readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: CAP_coded_UK_statutory_instruments
    results: []
    
  widget:
  - text: "The National Health Service (Charges for Drugs and Appliances) (Scotland) Regulations 2007"
    example_title: "example 1"
  - text: "The Inshore Fishing (Prohibited Methods of Fishing) (Luce Bay) Order 2015"
    example_title: "example 2"
    
  ---
  
  
  
  # CAP_coded_UK_statutory_instruments
  
  This model predicts the CAP code of parliamentary bills/instruments (https://www.comparativeagendas.net/pages/master-codebook)
  
  The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament. 
  The model is cased (case sensitive)
  
  Any questions on the model and training data feel free to message me on twitter - @sachary_
  
  
  - Train Loss: 0.1188
  - Train Sparse Categorical Accuracy: 0.9688
  - Validation Loss: 0.2032
  - Validation Sparse Categorical Accuracy: 0.9556
  
  
  
  
  The following hyperparameters were used during training:
  - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  - training_precision: float32
  
  ### Training results
  
  | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
  | 0.2167     | 0.9474                            | 0.2351          | 0.9444                                 | 0     |
  | 0.1539     | 0.9592                            | 0.2076          | 0.9536                                 | 1     |
  | 0.1188     | 0.9688                            | 0.2032          | 0.9556                                 | 2     |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - TensorFlow 2.8.2
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  