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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - image-classification
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+ - vision
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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: cifar10_outputs
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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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+ 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: 0.991421568627451
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+ ---
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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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+
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+ # cifar10_outputs
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+
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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: 0.0806
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+ - Accuracy: 0.9914
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 17
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+ - eval_batch_size: 17
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+ - seed: 1337
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+ - distributed_type: IPU
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+ - gradient_accumulation_steps: 128
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+ - total_train_batch_size: 8704
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+ - total_eval_batch_size: 272
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.25
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+ - num_epochs: 100.0
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+ - training precision: Mixed Precision
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.18.0
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+ - Pytorch 1.10.0+cpu
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+ - Datasets 2.3.3.dev0
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+ - Tokenizers 0.12.1