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: 0.9349593495934959
      - name: Recall
        type: recall
        value: 0.9468562874251497
      - name: F1
        type: f1
        value: 0.9408702119747119
      - name: Accuracy
        type: accuracy
        value: 0.9473684210526315
  ---
  
  <!-- 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: 0.2702
  - Precision: 0.9350
  - Recall: 0.9469
  - F1: 0.9409
  - Accuracy: 0.9474
  
  ## 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        | 4.17  | 250  | 1.0496          | 0.6714    | 0.7507 | 0.7088 | 0.7746   |
  | 1.4245        | 8.33  | 500  | 0.5492          | 0.8401    | 0.8728 | 0.8561 | 0.8735   |
  | 1.4245        | 12.5  | 750  | 0.3773          | 0.8934    | 0.9162 | 0.9047 | 0.9240   |
  | 0.3461        | 16.67 | 1000 | 0.3212          | 0.9287    | 0.9364 | 0.9325 | 0.9380   |
  | 0.3461        | 20.83 | 1250 | 0.2888          | 0.9276    | 0.9401 | 0.9338 | 0.9440   |
  | 0.1502        | 25.0  | 1500 | 0.2749          | 0.9299    | 0.9431 | 0.9365 | 0.9474   |
  | 0.1502        | 29.17 | 1750 | 0.2741          | 0.9321    | 0.9446 | 0.9383 | 0.9469   |
  | 0.0866        | 33.33 | 2000 | 0.2715          | 0.9328    | 0.9454 | 0.9390 | 0.9465   |
  | 0.0866        | 37.5  | 2250 | 0.2740          | 0.9314    | 0.9446 | 0.9379 | 0.9452   |
  | 0.0635        | 41.67 | 2500 | 0.2702          | 0.9350    | 0.9469 | 0.9409 | 0.9474   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  