readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - nielsr/funsd-layoutlmv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: pasha
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nielsr/funsd-layoutlmv3
        type: nielsr/funsd-layoutlmv3
        config: pasha
        split: test
        args: pasha
      metrics:
      - name: Precision
        type: precision
        value: 0.986704994610133
      - name: Recall
        type: recall
        value: 0.989193083573487
      - name: F1
        type: f1
        value: 0.9879474725670084
      - name: Accuracy
        type: accuracy
        value: 0.9905978784956606
  ---
  
  <!-- 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. -->
  
  # pasha
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0585
  - Precision: 0.9867
  - Recall: 0.9892
  - F1: 0.9879
  - Accuracy: 0.9906
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.13  | 100  | 0.2664          | 0.9534    | 0.9438 | 0.9486 | 0.9571   |
  | No log        | 4.26  | 200  | 0.1044          | 0.9756    | 0.9802 | 0.9779 | 0.9838   |
  | No log        | 6.38  | 300  | 0.0672          | 0.9853    | 0.9899 | 0.9876 | 0.9904   |
  | No log        | 8.51  | 400  | 0.0634          | 0.9824    | 0.9860 | 0.9842 | 0.9884   |
  | 0.2958        | 10.64 | 500  | 0.0585          | 0.9867    | 0.9892 | 0.9879 | 0.9906   |
  | 0.2958        | 12.77 | 600  | 0.0511          | 0.9889    | 0.9928 | 0.9908 | 0.9928   |
  | 0.2958        | 14.89 | 700  | 0.0503          | 0.9871    | 0.9921 | 0.9896 | 0.9925   |
  | 0.2958        | 17.02 | 800  | 0.0529          | 0.9860    | 0.9903 | 0.9881 | 0.9913   |
  | 0.2958        | 19.15 | 900  | 0.0581          | 0.9842    | 0.9892 | 0.9867 | 0.9904   |
  | 0.0256        | 21.28 | 1000 | 0.0571          | 0.9849    | 0.9888 | 0.9869 | 0.9901   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  