Audio Cls Sjs
This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: Loss: 1.2026 Accuracy: 0.7311
Modellquelle
- Art
- Other
- Dateigröße
- 361 MB
- Basismodell
- Kkonjeong/wav2vec2-base-korean
- Version
- vRevision def3ba7
- Aktualisiert
- 26. Sept. 2026
- Downloads
- 4
- Quelle
- Hugging Face
Quellenbeschreibung
audio_cls_sjs
This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2026
- Accuracy: 0.7311
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 15 | 2.6319 | 0.1008 |
| No log | 2.0 | 30 | 2.4686 | 0.2101 |
| No log | 3.0 | 45 | 2.2363 | 0.3361 |
| No log | 4.0 | 60 | 2.0202 | 0.4286 |
| No log | 5.0 | 75 | 1.7058 | 0.5714 |
| No log | 6.0 | 90 | 1.5203 | 0.6303 |
| No log | 7.0 | 105 | 1.4001 | 0.6471 |
| No log | 8.0 | 120 | 1.2837 | 0.6975 |
| No log | 9.0 | 135 | 1.2277 | 0.7227 |
| No log | 10.0 | 150 | 1.2026 | 0.7311 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 29. Aug.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- library_name: transformers language: - ko base_model: Kkonjeong/wav2vec2-base-korean tags: - audio-classification - minds14 - korean - generated_from_trainer metrics: - accuracy model-index: - name: audio_cls_sjs 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. --> # audio_cls_sjs This model is a fine-tuned version of [Kkonjeong/wav2vec2-base-korean](https://huggingface.co/Kkonjeong/wav2vec2-base-korean) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 1.2026 - Accuracy: 0.7311 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 2.6319 | 0.1008 | | No log | 2.0 | 30 | 2.4686 | 0.2101 | | No log | 3.0 | 45 | 2.2363 | 0.3361 | | No log | 4.0 | 60 | 2.0202 | 0.4286 | | No log | 5.0 | 75 | 1.7058 | 0.5714 | | No log | 6.0 | 90 | 1.5203 | 0.6303 | | No log | 7.0 | 105 | 1.4001 | 0.6471 | | No log | 8.0 | 120 | 1.2837 | 0.6975 | | No log | 9.0 | 135 | 1.2277 | 0.7227 | | No log | 10.0 | 150 | 1.2026 | 0.7311 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 4 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo seohyeon122333/audio_cls_sjs
