readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv1-er-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. -->
  
  # layoutlmv1-er-ner
  
  This model is a fine-tuned version of [renjithks/layoutlmv1-cord-ner](https://huggingface.co/renjithks/layoutlmv1-cord-ner) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2092
  - Precision: 0.7202
  - Recall: 0.7238
  - F1: 0.7220
  - Accuracy: 0.9639
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 41   | 0.2444          | 0.4045    | 0.3996 | 0.4020 | 0.9226   |
  | No log        | 2.0   | 82   | 0.1640          | 0.5319    | 0.6098 | 0.5682 | 0.9455   |
  | No log        | 3.0   | 123  | 0.1531          | 0.6324    | 0.6614 | 0.6466 | 0.9578   |
  | No log        | 4.0   | 164  | 0.1440          | 0.6927    | 0.6743 | 0.6834 | 0.9620   |
  | No log        | 5.0   | 205  | 0.1520          | 0.6750    | 0.6958 | 0.6853 | 0.9613   |
  | No log        | 6.0   | 246  | 0.1597          | 0.6840    | 0.6987 | 0.6913 | 0.9605   |
  | No log        | 7.0   | 287  | 0.1910          | 0.7002    | 0.6887 | 0.6944 | 0.9605   |
  | No log        | 8.0   | 328  | 0.1860          | 0.6834    | 0.6923 | 0.6878 | 0.9609   |
  | No log        | 9.0   | 369  | 0.1665          | 0.6785    | 0.7102 | 0.6940 | 0.9624   |
  | No log        | 10.0  | 410  | 0.1816          | 0.7016    | 0.7052 | 0.7034 | 0.9624   |
  | No log        | 11.0  | 451  | 0.1808          | 0.6913    | 0.7166 | 0.7038 | 0.9638   |
  | No log        | 12.0  | 492  | 0.2165          | 0.712     | 0.7023 | 0.7071 | 0.9628   |
  | 0.1014        | 13.0  | 533  | 0.2135          | 0.6979    | 0.7109 | 0.7043 | 0.9613   |
  | 0.1014        | 14.0  | 574  | 0.2154          | 0.6906    | 0.7109 | 0.7006 | 0.9612   |
  | 0.1014        | 15.0  | 615  | 0.2118          | 0.6902    | 0.7016 | 0.6958 | 0.9615   |
  | 0.1014        | 16.0  | 656  | 0.2091          | 0.6985    | 0.7080 | 0.7032 | 0.9623   |
  | 0.1014        | 17.0  | 697  | 0.2104          | 0.7118    | 0.7123 | 0.7121 | 0.9630   |
  | 0.1014        | 18.0  | 738  | 0.2081          | 0.7129    | 0.7231 | 0.7179 | 0.9638   |
  | 0.1014        | 19.0  | 779  | 0.2093          | 0.7205    | 0.7231 | 0.7218 | 0.9638   |
  | 0.1014        | 20.0  | 820  | 0.2092          | 0.7202    | 0.7238 | 0.7220 | 0.9639   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  