Popbert

🚀 PopBERT

PopBERT is a model designed for detecting populism in German political speeches within the German Bundestag. It builds on the deepset/gbert-large model and can identify both populist dimensions and ideological orientations.

🚀 Quick Start

PopBERT is a model for German-language populism detection in political speeches within the German Bundestag, based on the deepset/gbert-large model: https://huggingface.co/deepset/gbert-large. It is a multilabel model trained on a manually curated dataset of sentences from the 18th and 19th legislative periods. In addition to capturing the foundational dimensions of populism, namely "anti-elitism" and "people-centrism," the model was also fine-tuned to identify the underlying ideological orientation as either "left-wing" or "right-wing."

✨ Features

• Multilabel Detection: Capable of detecting both populist dimensions ("anti-elitism" and "people-centrism") and ideological orientations ("left-wing" and "right-wing").
• Based on gbert-large: Built upon the deepset/gbert-large model for better performance in the German language.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer

# load tokenizer
tokenizer = AutoTokenizer.from_pretrained("luerhard/PopBERT")

# load model
model = AutoModelForSequenceClassification.from_pretrained("luerhard/PopBERT")

# define text to be predicted
text = (
    "Das ist Klassenkampf von oben, das ist Klassenkampf im Interesse von "
    "Vermögenden und Besitzenden gegen die Mehrheit der Steuerzahlerinnen und "
    "Steuerzahler auf dieser Erde."
)

# encode text with tokenizer
encodings = tokenizer(text, return_tensors="pt")

# predict
with torch.inference_mode():
    out = model(**encodings)

# get probabilties
probs = torch.nn.functional.sigmoid(out.logits)
print(probs.detach().numpy())

Advanced Usage

The model outputs a Tensor of length 4. The table connects the position of the predicted probability to its dimension.

[[0.8765146  0.34838045 0.983123   0.02148379]]

📚 Documentation

Performance

To maximize performance, it is recommended to use the following thresholds per dimension:

[0.415961, 0.295400, 0.429109, 0.302714]

Using these thresholds, the model achieves the following performance on the test set:

📄 License

This project is licensed under the MIT license.