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README.md
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language: de
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---
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# REDEWIEDERGABE Tagger: indirect STWR
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language: de
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---
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# REDEWIEDERGABE Tagger: indirect STWR
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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:
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| STWR type | Example | Translation |
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|--------------------------------|-------------------------------------------------------------------------|----------------------------------------------------------|
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| direct | Dann sagte er: **"Ich habe Hunger."** | Then he said: **"I'm hungry."** |
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| 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?** |
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| indirect (**this tagger**) | Sie fragte, **wo das Essen sei.** | She asked **where the food was.** |
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| reported | **Sie sprachen über das Mittagessen.** | **They talked about lunch.** |
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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))
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**F1-Scores:**
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| STWR type | F1-Score |
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|-----------|-----------|
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| direct | 90.76 |
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| **indirect (this tagger)** | **79.16** |
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| free indirect | 58.00 |
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| reported | 70.47 |
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----
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**Demo Usage:**
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```python
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from flair.data import Sentence
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from flair.models import SequenceTagger
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sentence = Sentence('Sie sprachen über das Mittagessen. Sie fragte, wo das Essen sei. Woher sollte er das wissen? Dann sagte er: "Ich habe Hunger."')
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rwtypes = ['direct', 'indirect', 'freeindirect', 'reported']
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for rwtype in rwtypes:
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model = SequenceTagger.load(f'aehrm/redewiedergabe-{rwtype}')
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model.predict(sentence)
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print(rwtype, [ x.data_point.text for x in sentence.get_labels() ])
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# >>> direct ['"', 'Ich', 'habe', 'Hunger', '.', '"']
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# >>> indirect ['wo', 'das', 'Essen', 'sei', '.']
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# >>> freeindirect ['Woher', 'sollte', 'er', 'das', 'wissen', '?']
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# >>> reported ['Sie', 'sprachen', 'über', 'das', 'Mittagessen', '.', 'Woher', 'sollte', 'er', 'das', 'wissen', '?']
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```
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**Cite**:
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Please cite the following paper when using this model.
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```
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@inproceedings{ehrmanntraut-et-al-llpro-2023,
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address = {Ingolstadt, Germany},
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title = {{LLpro}: A Literary Language Processing Pipeline for {German} Narrative Text},
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booktitle = {Proceedings of the 10th Conference on Natural Language Processing ({KONVENS} 2022)},
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publisher = {{KONVENS} 2023 Organizers},
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author = {Ehrmanntraut, Anton and Konle, Leonard and Jannidis, Fotis},
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year = {2023},
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}
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```
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