readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: model_WS_CV1
    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. -->
  
  # model_WS_CV1
  
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2550
  - Ame: {'precision': 1.0, 'recall': 0.9166666666666666, 'f1': 0.9565217391304348, 'number': 24}
  - Anguage: {'precision': 0.851063829787234, 'recall': 0.851063829787234, 'f1': 0.8510638297872339, 'number': 47}
  - Du Degree: {'precision': 0.9529411764705882, 'recall': 0.9418604651162791, 'f1': 0.9473684210526314, 'number': 86}
  - Du End Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
  - Du University: {'precision': 0.8941176470588236, 'recall': 0.9620253164556962, 'f1': 0.926829268292683, 'number': 79}
  - Ears Ex: {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 22}
  - Er Name: {'precision': 0.2727272727272727, 'recall': 0.2727272727272727, 'f1': 0.2727272727272727, 'number': 11}
  - Kill: {'precision': 0.9290540540540541, 'recall': 0.9548611111111112, 'f1': 0.9417808219178083, 'number': 288}
  - Ractice: {'precision': 0.6428571428571429, 'recall': 0.8181818181818182, 'f1': 0.7200000000000001, 'number': 22}
  - Rade: {'precision': 0.7407407407407407, 'recall': 0.8695652173913043, 'f1': 0.7999999999999999, 'number': 23}
  - Ummarize: {'precision': 0.9539748953974896, 'recall': 0.9978118161925602, 'f1': 0.9754010695187165, 'number': 457}
  - Xpertise: {'precision': 0.8111111111111111, 'recall': 0.9125, 'f1': 0.8588235294117648, 'number': 80}
  - X Company: {'precision': 0.8909090909090909, 'recall': 0.875, 'f1': 0.8828828828828829, 'number': 224}
  - X Description: {'precision': 0.9808371154815936, 'recall': 0.9727431857964491, 'f1': 0.9767733835530447, 'number': 3999}
  - X End Date: {'precision': 0.5714285714285714, 'recall': 0.23529411764705882, 'f1': 0.3333333333333333, 'number': 17}
  - X Position: {'precision': 0.8827586206896552, 'recall': 0.9434889434889435, 'f1': 0.9121140142517814, 'number': 407}
  - X Start Date: {'precision': 1.0, 'recall': 0.7142857142857143, 'f1': 0.8333333333333333, 'number': 14}
  - Overall Precision: 0.9549
  - Overall Recall: 0.9598
  - Overall F1: 0.9574
  - Overall Accuracy: 0.9448
  
  ## 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: 3e-05
  - train_batch_size: 8
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 25
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Ame                                                                                                     | Anguage                                                                                                 | Du Degree                                                                                                | Du End Date                                               | Du University                                                                                           | Ears Ex                                                                                                 | Er Name                                                                                                    | Kill                                                                                                     | Ractice                                                                                                 | Rade                                                                                                    | Ummarize                                                                                                 | Xpertise                                                                                    | X Company                                                                                                 | X Description                                                                                             | X End Date                                                                                               | X Position                                                                                                   | X Start Date                                                                               | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------