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CAP_coded_US_Congressional_bills
z-dickson/CAP_coded_US_Congressional_bills
CAP_coded_US_Congressional_bills is a text classification model from z-dickson. Use it when you need a label for a piece of text. It is set up for transformers.
This model predicts the issue category of US Congressional bills.
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Repo size
434 MB
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8
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jun 9, 2022
- Updated
- Jun 9, 2025
- SHA
- 2cd84690
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Model card
From the Hugging Face model README
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)
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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Jun 9, 2022
- Updated
- Jun 9, 2025