readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - funsd-layoutlmv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: OCR-LayoutLMv3
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: funsd-layoutlmv3
        type: funsd-layoutlmv3
        config: funsd
        split: train
        args: funsd
      metrics:
      - name: Precision
        type: precision
        value: 0.8988653182042428
      - name: Recall
        type: recall
        value: 0.905116741182315
      - name: F1
        type: f1
        value: 0.9019801980198019
      - name: Accuracy
        type: accuracy
        value: 0.8403661000832046
  ---
  
  <!-- 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. -->
  
  # OCR-LayoutLMv3
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9788
  - Precision: 0.8989
  - Recall: 0.9051
  - F1: 0.9020
  - Accuracy: 0.8404
  
  ## Model description
  
  LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis.
  
  [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387)
  Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022.
  
  
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 2000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.33  | 100  | 0.6966          | 0.7418    | 0.8063 | 0.7727 | 0.7801   |
  | No log        | 2.67  | 200  | 0.5767          | 0.8104    | 0.8644 | 0.8365 | 0.8117   |
  | No log        | 4.0   | 300  | 0.5355          | 0.8246    | 0.8852 | 0.8539 | 0.8295   |
  | No log        | 5.33  | 400  | 0.5240          | 0.8706    | 0.8922 | 0.8813 | 0.8427   |
  | 0.5326        | 6.67  | 500  | 0.6337          | 0.8528    | 0.8778 | 0.8651 | 0.8260   |
  | 0.5326        | 8.0   | 600  | 0.6870          | 0.8698    | 0.8828 | 0.8762 | 0.8240   |
  | 0.5326        | 9.33  | 700  | 0.6584          | 0.8723    | 0.9061 | 0.8889 | 0.8342   |
  | 0.5326        | 10.67 | 800  | 0.7186          | 0.8868    | 0.9031 | 0.8949 | 0.8335   |
  | 0.5326        | 12.0  | 900  | 0.6822          | 0.9040    | 0.9076 | 0.9058 | 0.8526   |
  | 0.1248        | 13.33 | 1000 | 0.7042          | 0.8872    | 0.9021 | 0.8946 | 0.8511   |
  | 0.1248        | 14.67 | 1100 | 0.7920          | 0.9027    | 0.9036 | 0.9032 | 0.8480   |
  | 0.1248        | 16.0  | 1200 | 0.8052          | 0.8964    | 0.9151 | 0.9056 | 0.8389   |
  | 0.1248        | 17.33 | 1300 | 0.8932          | 0.8995    | 0.9066 | 0.9030 | 0.8329   |
  | 0.1248        | 18.67 | 1400 | 0.8728          | 0.8950    | 0.9061 | 0.9005 | 0.8398   |
  | 0.0442        | 20.0  | 1500 | 0.9051          | 0.8960    | 0.9116 | 0.9037 | 0.8347   |
  | 0.0442        | 21.33 | 1600 | 0.9587          | 0.8947    | 0.9031 | 0.8989 | 0.8401   |
  | 0.0442        | 22.67 | 1700 | 0.9822          | 0.9042    | 0.9046 | 0.9044 | 0.8389   |
  | 0.0442        | 24.0  | 1800 | 0.9734          | 0.9043    | 0.9061 | 0.9052 | 0.8391   |
  | 0.0442        | 25.33 | 1900 | 0.9842          | 0.9042    | 0.9091 | 0.9066 | 0.8410   |
  | 0.0225        | 26.67 | 2000 | 0.9788          | 0.8989    | 0.9051 | 0.9020 | 0.8404   |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  