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poltextlab / xlm-roberta-large-german-parlspeech-cap-v4
Model's Last Updated: August 24 2026
text-classification
Introduction of xlm-roberta-large-german-parlspeech-cap-v4
Model Details of xlm-roberta-large-german-parlspeech-cap-v4
xlm-roberta-large-german-parlspeech-cap-v4
Model description
An
xlm-roberta-large
model fine-tuned on german training data containing parliamentary speeches (oral questions, interpellations, bill debates, other plenary speeches, urgent questions) labeled with
major topic codes
from the
Comparative Agendas Project
.
To augment the lower-performing labels and the No Policy Content label, we used English parliamentary speeches that were translated into German using OpenNMT. This model is an improved version of
xlm-roberta-large-german-parlspeech-cap-v3
.
We follow the master codebook of the Comparative Agendas Project, and all of our models use the same major topic codes.
How to use the model
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
model="poltextlab/xlm-roberta-large-pooled-cap-v4",
task="text-classification",
tokenizer=tokenizer,
use_fast=False,
token="<your_hf_read_only_token>"
)
text = "Entwicklung der Regionalisierungsmittel fuer den Schienenpersonennahverkehr nach der ersten Revision zum 31. Dezember 1997"
pipe(text)
Gated access
Due to the gated access, you must pass the
token
parameter when loading the model. In earlier versions of the Transformers package, you may need to use the
use_auth_token
parameter instead.
Model performance
The model was evaluated on a test set of 1576 examples.
Overall Performance:
- Accuracy: 73.9%
- Macro Avg: Precision: 0.69, Recall: 0.64, F1-score: 0.65
- Weighted Avg: Precision: 0.73, Recall: 0.74, F1-score: 0.73
Per-Class Metrics:
| Label | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| (1) Macroeconomics | 0.86 | 0.61 | 0.72 | 62 |
| (2) Civil Rights | 0.76 | 0.81 | 0.78 | 152 |
| (3) Health | 0.68 | 0.83 | 0.75 | 46 |
| (4) Agriculture | 0.74 | 0.84 | 0.79 | 51 |
| (5) Labor | 0.57 | 0.63 | 0.60 | 51 |
| (6) Education | 0.43 | 0.43 | 0.43 | 28 |
| (7) Environment | 0.68 | 0.78 | 0.73 | 101 |
| (8) Energy | 0.80 | 0.85 | 0.82 | 65 |
| (9) Immigration | 0.81 | 0.77 | 0.79 | 88 |
| (10) Transportation | 0.87 | 0.88 | 0.87 | 105 |
| (12) Law and Crime | 0.61 | 0.53 | 0.57 | 77 |
| (13) Social Welfare | 0.61 | 0.55 | 0.58 | 40 |
| (14) Housing | 0.81 | 0.81 | 0.81 | 32 |
| (15) Banking, Finance, and Domestic Commerce | 0.55 | 0.59 | 0.57 | 44 |
| (16) Defense | 0.78 | 0.88 | 0.83 | 152 |
| (17) Technology | 0.72 | 0.56 | 0.63 | 32 |
| (18) Foreign Trade | 0.50 | 0.18 | 0.27 | 22 |
| (19) International Affairs | 0.74 | 0.81 | 0.77 | 175 |
| (20) Government Operations | 0.67 | 0.60 | 0.63 | 161 |
| (21) Public Lands | 0.00 | 0.00 | 0.00 | 8 |
| (23) Culture | 1.00 | 0.22 | 0.36 | 9 |
| (999) No Policy Content | 1.00 | 0.97 | 0.99 | 75 |
Fine-tuning procedure
This model was fine-tuned with the following key hyperparameters:
- Number of Training Epochs : 10
- Batch Size : 10
- Learning Rate : 5e-06
- Early Stopping : enabled with a patience of 1 epochs
Inference platform
This model is used by the CAP Babel Machine , an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.
Cooperation
Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the CAP Babel Machine .
Reference
Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434
Debugging and issues
This architecture uses the
sentencepiece
tokenizer. In order to use the model before
transformers==4.27
you need to install it manually.
If you encounter a
RuntimeError
when loading the model using the
from_pretrained()
method, adding
ignore_mismatched_sizes=True
should solve the issue.
Runs of poltextlab xlm-roberta-large-german-parlspeech-cap-v4 on huggingface.co
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More Information About xlm-roberta-large-german-parlspeech-cap-v4 huggingface.co Model
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