CAP_multilingual

Multilingual Bert base (multilingual uncased) model trained to predict CAP issue codes from text documents such as speeches, press releases, social media messages, news articles, bills, laws etc..

Model training on 120,000 assorted political documents -- mostly from the Comparative Agendas Project

Countries:

• Italy
• Sweden
• France
• Switzerland
• Poland
• Netherlands
• Germany
• Denmark
• Spain
• UK
• Austria
• Ireland

LABELS USED IN TRAINING

• Model labels -> CAP labels:

• {0: 1.0, 1: 2.0, 2: 3.0, 3: 4.0, 4: 5.0, 5: 6.0, 6: 7.0, 7: 8.0, 8: 9.0, 9: 10.0, 10: 12.0, 11: 13.0, 12: 14.0, 13: 15.0, 14: 16.0, 15: 17.0, 16: 18.0, 17: 19.0, 18: 20.0, 19: 23.0}

• Model labels -> CAP issues:

• {0: 'macroeconomics', 1: 'civil_rights', 2: 'healthcare', 3: 'agriculture', 4: 'labour', 5: 'education', 6: 'environment', 7: 'energy', 8: 'immigration', 9: 'transportation', 10: 'law_crime', 11: 'social_welfare', 12: 'housing', 13: 'domestic_commerce', 14: 'defense', 15: 'technology', 16: 'foreign_trade', 17: 'international_affairs', 18: 'government_operations', 19: 'culture'}

Validation

from transformers import AutoModelForSequenceClassification
from transformers import TextClassificationPipeline, AutoTokenizer

mp = 'z-dickson/CAP_multilingual'
model = AutoModelForSequenceClassification.from_pretrained(mp)
tokenizer =  AutoTokenizer.from_pretrained(mp)

classifier = TextClassificationPipeline(tokenizer=tokenizer, model=model, device=0)

classifier("""
To ask the Secretary of State for Energy and Climate \\
Change what estimate he has made of the proportion of carbon \\
dioxide emissions arising in the UK attributable to burning.
"""
)