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distilhubert-finetuned-gtzan-2

Audio Classification
Transformers
Safetensors
hubert
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ base_model: ntu-spml/distilhubert
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - marsyas/gtzan
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: distilhubert-finetuned-gtzan-2
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+ results:
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+ - task:
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+ name: Audio Classification
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+ type: audio-classification
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+ dataset:
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+ name: GTZAN
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+ type: marsyas/gtzan
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+ config: all
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+ split: train
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+ args: all
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.86
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+ ---
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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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+
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+ # distilhubert-finetuned-gtzan-2
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+
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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: 0.7203
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+ - Accuracy: 0.86
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 10
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.2
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.2521 | 1.0 | 90 | 2.2219 | 0.3 |
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+ | 1.8502 | 2.0 | 180 | 1.8299 | 0.54 |
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+ | 1.4155 | 3.0 | 270 | 1.4247 | 0.64 |
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+ | 0.9885 | 4.0 | 360 | 1.0313 | 0.7 |
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+ | 0.8111 | 5.0 | 450 | 0.8535 | 0.78 |
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+ | 0.7023 | 6.0 | 540 | 0.7743 | 0.79 |
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+ | 0.5663 | 7.0 | 630 | 0.6618 | 0.81 |
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+ | 0.3577 | 8.0 | 720 | 0.6937 | 0.77 |
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+ | 0.3003 | 9.0 | 810 | 0.6107 | 0.82 |
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+ | 0.1321 | 10.0 | 900 | 0.5648 | 0.81 |
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+ | 0.0488 | 11.0 | 990 | 0.5655 | 0.84 |
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+ | 0.0323 | 12.0 | 1080 | 0.5612 | 0.86 |
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+ | 0.0154 | 13.0 | 1170 | 0.6338 | 0.85 |
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+ | 0.0108 | 14.0 | 1260 | 0.7292 | 0.84 |
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+ | 0.0082 | 15.0 | 1350 | 0.7542 | 0.84 |
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+ | 0.0065 | 16.0 | 1440 | 0.7123 | 0.86 |
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+ | 0.0062 | 17.0 | 1530 | 0.6949 | 0.86 |
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+ | 0.0848 | 18.0 | 1620 | 0.7332 | 0.85 |
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+ | 0.0053 | 19.0 | 1710 | 0.7291 | 0.85 |
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+ | 0.005 | 20.0 | 1800 | 0.7203 | 0.86 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.1
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+ - Pytorch 2.2.1
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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