---
tags:
  - flair
  - token-classification
  - sequence-tagger-model
language: de
---
# REDEWIEDERGABE Tagger: indirect STWR

This model is part of an ensemble of binary taggers that recognize German speech, thought and writing representation. They can be used to automatically detect and annotate the following 4 types of speech, thought and writing representation in German texts:

| STWR type                      | Example                                                                 | Translation                                              |
|--------------------------------|-------------------------------------------------------------------------|----------------------------------------------------------|
| direct                         | Dann sagte er: **"Ich habe Hunger."**                                       | Then he said: **"I'm hungry."**                             |
| free indirect ('erlebte Rede') | Er war ratlos. **Woher sollte er denn hier bloß ein Mittagessen bekommen?** | He was at a loss. **Where should he ever find lunch here?** |
| indirect (**this tagger**)                 | Sie fragte, **wo das Essen sei.**                                           | She asked **where the food was.**                            |
| reported                  | **Sie sprachen über das Mittagessen.**                                      | **They talked about lunch.**                                 |

The ensemble is trained on the [REDEWIEDERGABE corpus](https://github.com/redewiedergabe/corpus) ([Annotation guidelines](http://redewiedergabe.de/richtlinien/richtlinien.html)), fine-tuning each tagger on the domain-adapted [lkonle/fiction-gbert-large](https://huggingface.co/lkonle/fiction-gbert-large). ([Training Code](https://github.com/cophi-wue/LLpro/blob/main/contrib/train_redewiedergabe.py))

**F1-Scores:**

| STWR type |  F1-Score |
|-----------|-----------|
| direct    | 90.76     |
| **indirect (this tagger)** | **79.16**  |
| free indirect | 58.00  |
| reported   | 70.47   |

----

**Demo Usage:**

```python
from flair.data import Sentence
from flair.models import SequenceTagger


sentence = Sentence('Sie sprachen über das Mittagessen. Sie fragte, wo das Essen sei. Woher sollte er das wissen? Dann sagte er: "Ich habe Hunger."')

rwtypes = ['direct', 'indirect', 'freeindirect', 'reported']
for rwtype in rwtypes:
    model = SequenceTagger.load(f'aehrm/redewiedergabe-{rwtype}')
    model.predict(sentence)
    print(rwtype, [ x.data_point.text for x in sentence.get_labels() ])
# >>> direct ['"', 'Ich', 'habe', 'Hunger', '.', '"']
# >>> indirect ['wo', 'das', 'Essen', 'sei', '.']
# >>> freeindirect ['Woher', 'sollte', 'er', 'das', 'wissen', '?']
# >>> reported ['Sie', 'sprachen', 'über', 'das', 'Mittagessen', '.', 'Woher', 'sollte', 'er', 'das', 'wissen', '?']
```

**Cite**:

Please cite the following paper when using this model.

```
@inproceedings{ehrmanntraut-et-al-llpro-2023,
	address = {Ingolstadt, Germany},
	title = {{LLpro}: A Literary Language Processing Pipeline for {German} Narrative Text},
	booktitle = {Proceedings of the 10th Conference on Natural Language Processing ({KONVENS} 2022)},
	publisher = {{KONVENS} 2023 Organizers},
	author = {Ehrmanntraut, Anton and Konle, Leonard and Jannidis, Fotis},
	year = {2023},
}
```