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Hcask
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distilhubert

Audio Classification
Transformers
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Safetensors
hubert
Generated from Trainer
Eval Results (legacy)
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Hcask commited on
Commit
02acc07
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1 Parent(s): 3c27c93

End of training

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Files changed (3) hide show
  1. README.md +27 -22
  2. model.safetensors +1 -1
  3. runs/May05_13-17-17_e4172518e05d/events.out.tfevents.1746451950.e4172518e05d.869.1 +2 -2
README.md CHANGED
@@ -1,7 +1,7 @@
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  ---
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  library_name: transformers
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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:
@@ -9,7 +9,7 @@ datasets:
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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
@@ -23,18 +23,18 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.782608695652174
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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: 1.2172
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- - Accuracy: 0.7826
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  ## Model description
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@@ -60,28 +60,33 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.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: 0.1
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- - num_epochs: 10
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  - mixed_precision_training: Native AMP
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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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- | 2.1308 | 1.0 | 25 | 2.1309 | 0.0870 |
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- | 1.9034 | 2.0 | 50 | 1.9650 | 0.3478 |
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- | 1.6085 | 3.0 | 75 | 1.7543 | 0.5217 |
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- | 1.4083 | 4.0 | 100 | 1.6225 | 0.5217 |
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- | 1.4712 | 5.0 | 125 | 1.4741 | 0.6957 |
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- | 1.1667 | 6.0 | 150 | 1.3947 | 0.6087 |
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- | 1.0986 | 7.0 | 175 | 1.3320 | 0.7391 |
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- | 1.0781 | 8.0 | 200 | 1.2441 | 0.7391 |
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- | 0.96 | 9.0 | 225 | 1.2146 | 0.7826 |
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- | 0.9224 | 10.0 | 250 | 1.2172 | 0.7826 |
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.48.3
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- - Pytorch 2.5.1+cu124
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- - Datasets 3.3.2
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- - Tokenizers 0.21.0
 
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  ---
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  library_name: transformers
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  license: apache-2.0
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+ base_model: facebook/hubert-base-ls960
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - accuracy
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  model-index:
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+ - name: hubert-base-ls960-finetuned-gtzan
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  results:
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  - task:
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  name: Audio Classification
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7391304347826086
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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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+ # hubert-base-ls960-finetuned-gtzan
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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: 1.1234
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+ - Accuracy: 0.7391
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  ## Model description
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  - optimizer: Use OptimizerNames.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: 0.1
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+ - num_epochs: 15
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  - mixed_precision_training: Native AMP
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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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+ | 1.4277 | 1.0 | 25 | 1.5627 | 0.4783 |
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+ | 1.4946 | 2.0 | 50 | 1.4727 | 0.5217 |
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+ | 1.051 | 3.0 | 75 | 1.3207 | 0.6087 |
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+ | 1.0897 | 4.0 | 100 | 1.3614 | 0.6522 |
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+ | 1.1461 | 5.0 | 125 | 1.3143 | 0.5652 |
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+ | 0.6919 | 6.0 | 150 | 1.1131 | 0.6087 |
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+ | 0.7273 | 7.0 | 175 | 1.4138 | 0.6522 |
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+ | 0.5955 | 8.0 | 200 | 1.2106 | 0.6957 |
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+ | 0.4823 | 9.0 | 225 | 1.1681 | 0.6087 |
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+ | 0.5178 | 10.0 | 250 | 1.1616 | 0.6522 |
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+ | 0.4635 | 11.0 | 275 | 0.9685 | 0.7826 |
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+ | 0.4622 | 12.0 | 300 | 0.9625 | 0.7826 |
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+ | 0.3048 | 13.0 | 325 | 1.0364 | 0.7391 |
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+ | 0.1576 | 14.0 | 350 | 1.0571 | 0.7391 |
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+ | 0.1876 | 15.0 | 375 | 1.1234 | 0.7391 |
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  ### Framework versions
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+ - Transformers 4.51.3
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.5.1
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+ - Tokenizers 0.21.1
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