | 250  | 3.4497          | 0.1454   | 0.0633    | 0.1454 | 0.0699 | 0.3975 |
| 4.2635        | 1.44  | 300  | 3.2114          | 0.1912   | 0.0913    | 0.1912 | 0.0994 | 0.4306 |
| 4.2635        | 1.68  | 350  | 2.9633          | 0.2399   | 0.1697    | 0.2399 | 0.1533 | 0.4645 |
| 4.2635        | 1.92  | 400  | 2.7158          | 0.2684   | 0.2035    | 0.2684 | 0.1801 | 0.4852 |
| 3.2875        | 2.16  | 450  | 2.3488          | 0.4033   | 0.3287    | 0.4033 | 0.3141 | 0.5813 |
| 3.2875        | 2.4   | 500  | 2.2100          | 0.4070   | 0.3778    | 0.4070 | 0.3362 | 0.5768 |
| 3.2875        | 2.63  | 550  | 1.8435          | 0.5232   | 0.4362    | 0.5232 | 0.4500 | 0.6669 |
| 3.2875        | 2.87  | 600  | 1.6225          | 0.5847   | 0.5594    | 0.5847 | 0.5397 | 0.7085 |
| 2.2923        | 3.11  | 650  | 1.4552          | 0.6027   | 0.5886    | 0.6027 | 0.5546 | 0.7217 |
| 2.2923        | 3.35  | 700  | 1.2618          | 0.6529   | 0.6733    | 0.6529 | 0.6201 | 0.7570 |
| 2.2923        | 3.59  | 750  | 1.1393          | 0.6904   | 0.7135    | 0.6904 | 0.6672 | 0.7822 |
| 2.2923        | 3.83  | 800  | 1.1629          | 0.6904   | 0.7141    | 0.6904 | 0.6713 | 0.7825 |
| 1.6069        | 4.07  | 850  | 1.0495          | 0.7264   | 0.7485    | 0.7264 | 0.7120 | 0.8105 |
| 1.6069        | 4.31  | 900  | 0.9086          | 0.7571   | 0.7644    | 0.7571 | 0.7409 | 0.8304 |
| 1.6069        | 4.55  | 950  | 0.8281          | 0.7669   | 0.7855    | 0.7669 | 0.7580 | 0.8379 |
| 1.6069        | 4.79  | 1000 | 0.8820          | 0.7534   | 0.7784    | 0.7534 | 0.7412 | 0.8272 |
| 1.2646        | 5.03  | 1050 | 0.7262          | 0.7886   | 0.7982    | 0.7886 | 0.7817 | 0.8525 |
| 1.2646        | 5.27  | 1100 | 0.7475          | 0.7954   | 0.8113    | 0.7954 | 0.7923 | 0.8577 |
| 1.2646        | 5.51  | 1150 | 0.7535          | 0.7999   | 0.8152    | 0.7999 | 0.7960 | 0.8600 |
| 1.2646        | 5.75  | 1200 | 0.7443          | 0.8021   | 0.8159    | 0.8021 | 0.7997 | 0.8624 |
| 1.2646        | 5.99  | 1250 | 0.5991          | 0.8351   | 0.8471    | 0.8351 | 0.8301 | 0.8854 |
| 1.0169        | 6.23  | 1300 | 0.6740          | 0.8193   | 0.8387    | 0.8193 | 0.8142 | 0.8741 |
| 1.0169        | 6.47  | 1350 | 0.6129          | 0.8358   | 0.8535    | 0.8358 | 0.8326 | 0.8843 |
| 1.0169        | 6.71  | 1400 | 0.6051          | 0.8358   | 0.8486    | 0.8358 | 0.8324 | 0.8851 |
| 1.0169        | 6.95  | 1450 | 0.6603          | 0.8216   | 0.8388    | 0.8216 | 0.8176 | 0.8748 |
| 0.89          | 7.19  | 1500 | 0.6105          | 0.8388   | 0.8499    | 0.8388 | 0.8363 | 0.8871 |
| 0.89          | 7.43  | 1550 | 0.6328          | 0.8298   | 0.8433    | 0.8298 | 0.8265 | 0.8808 |
| 0.89          | 7.66  | 1600 | 0.7041          | 0.8246   | 0.8405    | 0.8246 | 0.8203 | 0.8765 |
| 0.89          | 7.9   | 1650 | 0.6693          | 0.8268   | 0.8453    | 0.8268 | 0.8257 | 0.8788 |
| 0.7796        | 8.14  | 1700 | 0.6774          | 0.8223   | 0.8399    | 0.8223 | 0.8198 | 0.8768 |
| 0.7796        | 8.38  | 1750 | 0.6875          | 0.8201   | 0.8334    | 0.8201 | 0.8181 | 0.8755 |
| 0.7796        | 8.62  | 1800 | 0.6783          | 0.8253   | 0.8409    | 0.8253 | 0.8225 | 0.8795 |
| 0.7796        | 8.86  | 1850 | 0.6815          | 0.8261   | 0.8431    | 0.8261 | 0.8241 | 0.8798 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1