readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  model-index:
  - name: layoutlmv2-finetuned-sroie
    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-finetuned-sroie
  
  This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the sroie dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0291
  - Address Precision: 0.9341
  - Address Recall: 0.9395
  - Address F1: 0.9368
  - Address Number: 347
  - Company Precision: 0.9570
  - Company Recall: 0.9625
  - Company F1: 0.9598
  - Company Number: 347
  - Date Precision: 0.9885
  - Date Recall: 0.9885
  - Date F1: 0.9885
  - Date Number: 347
  - Total Precision: 0.9253
  - Total Recall: 0.9280
  - Total F1: 0.9266
  - Total Number: 347
  - Overall Precision: 0.9512
  - Overall Recall: 0.9546
  - Overall F1: 0.9529
  - Overall Accuracy: 0.9961
  
  ## 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
  - lr_scheduler_warmup_ratio: 0.1
  - training_steps: 3000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Address Precision | Address Recall | Address F1 | Address Number | Company Precision | Company Recall | Company F1 | Company Number | Date Precision | Date Recall | Date F1 | Date Number | Total Precision | Total Recall | Total F1 | Total Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------------:|:--------------:|:-----------:|:-------:|:-----------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | No log        | 0.05  | 157  | 0.8162          | 0.3670            | 0.7233         | 0.4869     | 347            | 0.0617            | 0.0144         | 0.0234     | 347            | 0.0            | 0.0         | 0.0     | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.3346            | 0.1844         | 0.2378     | 0.9342           |
  | No log        | 1.05  | 314  | 0.3490          | 0.8564            | 0.8934         | 0.8745     | 347            | 0.8610            | 0.9280         | 0.8932     | 347            | 0.7297         | 0.8559      | 0.7878  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.8128            | 0.6693         | 0.7341     | 0.9826           |
  | No log        | 2.05  | 471  | 0.1845          | 0.7970            | 0.9049         | 0.8475     | 347            | 0.9211            | 0.9424         | 0.9316     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.8978            | 0.7089         | 0.7923     | 0.9835           |
  | 0.7027        | 3.05  | 628  | 0.1194          | 0.9040            | 0.9222         | 0.9130     | 347            | 0.8880            | 0.9135         | 0.9006     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.0             | 0.0          | 0.0      | 347          | 0.9263            | 0.7061         | 0.8013     | 0.9853           |
  | 0.7027        | 4.05  | 785  | 0.0762          | 0.9397            | 0.9424         | 0.9410     | 347            | 0.8889            | 0.9222         | 0.9052     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.7740          | 0.9078       | 0.8355   | 347          | 0.8926            | 0.9402         | 0.9158     | 0.9928           |
  | 0.7027        | 5.05  | 942  | 0.0564          | 0.9282            | 0.9308         | 0.9295     | 347            | 0.9296            | 0.9510         | 0.9402     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.7801          | 0.8588       | 0.8176   | 347          | 0.9036            | 0.9323         | 0.9177     | 0.9946           |
  | 0.0935        | 6.05  | 1099 | 0.0548          | 0.9222            | 0.9222         | 0.9222     | 347            | 0.6975            | 0.7378         | 0.7171     | 347            | 0.9885         | 0.9885      | 0.9885  | 347         | 0.8608          | 0.8732       | 0.8670   | 347          | 0.8648            | 0.8804         | 0.8725     | 0.9921           |
  | 0.0935        | 7.05  | 1256 | 0.0410          | 0.92              | 0.9280  