metadata
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