End of training

README.md

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+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- gtzan
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: music-genre-detector-finetuned-gtzan_dset
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: GTZAN
+      type: gtzan
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9298245614035088
+    - name: Precision
+      type: precision
+      value: 0.9292447472185437
+    - name: Recall
+      type: recall
+      value: 0.9298245614035088
+    - name: F1
+      type: f1
+      value: 0.9293437948869628
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# music-genre-detector-finetuned-gtzan_dset
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2288
+- Accuracy: 0.9298
+- Precision: 0.9292
+- Recall: 0.9298
+- F1: 0.9293
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 9e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 2.2522        | 0.98  | 49   | 1.6370          | 0.6090   | 0.6189    | 0.6090 | 0.5764 |
+| 1.2901        | 1.98  | 99   | 0.9974          | 0.7556   | 0.7655    | 0.7556 | 0.7426 |
+| 1.0046        | 2.99  | 149  | 0.6645          | 0.8195   | 0.8226    | 0.8195 | 0.8162 |
+| 0.5952        | 3.99  | 199  | 0.5054          | 0.8459   | 0.8561    | 0.8459 | 0.8460 |
+| 0.3596        | 4.99  | 249  | 0.3729          | 0.9023   | 0.9117    | 0.9023 | 0.9041 |
+| 0.2534        | 5.99  | 299  | 0.2953          | 0.9073   | 0.9088    | 0.9073 | 0.9075 |
+| 0.1413        | 7.0   | 349  | 0.2545          | 0.9223   | 0.9229    | 0.9223 | 0.9216 |
+| 0.0759        | 8.0   | 399  | 0.2593          | 0.9198   | 0.9209    | 0.9198 | 0.9190 |
+| 0.0491        | 8.98  | 448  | 0.2288          | 0.9298   | 0.9292    | 0.9298 | 0.9293 |
+| 0.0355        | 9.82  | 490  | 0.2392          | 0.9223   | 0.9231    | 0.9223 | 0.9221 |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 1.10.2+cu111
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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