---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: superb_ks_42
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: superb
      type: superb
      config: ks
      split: validation
      args: ks
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9833774639599883
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# superb_ks_42

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co./facebook/hubert-base-ls960) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- Accuracy: 0.9834

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5858        | 1.0   | 1597  | 0.1746          | 0.9701   |
| 0.2268        | 2.0   | 3194  | 0.1038          | 0.9773   |
| 0.1937        | 3.0   | 4791  | 0.0887          | 0.9797   |
| 0.163         | 4.0   | 6388  | 0.0786          | 0.9826   |
| 0.1468        | 5.0   | 7985  | 0.0982          | 0.9815   |
| 0.1327        | 6.0   | 9582  | 0.0888          | 0.9821   |
| 0.1175        | 7.0   | 11179 | 0.0967          | 0.9812   |
| 0.1164        | 8.0   | 12776 | 0.0922          | 0.9815   |
| 0.1023        | 9.0   | 14373 | 0.0875          | 0.9828   |
| 0.0983        | 10.0  | 15970 | 0.0857          | 0.9834   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1