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--- |
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library_name: transformers |
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license: bsd-3-clause |
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base_model: MIT/ast-finetuned-audioset-10-10-0.4593 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: AST_EmoRecog_Model_v4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# AST_EmoRecog_Model_v4 |
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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 [IEMOCAP](https://sail.usc.edu/iemocap/) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4615 |
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- Accuracy: 0.5159 |
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- Recall: 0.4007 |
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- Precision: 0.4956 |
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- F1: 0.4090 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.4443 | 1.0 | 377 | 1.3359 | 0.4695 | 0.3408 | 0.4793 | 0.3099 | |
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| 1.1556 | 2.0 | 754 | 1.2506 | 0.5266 | 0.3877 | 0.6026 | 0.3970 | |
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| 0.8988 | 3.0 | 1131 | 1.2633 | 0.5279 | 0.4175 | 0.5148 | 0.4208 | |
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| 0.6187 | 4.0 | 1508 | 1.3426 | 0.5279 | 0.4031 | 0.5425 | 0.4153 | |
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| 0.3944 | 5.0 | 1885 | 1.4266 | 0.5206 | 0.4021 | 0.5256 | 0.4152 | |
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| 0.2555 | 6.0 | 2262 | 1.4615 | 0.5159 | 0.4007 | 0.4956 | 0.4090 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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