readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - funsd
  model-index:
  - name: ananth-docai1
    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. -->
  
  # ananth-docai1
  
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7024
  - Answer: {'precision': 0.7113513513513513, 'recall': 0.8133498145859085, 'f1': 0.7589388696655133, 'number': 809}
  - Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
  - Question: {'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}
  - Overall Precision: 0.7241
  - Overall Recall: 0.7903
  - Overall F1: 0.7558
  - Overall Accuracy: 0.8106
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 1.7944        | 1.0   | 10   | 1.6233          | {'precision': 0.01929260450160772, 'recall': 0.014833127317676144, 'f1': 0.016771488469601678, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.27685325264750377, 'recall': 0.17183098591549295, 'f1': 0.2120509849362688, 'number': 1065} | 0.1520            | 0.0978         | 0.1190     | 0.3505           |
  | 1.5001        | 2.0   | 20   | 1.2971          | {'precision': 0.11125, 'recall': 0.1100123609394314, 'f1': 0.11062771908017402, 'number': 809}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4044059795436664, 'recall': 0.48262910798122066, 'f1': 0.4400684931506849, 'number': 1065}  | 0.2912            | 0.3026         | 0.2968     | 0.5348           |
  | 1.136         | 3.0   | 30   | 0.9852          | {'precision': 0.4911699779249448, 'recall': 0.5500618046971569, 'f1': 0.5189504373177842, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.6086587436332768, 'recall': 0.6732394366197183, 'f1': 0.6393223361569326, 'number': 1065}   | 0.5562            | 0.5830         | 0.5693     | 0.6941           |
  | 0.8567        | 4.0   | 40   | 0.8143          | {'precision': 0.627744510978044, 'recall': 0.7775030902348579, 'f1': 0.6946438431805633, 'number': 809}       | {'precision': 0.06666666666666667, 'recall': 0.01680672268907563, 'f1': 0.026845637583892617, 'number': 119} | {'precision': 0.6987179487179487, 'recall': 0.7164319248826291, 'f1': 0.7074640704682429, 'number': 1065}   | 0.6563            | 0.6994         | 0.6772     | 0.7467           |
  | 0.6998        | 5.0   | 50   | 0.7133          | {'precision': 0.6534859521331946, 'recall': 0.7762669962917181, 'f1': 0.7096045197740113, 'number': 809}      | {'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119}                  | {'precision': 0.7243532560214094, 'recall': 0.7624413145539906, 'f1': 0.7429094236047575, 'number': 1065}   | 0.6757            | 0.7296         | 0.7016     | 0.7781           |
  | 0.5886        | 6.0   | 60   | 0.6775          | {'precision': 0.648406374501992, 'recall': 0.8046971569839307, 'f1': 0.7181467181467182, 'number': 809}       | {'precision': 0.25806451612903225, 'recall': 0.13445378151260504, 'f1': 0.17679558011049723, 'number': 119}  | {'precision': 0.712947189097104, 'recall': 0.78591549