| --- |
| license: cc-by-4.0 |
| language: |
| - de |
| base_model: |
| - dbmdz/bert-base-german-cased |
| pipeline_tag: text-classification |
| --- |
| |
| # IP-BERT |
|
|
| **IP-BERT** is a transformer-based model designed to detect **economic inequality** in German texts. |
| It was fine-tuned on **German Bundestag debates** (sourced from [OpenDiscourse](https://doi.org/10.7910/DVN/FIKIBO)), where each training instance consists of 3-sentence segments. |
|
|
| --- |
|
|
| ## Model Description |
|
|
| - **Architecture**: `bert-base-german-cased` (from [dbmdz](https://huggingface.co/dbmdz/bert-base-german-cased)) |
| - **Task**: Binary classification – detecting economic inequality in German texts |
| - **Labels**: |
| - `0` = no economic inequality |
| - `1` = economic 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 |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| - **Primary use case**: Analysis of parliamentary discourse on economic inequality |
| - **Research contexts**: Political science, computational social science, discourse analysis |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## Usage |
|
|
| You can load the model with the Hugging Face `transformers` library: |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| tokenizer = AutoTokenizer.from_pretrained("miriamex/IP-BERT") |
| model = AutoModelForSequenceClassification.from_pretrained("miriamex/IP-BERT") |
| |
| inputs = tokenizer("Hier ein Beispieltext über ökonomische Ungleichheit.", return_tensors="pt") |
| outputs = model(**inputs) |
| ``` |
