---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rio-model
  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. -->

# rio-model

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1567
- Accuracy: 0.96

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.597         | 1.0   | 28   | 0.1618          | 0.92     |
| 0.1129        | 2.0   | 56   | 0.7487          | 0.8      |
| 0.0545        | 3.0   | 84   | 0.0181          | 1.0      |
| 0.001         | 4.0   | 112  | 0.7661          | 0.84     |
| 0.0003        | 5.0   | 140  | 0.1578          | 0.96     |
| 0.0001        | 6.0   | 168  | 0.1282          | 0.96     |
| 0.0001        | 7.0   | 196  | 0.1429          | 0.96     |
| 0.0           | 8.0   | 224  | 0.1519          | 0.96     |
| 0.0           | 9.0   | 252  | 0.1562          | 0.96     |
| 0.0           | 10.0  | 280  | 0.1567          | 0.96     |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1