update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: roman_numerals-digit-classification-2022-09-04
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8333333333333334
+---
+
+<!-- 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. -->
+
+# roman_numerals-digit-classification-2022-09-04
+
+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 imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7018
+- Accuracy: 0.8333
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.9053        | 1.0   | 289  | 1.3680          | 0.7132   |
+| 1.2788        | 2.0   | 578  | 0.9499          | 0.7966   |
+| 1.1232        | 3.0   | 867  | 0.8679          | 0.7279   |
+| 1.0373        | 4.0   | 1156 | 0.7324          | 0.8088   |
+| 0.9658        | 5.0   | 1445 | 0.7018          | 0.8333   |
+
+
+### Framework versions
+
+- Transformers 4.22.0.dev0
+- Pytorch 1.12.1+cu102
+- Datasets 2.4.0
+- Tokenizers 0.12.1