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Barani1-t
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hubert-base-ls960

Audio Classification
Transformers
PyTorch
hubert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +25 -21
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.82
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9359
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- - Accuracy: 0.82
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  ## Model description
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@@ -63,24 +63,28 @@ The following hyperparameters were used during training:
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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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- | 2.2494 | 1.0 | 113 | 2.1568 | 0.36 |
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- | 1.7795 | 2.0 | 226 | 1.7904 | 0.38 |
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- | 1.5798 | 3.0 | 339 | 1.6144 | 0.5 |
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- | 1.6354 | 4.0 | 452 | 1.2584 | 0.66 |
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- | 0.9675 | 5.0 | 565 | 1.1453 | 0.64 |
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- | 0.995 | 6.0 | 678 | 0.9740 | 0.67 |
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- | 1.2052 | 7.0 | 791 | 1.0552 | 0.68 |
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- | 0.7028 | 8.0 | 904 | 0.8980 | 0.74 |
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- | 0.7472 | 9.0 | 1017 | 0.9431 | 0.72 |
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- | 0.3181 | 10.0 | 1130 | 0.8750 | 0.75 |
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- | 0.3948 | 11.0 | 1243 | 1.0047 | 0.73 |
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- | 0.3507 | 12.0 | 1356 | 0.8054 | 0.81 |
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- | 0.1785 | 13.0 | 1469 | 0.7866 | 0.84 |
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- | 0.2453 | 14.0 | 1582 | 0.8960 | 0.82 |
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- | 0.2832 | 15.0 | 1695 | 1.0770 | 0.81 |
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- | 0.2132 | 16.0 | 1808 | 0.9359 | 0.82 |
 
 
 
 
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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: 0.83
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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 [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0283
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+ - Accuracy: 0.83
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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+ |:-------------:|:-----:|:----:|:--------:|:---------------:|
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+ | 2.2494 | 1.0 | 113 | 0.36 | 2.1568 |
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+ | 1.7795 | 2.0 | 226 | 0.38 | 1.7904 |
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+ | 1.5798 | 3.0 | 339 | 0.5 | 1.6144 |
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+ | 1.6354 | 4.0 | 452 | 0.66 | 1.2584 |
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+ | 0.9675 | 5.0 | 565 | 0.64 | 1.1453 |
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+ | 0.995 | 6.0 | 678 | 0.67 | 0.9740 |
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+ | 1.2052 | 7.0 | 791 | 0.68 | 1.0552 |
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+ | 0.7028 | 8.0 | 904 | 0.74 | 0.8980 |
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+ | 0.7472 | 9.0 | 1017 | 0.72 | 0.9431 |
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+ | 0.3181 | 10.0 | 1130 | 0.75 | 0.8750 |
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+ | 0.3948 | 11.0 | 1243 | 0.73 | 1.0047 |
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+ | 0.3507 | 12.0 | 1356 | 0.81 | 0.8054 |
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+ | 0.1785 | 13.0 | 1469 | 0.84 | 0.7866 |
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+ | 0.2453 | 14.0 | 1582 | 0.82 | 0.8960 |
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+ | 0.2832 | 15.0 | 1695 | 0.81 | 1.0770 |
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+ | 0.2132 | 16.0 | 1808 | 0.82 | 0.9359 |
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+ | 0.1398 | 17.0 | 1921 | 0.81 | 1.0800 |
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+ | 0.292 | 18.0 | 2034 | 0.84 | 0.9867 |
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+ | 0.0181 | 19.0 | 2147 | 0.82 | 1.0585 |
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+ | 0.0399 | 20.0 | 2260 | 1.0283 | 0.83 |
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  ### Framework versions
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