SIP-BERT

SIP-BERT is a transformer-based model designed to detect social inequality in German texts.
It was fine-tuned on German Bundestag debates (sourced from OpenDiscourse), where each training instance consists of 3-sentence segments.

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Model Description

• Architecture: bert-base-german-cased (from dbmdz)
• Task: Binary classification – detecting social inequality in German texts
• Labels:
  • 0 = no social inequality
  • 1 = social inequality
• Language: German
• Training Data: 1,950 annotated text passages from Bundestag debates (via OpenDiscourse)
• Segmenting: Data split into 3-sentence units
• Evaluation: Accuracy 0.97; F1 Score 0.95

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Intended Use

• Primary use case: Analysis of parliamentary discourse on social inequality
• Research contexts: Political science, computational social science, discourse analysis

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Limitations

• The model is trained on Bundestag debates (1949–2021), but is specialized for texts from 1990 onwards.
• It may be less reliable for earlier parliamentary language (1949–1989) and for non-parliamentary speech.
• It was designed primarily to detect economic inequality, and it may not be applicable to other types of inequality.

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Usage

You can load the model with the Hugging Face transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("miriamex/SIP-BERT")
model = AutoModelForSequenceClassification.from_pretrained("miriamex/SIP-BERT")

inputs = tokenizer("Hier ein Beispieltext über soziale Ungleichheit.", return_tensors="pt")
outputs = model(**inputs)