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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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- marsyas/gtzan |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilhubert-finetuned-gtzan |
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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: GTZAN |
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type: marsyas/gtzan |
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config: all |
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split: train |
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args: all |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.81 |
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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-gtzan |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7392 |
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- Accuracy: 0.81 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 20 |
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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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| 1.3055 | 0.97 | 7 | 1.2863 | 0.73 | |
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| 1.2903 | 1.93 | 14 | 1.2504 | 0.7 | |
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| 1.2118 | 2.9 | 21 | 1.1450 | 0.77 | |
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| 1.1443 | 4.0 | 29 | 1.1224 | 0.74 | |
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| 1.006 | 4.97 | 36 | 1.0376 | 0.79 | |
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| 1.0174 | 5.93 | 43 | 0.9681 | 0.8 | |
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| 0.9155 | 6.9 | 50 | 0.9322 | 0.81 | |
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| 0.8781 | 8.0 | 58 | 0.9266 | 0.78 | |
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| 0.819 | 8.97 | 65 | 0.8473 | 0.79 | |
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| 0.7984 | 9.93 | 72 | 0.8225 | 0.77 | |
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| 0.7254 | 10.9 | 79 | 0.8096 | 0.81 | |
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| 0.6752 | 12.0 | 87 | 0.7801 | 0.81 | |
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| 0.6132 | 12.97 | 94 | 0.7687 | 0.8 | |
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| 0.615 | 13.93 | 101 | 0.7603 | 0.79 | |
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| 0.6162 | 14.9 | 108 | 0.7599 | 0.82 | |
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| 0.5678 | 16.0 | 116 | 0.7414 | 0.81 | |
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| 0.548 | 16.97 | 123 | 0.7423 | 0.81 | |
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| 0.5495 | 17.93 | 130 | 0.7378 | 0.81 | |
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| 0.5185 | 18.9 | 137 | 0.7396 | 0.81 | |
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| 0.5544 | 19.31 | 140 | 0.7392 | 0.81 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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