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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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  1. README.md +20 -20
  2. model.safetensors +1 -1
  3. runs/Jan26_06-33-16_d759b9d8f8ea/events.out.tfevents.1706250835.d759b9d8f8ea.13261.5 +3 -0
  4. runs/Jan26_06-34-14_d759b9d8f8ea/events.out.tfevents.1706250858.d759b9d8f8ea.13261.6 +3 -0
  5. runs/Jan26_06-34-33_d759b9d8f8ea/events.out.tfevents.1706250876.d759b9d8f8ea.13261.7 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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: 0.6195
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  - Accuracy: 0.84
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  ## Model description
@@ -52,7 +52,7 @@ More information needed
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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: 2
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  - eval_batch_size: 2
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  - seed: 42
@@ -68,24 +68,24 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.1191 | 0.11 | 5 | 0.6123 | 0.84 |
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- | 0.1533 | 0.22 | 10 | 0.6686 | 0.81 |
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- | 0.1185 | 0.33 | 15 | 0.6119 | 0.82 |
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- | 0.1053 | 0.44 | 20 | 0.6488 | 0.82 |
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- | 0.1608 | 0.56 | 25 | 0.7321 | 0.82 |
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- | 0.1185 | 0.67 | 30 | 0.5918 | 0.88 |
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- | 0.1096 | 0.78 | 35 | 0.5839 | 0.82 |
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- | 0.1207 | 0.89 | 40 | 0.5980 | 0.84 |
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- | 0.0845 | 1.0 | 45 | 0.6426 | 0.86 |
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- | 0.1001 | 1.11 | 50 | 0.6154 | 0.84 |
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- | 0.083 | 1.22 | 55 | 0.5982 | 0.85 |
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- | 0.0564 | 1.33 | 60 | 0.5937 | 0.87 |
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- | 0.0935 | 1.44 | 65 | 0.6189 | 0.87 |
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- | 0.0591 | 1.56 | 70 | 0.5973 | 0.84 |
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- | 0.0951 | 1.67 | 75 | 0.6225 | 0.84 |
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- | 0.0484 | 1.78 | 80 | 0.6454 | 0.81 |
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- | 0.0621 | 1.89 | 85 | 0.6284 | 0.83 |
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- | 0.1254 | 2.0 | 90 | 0.6195 | 0.84 |
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  ### Framework versions
 
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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: 0.5771
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  - Accuracy: 0.84
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  ## Model description
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-06
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.1148 | 0.11 | 5 | 0.5865 | 0.83 |
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+ | 0.1411 | 0.22 | 10 | 0.5951 | 0.83 |
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+ | 0.1014 | 0.33 | 15 | 0.5964 | 0.83 |
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+ | 0.085 | 0.44 | 20 | 0.5901 | 0.83 |
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+ | 0.1362 | 0.56 | 25 | 0.5894 | 0.82 |
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+ | 0.0917 | 0.67 | 30 | 0.5862 | 0.83 |
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+ | 0.097 | 0.78 | 35 | 0.5759 | 0.84 |
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+ | 0.1206 | 0.89 | 40 | 0.5701 | 0.84 |
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+ | 0.0909 | 1.0 | 45 | 0.5649 | 0.84 |
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+ | 0.1269 | 1.11 | 50 | 0.5674 | 0.84 |
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+ | 0.1117 | 1.22 | 55 | 0.5714 | 0.84 |
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+ | 0.0791 | 1.33 | 60 | 0.5730 | 0.86 |
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+ | 0.1016 | 1.44 | 65 | 0.5745 | 0.84 |
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+ | 0.0712 | 1.56 | 70 | 0.5744 | 0.85 |
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+ | 0.1212 | 1.67 | 75 | 0.5773 | 0.85 |
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+ | 0.0724 | 1.78 | 80 | 0.5782 | 0.85 |
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+ | 0.0831 | 1.89 | 85 | 0.5777 | 0.85 |
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+ | 0.1429 | 2.0 | 90 | 0.5771 | 0.84 |
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  ### Framework versions
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