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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: 0.6676 |
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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: 3e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 12 |
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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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| 2.2592 | 1.0 | 75 | 2.2017 | 0.35 | |
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| 1.8413 | 2.0 | 150 | 1.8071 | 0.45 | |
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| 1.5432 | 3.0 | 225 | 1.4808 | 0.65 | |
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| 1.2137 | 4.0 | 300 | 1.2621 | 0.7 | |
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| 1.1546 | 5.0 | 375 | 1.0581 | 0.77 | |
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| 0.9996 | 6.0 | 450 | 0.9858 | 0.75 | |
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| 0.7508 | 7.0 | 525 | 0.9087 | 0.78 | |
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| 0.6669 | 8.0 | 600 | 0.7710 | 0.81 | |
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| 0.6834 | 9.0 | 675 | 0.7663 | 0.8 | |
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| 0.4495 | 10.0 | 750 | 0.7184 | 0.79 | |
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| 0.3677 | 11.0 | 825 | 0.6589 | 0.81 | |
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| 0.3092 | 12.0 | 900 | 0.7223 | 0.8 | |
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| 0.1846 | 13.0 | 975 | 0.6665 | 0.82 | |
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| 0.1797 | 14.0 | 1050 | 0.6500 | 0.8 | |
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| 0.1695 | 15.0 | 1125 | 0.6549 | 0.81 | |
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| 0.1104 | 16.0 | 1200 | 0.6636 | 0.81 | |
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| 0.1192 | 17.0 | 1275 | 0.6722 | 0.81 | |
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| 0.1226 | 18.0 | 1350 | 0.6540 | 0.82 | |
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| 0.1218 | 19.0 | 1425 | 0.6646 | 0.79 | |
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| 0.067 | 20.0 | 1500 | 0.6676 | 0.81 | |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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