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
+model-index:
+- name: fine-tuned-gtzan
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: gtzan
+      type: gtzan
+      config: all
+      split: train
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.89
+---
+
+<!-- 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. -->
+
+# fine-tuned-gtzan
+
+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: 0.5559
+- Accuracy: 0.89
+
+## 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: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.946         | 1.0   | 112  | 1.7832          | 0.52     |
+| 1.3084        | 2.0   | 225  | 1.2228          | 0.65     |
+| 0.8713        | 3.0   | 337  | 0.8974          | 0.75     |
+| 0.6942        | 4.0   | 450  | 0.9075          | 0.77     |
+| 0.4072        | 5.0   | 562  | 0.7447          | 0.78     |
+| 0.2349        | 6.0   | 675  | 0.6456          | 0.81     |
+| 0.197         | 7.0   | 787  | 0.6228          | 0.8      |
+| 0.2246        | 8.0   | 900  | 0.5927          | 0.84     |
+| 0.1987        | 9.0   | 1012 | 0.5558          | 0.88     |
+| 0.1152        | 9.96  | 1120 | 0.5559          | 0.89     |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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