readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: CAP_coded_US_Congressional_bills
    results: []
  widget:
  - text: "A bill to prohibt discrimination in employment because of race, color, religion, national origin, or ancestry"
    example_title: "example 1"
  - text: "A bill to require the promulgation of regulations to improve aviation safety in adverse weather conditions, and for other purposes."
    example_title: "example 2"
    
  ---
  
  This model predicts the issue category of US Congressional bills.
  
  The model is trained on ~250k US Congressional bills from 1950-2015.
  
  The issue coding scheme follows the Comparative Agenda Project: https://www.comparativeagendas.net/pages/master-codebook
  
  The model is cased (case sensitive)
  
  Any questions on the model and training data feel free to message me on twitter - @sachary_
  
  Train Loss: 0.1318; 
  Train Sparse Categorical Accuracy: 0.9268; 
  Validation Loss: 0.2439; 
  Validation Sparse Categorical Accuracy: 0.9161
  
  
  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 hyperparameters
  
  
  
  
  
  ### Framework versions
  
  - Transformers 4.19.3
  - TensorFlow 2.8.2
  - Tokenizers 0.12.1
  