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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-4 |
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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: 0.82 |
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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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# distilhubert-finetuned-gtzan-4 |
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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: 2.8452 |
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- Accuracy: 0.82 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 4 |
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- eval_batch_size: 4 |
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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: 80 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.2676 | 1.0 | 225 | 2.2400 | 0.33 | |
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| 1.9086 | 2.0 | 450 | 1.8716 | 0.55 | |
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| 1.417 | 3.0 | 675 | 1.4094 | 0.69 | |
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| 1.0484 | 4.0 | 900 | 1.1065 | 0.77 | |
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| 1.0122 | 5.0 | 1125 | 0.9172 | 0.75 | |
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| 0.8937 | 6.0 | 1350 | 1.1343 | 0.66 | |
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| 0.2979 | 7.0 | 1575 | 0.6102 | 0.83 | |
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| 0.3413 | 8.0 | 1800 | 1.1212 | 0.71 | |
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| 1.459 | 9.0 | 2025 | 1.5433 | 0.75 | |
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| 0.5177 | 10.0 | 2250 | 1.3990 | 0.78 | |
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| 0.0494 | 11.0 | 2475 | 2.7712 | 0.78 | |
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| 0.792 | 12.0 | 2700 | 2.7047 | 0.81 | |
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| 0.0 | 13.0 | 2925 | 2.8097 | 0.82 | |
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| 0.0 | 14.0 | 3150 | 3.3873 | 0.79 | |
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| 0.0 | 15.0 | 3375 | 2.6185 | 0.81 | |
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| 0.0 | 16.0 | 3600 | 3.0773 | 0.81 | |
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| 0.0 | 17.0 | 3825 | 2.2380 | 0.84 | |
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| 0.0 | 18.0 | 4050 | 2.5949 | 0.79 | |
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| 0.0 | 19.0 | 4275 | 3.4890 | 0.75 | |
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| 0.0 | 20.0 | 4500 | 2.7776 | 0.82 | |
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| 0.0 | 21.0 | 4725 | 3.8952 | 0.77 | |
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| 0.0 | 22.0 | 4950 | 2.8020 | 0.8 | |
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| 0.0 | 23.0 | 5175 | 3.7968 | 0.72 | |
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| 0.0 | 24.0 | 5400 | 2.8630 | 0.81 | |
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| 0.0 | 25.0 | 5625 | 2.2026 | 0.84 | |
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| 0.0 | 26.0 | 5850 | 1.8612 | 0.87 | |
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| 0.0 | 27.0 | 6075 | 3.2650 | 0.78 | |
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| 0.0 | 28.0 | 6300 | 2.6739 | 0.82 | |
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| 0.0 | 29.0 | 6525 | 3.0167 | 0.8 | |
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| 0.0 | 30.0 | 6750 | 1.9316 | 0.84 | |
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| 0.0 | 31.0 | 6975 | 3.0458 | 0.8 | |
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| 0.0 | 32.0 | 7200 | 3.3569 | 0.77 | |
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| 0.0 | 33.0 | 7425 | 3.0012 | 0.79 | |
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| 0.0 | 34.0 | 7650 | 3.2477 | 0.79 | |
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| 0.0 | 35.0 | 7875 | 3.2145 | 0.79 | |
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| 0.0 | 36.0 | 8100 | 3.0645 | 0.79 | |
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| 0.0 | 37.0 | 8325 | 3.2974 | 0.77 | |
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| 0.0 | 38.0 | 8550 | 3.4422 | 0.77 | |
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| 0.0 | 39.0 | 8775 | 2.7268 | 0.8 | |
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| 0.0 | 40.0 | 9000 | 2.6908 | 0.8 | |
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| 0.0 | 41.0 | 9225 | 2.4034 | 0.82 | |
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| 0.0 | 42.0 | 9450 | 3.1446 | 0.79 | |
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| 0.0 | 43.0 | 9675 | 2.9127 | 0.8 | |
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| 0.0 | 44.0 | 9900 | 2.3812 | 0.81 | |
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| 0.0 | 45.0 | 10125 | 2.4215 | 0.81 | |
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| 0.0 | 46.0 | 10350 | 2.6125 | 0.82 | |
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| 0.7338 | 47.0 | 10575 | 2.5113 | 0.82 | |
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| 0.0 | 48.0 | 10800 | 2.9264 | 0.81 | |
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| 0.0 | 49.0 | 11025 | 2.7811 | 0.81 | |
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| 0.0 | 50.0 | 11250 | 2.6749 | 0.8 | |
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| 0.0 | 51.0 | 11475 | 3.2003 | 0.78 | |
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| 0.0 | 52.0 | 11700 | 3.2670 | 0.78 | |
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| 0.0 | 53.0 | 11925 | 3.4001 | 0.76 | |
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| 0.0 | 54.0 | 12150 | 2.8570 | 0.76 | |
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| 0.0 | 55.0 | 12375 | 2.1772 | 0.83 | |
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| 0.0 | 56.0 | 12600 | 2.7977 | 0.81 | |
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| 0.0 | 57.0 | 12825 | 2.9106 | 0.78 | |
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| 0.0 | 58.0 | 13050 | 2.8428 | 0.8 | |
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| 0.0 | 59.0 | 13275 | 2.7308 | 0.78 | |
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| 0.0 | 60.0 | 13500 | 2.8214 | 0.8 | |
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| 0.0 | 61.0 | 13725 | 2.8194 | 0.79 | |
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| 0.0 | 62.0 | 13950 | 1.7708 | 0.85 | |
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| 0.0 | 63.0 | 14175 | 2.6017 | 0.81 | |
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| 0.0 | 64.0 | 14400 | 2.7698 | 0.78 | |
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| 0.0 | 65.0 | 14625 | 2.8218 | 0.81 | |
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| 0.0 | 66.0 | 14850 | 2.8252 | 0.82 | |
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| 0.0 | 67.0 | 15075 | 2.9149 | 0.81 | |
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| 0.0 | 68.0 | 15300 | 2.8106 | 0.81 | |
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| 0.0 | 69.0 | 15525 | 2.8514 | 0.8 | |
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| 0.0 | 70.0 | 15750 | 2.6649 | 0.82 | |
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| 0.0 | 71.0 | 15975 | 2.5629 | 0.81 | |
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| 0.0 | 72.0 | 16200 | 2.8140 | 0.81 | |
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| 0.0 | 73.0 | 16425 | 2.8164 | 0.79 | |
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| 0.0 | 74.0 | 16650 | 2.7022 | 0.81 | |
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| 0.0 | 75.0 | 16875 | 2.7376 | 0.81 | |
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| 0.0 | 76.0 | 17100 | 2.6498 | 0.8 | |
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| 0.0 | 77.0 | 17325 | 2.7363 | 0.81 | |
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| 0.0 | 78.0 | 17550 | 2.8057 | 0.81 | |
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| 0.0 | 79.0 | 17775 | 2.8526 | 0.81 | |
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| 0.0 | 80.0 | 18000 | 2.8452 | 0.82 | |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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