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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: distill-vit |
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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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# distill-vit |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4751 |
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- Accuracy: 0.7656 |
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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: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 30 |
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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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| 0.8986 | 1.0 | 65 | 0.7788 | 0.4286 | |
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| 0.8556 | 2.0 | 130 | 0.9774 | 0.4812 | |
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| 0.7581 | 3.0 | 195 | 0.6150 | 0.6541 | |
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| 0.6434 | 4.0 | 260 | 0.6455 | 0.6090 | |
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| 0.609 | 5.0 | 325 | 0.5329 | 0.7143 | |
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| 0.5503 | 6.0 | 390 | 0.5829 | 0.6466 | |
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| 0.5492 | 7.0 | 455 | 0.6716 | 0.6917 | |
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| 0.504 | 8.0 | 520 | 0.5342 | 0.6917 | |
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| 0.4966 | 9.0 | 585 | 0.5668 | 0.6617 | |
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| 0.4978 | 10.0 | 650 | 0.5347 | 0.6767 | |
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| 0.4535 | 11.0 | 715 | 0.5580 | 0.6090 | |
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| 0.4415 | 12.0 | 780 | 0.5085 | 0.7444 | |
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| 0.4308 | 13.0 | 845 | 0.5131 | 0.7068 | |
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| 0.4247 | 14.0 | 910 | 0.4808 | 0.7068 | |
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| 0.4307 | 15.0 | 975 | 0.5542 | 0.6917 | |
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| 0.4165 | 16.0 | 1040 | 0.5410 | 0.6992 | |
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| 0.3975 | 17.0 | 1105 | 0.5944 | 0.6015 | |
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| 0.3942 | 18.0 | 1170 | 0.4730 | 0.6917 | |
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| 0.3932 | 19.0 | 1235 | 0.4806 | 0.6917 | |
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| 0.3437 | 20.0 | 1300 | 0.5341 | 0.6842 | |
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| 0.3628 | 21.0 | 1365 | 0.5836 | 0.6692 | |
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| 0.3483 | 22.0 | 1430 | 0.6234 | 0.6316 | |
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| 0.3318 | 23.0 | 1495 | 0.4950 | 0.7143 | |
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| 0.3189 | 24.0 | 1560 | 0.4590 | 0.7068 | |
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| 0.3243 | 25.0 | 1625 | 0.5789 | 0.6692 | |
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| 0.3169 | 26.0 | 1690 | 0.5702 | 0.7218 | |
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| 0.3031 | 27.0 | 1755 | 0.4415 | 0.7519 | |
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| 0.2928 | 28.0 | 1820 | 0.4680 | 0.7368 | |
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| 0.3117 | 29.0 | 1885 | 0.5384 | 0.6842 | |
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| 0.3127 | 30.0 | 1950 | 0.5148 | 0.6767 | |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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