metadata

license: cc-by-sa-4.0
language:
  - de
  - en
  - es
  - da
  - pl
  - sv
  - nl
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - partypress
  - political science
  - parties
  - press releases

PARTYPRESS multilingual

Fine-tuned model in seven languages on texts from nine countries, based on bert-base-multilingual-cased. It used in Erfort et al. (2023).

Model description

tbs

Model variations

tbd (monolingual)

Intended uses & limitations

tbd

How to use

tbd

Limitations and bias

tbd

Training data

For the training data, please refer to bert-base-multilingual-cased

Training procedure

Preprocessing

For the preprocessing, please refer to bert-base-multilingual-cased

Pretraining

For the pretraining, please refer to bert-base-multilingual-cased

Evaluation results

Fine-tuned on our downstream task, this model achieves the following results:

BibTeX entry and citation info

@article{erfort_partypress_2023,
  author    = {Cornelius Erfort and
               Lukas F. Stoetzer and
               Heike Klüver},
  title     = {The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases},
  journal   = {Research and Politics},
  volume    = {forthcoming},
  year      = {2023},
}