readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv2-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. -->
  
  # layoutlmv2-cord-ner
  
  This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0952
  - Precision: 0.9639
  - Recall: 0.9741
  - F1: 0.9690
  - Accuracy: 0.9911
  
  ## 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  | 0.5962          | 0.8714    | 0.8973 | 0.8842 | 0.9405   |
  | No log        | 2.0   | 226  | 0.4064          | 0.8713    | 0.9098 | 0.8901 | 0.9511   |
  | No log        | 3.0   | 339  | 0.2687          | 0.9314    | 0.9386 | 0.9350 | 0.9754   |
  | No log        | 4.0   | 452  | 0.2007          | 0.9355    | 0.9472 | 0.9413 | 0.9792   |
  | 0.4677        | 5.0   | 565  | 0.1625          | 0.9497    | 0.9597 | 0.9547 | 0.9834   |
  | 0.4677        | 6.0   | 678  | 0.1326          | 0.9526    | 0.9645 | 0.9585 | 0.9868   |
  | 0.4677        | 7.0   | 791  | 0.1212          | 0.9508    | 0.9645 | 0.9576 | 0.9851   |
  | 0.4677        | 8.0   | 904  | 0.1019          | 0.9675    | 0.9712 | 0.9693 | 0.9911   |
  | 0.1131        | 9.0   | 1017 | 0.1029          | 0.9545    | 0.9664 | 0.9604 | 0.9881   |
  | 0.1131        | 10.0  | 1130 | 0.0952          | 0.9639    | 0.9741 | 0.9690 | 0.9911   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.9.0+cu111
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  