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update model card README.md

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@@ -16,11 +16,13 @@ model-index:
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  dataset:
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  name: food101
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  type: food101
 
 
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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: 0.8558811881188119
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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
@@ -30,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 food101 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5434
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- - Accuracy: 0.8559
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  ## Model description
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@@ -63,15 +65,15 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 1.6283 | 1.0 | 4735 | 0.9875 | 0.7409 |
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- | 0.9874 | 2.0 | 9470 | 0.7967 | 0.7894 |
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- | 0.7102 | 3.0 | 14205 | 0.6455 | 0.8255 |
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- | 0.4917 | 4.0 | 18940 | 0.5502 | 0.8524 |
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  ### Framework versions
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- - Transformers 4.19.2
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- - Pytorch 1.11.0+cu113
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- - Datasets 2.2.1
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  - Tokenizers 0.12.1
 
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  dataset:
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  name: food101
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  type: food101
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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: 0.8539405940594059
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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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  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 food101 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5493
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+ - Accuracy: 0.8539
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.657 | 1.0 | 4735 | 0.9732 | 0.7459 |
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+ | 0.9869 | 2.0 | 9470 | 0.7987 | 0.7884 |
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+ | 0.71 | 3.0 | 14205 | 0.6364 | 0.8311 |
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+ | 0.4961 | 4.0 | 18940 | 0.5595 | 0.8487 |
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  ### Framework versions
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+ - Transformers 4.21.1
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+ - Pytorch 1.12.0+cu113
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+ - Datasets 2.4.0
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  - Tokenizers 0.12.1