update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- vision
+- generated_from_trainer
+datasets:
+- cifar10
+metrics:
+- accuracy
+model-index:
+- name: cifar10_outputs
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: cifar10
+      type: cifar10
+      args: plain_text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.991421568627451
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# cifar10_outputs
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.0806
+- Accuracy: 0.9914
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 17
+- eval_batch_size: 17
+- seed: 1337
+- distributed_type: IPU
+- gradient_accumulation_steps: 128
+- total_train_batch_size: 8704
+- total_eval_batch_size: 272
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.25
+- num_epochs: 100.0
+- training precision: Mixed Precision
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.10.0+cpu
+- Datasets 2.3.3.dev0
+- Tokenizers 0.12.1