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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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- mechanicalkeystrokes |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilhubert-finetuned-mechanicalkeystrokes |
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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: MechanicalKeystrokes |
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type: mechanicalkeystrokes |
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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: 0.9666666666666667 |
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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-mechanicalkeystrokes |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the MechanicalKeystrokes dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1155 |
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- Accuracy: 0.9667 |
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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: 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: 50 |
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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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| 2.2923 | 1.0 | 30 | 2.3063 | 0.0333 | |
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| 2.2788 | 2.0 | 60 | 2.2863 | 0.1333 | |
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| 2.1377 | 3.0 | 90 | 2.1583 | 0.35 | |
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| 1.8769 | 4.0 | 120 | 1.8170 | 0.6167 | |
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| 1.5083 | 5.0 | 150 | 1.4282 | 0.7833 | |
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| 1.0911 | 6.0 | 180 | 1.0241 | 0.9333 | |
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| 0.7235 | 7.0 | 210 | 0.7131 | 0.9667 | |
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| 0.4517 | 8.0 | 240 | 0.4509 | 0.9667 | |
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| 0.2529 | 9.0 | 270 | 0.2682 | 0.9833 | |
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| 0.14 | 10.0 | 300 | 0.1799 | 0.9833 | |
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| 0.0791 | 11.0 | 330 | 0.1155 | 0.9667 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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