readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv1-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. -->
  
  # layoutlmv1-cord-ner
  
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1438
  - Precision: 0.9336
  - Recall: 0.9453
  - F1: 0.9394
  - Accuracy: 0.9767
  
  ## 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  | 0.1251          | 0.9054    | 0.9184 | 0.9119 | 0.9651   |
  | No log        | 2.0   | 226  | 0.1343          | 0.9002    | 0.9261 | 0.9130 | 0.9635   |
  | No log        | 3.0   | 339  | 0.1264          | 0.9189    | 0.9357 | 0.9272 | 0.9647   |
  | No log        | 4.0   | 452  | 0.1235          | 0.9122    | 0.9376 | 0.9248 | 0.9681   |
  | 0.1371        | 5.0   | 565  | 0.1353          | 0.9378    | 0.9405 | 0.9391 | 0.9717   |
  | 0.1371        | 6.0   | 678  | 0.1431          | 0.9233    | 0.9357 | 0.9295 | 0.9709   |
  | 0.1371        | 7.0   | 791  | 0.1473          | 0.9289    | 0.9405 | 0.9347 | 0.9759   |
  | 0.1371        | 8.0   | 904  | 0.1407          | 0.9473    | 0.9491 | 0.9482 | 0.9784   |
  | 0.0106        | 9.0   | 1017 | 0.1440          | 0.9301    | 0.9453 | 0.9376 | 0.9769   |
  | 0.0106        | 10.0  | 1130 | 0.1438          | 0.9336    | 0.9453 | 0.9394 | 0.9767   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  