End of training

README.md

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+---
+license: bsd-3-clause
+base_model: MIT/ast-finetuned-audioset-10-10-0.450
+tags:
+- generated_from_trainer
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+model-index:
+- name: ast-finetuned-audioset-10-10-0.450-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: 0.89
+---
+
+<!-- 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. -->
+
+# ast-finetuned-audioset-10-10-0.450-finetuned-gtzan
+
+This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.450](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.450) on the GTZAN dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5513
+- 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- 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: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.4812        | 1.0   | 100  | 0.4780          | 0.86     |
+| 0.4555        | 2.0   | 200  | 0.6969          | 0.795    |
+| 0.106         | 3.0   | 300  | 0.6725          | 0.85     |
+| 0.0063        | 4.0   | 400  | 0.5885          | 0.875    |
+| 0.0004        | 5.0   | 500  | 0.5513          | 0.89     |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.2
+- Datasets 2.14.7
+- Tokenizers 0.15.0