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--- |
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license: apache-2.0 |
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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 |
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results: [] |
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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 |
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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 on best epoch: |
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- Loss: 0.7305 |
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- Accuracy: 0.9 |
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## Model description |
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Distilhubert is distilled version of the [HuBERT](https://huggingface.co/docs/transformers/model_doc/hubert) and pretrained on data set with 16k frequency. <br/> |
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Architecture of this model is CTC or Connectionist Temporal Classification is a technique that is used with encoder-only transformer. <br/> |
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## Training and evaluation data |
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Training + Evaluation data set is GTZAN which is a popular dataset of 999 songs for music genre classification. <br/> |
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Each song is a 30-second clip from one of 10 genres of music, spanning disco to metal.<br/> |
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Train set is 899 songs and Evaluation set is 100 songs remainings. |
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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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- distributed_type: multi-GPU |
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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: 35 |
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- mixed_precision_training: Native AMP |
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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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| 2.1728 | 1.0 | 225 | 2.0896 | 0.42 | |
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| 1.4211 | 2.0 | 450 | 1.4951 | 0.55 | |
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| 1.2155 | 3.0 | 675 | 1.0669 | 0.72 | |
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| 1.0175 | 4.0 | 900 | 0.8862 | 0.69 | |
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| 0.3516 | 5.0 | 1125 | 0.6265 | 0.83 | |
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| 0.6135 | 6.0 | 1350 | 0.6485 | 0.78 | |
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| 0.0807 | 7.0 | 1575 | 0.6567 | 0.78 | |
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| 0.0303 | 8.0 | 1800 | 0.7615 | 0.83 | |
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| 0.2663 | 9.0 | 2025 | 0.6612 | 0.86 | |
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| 0.0026 | 10.0 | 2250 | 0.8354 | 0.85 | |
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| 0.0337 | 11.0 | 2475 | 0.6768 | 0.87 | |
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| 0.0013 | 12.0 | 2700 | 0.7718 | 0.87 | |
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| 0.001 | 13.0 | 2925 | 0.7570 | 0.88 | |
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| 0.0008 | 14.0 | 3150 | 0.8170 | 0.89 | |
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| 0.0006 | 15.0 | 3375 | 0.7920 | 0.89 | |
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| 0.0005 | 16.0 | 3600 | 0.9859 | 0.83 | |
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| 0.0004 | 17.0 | 3825 | 0.8190 | 0.9 | |
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| 0.0003 | 18.0 | 4050 | 0.7305 | 0.9 | |
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| 0.0003 | 19.0 | 4275 | 0.8025 | 0.88 | |
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| 0.0002 | 20.0 | 4500 | 0.8208 | 0.87 | |
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| 0.0003 | 21.0 | 4725 | 0.7358 | 0.88 | |
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| 0.0002 | 22.0 | 4950 | 0.8681 | 0.87 | |
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| 0.0002 | 23.0 | 5175 | 0.7831 | 0.9 | |
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| 0.0003 | 24.0 | 5400 | 0.8583 | 0.88 | |
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| 0.0002 | 25.0 | 5625 | 0.8138 | 0.88 | |
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| 0.0002 | 26.0 | 5850 | 0.7871 | 0.89 | |
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| 0.0002 | 27.0 | 6075 | 0.8893 | 0.88 | |
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| 0.0002 | 28.0 | 6300 | 0.8284 | 0.89 | |
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| 0.0001 | 29.0 | 6525 | 0.8388 | 0.89 | |
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| 0.0001 | 30.0 | 6750 | 0.8305 | 0.9 | |
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| 0.0001 | 31.0 | 6975 | 0.8377 | 0.88 | |
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| 0.0153 | 32.0 | 7200 | 0.8496 | 0.88 | |
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| 0.0001 | 33.0 | 7425 | 0.8381 | 0.88 | |
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| 0.0001 | 34.0 | 7650 | 0.8440 | 0.88 | |
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| 0.0001 | 35.0 | 7875 | 0.8458 | 0.88 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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