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update model card README.md
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README.md
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---
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license: apache-2.0
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base_model: ntu-spml/distilhubert
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tags:
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- generated_from_trainer
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datasets:
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- aesdd
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metrics:
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- accuracy
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model-index:
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- name: distilhubert-finetuned-AESDD
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: aesdd
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type: aesdd
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config: AESDD
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split: train
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args: AESDD
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9016393442622951
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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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# distilhubert-finetuned-AESDD
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the aesdd dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4389
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- Accuracy: 0.9016
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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: 5e-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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.1249 | 1.0 | 68 | 1.1905 | 0.5082 |
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| 0.7441 | 2.0 | 136 | 0.8850 | 0.6721 |
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| 0.5941 | 3.0 | 204 | 0.6579 | 0.8361 |
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| 0.4349 | 4.0 | 272 | 0.9638 | 0.6721 |
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| 0.2612 | 5.0 | 340 | 0.5081 | 0.8689 |
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| 0.1883 | 6.0 | 408 | 0.6223 | 0.8197 |
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| 0.0978 | 7.0 | 476 | 0.4671 | 0.8689 |
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| 0.0425 | 8.0 | 544 | 0.4338 | 0.8852 |
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| 0.0264 | 9.0 | 612 | 0.4488 | 0.8525 |
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| 0.0219 | 10.0 | 680 | 0.4389 | 0.9016 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.2
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- Tokenizers 0.13.3
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