Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
politics
agenda
issues
comparative agendas project
political communication
bills
laws
parliament
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("z-dickson/CAP_coded_US_Congressional_bills")
model = AutoModelForSequenceClassification.from_pretrained("z-dickson/CAP_coded_US_Congressional_bills")Quick Links
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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/CAP_coded_US_Congressional_bills")