Naat Classifier Model
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: Loss: 0.1137 Accuracy: 0.9773
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 361 MB
- Modelo base
- facebook/wav2vec2-base
- Versão
- vRevision eba3838
- Atualizado
- 22 de set. de 2026
- Downloads
- 20
- Fonte
- Hugging Face
Descrição da fonte
naat-classifier-model
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1137
- Accuracy: 0.9773
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3383 | 1.0 | 749 | 0.2574 | 0.9446 |
| 0.1287 | 2.0 | 1498 | 0.1934 | 0.9493 |
| 0.0620 | 3.0 | 2247 | 0.1921 | 0.9413 |
| 0.0264 | 4.0 | 2996 | 0.1394 | 0.9746 |
| 0.3313 | 5.0 | 3745 | 0.1415 | 0.9586 |
| 0.0112 | 6.0 | 4494 | 0.1009 | 0.9740 |
| 0.1507 | 7.0 | 5243 | 0.1112 | 0.9760 |
| 0.0070 | 8.0 | 5992 | 0.1137 | 0.9773 |
| 0.1192 | 9.0 | 6741 | 0.1203 | 0.9746 |
| 0.0038 | 10.0 | 7490 | 0.1258 | 0.9740 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Fontes
1 fonte
Fontes
1 fonteVerificado 6 de set.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - audiofolder metrics: - accuracy model-index: - name: naat-classifier-model results: - task: type: audio-classification name: Audio Classification dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - type: accuracy value: 0.9773030707610146 name: Accuracy --- <!-- 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. --> # naat-classifier-model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1137 - Accuracy: 0.9773 ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 - lr_scheduler_warmup_steps: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3383 | 1.0 | 749 | 0.2574 | 0.9446 | | 0.1287 | 2.0 | 1498 | 0.1934 | 0.9493 | | 0.0620 | 3.0 | 2247 | 0.1921 | 0.9413 | | 0.0264 | 4.0 | 2996 | 0.1394 | 0.9746 | | 0.3313 | 5.0 | 3745 | 0.1415 | 0.9586 | | 0.0112 | 6.0 | 4494 | 0.1009 | 0.9740 | | 0.1507 | 7.0 | 5243 | 0.1112 | 0.9760 | | 0.0070 | 8.0 | 5992 | 0.1137 | 0.9773 | | 0.1192 | 9.0 | 6741 | 0.1203 | 0.9746 | | 0.0038 | 10.0 | 7490 | 0.1258 | 0.9740 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 20 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo sahilhasnain07/naat-classifier-model
