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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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- gtzan |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: music-genre-detector-finetuned-gtzan_dset |
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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: gtzan |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9298245614035088 |
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- name: Precision |
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type: precision |
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value: 0.9292447472185437 |
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- name: Recall |
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type: recall |
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value: 0.9298245614035088 |
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- name: F1 |
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type: f1 |
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value: 0.9293437948869628 |
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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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# music-genre-detector-finetuned-gtzan_dset |
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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: 0.2288 |
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- Accuracy: 0.9298 |
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- Precision: 0.9292 |
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- Recall: 0.9298 |
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- F1: 0.9293 |
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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: 9e-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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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 2.2522 | 0.98 | 49 | 1.6370 | 0.6090 | 0.6189 | 0.6090 | 0.5764 | |
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| 1.2901 | 1.98 | 99 | 0.9974 | 0.7556 | 0.7655 | 0.7556 | 0.7426 | |
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| 1.0046 | 2.99 | 149 | 0.6645 | 0.8195 | 0.8226 | 0.8195 | 0.8162 | |
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| 0.5952 | 3.99 | 199 | 0.5054 | 0.8459 | 0.8561 | 0.8459 | 0.8460 | |
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| 0.3596 | 4.99 | 249 | 0.3729 | 0.9023 | 0.9117 | 0.9023 | 0.9041 | |
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| 0.2534 | 5.99 | 299 | 0.2953 | 0.9073 | 0.9088 | 0.9073 | 0.9075 | |
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| 0.1413 | 7.0 | 349 | 0.2545 | 0.9223 | 0.9229 | 0.9223 | 0.9216 | |
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| 0.0759 | 8.0 | 399 | 0.2593 | 0.9198 | 0.9209 | 0.9198 | 0.9190 | |
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| 0.0491 | 8.98 | 448 | 0.2288 | 0.9298 | 0.9292 | 0.9298 | 0.9293 | |
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| 0.0355 | 9.82 | 490 | 0.2392 | 0.9223 | 0.9231 | 0.9223 | 0.9221 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 1.10.2+cu111 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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