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distilhubert-finetuned-gtzan

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  1. README.md +22 -20
  2. model.safetensors +1 -1
  3. runs/Aug14_08-19-17_26abd6e5c87d/events.out.tfevents.1723634970.26abd6e5c87d.34.1 +3 -0
README.md CHANGED
@@ -28,29 +28,28 @@ model-index:
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  value: 0.87
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  - name: Precision
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  type: precision
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- value: 0.8753213453213452
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  - name: Recall
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  type: recall
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  value: 0.87
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  - name: F1
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  type: f1
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- value: 0.8641214483158217
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- pipeline_tag: audio-classification
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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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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/cefsu57q)
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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: 0.5488
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  - Accuracy: 0.87
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- - Precision: 0.8753
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  - Recall: 0.87
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- - F1: 0.8641
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  ## Model description
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@@ -83,18 +82,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 2.1729 | 1.0 | 113 | 2.0581 | 0.63 | 0.6670 | 0.63 | 0.5957 |
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- | 1.6552 | 2.0 | 226 | 1.3957 | 0.7 | 0.6894 | 0.7 | 0.6857 |
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- | 1.0753 | 3.0 | 339 | 0.9783 | 0.75 | 0.8154 | 0.75 | 0.7277 |
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- | 0.8519 | 4.0 | 452 | 0.8087 | 0.75 | 0.8120 | 0.75 | 0.7380 |
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- | 0.8623 | 5.0 | 565 | 0.7393 | 0.75 | 0.7622 | 0.75 | 0.7373 |
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- | 0.506 | 6.0 | 678 | 0.6861 | 0.81 | 0.8449 | 0.81 | 0.7997 |
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- | 0.2052 | 7.0 | 791 | 0.6505 | 0.81 | 0.8254 | 0.81 | 0.8024 |
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- | 0.1583 | 8.0 | 904 | 0.5365 | 0.86 | 0.8770 | 0.86 | 0.8545 |
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- | 0.0699 | 9.0 | 1017 | 0.5488 | 0.87 | 0.8753 | 0.87 | 0.8641 |
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- | 0.0177 | 10.0 | 1130 | 0.6330 | 0.83 | 0.8312 | 0.83 | 0.8245 |
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- | 0.0071 | 11.0 | 1243 | 0.6268 | 0.84 | 0.8410 | 0.84 | 0.8348 |
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- | 0.0746 | 12.0 | 1356 | 0.6051 | 0.87 | 0.8732 | 0.87 | 0.8675 |
 
 
 
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  ### Framework versions
@@ -102,4 +104,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.42.3
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  - Pytorch 2.1.2
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  - Datasets 2.20.0
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- - Tokenizers 0.19.1
 
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  value: 0.87
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  - name: Precision
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  type: precision
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+ value: 0.8802816627816629
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  - name: Recall
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  type: recall
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  value: 0.87
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  - name: F1
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  type: f1
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+ value: 0.8627110595989314
 
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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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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/8epo656a)
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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: 0.6501
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  - Accuracy: 0.87
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+ - Precision: 0.8803
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  - Recall: 0.87
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+ - F1: 0.8627
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.1743 | 1.0 | 113 | 2.0604 | 0.38 | 0.5273 | 0.38 | 0.3101 |
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+ | 1.6179 | 2.0 | 226 | 1.4299 | 0.62 | 0.6136 | 0.62 | 0.5877 |
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+ | 1.0981 | 3.0 | 339 | 1.0223 | 0.79 | 0.8516 | 0.79 | 0.7669 |
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+ | 0.9785 | 4.0 | 452 | 0.8722 | 0.71 | 0.7748 | 0.71 | 0.6733 |
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+ | 0.8834 | 5.0 | 565 | 0.8363 | 0.76 | 0.7691 | 0.76 | 0.7449 |
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+ | 0.4936 | 6.0 | 678 | 0.6241 | 0.82 | 0.8313 | 0.82 | 0.8193 |
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+ | 0.2772 | 7.0 | 791 | 0.5648 | 0.85 | 0.8623 | 0.85 | 0.8459 |
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+ | 0.1213 | 8.0 | 904 | 0.6919 | 0.81 | 0.8429 | 0.81 | 0.7997 |
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+ | 0.0958 | 9.0 | 1017 | 0.5527 | 0.86 | 0.8682 | 0.86 | 0.8541 |
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+ | 0.0194 | 10.0 | 1130 | 0.6840 | 0.85 | 0.8645 | 0.85 | 0.8420 |
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+ | 0.0151 | 11.0 | 1243 | 0.6214 | 0.86 | 0.8642 | 0.86 | 0.8542 |
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+ | 0.1239 | 12.0 | 1356 | 0.6501 | 0.87 | 0.8803 | 0.87 | 0.8627 |
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+ | 0.0049 | 13.0 | 1469 | 0.6651 | 0.87 | 0.8803 | 0.87 | 0.8627 |
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+ | 0.0043 | 14.0 | 1582 | 0.7188 | 0.87 | 0.8803 | 0.87 | 0.8627 |
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+ | 0.0035 | 15.0 | 1695 | 0.6808 | 0.87 | 0.8803 | 0.87 | 0.8627 |
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  ### Framework versions
 
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  - Transformers 4.42.3
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  - Pytorch 2.1.2
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  - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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