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update model card README.md
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README.md
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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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- cifar10
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metrics:
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- accuracy
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model-index:
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- name: vit-base-cifar10
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: cifar10
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type: cifar10
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config: plain_text
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split: train
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args: plain_text
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9885333333333334
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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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# vit-base-cifar10
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0477
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- Accuracy: 0.9885
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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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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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: 5.0
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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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| 0.3034 | 1.0 | 664 | 0.1245 | 0.9829 |
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| 0.2703 | 2.0 | 1328 | 0.0783 | 0.9841 |
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| 0.2057 | 3.0 | 1992 | 0.0600 | 0.9865 |
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| 0.218 | 4.0 | 2656 | 0.0505 | 0.988 |
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| 0.2166 | 5.0 | 3320 | 0.0477 | 0.9885 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.1+cu117
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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