---
library_name: transformers
base_model: Kkonjeong/wav2vec2-base-korean
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: kws
  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. -->

# kws

This model is a fine-tuned version of [Kkonjeong/wav2vec2-base-korean](https://huggingface.co/Kkonjeong/wav2vec2-base-korean) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4170
- Accuracy: 0.9474

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 1.8732          | 0.3158   |
| No log        | 2.0   | 4    | 1.9113          | 0.2105   |
| No log        | 3.0   | 6    | 1.7446          | 0.4737   |
| No log        | 4.0   | 8    | 1.6420          | 0.7895   |
| No log        | 5.0   | 10   | 1.5209          | 0.6842   |
| No log        | 6.0   | 12   | 1.4078          | 0.6842   |
| No log        | 7.0   | 14   | 1.3386          | 0.7895   |
| No log        | 8.0   | 16   | 1.2170          | 0.7895   |
| No log        | 9.0   | 18   | 1.1401          | 0.9474   |
| No log        | 10.0  | 20   | 1.0615          | 0.8947   |
| No log        | 11.0  | 22   | 1.0216          | 0.8947   |
| No log        | 12.0  | 24   | 1.0372          | 0.7895   |
| No log        | 13.0  | 26   | 0.9032          | 0.9474   |
| No log        | 14.0  | 28   | 0.8303          | 0.9474   |
| No log        | 15.0  | 30   | 0.7810          | 0.9474   |
| No log        | 16.0  | 32   | 0.7487          | 0.9474   |
| No log        | 17.0  | 34   | 0.6966          | 0.9474   |
| No log        | 18.0  | 36   | 0.6569          | 0.9474   |
| No log        | 19.0  | 38   | 0.6278          | 0.9474   |
| No log        | 20.0  | 40   | 0.5570          | 0.9474   |
| No log        | 21.0  | 42   | 0.5196          | 0.9474   |
| No log        | 22.0  | 44   | 0.5068          | 0.9474   |
| No log        | 23.0  | 46   | 0.5700          | 0.9474   |
| No log        | 24.0  | 48   | 0.5661          | 0.9474   |
| No log        | 25.0  | 50   | 0.5427          | 0.9474   |
| No log        | 26.0  | 52   | 0.4936          | 0.9474   |
| No log        | 27.0  | 54   | 0.4339          | 0.9474   |
| No log        | 28.0  | 56   | 0.4257          | 0.9474   |
| No log        | 29.0  | 58   | 0.4186          | 0.9474   |
| No log        | 30.0  | 60   | 0.4170          | 0.9474   |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1