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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: 0.66 |
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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: 1.6170 |
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- Accuracy: 0.66 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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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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| 2.2999 | 0.97 | 7 | 2.2700 | 0.28 | |
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| 2.2713 | 1.93 | 14 | 2.1859 | 0.36 | |
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| 2.1478 | 2.9 | 21 | 2.0656 | 0.47 | |
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| 2.0863 | 4.0 | 29 | 1.9387 | 0.53 | |
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| 1.9229 | 4.97 | 36 | 1.8303 | 0.62 | |
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| 1.8399 | 5.93 | 43 | 1.7453 | 0.59 | |
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| 1.7467 | 6.9 | 50 | 1.6898 | 0.58 | |
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| 1.7223 | 8.0 | 58 | 1.6360 | 0.6 | |
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| 1.6716 | 8.97 | 65 | 1.6243 | 0.65 | |
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| 1.6509 | 9.66 | 70 | 1.6170 | 0.66 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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