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--- |
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license: apache-2.0 |
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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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--- |
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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: 1.2658 |
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- Accuracy: 0.8 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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: 30 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.1071 | 1.0 | 112 | 2.1453 | 0.33 | |
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| 1.6165 | 2.0 | 225 | 1.6129 | 0.59 | |
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| 1.2842 | 3.0 | 337 | 1.2084 | 0.68 | |
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| 0.9805 | 4.0 | 450 | 0.8842 | 0.74 | |
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| 0.5216 | 5.0 | 562 | 0.7350 | 0.78 | |
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| 0.5017 | 6.0 | 675 | 0.8196 | 0.77 | |
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| 0.1998 | 7.0 | 787 | 0.6709 | 0.8 | |
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| 0.3662 | 8.0 | 900 | 0.8483 | 0.78 | |
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| 0.2711 | 9.0 | 1012 | 0.8567 | 0.81 | |
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| 0.0183 | 10.0 | 1125 | 0.8994 | 0.82 | |
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| 0.0299 | 11.0 | 1237 | 1.2142 | 0.8 | |
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| 0.0064 | 12.0 | 1350 | 1.0208 | 0.81 | |
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| 0.004 | 13.0 | 1462 | 1.0619 | 0.81 | |
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| 0.0031 | 14.0 | 1575 | 1.1454 | 0.79 | |
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| 0.0028 | 15.0 | 1687 | 1.1010 | 0.81 | |
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| 0.0023 | 16.0 | 1800 | 1.0595 | 0.8 | |
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| 0.0017 | 17.0 | 1912 | 1.1340 | 0.8 | |
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| 0.0015 | 18.0 | 2025 | 1.1760 | 0.81 | |
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| 0.0014 | 19.0 | 2137 | 1.1361 | 0.81 | |
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| 0.0012 | 20.0 | 2250 | 1.2138 | 0.81 | |
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| 0.0011 | 21.0 | 2362 | 1.1366 | 0.81 | |
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| 0.0012 | 22.0 | 2475 | 1.1662 | 0.8 | |
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| 0.0011 | 23.0 | 2587 | 1.1491 | 0.8 | |
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| 0.0009 | 24.0 | 2700 | 1.1287 | 0.81 | |
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| 0.0009 | 25.0 | 2812 | 1.2027 | 0.81 | |
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| 0.0009 | 26.0 | 2925 | 1.1740 | 0.81 | |
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| 0.0009 | 27.0 | 3037 | 1.2011 | 0.81 | |
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| 0.0009 | 28.0 | 3150 | 1.2523 | 0.8 | |
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| 0.0008 | 29.0 | 3262 | 1.2494 | 0.81 | |
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| 0.0007 | 29.87 | 3360 | 1.2658 | 0.8 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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