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---
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library_name: transformers
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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: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.86
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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: 1.1749
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- Accuracy: 0.86
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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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- 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: 25
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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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| 2.0517 | 1.0 | 225 | 2.0004 | 0.47 |
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| 1.3283 | 2.0 | 450 | 1.3458 | 0.57 |
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| 0.729 | 3.0 | 675 | 0.8563 | 0.76 |
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| 0.4007 | 4.0 | 900 | 0.6748 | 0.8 |
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| 0.3923 | 5.0 | 1125 | 0.7340 | 0.78 |
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| 0.2193 | 6.0 | 1350 | 0.8712 | 0.76 |
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| 0.2383 | 7.0 | 1575 | 0.7414 | 0.79 |
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| 0.3 | 8.0 | 1800 | 0.7387 | 0.86 |
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| 0.006 | 9.0 | 2025 | 0.9203 | 0.85 |
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| 0.002 | 10.0 | 2250 | 0.8956 | 0.85 |
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| 0.0014 | 11.0 | 2475 | 0.9831 | 0.86 |
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| 0.001 | 12.0 | 2700 | 0.9406 | 0.86 |
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| 0.0009 | 13.0 | 2925 | 1.0288 | 0.86 |
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| 0.0007 | 14.0 | 3150 | 1.0172 | 0.86 |
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| 0.0007 | 15.0 | 3375 | 0.9912 | 0.89 |
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| 0.0005 | 16.0 | 3600 | 1.0282 | 0.86 |
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| 0.0006 | 17.0 | 3825 | 1.3495 | 0.83 |
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| 0.2453 | 18.0 | 4050 | 1.0340 | 0.87 |
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| 0.0004 | 19.0 | 4275 | 1.1048 | 0.86 |
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| 0.0004 | 20.0 | 4500 | 1.3051 | 0.85 |
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| 0.0003 | 21.0 | 4725 | 1.2280 | 0.85 |
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| 0.0003 | 22.0 | 4950 | 1.2530 | 0.85 |
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| 0.0003 | 23.0 | 5175 | 1.1992 | 0.85 |
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| 0.0003 | 24.0 | 5400 | 1.1881 | 0.85 |
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| 0.0003 | 25.0 | 5625 | 1.1749 | 0.86 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.3.1
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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