An `xlm-roberta-large` model finetuned on a multilingual training data containing major topic codes from the Comparative Agendas Project.

# Training data:
- US Presidential occasional remarks (English, sentences)
- NYTimes lead paragraphs (English, full documents)
- Spanish parliamentary speeches (Spanish, sentences)
- Hungarian bills and laws (Hungarian, full documents)
- Polish laws (Polish, full documents)
- Danish parliamentary questions (Danish, sentences)
- Flemish Facebook comments (sentences), newspaper articles

# Model performance 

Model accuracy is 86%

| label        | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| 0            | 0,82      | 0,80   | 0,81     | 1245    |
| 1            | 0,82      | 0,71   | 0,76     | 642     |
| 2            | 0,90      | 0,92   | 0,91     | 1229    |
| 3            | 0,88      | 0,88   | 0,88     | 710     |
| 4            | 0,83      | 0,81   | 0,82     | 906     |
| 5            | 0,91      | 0,92   | 0,92     | 1059    |
| 6            | 0,85      | 0,85   | 0,85     | 771     |
| 7            | 0,86      | 0,91   | 0,88     | 474     |
| 8            | 0,86      | 0,88   | 0,87     | 676     |
| 9            | 0,89      | 0,93   | 0,91     | 1350    |
| 10           | 0,87      | 0,87   | 0,87     | 1726    |
| 11           | 0,81      | 0,81   | 0,81     | 761     |
| 12           | 0,82      | 0,82   | 0,82     | 424     |
| 13           | 0,80      | 0,80   | 0,80     | 847     |
| 14           | 0,84      | 0,88   | 0,86     | 1088    |
| 15           | 0,83      | 0,87   | 0,85     | 535     |
| 16           | 0,74      | 0,72   | 0,73     | 218     |
| 17           | 0,84      | 0,88   | 0,86     | 2351    |
| 18           | 0,84      | 0,82   | 0,83     | 2013    |
| 19           | 0,86      | 0,82   | 0,84     | 414     |
| 20           | 0,93      | 0,92   | 0,93     | 3293    |
| 21           | 0,64      | 0,58   | 0,61     | 663     |
| 22           | 1,00      | 0,08   | 0,14     | 13      |
| 23           | 1,00      | 0,15   | 0,27     | 13      |
| 24           | 0,83      | 0,83   | 0,83     | 35      |
| 25           | 0,00      | 0,00   | 0,00     | 1       |
| 26           | 0,00      | 0,00   | 0,00     | 8       |
| macro avg    | 0,79      | 0,72   | 0,73     | 23465   |
| weighted avg | 0,86      | 0,86   | 0,86     | 23465   |

{"pipeline_tag": "text-classification"}
text-classification region:us
TAGS
#text-classification #region-us 

An 'xlm-roberta-large' model finetuned on a multilingual training data containing major topic codes from the Comparative Agendas Project.


Training data:
==============


* US Presidential occasional remarks (English, sentences)
* NYTimes lead paragraphs (English, full documents)
* Spanish parliamentary speeches (Spanish, sentences)
* Hungarian bills and laws (Hungarian, full documents)
* Polish laws (Polish, full documents)
* Danish parliamentary questions (Danish, sentences)
* Flemish Facebook comments (sentences), newspaper articles


Model performance
=================


Model accuracy is 86%




TAGS
#text-classification #region-us 

TAGS
#text-classification #region-us 
