File size: 2,045 Bytes
476e65a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ast_8-finetuned-ICBHI
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_8-finetuned-ICBHI
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: 1.1190
- Accuracy: 0.6641
- Sensitivity: 0.4474
- Specificity: 0.8579
- Score: 0.6527
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.9271 | 1.0 | 258 | 1.0487 | 0.5793 | 0.5480 | 0.6074 | 0.5777 |
| 0.8124 | 2.0 | 517 | 0.8780 | 0.6366 | 0.3369 | 0.9046 | 0.6208 |
| 0.714 | 3.0 | 776 | 0.9018 | 0.6482 | 0.5510 | 0.7351 | 0.6431 |
| 0.2385 | 4.0 | 1035 | 1.1190 | 0.6641 | 0.4474 | 0.8579 | 0.6527 |
| 0.0712 | 4.99 | 1290 | 1.3453 | 0.6594 | 0.5173 | 0.7865 | 0.6519 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|