
Multilingual Bert base (multilingual uncased) model trained to predict [CAP issue codes](https://www.comparativeagendas.net/pages/master-codebook). 

Model training on 120,000 assorted political documents -- mostly from the [Comparative Agendas Project](https://www.comparativeagendas.net/) 

# 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
 
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 | 0.72 | 0.83 | 0.77 | 211 |
| 1 | 0.82 | 0.77 | 0.79 | 242 |
| 2 | 0.82 | 0.86 | 0.84 | 251 |
| 3 | 0.92 | 0.89 | 0.90 | 228 |
| 4 | 0.81 | 0.85 | 0.83 | 220 |
| 5 | 0.90 | 0.93 | 0.91 | 244 |
| 6 | 0.87 | 0.87 | 0.87 | 230 |
| 7 | 0.92 | 0.88 | 0.90 | 251 |
| 8 | 0.94 | 0.90 | 0.92 | 237 |
| 9 | 0.87 | 0.88 | 0.87 | 263 |
| 10 | 0.70 | 0.88 | 0.78 | 189 |
| 11 | 0.90 | 0.81 | 0.85 | 248 |
| 12 | 0.87 | 0.90 | 0.88 | 222 |
| 13 | 0.76 | 0.72 | 0.74 | 255 |
| 14 | 0.84 | 0.84 | 0.84 | 241 |
| 15 | 0.92 | 0.79 | 0.85 | 276 |
| 16 | 0.95 | 0.90 | 0.92 | 258 |
| 17 | 0.71 | 0.82 | 0.76 | 200 |
| 18 | 0.77 | 0.73 | 0.75 | 215 |
| 19 | 0.92 | 0.91 | 0.92 | 239 |
| Accuracy | --- 0.85 --- | | | |
| Macro Avg | 0.85 | 0.85 | 0.85 | 4720 |
| Weighted Avg | 0.85 | 0.85 | 0.85 | 4720 |

{"license": "afl-3.0", "widget": [{"text": "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.", "example_title": "English (UK House of Commons Question)"}, {"text": "To ask the Scottish Government what action it is taking to ensure that women who are prescribed sodium valproate are (a) adequately counselled regarding the risks of taking the drug while pregnant and (b) supported to plan their pregnancies in order to minimise the risk of foetal abnormalities.", "example_title": "English (Scottish Parliamentary Question)"}]}
transformers pytorch bert text-classification license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

Multilingual Bert base (multilingual uncased) model trained to predict CAP issue codes.


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
==========




TAGS
#transformers #pytorch #bert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
