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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: ntu-spml/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: 0.87 |
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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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# ntu-spml/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: 0.7428 |
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- Accuracy: 0.87 |
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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: 3.992986714871485e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9807885777224674,0.996064720140604) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 509 |
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- num_epochs: 100 |
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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 | 1.0 | 57 | 2.2832 | 0.3 | |
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| No log | 2.0 | 114 | 2.2273 | 0.28 | |
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| No log | 3.0 | 171 | 2.0861 | 0.46 | |
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| No log | 4.0 | 228 | 1.8473 | 0.5 | |
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| No log | 5.0 | 285 | 1.5146 | 0.6 | |
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| No log | 6.0 | 342 | 1.2140 | 0.69 | |
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| No log | 7.0 | 399 | 0.9856 | 0.74 | |
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| No log | 8.0 | 456 | 0.8056 | 0.79 | |
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| 1.6591 | 9.0 | 513 | 0.7135 | 0.8 | |
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| 1.6591 | 10.0 | 570 | 0.7642 | 0.75 | |
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| 1.6591 | 11.0 | 627 | 0.6344 | 0.79 | |
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| 1.6591 | 12.0 | 684 | 0.5982 | 0.83 | |
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| 1.6591 | 13.0 | 741 | 0.5369 | 0.86 | |
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| 1.6591 | 14.0 | 798 | 0.7501 | 0.79 | |
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| 1.6591 | 15.0 | 855 | 0.7493 | 0.78 | |
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| 1.6591 | 16.0 | 912 | 0.6891 | 0.83 | |
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| 1.6591 | 17.0 | 969 | 0.7492 | 0.8 | |
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| 0.2402 | 18.0 | 1026 | 0.6663 | 0.88 | |
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| 0.2402 | 19.0 | 1083 | 0.5750 | 0.89 | |
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| 0.2402 | 20.0 | 1140 | 0.8215 | 0.81 | |
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| 0.2402 | 21.0 | 1197 | 0.7435 | 0.79 | |
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| 0.2402 | 22.0 | 1254 | 0.8305 | 0.86 | |
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| 0.2402 | 23.0 | 1311 | 0.7636 | 0.83 | |
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| 0.2402 | 24.0 | 1368 | 0.9786 | 0.77 | |
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| 0.2402 | 25.0 | 1425 | 0.7082 | 0.88 | |
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| 0.2402 | 26.0 | 1482 | 0.7698 | 0.85 | |
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| 0.0206 | 27.0 | 1539 | 0.7360 | 0.87 | |
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| 0.0206 | 28.0 | 1596 | 0.8575 | 0.84 | |
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| 0.0206 | 29.0 | 1653 | 0.7428 | 0.87 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.2.dev1 |
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- Tokenizers 0.13.3 |
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