
# PARTYPRESS monolingual Spain




Fine-tuned model, based on [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased). Used in [Erfort et al. (2023)](https://doi.org/10.1177/20531680231183512), building on the PARTYPRESS database. For the downstream task of classyfing press releases from political parties into 23 unique policy areas we achieve a performance comparable to expert human coders.



## Model description

The PARTYPRESS monolingual model builds on [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) but has a supervised component. This means, it was fine-tuned using texts labeled by humans. The labels indicate 23 different political issue categories derived from the Comparative Agendas Project (CAP):
| Code | Issue |
|--|-------|
| 1 | Macroeconomics |
| 2 | Civil Rights |
| 3 | Health |
| 4 | Agriculture |
| 5 | Labor |
| 6 | Education |
| 7 | Environment |
| 8 | Energy |
| 9 | Immigration |
| 10 | Transportation |
| 12 | Law and Crime |
| 13 | Social Welfare |
| 14 | Housing |
| 15 | Domestic Commerce |
| 16 | Defense |
| 17 | Technology |
| 18 | Foreign Trade |
| 19.1 | International Affairs |
| 19.2 | European Union |
| 20 | Government Operations |
| 23 | Culture |
| 98 | Non-thematic |
| 99 | Other |

## Model variations

There are several monolingual models for different countries, and a multilingual model. The multilingual model can be easily extended to other languages, country contexts, or time periods by fine-tuning it with minimal additional labeled texts.

## Intended uses & limitations

The main use of the model is for text classification of press releases from political parties. It may also be useful for other political texts.

The classification can then be used to measure which issues parties are discussing in their communication.

### How to use

This model can be used directly with a pipeline for text classification:

```python
>>> from transformers import pipeline
>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
>>> partypress = pipeline("text-classification", model = "cornelius/partypress-monolingual-spain", tokenizer = "cornelius/partypress-monolingual-spain", **tokenizer_kwargs)
>>> partypress("Your text here.")
```

### Limitations and bias

The model was trained with data from parties in Spain. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.

The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database. For example, the performance is highest for press releases from Ireland (75%) and lowest for Poland (55%).

## Training data

The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in Spain. The press releases were labeled by two expert human coders.

For the training data of the underlying model, please refer to [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased)

## Training procedure

### Preprocessing

For the preprocessing, please refer to [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased)



### Pretraining

For the pretraining, please refer to [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased)

### Fine-tuning

We fine-tuned the model using about 3,000 labeled press releases from political parties in Spain. 

#### Training Hyperparameters

The batch size for training was 12, for testing 2, with four epochs. All other hyperparameters were the standard from the transformers library.


#### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3


## Evaluation results

Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation that are comparable to the performance of our expert human coders. Please refer to Erfort et al. (2023)

### 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    = {10},
  number    = {3},
  year      = {2023},
  doi       = {10.1177/20531680231183512},
  URL       = {https://doi.org/10.1177/20531680231183512}

