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README.md
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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: 0.87
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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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# 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: 0.7127
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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: 0.0001
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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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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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: 0.2
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- num_epochs: 16
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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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| 2.2072 | 0.99 | 56 | 2.1364 | 0.37 |
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| 1.6502 | 2.0 | 113 | 1.5282 | 0.63 |
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| 1.2965 | 2.99 | 169 | 1.1371 | 0.69 |
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| 1.0407 | 4.0 | 226 | 0.9643 | 0.74 |
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| 0.6558 | 4.99 | 282 | 0.7303 | 0.76 |
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| 0.3615 | 6.0 | 339 | 0.7688 | 0.78 |
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| 0.3705 | 6.99 | 395 | 0.5905 | 0.85 |
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| 0.2165 | 8.0 | 452 | 0.6988 | 0.81 |
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| 0.1098 | 8.99 | 508 | 0.4604 | 0.9 |
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| 0.0647 | 10.0 | 565 | 0.6756 | 0.87 |
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| 0.0179 | 10.99 | 621 | 0.8108 | 0.83 |
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| 0.0278 | 12.0 | 678 | 0.6674 | 0.87 |
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| 0.0075 | 12.99 | 734 | 0.8230 | 0.83 |
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| 0.0061 | 14.0 | 791 | 0.8155 | 0.85 |
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| 0.0056 | 14.99 | 847 | 0.7233 | 0.87 |
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| 0.0055 | 15.86 | 896 | 0.7127 | 0.87 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.1
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- Tokenizers 0.13.3
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