metadata
language: de
license: cc-by-4.0
base_model: lkonle/fiction-gbert-large
pipeline_tag: text-classification
tags:
- german
- literature
- digital-humanities
- narratology
widget:
- text: Er trat durch die Tür und schloss sie hinter sich.
example_title: action_space
- text: Vom Hügel aus konnte sie das ganze Tal unter sich liegen sehen.
example_title: visual_space
- text: Das Zimmer wirkte schwer und bedrückend, als rückten die Wände näher.
example_title: perceived_space
- text: In der Ecke stand ein Tisch, bedeckt mit staubigen Büchern.
example_title: descriptive_space
- text: Sie fragte sich, ob er ihr jemals zurückschreiben würde.
example_title: no_space
de-setting-classifier
Sentence-level classifier for the representation of space in German narrative prose.
Fine-tuned from lkonle/fiction-gbert-large
(BERT-large, pre-trained on German fiction).
German counterpart to
katrohrb/en-setting-classifier;
both models use the same five categories and the same label encoding.
Categories
| Label | German term | Description |
|---|---|---|
action_space |
Aktionsraum | Space registered through a character's movement and direct physical contact |
visual_space |
Anschauungsraum | Space as perceived by a character from a distance |
perceived_space |
gestimmter Raum | Space as it carries mood and atmosphere |
descriptive_space |
Raumbeschreibung | Things and objects in space, without reference to a character |
no_space |
kein Raum | No spatial reference |
Label ids: 0 perceived_space, 1 action_space, 2 visual_space, 3 descriptive_space,
4 no_space (stored in config.json as id2label).
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
name = "katrohrb/de-setting-classifier"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()
sentence = "Er trat durch die Tür und schloss sie hinter sich."
with torch.no_grad():
probs = model(**tok(sentence, return_tensors="pt")).logits.softmax(-1)[0]
print(model.config.id2label[int(probs.argmax())])
# → action_space
Or with a pipeline:
from transformers import pipeline
clf = pipeline("text-classification", model="katrohrb/de-setting-classifier")
clf("Vom Hügel aus konnte sie das ganze Tal unter sich liegen sehen.")
# → [{'label': 'visual_space', 'score': 0.93}]
Citation
Rohrbacher, K. (2025). Opening Worlds: Narrative Beginnings and the Role of Setting. CCLS2025 Conference Preprints, 4(1). https://doi.org/10.26083/tuprints-00030149