}
```

Erfort, C., Stoetzer, L. F., & Klüver, H. (2023). The PARTYPRESS Database: A new comparative database of parties’ press releases. Research & Politics, 10(3). [https://doi.org/10.1177/20531680231183512](https://doi.org/10.1177/20531680231183512)

### Further resources

Github: [cornelius-erfort/partypress](https://github.com/cornelius-erfort/partypress)

Research and Politics Dataverse: [Replication Data for: The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FOINX7Q)



## Acknowledgements

Research for this contribution is part of the Cluster of Excellence "Contestations of the Liberal Script" (EXC 2055, Project-ID: 390715649), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy. Cornelius Erfort is moreover grateful for generous funding provided by the DFG through the Research Training Group DYNAMICS (GRK 2458/1).

## Contact

Cornelius Erfort

Humboldt-Universität zu Berlin

[corneliuserfort.de](corneliuserfort.de)


{"language": ["es"], "license": "cc-by-sa-4.0", "tags": ["partypress", "political science", "parties", "press releases"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "\"Los soldados espa\u00f1oles que est\u00e1n en Afganist\u00e1n cuentan con las m\u00e1ximas medidas de seguridad para su protecci\u00f3n\" Jes\u00fas Cuadrado, portavoz socialista en la Comisi\u00f3n de Defensa del Congreso, ha enviado esta tarde, en nombre del PSOE, un mensaje de apoyo y solidaridad a los soldados heridos hoy en Afganist\u00e1n y a sus familias, as\u00ed como a sus compa\u00f1eros en esta misi\u00f3n internacional. Cuadrado ha resaltado el \u201cenorme sacrificio\u201d que supone para los soldados una misi\u00f3n de estas caracter\u00edsticas. Un sacrificio \u201cque est\u00e1 al servicio de todo los espa\u00f1oles\u201d, ha explicado, \u201cporque contribuyen a la creaci\u00f3n de un Estado\u201d, en un lugar que ha sido usado hasta el momento por los terroristas para cometer atentados en su pa\u00eds y en resto del mundo.  Gracias al trabajo de \u201cnuestros soldados\u201d, ha a\u00f1adido, \u201cahora hay un ej\u00e9rcito compuesto por militares afganos y hay una polic\u00eda\u201d. As\u00ed, \u201cel trabajo de los militares espa\u00f1oles est\u00e1 al servicio de Espa\u00f1a y de los dem\u00e1s pa\u00edses\u201d, que participan en esta misi\u00f3n por mandato de la OTAN, ha recordado. \u201cEs uno de los trabajos m\u00e1s solidarios y comprometidos que se pueden hacer en el mundo\u201d, ha resaltado el portavoz socialista.  La seguridad de nuestras tropas, una prioridad absoluta  \u201cLa seguridad al cien por cien es imposible\u201d, ha admitido Cuadrado. As\u00ed lo ha se\u00f1alado en numerosas ocasiones tanto el Ministerio de Defensa como el resto del Gobierno. \u201cLos riesgos que asumimos all\u00ed son muy elevados y, precisamente por eso, durante estos a\u00f1os, el Gobierno ha hecho de la seguridad de nuestras tropas una prioridad absoluta\u201d.  Cuadrado ha recordado que \u201ctodos los blindados que utilizan los militares espa\u00f1oles en Afganist\u00e1n han sido renovados\u201d. Los soldados espa\u00f1oles, en concreto, cuentan con 67 RG31 y 131 blindados tipo Lince. Tambi\u00e9n se ha construido una nueva base y se ha dotado, a todo el material que utilizan, de las m\u00e1s modernas medidas de seguridad, as\u00ed como de unos servicios sanitarios de alto nivel. \u201cIgualmente, es conocido que se ha ido mejorando el sistema de transporte de nuestras tropas con las mejores medidas de seguridad\u201d, concluy\u00f3 el portavoz en la Comisi\u00f3n de Defensa."}]}
transformers pytorch tf bert text-classification partypress political science parties press releases es license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #text-classification #partypress #political science #parties #press releases #es #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

PARTYPRESS monolingual Spain
============================


Fine-tuned model, based on dccuchile/bert-base-spanish-wwm-uncased. Used in Erfort et al. (2023), building on the PARTYPRESS database. For the downstream task of classyfing press releases from political parties into 23 unique policy areas we achieve a performance comparable to expert human coders.


Model description
-----------------


The PARTYPRESS monolingual model builds on dccuchile/bert-base-spanish-wwm-uncased but has a supervised component. This means, it was fine-tuned using texts labeled by humans. The labels indicate 23 different political issue categories derived from the Comparative Agendas Project (CAP):



Model variations
----------------


There are several monolingual models for different countries, and a multilingual model. The multilingual model can be easily extended to other languages, country contexts, or time periods by fine-tuning it with minimal additional labeled texts.


Intended uses & limitations
---------------------------


The main use of the model is for text classification of press releases from political parties. It may also be useful for other political texts.


The classification can then be used to measure which issues parties are discussing in their communication.


### How to use


This model can be used directly with a pipeline for text classification:


### Limitations and bias


The model was trained with data from parties in Spain. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.


The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database. For example, the performance is highest for press releases from Ireland (75%) and lowest for Poland (55%).


Training data
-------------


The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in Spain. The press releases were labeled by two expert human coders.


For the training data of the underlying model, please refer to dccuchile/bert-base-spanish-wwm-uncased


Training procedure
------------------


### Preprocessing


For the preprocessing, please refer to dccuchile/bert-base-spanish-wwm-uncased


### Pretraining


For the pretraining, please refer to dccuchile/bert-base-spanish-wwm-uncased


