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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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- audiofolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilhubert-finetuned-RHD_Dataset |
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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: audiofolder |
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type: audiofolder |
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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: 0.8048780487804879 |
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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-RHD_Dataset |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9447 |
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- Accuracy: 0.8049 |
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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: 0.0001 |
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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: 10 |
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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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| 1.0412 | 1.0 | 46 | 1.0084 | 0.6829 | |
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| 0.8547 | 2.0 | 92 | 0.8433 | 0.6585 | |
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| 0.7936 | 3.0 | 138 | 0.7128 | 0.7073 | |
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| 0.5984 | 4.0 | 184 | 0.7778 | 0.7317 | |
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| 0.3888 | 5.0 | 230 | 0.6361 | 0.7317 | |
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| 0.4947 | 6.0 | 276 | 0.7471 | 0.7805 | |
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| 0.1663 | 7.0 | 322 | 0.8244 | 0.7561 | |
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| 0.1379 | 8.0 | 368 | 0.7986 | 0.8049 | |
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| 0.0405 | 9.0 | 414 | 0.8892 | 0.8049 | |
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| 0.0229 | 10.0 | 460 | 0.9447 | 0.8049 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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