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language: en
license: cc-by-4.0
base_model: FacebookAI/roberta-base
pipeline_tag: text-classification
tags:
- english
- literature
- digital-humanities
- narratology
widget:
- text: "He walked through the door and shut it behind him."
example_title: action_space
- text: "From the hilltop she could see the whole valley spread out below."
example_title: visual_space
- text: "The room felt heavy and oppressive, as if the walls were closing in."
example_title: perceived_space
- text: "A table stood in the corner, covered with dusty books."
example_title: descriptive_space
- text: "She wondered whether he would ever write back."
example_title: no_space
---
# en-setting-classifier
Sentence-level classifier for the representation of space in English narrative prose.
Fine-tuned from [`FacebookAI/roberta-base`](https://huggingface.co/FacebookAI/roberta-base).
English counterpart to
[`katrohrb/de-setting-classifier`](https://huggingface.co/katrohrb/de-setting-classifier);
both models use the same five categories and the same label encoding.
## Categories
| Label | Description |
|---------------------|-----------------------------------------------------------------------------|
| `action_space` | Space registered through a character's movement and direct physical contact |
| `visual_space` | Space as perceived by a character from a distance |
| `perceived_space` | Space as it carries mood and atmosphere |
| `descriptive_space` | Things and objects in space, without reference to a character |
| `no_space` | 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
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
name = "katrohrb/en-setting-classifier"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()
sentence = "He walked through the door and shut it behind him."
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:
```python
from transformers import pipeline
clf = pipeline("text-classification", model="katrohrb/en-setting-classifier")
clf("From the hilltop she could see the whole valley spread out below.")
# → [{'label': 'visual_space', 'score': 0.87}]
```
## Citation
Rohrbacher, K., Nieth, B., Salin, E., Eskofier, B., Mahlberg, M. (2026).
How LLMs Build Fictional Worlds: Measuring Setting and Narrative Space in AI-Generated Creative Storytelling.
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP).
To appear. Preprint: https://doi.org/10.48550/arXiv.2609.02482
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