readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - cord-layoutlmv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: LayoutLMv3-Finetuned-CORD_100
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: cord-layoutlmv3
        type: cord-layoutlmv3
        config: cord
        split: train
        args: cord
      metrics:
      - name: Precision
        type: precision
        value: 0.9524870081662955
      - name: Recall
        type: recall
        value: 0.9603293413173652
      - name: F1
        type: f1
        value: 0.9563920983973164
      - name: Accuracy
        type: accuracy
        value: 0.9647707979626485
  ---
  
  <!-- 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. -->
  
  # LayoutLMv3-Finetuned-CORD_100
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1948
  - Precision: 0.9525
  - Recall: 0.9603
  - F1: 0.9564
  - Accuracy: 0.9648
  
  ## 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: 1.1e-05
  - train_batch_size: 5
  - eval_batch_size: 5
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 3000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.56  | 250  | 0.9568          | 0.7298    | 0.7844 | 0.7561 | 0.7992   |
  | 1.3271        | 3.12  | 500  | 0.5239          | 0.8398    | 0.8713 | 0.8553 | 0.8858   |
  | 1.3271        | 4.69  | 750  | 0.3586          | 0.8945    | 0.9207 | 0.9074 | 0.9300   |
  | 0.3495        | 6.25  | 1000 | 0.2716          | 0.9298    | 0.9416 | 0.9357 | 0.9410   |
  | 0.3495        | 7.81  | 1250 | 0.2331          | 0.9198    | 0.9356 | 0.9276 | 0.9474   |
  | 0.1725        | 9.38  | 1500 | 0.2134          | 0.9379    | 0.9499 | 0.9438 | 0.9529   |
  | 0.1725        | 10.94 | 1750 | 0.2079          | 0.9401    | 0.9513 | 0.9457 | 0.9605   |
  | 0.1116        | 12.5  | 2000 | 0.1992          | 0.9554    | 0.9618 | 0.9586 | 0.9656   |
  | 0.1116        | 14.06 | 2250 | 0.1941          | 0.9517    | 0.9588 | 0.9553 | 0.9631   |
  | 0.0762        | 15.62 | 2500 | 0.1966          | 0.9503    | 0.9588 | 0.9545 | 0.9639   |
  | 0.0762        | 17.19 | 2750 | 0.1951          | 0.9510    | 0.9588 | 0.9549 | 0.9626   |
  | 0.0636        | 18.75 | 3000 | 0.1948          | 0.9525    | 0.9603 | 0.9564 | 0.9648   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  