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update model card README.md

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+ ---
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+ license: apache-2.0
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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: 0.85
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+ ---
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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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+
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+ # distilhubert-finetuned-gtzan
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+
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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: 0.5569
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+ - Accuracy: 0.85
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.0203 | 1.0 | 113 | 1.8486 | 0.5 |
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+ | 1.3421 | 2.0 | 226 | 1.2434 | 0.67 |
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+ | 0.9927 | 3.0 | 339 | 0.9158 | 0.76 |
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+ | 0.8987 | 4.0 | 452 | 0.8062 | 0.76 |
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+ | 0.6031 | 5.0 | 565 | 0.6789 | 0.8 |
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+ | 0.3869 | 6.0 | 678 | 0.6774 | 0.79 |
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+ | 0.4401 | 7.0 | 791 | 0.5672 | 0.84 |
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+ | 0.1752 | 8.0 | 904 | 0.5165 | 0.86 |
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+ | 0.2991 | 9.0 | 1017 | 0.5699 | 0.84 |
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+ | 0.1433 | 10.0 | 1130 | 0.5569 | 0.85 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0.dev0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3