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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-gtzan4 |
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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: 0.78 |
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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-gtzan4 |
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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: 1.0945 |
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- Accuracy: 0.78 |
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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: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 192 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.85 | 4 | 2.2991 | 0.06 | |
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| 2.2997 | 1.92 | 9 | 2.2668 | 0.28 | |
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| 2.2819 | 2.99 | 14 | 2.1877 | 0.33 | |
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| 2.2336 | 3.84 | 18 | 2.1023 | 0.47 | |
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| 2.1493 | 4.91 | 23 | 1.9895 | 0.52 | |
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| 2.0571 | 5.97 | 28 | 1.8745 | 0.51 | |
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| 1.9341 | 6.83 | 32 | 1.7838 | 0.57 | |
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| 1.8274 | 7.89 | 37 | 1.6784 | 0.64 | |
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| 1.724 | 8.96 | 42 | 1.5859 | 0.66 | |
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| 1.6407 | 9.81 | 46 | 1.5234 | 0.66 | |
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| 1.5593 | 10.88 | 51 | 1.4508 | 0.7 | |
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| 1.4735 | 11.95 | 56 | 1.3982 | 0.69 | |
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| 1.4185 | 12.8 | 60 | 1.3501 | 0.72 | |
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| 1.3613 | 13.87 | 65 | 1.3131 | 0.74 | |
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| 1.3099 | 14.93 | 70 | 1.2742 | 0.72 | |
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| 1.2762 | 16.0 | 75 | 1.2485 | 0.73 | |
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| 1.2762 | 16.85 | 79 | 1.2102 | 0.74 | |
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| 1.2379 | 17.92 | 84 | 1.1931 | 0.75 | |
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| 1.193 | 18.99 | 89 | 1.1647 | 0.75 | |
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| 1.1863 | 19.84 | 93 | 1.1488 | 0.77 | |
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| 1.1435 | 20.91 | 98 | 1.1349 | 0.78 | |
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| 1.1424 | 21.97 | 103 | 1.1166 | 0.79 | |
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| 1.0961 | 22.83 | 107 | 1.1025 | 0.78 | |
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| 1.0887 | 23.89 | 112 | 1.0993 | 0.78 | |
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| 1.0977 | 24.96 | 117 | 1.0952 | 0.78 | |
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| 1.0661 | 25.6 | 120 | 1.0945 | 0.78 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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