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: 0.9387001477104875
      - name: Recall
        type: recall
        value: 0.9513473053892215
      - name: F1
        type: f1
        value: 0.9449814126394053
      - name: Accuracy
        type: accuracy
        value: 0.9567062818336163
  ---
  
  <!-- 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: 0.2137
  - Precision: 0.9387
  - Recall: 0.9513
  - F1: 0.9450
  - Accuracy: 0.9567
  
  ## 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: 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: 2500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.56  | 250  | 1.0609          | 0.6596    | 0.7440 | 0.6993 | 0.7687   |
  | 1.4193        | 3.12  | 500  | 0.5989          | 0.8403    | 0.8623 | 0.8511 | 0.8663   |
  | 1.4193        | 4.69  | 750  | 0.4037          | 0.8795    | 0.9012 | 0.8902 | 0.9087   |
  | 0.4182        | 6.25  | 1000 | 0.3264          | 0.8980    | 0.9162 | 0.9070 | 0.9257   |
  | 0.4182        | 7.81  | 1250 | 0.2705          | 0.9190    | 0.9341 | 0.9265 | 0.9410   |
  | 0.2258        | 9.38  | 1500 | 0.2450          | 0.9311    | 0.9401 | 0.9356 | 0.9461   |
  | 0.2258        | 10.94 | 1750 | 0.2350          | 0.9341    | 0.9439 | 0.9389 | 0.9491   |
  | 0.1576        | 12.5  | 2000 | 0.2219          | 0.9350    | 0.9476 | 0.9413 | 0.9508   |
  | 0.1576        | 14.06 | 2250 | 0.2122          | 0.9373    | 0.9506 | 0.9439 | 0.9559   |
  | 0.1207        | 15.62 | 2500 | 0.2137          | 0.9387    | 0.9513 | 0.9450 | 0.9567   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  