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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: 0.84 |
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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: 0.9085 |
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- Accuracy: 0.84 |
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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 | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 2.0825 | 0.88 | 100 | 0.47 | 1.8392 | |
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| 1.4043 | 1.77 | 200 | 0.67 | 1.2675 | |
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| 1.0686 | 2.65 | 300 | 0.71 | 1.0186 | |
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| 0.8037 | 3.54 | 400 | 0.74 | 0.9198 | |
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| 0.6215 | 4.42 | 500 | 0.78 | 0.7636 | |
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| 0.5106 | 5.31 | 600 | 0.76 | 0.7937 | |
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| 0.3844 | 6.19 | 700 | 0.78 | 0.6909 | |
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| 0.3043 | 7.08 | 800 | 0.77 | 0.7279 | |
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| 0.2453 | 7.96 | 900 | 0.82 | 0.6447 | |
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| 0.211 | 8.85 | 1000 | 0.84 | 0.6404 | |
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| 0.2268 | 9.73 | 1100 | 0.77 | 0.7198 | |
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| 0.1565 | 10.62 | 1200 | 0.83 | 0.6704 | |
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| 0.0694 | 11.5 | 1300 | 0.83 | 0.8017 | |
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| 0.0568 | 12.39 | 1400 | 0.8 | 0.7841 | |
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| 0.0441 | 13.27 | 1500 | 0.81 | 0.7757 | |
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| 0.0302 | 14.16 | 1600 | 0.84 | 0.7819 | |
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| 0.0116 | 15.04 | 1700 | 0.83 | 0.7949 | |
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| 0.0289 | 15.93 | 1800 | 0.85 | 0.8057 | |
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| 0.0115 | 16.81 | 1900 | 0.83 | 0.8271 | |
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| 0.0081 | 17.7 | 2000 | 0.86 | 0.8005 | |
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| 0.0124 | 18.58 | 2100 | 0.8 | 0.8927 | |
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| 0.0219 | 19.47 | 2200 | 0.85 | 0.8126 | |
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| 0.0161 | 20.35 | 2300 | 0.85 | 0.8464 | |
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| 0.0157 | 21.24 | 2400 | 0.86 | 0.8459 | |
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| 0.0039 | 22.12 | 2500 | 0.8 | 1.0282 | |
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| 0.0145 | 23.01 | 2600 | 0.9218 | 0.84 | |
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| 0.0149 | 23.89 | 2700 | 0.9085 | 0.84 | |
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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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