### Fine-tuning


We fine-tuned the model using about 3,000 labeled press releases from political parties in Spain.


#### Training Hyperparameters


The batch size for training was 12, for testing 2, with four epochs. All other hyperparameters were the standard from the transformers library.


#### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3


Evaluation results
------------------


Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation that are comparable to the performance of our expert human coders. Please refer to Erfort et al. (2023)


### BibTeX entry and citation info


Erfort, C., Stoetzer, L. F., & Klüver, H. (2023). The PARTYPRESS Database: A new comparative database of parties’ press releases. Research & Politics, 10(3). URL


### Further resources


Github: cornelius-erfort/partypress


Research and Politics Dataverse: Replication Data for: The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases


Acknowledgements
----------------


Research for this contribution is part of the Cluster of Excellence "Contestations of the Liberal Script" (EXC 2055, Project-ID: 390715649), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy. Cornelius Erfort is moreover grateful for generous funding provided by the DFG through the Research Training Group DYNAMICS (GRK 2458/1).


Contact
-------


Cornelius Erfort


Humboldt-Universität zu Berlin


URL



### How to use


This model can be used directly with a pipeline for text classification: ### Limitations and bias


The model was trained with data from parties in Spain. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.


The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database. For example, the performance is highest for press releases from Ireland (75%) and lowest for Poland (55%).


Training data
-------------


The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in Spain. The press releases were labeled by two expert human coders.


For the training data of the underlying model, please refer to dccuchile/bert-base-spanish-wwm-uncased


Training procedure
------------------ ### Preprocessing


For the preprocessing, please refer to dccuchile/bert-base-spanish-wwm-uncased ### Pretraining


For the pretraining, please refer to dccuchile/bert-base-spanish-wwm-uncased ### Fine-tuning


We fine-tuned the model using about 3,000 labeled press releases from political parties in Spain. #### Training Hyperparameters


The batch size for training was 12, for testing 2, with four epochs. All other hyperparameters were the standard from the transformers library. #### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3


Evaluation results
------------------


Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation that are comparable to the performance of our expert human coders. Please refer to Erfort et al. (2023) ### BibTeX entry and citation info


Erfort, C., Stoetzer, L. F., & Klüver, H. (2023). The PARTYPRESS Database: A new comparative database of parties’ press releases. Research & Politics, 10(3). URL ### Further resources


Github: cornelius-erfort/partypress


Research and Politics Dataverse: Replication Data for: The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases


Acknowledgements
----------------


Research for this contribution is part of the Cluster of Excellence "Contestations of the Liberal Script" (EXC 2055, Project-ID: 390715649), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy. Cornelius Erfort is moreover grateful for generous funding provided by the DFG through the Research Training Group DYNAMICS (GRK 2458/1).


Contact
-------


Cornelius Erfort


Humboldt-Universität zu Berlin


URL
TAGS
#transformers #pytorch #tf #bert #text-classification #partypress #political science #parties #press releases #es #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use


This model can be used directly with a pipeline for text classification: ### Limitations and bias


The model was trained with data from parties in Spain. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.


The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database. For example, the performance is highest for press releases from Ireland (75%) and lowest for Poland (55%).


Training data
-------------


The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in Spain. The press releases were labeled by two expert human coders.


For the training data of the underlying model, please refer to dccuchile/bert-base-spanish-wwm-uncased


Training procedure
------------------ ### Preprocessing


For the preprocessing, please refer to dccuchile/bert-base-spanish-wwm-uncased ### Pretraining


For the pretraining, please refer to dccuchile/bert-base-spanish-wwm-uncased ### Fine-tuning


We fine-tuned the model using about 3,000 labeled press releases from political parties in Spain. #### Training Hyperparameters


The batch size for training was 12, for testing 2, with four epochs. All other hyperparameters were the standard from the transformers library. #### Framework versions


* Transformers 4.28.0
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3


Evaluation results
------------------


Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation that are comparable to the performance of our expert human coders. Please refer to Erfort et al. (2023) ### BibTeX entry and citation info


Erfort, C., Stoetzer, L. F., & Klüver, H. (2023). The PARTYPRESS Database: A new comparative database of parties’ press releases. Research & Politics, 10(3). URL ### Further resources


Github: cornelius-erfort/partypress


Research and Politics Dataverse: Replication Data for: The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases


Acknowledgements
----------------


Research for this contribution is part of the Cluster of Excellence "Contestations of the Liberal Script" (EXC 2055, Project-ID: 390715649), funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy. Cornelius Erfort is moreover grateful for generous funding provided by the DFG through the Research Training Group DYNAMICS (GRK 2458/1).


Contact
-------


Cornelius Erfort


Humboldt-Universität zu Berlin


URL