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NiloofarMomeni
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distilhubert-finetuned-VD

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-VD
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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: 0.8349877949552482
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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-VD
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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: 0.4702
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+ - Accuracy: 0.8350
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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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+ | 0.555 | 1.0 | 167 | 0.4702 | 0.8350 |
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+ | 0.3965 | 2.0 | 334 | 0.4398 | 0.7570 |
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+ | 0.4106 | 3.0 | 501 | 0.7742 | 0.6713 |
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+ | 0.4372 | 4.0 | 668 | 0.9340 | 0.6827 |
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+ | 0.2087 | 5.0 | 835 | 1.0133 | 0.7574 |
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+ | 0.124 | 6.0 | 1002 | 1.1049 | 0.7437 |
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+ | 0.0509 | 7.0 | 1169 | 1.2264 | 0.7590 |
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+ | 0.0016 | 8.0 | 1336 | 1.2315 | 0.7845 |
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+ | 0.0064 | 9.0 | 1503 | 1.3620 | 0.7762 |
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+ | 0.0006 | 10.0 | 1670 | 1.3149 | 0.8039 |
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+ | 0.0007 | 11.0 | 1837 | 1.2818 | 0.8116 |
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+ | 0.0003 | 12.0 | 2004 | 1.2635 | 0.8298 |
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+ | 0.0003 | 13.0 | 2171 | 1.3287 | 0.8225 |
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+ | 0.0002 | 14.0 | 2338 | 1.3200 | 0.8295 |
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+ | 0.0001 | 15.0 | 2505 | 1.4146 | 0.8226 |
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+ | 0.0001 | 16.0 | 2672 | 1.4359 | 0.8221 |
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+ | 0.0001 | 17.0 | 2839 | 1.4443 | 0.8233 |
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+ | 0.0001 | 18.0 | 3006 | 1.5031 | 0.8184 |
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+ | 0.0001 | 19.0 | 3173 | 1.5111 | 0.8182 |
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+ | 0.0001 | 20.0 | 3340 | 1.5145 | 0.8182 |
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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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