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Isaacgv
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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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update model card README.md

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@@ -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: 0.86
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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 [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: 0.8540
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- - Accuracy: 0.86
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  ## Model description
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@@ -52,49 +52,69 @@ 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: 16
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- - eval_batch_size: 16
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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: 30
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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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- | 2.2594 | 1.0 | 57 | 2.2216 | 0.37 |
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- | 1.941 | 2.0 | 114 | 1.8715 | 0.59 |
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- | 1.4613 | 3.0 | 171 | 1.4244 | 0.65 |
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- | 1.2449 | 4.0 | 228 | 1.1359 | 0.71 |
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- | 0.8682 | 5.0 | 285 | 0.9472 | 0.74 |
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- | 0.6808 | 6.0 | 342 | 0.7817 | 0.78 |
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- | 0.4759 | 7.0 | 399 | 0.7428 | 0.74 |
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- | 0.3316 | 8.0 | 456 | 0.6441 | 0.78 |
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- | 0.2228 | 9.0 | 513 | 0.5838 | 0.83 |
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- | 0.1367 | 10.0 | 570 | 0.5843 | 0.86 |
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- | 0.0921 | 11.0 | 627 | 0.5745 | 0.86 |
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- | 0.0462 | 12.0 | 684 | 0.7029 | 0.83 |
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- | 0.0513 | 13.0 | 741 | 0.7116 | 0.86 |
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- | 0.0151 | 14.0 | 798 | 0.7017 | 0.86 |
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- | 0.0113 | 15.0 | 855 | 0.7439 | 0.85 |
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- | 0.0572 | 16.0 | 912 | 0.7691 | 0.84 |
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- | 0.0073 | 17.0 | 969 | 0.7918 | 0.84 |
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- | 0.0076 | 18.0 | 1026 | 0.8202 | 0.84 |
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- | 0.0053 | 19.0 | 1083 | 0.8238 | 0.86 |
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- | 0.0547 | 20.0 | 1140 | 0.8147 | 0.86 |
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- | 0.0045 | 21.0 | 1197 | 0.8201 | 0.86 |
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- | 0.004 | 22.0 | 1254 | 0.8282 | 0.83 |
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- | 0.0038 | 23.0 | 1311 | 0.8387 | 0.86 |
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- | 0.0035 | 24.0 | 1368 | 0.8398 | 0.86 |
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- | 0.0033 | 25.0 | 1425 | 0.8403 | 0.86 |
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- | 0.0031 | 26.0 | 1482 | 0.8464 | 0.86 |
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- | 0.0032 | 27.0 | 1539 | 0.8456 | 0.86 |
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- | 0.0031 | 28.0 | 1596 | 0.8505 | 0.86 |
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- | 0.0031 | 29.0 | 1653 | 0.8517 | 0.86 |
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- | 0.003 | 30.0 | 1710 | 0.8540 | 0.86 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.82
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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
 
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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: 1.0389
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+ - Accuracy: 0.82
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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: 0.0001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 50
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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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+ | 2.2599 | 1.0 | 29 | 2.2083 | 0.3 |
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+ | 2.0587 | 2.0 | 58 | 1.9010 | 0.48 |
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+ | 1.7008 | 3.0 | 87 | 1.5654 | 0.57 |
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+ | 1.3211 | 4.0 | 116 | 1.2700 | 0.64 |
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+ | 1.0132 | 5.0 | 145 | 1.0324 | 0.7 |
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+ | 0.8396 | 6.0 | 174 | 0.9723 | 0.7 |
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+ | 0.6316 | 7.0 | 203 | 0.8609 | 0.78 |
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+ | 0.4653 | 8.0 | 232 | 0.7132 | 0.82 |
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+ | 0.3607 | 9.0 | 261 | 0.7140 | 0.82 |
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+ | 0.2226 | 10.0 | 290 | 0.6465 | 0.85 |
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+ | 0.0988 | 11.0 | 319 | 0.6593 | 0.83 |
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+ | 0.0767 | 12.0 | 348 | 0.8247 | 0.83 |
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+ | 0.0674 | 13.0 | 377 | 0.7143 | 0.84 |
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+ | 0.0396 | 14.0 | 406 | 0.9098 | 0.77 |
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+ | 0.0467 | 15.0 | 435 | 0.9512 | 0.76 |
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+ | 0.0101 | 16.0 | 464 | 0.9639 | 0.79 |
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+ | 0.0064 | 17.0 | 493 | 0.7893 | 0.87 |
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+ | 0.0322 | 18.0 | 522 | 0.9502 | 0.84 |
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+ | 0.0055 | 19.0 | 551 | 0.9125 | 0.82 |
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+ | 0.0043 | 20.0 | 580 | 1.1760 | 0.79 |
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+ | 0.0033 | 21.0 | 609 | 1.0341 | 0.81 |
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+ | 0.0031 | 22.0 | 638 | 0.9639 | 0.82 |
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+ | 0.0024 | 23.0 | 667 | 1.0063 | 0.81 |
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+ | 0.0022 | 24.0 | 696 | 0.9636 | 0.83 |
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+ | 0.0018 | 25.0 | 725 | 1.0178 | 0.82 |
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+ | 0.0076 | 26.0 | 754 | 0.9735 | 0.82 |
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+ | 0.0018 | 27.0 | 783 | 1.0344 | 0.83 |
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+ | 0.0016 | 28.0 | 812 | 0.9294 | 0.83 |
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+ | 0.0126 | 29.0 | 841 | 0.9821 | 0.83 |
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+ | 0.0153 | 30.0 | 870 | 1.0450 | 0.81 |
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+ | 0.0014 | 31.0 | 899 | 0.9760 | 0.82 |
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+ | 0.0078 | 32.0 | 928 | 0.9816 | 0.82 |
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+ | 0.0012 | 33.0 | 957 | 1.0110 | 0.84 |
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+ | 0.0012 | 34.0 | 986 | 1.0529 | 0.84 |
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+ | 0.0011 | 35.0 | 1015 | 1.0165 | 0.81 |
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+ | 0.0011 | 36.0 | 1044 | 0.9932 | 0.82 |
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+ | 0.0011 | 37.0 | 1073 | 1.0577 | 0.83 |
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+ | 0.001 | 38.0 | 1102 | 1.0322 | 0.82 |
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+ | 0.001 | 39.0 | 1131 | 1.0170 | 0.82 |
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+ | 0.001 | 40.0 | 1160 | 1.0243 | 0.84 |
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+ | 0.0009 | 41.0 | 1189 | 1.0295 | 0.82 |
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+ | 0.0056 | 42.0 | 1218 | 1.0291 | 0.82 |
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+ | 0.0066 | 43.0 | 1247 | 1.0272 | 0.82 |
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+ | 0.0009 | 44.0 | 1276 | 1.0522 | 0.83 |
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+ | 0.0009 | 45.0 | 1305 | 1.0418 | 0.82 |
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+ | 0.0009 | 46.0 | 1334 | 1.0448 | 0.82 |
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+ | 0.0009 | 47.0 | 1363 | 1.0423 | 0.82 |
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+ | 0.0058 | 48.0 | 1392 | 1.0344 | 0.82 |
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+ | 0.0054 | 49.0 | 1421 | 1.0364 | 0.82 |
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+ | 0.0009 | 50.0 | 1450 | 1.0389 | 0.82 |
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  ### Framework versions