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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-gtzanVD |
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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: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9839786381842457 |
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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-gtzanVD |
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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: 0.1334 |
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- Accuracy: 0.9840 |
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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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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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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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| 0.3554 | 1.0 | 842 | 0.1898 | 0.9439 | |
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| 0.1136 | 2.0 | 1684 | 0.1657 | 0.9626 | |
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| 0.1571 | 3.0 | 2526 | 0.1132 | 0.9693 | |
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| 0.0004 | 4.0 | 3368 | 0.1235 | 0.9786 | |
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| 0.0011 | 5.0 | 4210 | 0.1555 | 0.9680 | |
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| 0.0001 | 6.0 | 5052 | 0.3138 | 0.9493 | |
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| 0.0001 | 7.0 | 5894 | 0.1825 | 0.9680 | |
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| 0.0001 | 8.0 | 6736 | 0.1982 | 0.9706 | |
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| 0.0001 | 9.0 | 7578 | 0.1690 | 0.9693 | |
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| 0.3166 | 10.0 | 8420 | 0.1487 | 0.9733 | |
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| 0.0 | 11.0 | 9262 | 0.2615 | 0.9680 | |
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| 0.0 | 12.0 | 10104 | 0.1536 | 0.9800 | |
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| 0.0001 | 13.0 | 10946 | 0.5478 | 0.9399 | |
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| 0.0 | 14.0 | 11788 | 0.1334 | 0.9840 | |
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| 0.0 | 15.0 | 12630 | 0.1270 | 0.9746 | |
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| 0.0 | 16.0 | 13472 | 0.1053 | 0.9840 | |
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| 0.0 | 17.0 | 14314 | 0.1181 | 0.9813 | |
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| 0.0 | 18.0 | 15156 | 0.1165 | 0.9826 | |
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| 0.0 | 19.0 | 15998 | 0.1191 | 0.9826 | |
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| 0.0 | 20.0 | 16840 | 0.1188 | 0.9826 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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