PARTYPRESS monolingual UK
Fine-tuned model, based on
distilbert-base-uncased-finetuned-sst-2-english. 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
distilbert-base-uncased-finetuned-sst-2-english 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:
1>>> from transformers import pipeline
2>>> tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
3>>> partypress = pipeline("text-classification", model = "cornelius/partypress-monolingual-uk", tokenizer = "cornelius/partypress-monolingual-uk", **tokenizer_kwargs)
4>>> partypress("Your text here.")
Limitations and bias
The model was trained with data from parties in the UK. 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 UK (75%) and lowest for Poland (55%).
Training data
The PARTYPRESS multilingual model was fine-tuned with about 3,000 press releases from parties in the UK. The press releases were labeled by two expert human coders.
For the training data of the underlying model, please refer to
distilbert-base-uncased-finetuned-sst-2-englishTraining procedure
Preprocessing
Pretraining
Fine-tuning
We fine-tuned the model using about 3,000 labeled press releases from political parties in the UK.
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
1@article{erfort_partypress_2023,
2 author = {Cornelius Erfort and
3 Lukas F. Stoetzer and
4 Heike Klüver},
5 title = {The PARTYPRESS Database: A new comparative database of parties’ press releases},
6 journal = {Research and Politics},
7 volume = {10},
8 number = {3},
9 year = {2023},
10 doi = {10.1177/20531680231183512},
11 URL = {https://doi.org/10.1177/20531680231183512}
12
13}
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/10.1177/20531680231183512Further resources
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
