
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

{"tags": ["generated_from_keras_callback"], "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"}], "model-index": [{"name": "CAP_coded_US_Congressional_bills", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 


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: URL

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

### Training hyperparameters ### Framework versions

- Transformers 4.19.3
- TensorFlow 2.8.2
- Tokenizers 0.12.1
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters ### Framework versions

- Transformers 4.19.3
- TensorFlow 2.8.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters### Framework versions

- Transformers 4.19.3
- TensorFlow 2.8.2
- Tokenizers 0.12.1