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README.md
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---
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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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- f1
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- recall
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- precision
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model-index:
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- name: ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
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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-finetuned-audioset-10-10-0.4593-finetuned-common_voice
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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 None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4652
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- Accuracy: 0.905
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- F1: 0.9049
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- Recall: 0.905
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- Precision: 0.9057
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- Mcc: 0.8814
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- Auc: 0.9874
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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: 3e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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| 1.0765 | 1.0 | 200 | 0.9662 | 0.5925 | 0.5411 | 0.5925 | 0.6493 | 0.5098 | 0.9152 |
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| 0.5977 | 2.0 | 400 | 0.5536 | 0.8 | 0.7999 | 0.8 | 0.8146 | 0.7532 | 0.9670 |
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| 0.1826 | 3.0 | 600 | 0.5388 | 0.8375 | 0.8385 | 0.8375 | 0.8491 | 0.7994 | 0.9759 |
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| 0.1125 | 4.0 | 800 | 0.6617 | 0.85 | 0.8486 | 0.85 | 0.8636 | 0.8161 | 0.9798 |
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| 0.0025 | 5.0 | 1000 | 0.5859 | 0.865 | 0.8653 | 0.865 | 0.8733 | 0.8333 | 0.984 |
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| 0.0058 | 6.0 | 1200 | 0.5043 | 0.8975 | 0.8968 | 0.8975 | 0.9001 | 0.8728 | 0.9882 |
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| 0.0 | 7.0 | 1400 | 0.4883 | 0.8925 | 0.8932 | 0.8925 | 0.8957 | 0.8660 | 0.9859 |
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| 0.0 | 8.0 | 1600 | 0.4652 | 0.905 | 0.9050 | 0.905 | 0.9055 | 0.8814 | 0.9871 |
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| 0.0 | 9.0 | 1800 | 0.4655 | 0.905 | 0.9049 | 0.905 | 0.9057 | 0.8814 | 0.9873 |
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| 0.0 | 10.0 | 2000 | 0.4652 | 0.905 | 0.9049 | 0.905 | 0.9057 | 0.8814 | 0.9874 |
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### Framework versions
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- Transformers 4.41.0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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model.safetensors
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