readme: ---
  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
  