---
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](https://huggingface.co/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](https://huggingface.co/bert-base-multilingual-cased)

## Training procedure

### Preprocessing

For the preprocessing, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)

### Pretraining

For the pretraining, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)

## Evaluation results

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


### BibTeX entry and citation info

```bibtex
@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},
}
```