End of training

README.md

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+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+model-index:
+- name: distilhubert-finetuned-gtzan
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: GTZAN
+      type: marsyas/gtzan
+      config: all
+      split: train
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.82
+---
+
+<!-- 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. -->
+
+# distilhubert-finetuned-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: 0.6665
+- Accuracy: 0.82
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.4758        | 1.0   | 113  | 1.4456          | 0.56     |
+| 0.9722        | 2.0   | 226  | 1.0866          | 0.65     |
+| 0.8148        | 3.0   | 339  | 0.8447          | 0.79     |
+| 0.531         | 4.0   | 452  | 0.7676          | 0.76     |
+| 0.3591        | 5.0   | 565  | 0.6793          | 0.8      |
+| 0.2623        | 6.0   | 678  | 0.6151          | 0.83     |
+| 0.1858        | 7.0   | 791  | 0.6248          | 0.84     |
+| 0.06          | 8.0   | 904  | 0.7053          | 0.81     |
+| 0.0818        | 9.0   | 1017 | 0.6606          | 0.81     |
+| 0.0498        | 10.0  | 1130 | 0.6665          | 0.82     |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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