update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- rvl_cdip
+metrics:
+- accuracy
+model-index:
+- name: invoicevsadvertisement
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: rvl_cdip
+      type: rvl_cdip
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9892257579553997
+---
+
+<!-- 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. -->
+
+# invoicevsadvertisement
+
+This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the rvl_cdip dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0292
+- Accuracy: 0.9892
+
+## 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: 192
+- eval_batch_size: 192
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 768
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.4353        | 0.98  | 41   | 0.0758          | 0.9837   |
+| 0.0542        | 1.98  | 82   | 0.0359          | 0.9860   |
+| 0.0349        | 2.98  | 123  | 0.0336          | 0.9867   |
+| 0.0323        | 3.98  | 164  | 0.0304          | 0.9876   |
+| 0.0288        | 4.98  | 205  | 0.0292          | 0.9892   |
+
+
+### Framework versions
+
+- Transformers 4.21.3
+- Pytorch 1.12.1
+- Datasets 2.3.2
+- Tokenizers 0.12.1