metadata
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: AST_EmoRecog_Model_v4
results: []
AST_EmoRecog_Model_v4
This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the IEMOCAP dataset. It achieves the following results on the evaluation set:
- Loss: 1.4615
- Accuracy: 0.5159
- Recall: 0.4007
- Precision: 0.4956
- F1: 0.4090
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 1.4443 | 1.0 | 377 | 1.3359 | 0.4695 | 0.3408 | 0.4793 | 0.3099 |
| 1.1556 | 2.0 | 754 | 1.2506 | 0.5266 | 0.3877 | 0.6026 | 0.3970 |
| 0.8988 | 3.0 | 1131 | 1.2633 | 0.5279 | 0.4175 | 0.5148 | 0.4208 |
| 0.6187 | 4.0 | 1508 | 1.3426 | 0.5279 | 0.4031 | 0.5425 | 0.4153 |
| 0.3944 | 5.0 | 1885 | 1.4266 | 0.5206 | 0.4021 | 0.5256 | 0.4152 |
| 0.2555 | 6.0 | 2262 | 1.4615 | 0.5159 | 0.4007 | 0.4956 | 0.4090 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0