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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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+ - generated_from_trainer
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+ datasets:
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+ - image_folder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05
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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: image_folder
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+ type: image_folder
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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: 1.0
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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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+ # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05
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+
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+ This model is a fine-tuned version of [lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05](https://huggingface.co/lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05) on the image_folder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0127
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+ - Accuracy: 1.0
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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: 7e-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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.9364 | 0.97 | 27 | 0.1873 | 0.9645 |
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+ | 0.3365 | 1.97 | 54 | 0.0951 | 0.9848 |
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+ | 0.2482 | 2.97 | 81 | 0.0562 | 0.9949 |
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+ | 0.1844 | 3.97 | 108 | 0.0213 | 0.9949 |
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+ | 0.1693 | 4.97 | 135 | 0.0127 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.1
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+ - Pytorch 1.11.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.12.1