update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: dit-base-Document_Classification-RVL_CDIP
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: data
+      split: train
+      args: data
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.976678084687705
+---
+
+<!-- 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. -->
+
+# dit-base-Document_Classification-RVL_CDIP
+
+This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0786
+- Accuracy: 0.9767
+- Weighted f1: 0.9768
+- Micro f1: 0.9767
+- Macro f1: 0.9154
+- Weighted recall: 0.9767
+- Micro recall: 0.9767
+- Macro recall: 0.9019
+- Weighted precision: 0.9771
+- Micro precision: 0.9767
+- Macro precision: 0.9314
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 0.1535        | 1.0   | 208  | 0.1126          | 0.9622   | 0.9597      | 0.9622   | 0.5711   | 0.9622          | 0.9622       | 0.5925       | 0.9577             | 0.9622          | 0.5531          |
+| 0.1195        | 2.0   | 416  | 0.0843          | 0.9738   | 0.9736      | 0.9738   | 0.8502   | 0.9738          | 0.9738       | 0.8037       | 0.9741             | 0.9738          | 0.9287          |
+| 0.0979        | 3.0   | 624  | 0.0786          | 0.9767   | 0.9768      | 0.9767   | 0.9154   | 0.9767          | 0.9767       | 0.9019       | 0.9771             | 0.9767          | 0.9314          |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3