readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-cord-ner
    results: []
  ---
  
  <!-- 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-cord-ner
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1215
  - Precision: 0.9448
  - Recall: 0.9520
  - F1: 0.9484
  - Accuracy: 0.9762
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 113  | 0.1771          | 0.8485    | 0.8925 | 0.8700 | 0.9393   |
  | No log        | 2.0   | 226  | 0.1584          | 0.8915    | 0.9146 | 0.9029 | 0.9524   |
  | No log        | 3.0   | 339  | 0.1153          | 0.9160    | 0.9309 | 0.9234 | 0.9686   |
  | No log        | 4.0   | 452  | 0.1477          | 0.9110    | 0.9136 | 0.9123 | 0.9592   |
  | 0.1562        | 5.0   | 565  | 0.0861          | 0.9363    | 0.9443 | 0.9403 | 0.9741   |
  | 0.1562        | 6.0   | 678  | 0.1165          | 0.9109    | 0.9415 | 0.9259 | 0.9673   |
  | 0.1562        | 7.0   | 791  | 0.1280          | 0.9278    | 0.9367 | 0.9322 | 0.9707   |
  | 0.1562        | 8.0   | 904  | 0.1122          | 0.9462    | 0.9453 | 0.9458 | 0.9762   |
  | 0.0224        | 9.0   | 1017 | 0.1265          | 0.9431    | 0.9539 | 0.9485 | 0.9771   |
  | 0.0224        | 10.0  | 1130 | 0.1215          | 0.9448    | 0.9520 | 0.9484 | 0.9762   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0.dev0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  