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distilhubert-finetuned-gtzan
PhysHunter/distilhubert-finetuned-gtzan
distilhubert-finetuned-gtzan is a audio classification model from PhysHunter. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- hubert
- Access
- Public
- Created
- Aug 14, 2023
- Updated
- Aug 15, 2023
- SHA
- ad82ce8e
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.5088
- Accuracy: 0.89
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0521 | 0.99 | 14 | 0.5613 | 0.86 |
| 0.0456 | 1.98 | 28 | 0.5497 | 0.86 |
| 0.0304 | 2.97 | 42 | 0.5402 | 0.87 |
| 0.0303 | 3.96 | 56 | 0.5084 | 0.9 |
| 0.0277 | 4.96 | 70 | 0.5088 | 0.89 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Audio Classification
- Library
- transformers
- Type
- hubert
- License
- apache-2.0
- Created
- Aug 14, 2023
- Updated
- Aug 15, 2023