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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: 0.6540 |
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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: 7 |
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- eval_batch_size: 7 |
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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: 13 |
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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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| 2.036 | 1.0 | 129 | 1.8621 | 0.54 | |
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| 1.272 | 2.0 | 258 | 1.2237 | 0.67 | |
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| 1.1092 | 3.0 | 387 | 0.9957 | 0.67 | |
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| 0.5955 | 4.0 | 516 | 0.8160 | 0.72 | |
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| 0.3345 | 5.0 | 645 | 0.6607 | 0.79 | |
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| 0.3451 | 6.0 | 774 | 0.7320 | 0.75 | |
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| 0.2405 | 7.0 | 903 | 0.4956 | 0.85 | |
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| 0.2242 | 8.0 | 1032 | 0.6112 | 0.81 | |
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| 0.0447 | 9.0 | 1161 | 0.6542 | 0.82 | |
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| 0.0194 | 10.0 | 1290 | 0.7455 | 0.84 | |
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| 0.0122 | 11.0 | 1419 | 0.6341 | 0.85 | |
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| 0.0119 | 12.0 | 1548 | 0.6671 | 0.84 | |
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| 0.0107 | 13.0 | 1677 | 0.6540 | 0.85 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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