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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: 0.85 |
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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: 0.9971 |
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- Accuracy: 0.85 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- training_steps: 3000 |
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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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| 2.2793 | 0.88 | 100 | 2.1792 | 0.41 | |
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| 1.992 | 1.77 | 200 | 1.6741 | 0.56 | |
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| 1.4928 | 2.65 | 300 | 1.2795 | 0.56 | |
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| 1.1156 | 3.54 | 400 | 0.9983 | 0.69 | |
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| 0.9162 | 4.42 | 500 | 0.8222 | 0.73 | |
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| 0.6785 | 5.31 | 600 | 0.8422 | 0.78 | |
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| 0.4695 | 6.19 | 700 | 0.7034 | 0.8 | |
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| 0.3362 | 7.08 | 800 | 0.9594 | 0.72 | |
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| 0.2051 | 7.96 | 900 | 0.6157 | 0.84 | |
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| 0.1242 | 8.85 | 1000 | 0.6059 | 0.86 | |
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| 0.0678 | 9.73 | 1100 | 0.7626 | 0.86 | |
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| 0.0479 | 10.62 | 1200 | 0.7886 | 0.84 | |
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| 0.0216 | 11.5 | 1300 | 0.8302 | 0.85 | |
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| 0.0202 | 12.39 | 1400 | 0.8921 | 0.86 | |
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| 0.0155 | 13.27 | 1500 | 0.9971 | 0.85 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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