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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: 0.73
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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: 1.3004
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- Accuracy: 0.73
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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: 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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| 2.3007 | 0.97 | 7 | 2.2260 | 0.34 |
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| 2.2424 | 1.93 | 14 | 2.0328 | 0.39 |
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| 1.9803 | 2.9 | 21 | 1.8298 | 0.41 |
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| 1.8344 | 4.0 | 29 | 1.6637 | 0.52 |
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| 1.608 | 4.97 | 36 | 1.5523 | 0.58 |
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| 1.5644 | 5.93 | 43 | 1.4443 | 0.67 |
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| 1.4354 | 6.9 | 50 | 1.3870 | 0.7 |
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| 1.38 | 8.0 | 58 | 1.3434 | 0.69 |
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| 1.3521 | 8.97 | 65 | 1.3051 | 0.76 |
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| 1.3542 | 9.66 | 70 | 1.3004 | 0.73 |
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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 2.13.1
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- Tokenizers 0.13.3
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