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distilhubert-finetuned-gtzanVD

Audio Classification
Transformers
Safetensors
hubert
Generated from Trainer
Eval Results
Inference Endpoints
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README.md ADDED
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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-gtzanVD
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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: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9839786381842457
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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-gtzanVD
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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: 0.1334
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+ - Accuracy: 0.9840
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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: 8
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+ - eval_batch_size: 8
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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: 0.1
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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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+ | 0.3554 | 1.0 | 842 | 0.1898 | 0.9439 |
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+ | 0.1136 | 2.0 | 1684 | 0.1657 | 0.9626 |
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+ | 0.1571 | 3.0 | 2526 | 0.1132 | 0.9693 |
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+ | 0.0004 | 4.0 | 3368 | 0.1235 | 0.9786 |
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+ | 0.0011 | 5.0 | 4210 | 0.1555 | 0.9680 |
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+ | 0.0001 | 6.0 | 5052 | 0.3138 | 0.9493 |
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+ | 0.0001 | 7.0 | 5894 | 0.1825 | 0.9680 |
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+ | 0.0001 | 8.0 | 6736 | 0.1982 | 0.9706 |
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+ | 0.0001 | 9.0 | 7578 | 0.1690 | 0.9693 |
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+ | 0.3166 | 10.0 | 8420 | 0.1487 | 0.9733 |
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+ | 0.0 | 11.0 | 9262 | 0.2615 | 0.9680 |
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+ | 0.0 | 12.0 | 10104 | 0.1536 | 0.9800 |
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+ | 0.0001 | 13.0 | 10946 | 0.5478 | 0.9399 |
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+ | 0.0 | 14.0 | 11788 | 0.1334 | 0.9840 |
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+ | 0.0 | 15.0 | 12630 | 0.1270 | 0.9746 |
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+ | 0.0 | 16.0 | 13472 | 0.1053 | 0.9840 |
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+ | 0.0 | 17.0 | 14314 | 0.1181 | 0.9813 |
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+ | 0.0 | 18.0 | 15156 | 0.1165 | 0.9826 |
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+ | 0.0 | 19.0 | 15998 | 0.1191 | 0.9826 |
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+ | 0.0 | 20.0 | 16840 | 0.1188 | 0.9826 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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