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apv53-fit
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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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End of training

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  1. README.md +15 -15
  2. model.safetensors +1 -1
  3. runs/Dec23_14-18-47_458d8aa29fd2/events.out.tfevents.1734963547.458d8aa29fd2.1283.0 +2 -2
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.7063
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- - Accuracy: 0.8
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  ## Model description
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@@ -58,8 +58,8 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- - lr_scheduler_type: linear
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- - lr_scheduler_warmup_ratio: 0.5
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  - num_epochs: 10
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  - mixed_precision_training: Native AMP
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.2776 | 1.0 | 113 | 2.2687 | 0.2 |
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- | 2.0615 | 2.0 | 226 | 2.0397 | 0.55 |
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- | 1.8286 | 3.0 | 339 | 1.7089 | 0.56 |
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- | 1.4052 | 4.0 | 452 | 1.3901 | 0.66 |
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- | 1.232 | 5.0 | 565 | 1.1751 | 0.69 |
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- | 0.9855 | 6.0 | 678 | 0.9499 | 0.74 |
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- | 0.8087 | 7.0 | 791 | 0.8492 | 0.75 |
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- | 0.5098 | 8.0 | 904 | 0.7997 | 0.77 |
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- | 0.5883 | 9.0 | 1017 | 0.7144 | 0.77 |
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- | 0.4644 | 10.0 | 1130 | 0.7063 | 0.8 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.79
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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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  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: 0.6878
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+ - Accuracy: 0.79
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  ## Model description
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 10
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  - mixed_precision_training: Native AMP
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.091 | 1.0 | 113 | 2.0139 | 0.6 |
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+ | 1.4629 | 2.0 | 226 | 1.4632 | 0.63 |
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+ | 1.2623 | 3.0 | 339 | 1.1626 | 0.74 |
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+ | 0.9162 | 4.0 | 452 | 0.9752 | 0.68 |
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+ | 0.7541 | 5.0 | 565 | 0.8230 | 0.81 |
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+ | 0.7539 | 6.0 | 678 | 0.7603 | 0.78 |
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+ | 0.564 | 7.0 | 791 | 0.7347 | 0.81 |
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+ | 0.3841 | 8.0 | 904 | 0.6810 | 0.79 |
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+ | 0.5169 | 9.0 | 1017 | 0.6859 | 0.79 |
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+ | 0.4292 | 10.0 | 1130 | 0.6878 | 0.79 |
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  ### Framework versions
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