|
--- |
|
license: bsd-3-clause |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: ast-finetuned-audioset-10-10-0.4593_ft_env_0-12 |
|
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. --> |
|
|
|
# ast-finetuned-audioset-10-10-0.4593_ft_env_0-12 |
|
|
|
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 the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3804 |
|
- Accuracy: 0.9643 |
|
- Precision: 0.9702 |
|
- Recall: 0.9643 |
|
- F1: 0.9643 |
|
|
|
## 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: 1.5e-06 |
|
- train_batch_size: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 8 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 56 |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 2.0371 | 1.0 | 28 | 1.9267 | 0.1429 | 0.3214 | 0.1429 | 0.1482 | |
|
| 1.7315 | 2.0 | 56 | 1.5823 | 0.3214 | 0.3667 | 0.3214 | 0.2973 | |
|
| 1.3081 | 3.0 | 84 | 1.2250 | 0.75 | 0.8423 | 0.75 | 0.7499 | |
|
| 0.9664 | 4.0 | 112 | 0.9526 | 0.8214 | 0.8616 | 0.8214 | 0.8078 | |
|
| 0.6607 | 5.0 | 140 | 0.7525 | 0.8571 | 0.8795 | 0.8571 | 0.8520 | |
|
| 0.5239 | 6.0 | 168 | 0.6080 | 0.8929 | 0.9152 | 0.8929 | 0.8866 | |
|
| 0.453 | 7.0 | 196 | 0.5089 | 0.9286 | 0.9286 | 0.9286 | 0.9286 | |
|
| 0.323 | 8.0 | 224 | 0.4353 | 0.9286 | 0.9286 | 0.9286 | 0.9286 | |
|
| 0.296 | 9.0 | 252 | 0.3804 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.2167 | 10.0 | 280 | 0.3382 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.186 | 11.0 | 308 | 0.3157 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.1748 | 12.0 | 336 | 0.2931 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.1367 | 13.0 | 364 | 0.2781 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.1469 | 14.0 | 392 | 0.2705 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
| 0.1308 | 15.0 | 420 | 0.2679 | 0.9643 | 0.9702 | 0.9643 | 0.9643 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.11.0 |
|
|
