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library_name: peft
base_model: LSX-UniWue/ModernGBERT_1B
tags:
- base_model:adapter:LSX-UniWue/ModernGBERT_1B
- lora
- transformers
- token-classification
---
# ModernGBERT Redewiedergabe Tagger
This model is a token classifier that recognizes German speech, thought and writing representation (STWR), that is being used in [LLpro](https://github.com/cophi-wue/LLpro). Besides the *medium* (speech, thought, writing) the model also predicts the *type* (direct, free indirect, indirect, reported) by providing 36 classification outputs (3 media × 4 types × B,I,O).
| STWR type | Example | Translation |
|--------------------------------|-------------------------------------------------------------------------|----------------------------------------------------------|
| direct | Dann schrieb er: **"Ich habe Hunger."** | Then he wrote: **"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 | Sie fragte, **wo das Essen sei.** | She asked **where the food was.** |
| reported | **Sie dachte über das Mittagessen.** | **She thought about lunch.** |
This model is a fine-tuned version of [LSX-UniWue/ModernGBERT_1B](https://huggingface.co/LSX-UniWue/ModernGBERT_1B) on the [REDEWIEDERGABE corpus](https://github.com/redewiedergabe/corpus) ([Annotation guidelines](http://redewiedergabe.de/richtlinien/richtlinien.html)).
[Training Script](https://github.com/cophi-wue/LLpro/blob/main/contrib/train_redewiedergabe.py).
### Performance
We report simplified F1 scores on a binarized variant (O vs B/I) for each speech type resp. medium.
| Type, Medium | F1 Score | support |
|:----------------------|---------:|---------:|
| direct.speech | 0.96 | 13598 |
| direct.thought | 0.79 | 715 |
| direct.writing | 0.19 | 996 |
| indirect.speech | 0.77 | 1226 |
| indirect.thought | 0.71 | 802 |
| indirect.writing | 0.00 | 11 |
| freeIndirect.speech | 0.73 | 198 |
| freeIndirect.thought | 0.45 | 251 |
| freeIndirect.writing | – | – |
| reported.speech | 0.69 | 1684 |
| reported.thought | 0.59 | 799 |
| reported.writing | 0.56 | 135 |
| *micro avg* | *0.86* | 20415 |
| *macro avg* | *0.54* | 20415 |
### Demo Usage
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
speech_labels = [
"direct.speech",
"direct.thought",
"direct.writing",
"indirect.speech",
"indirect.thought",
"indirect.writing",
"freeIndirect.speech",
"freeIndirect.thought",
"freeIndirect.writing",
"reported.speech",
"reported.thought",
"reported.writing",
]
text = """
Dann schrieb er: 'Ich habe Hunger."
Er war ratlos. Woher sollte er denn hier bloß ein Mittagessen bekommen?
Sie fragte, wo das Essen sei.
Sie dachte über das Mittagessen."""
model_id = 'aehrm/moderngbert-redewiedergabe'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id, num_labels=3*len(speech_labels))
inputs = tokenizer(text, return_tensors='pt')
out = model(**inputs)
batch_size, seq_len, _ = out.logits.shape
prediction = out.logits.reshape(batch_size, seq_len, 12, 3).argmax(-1)
for i, speech_label in enumerate(speech_labels):
for tok, pred in zip(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0]), prediction[0,:,i]):
pred = 'OBI'[pred]
print(tok, pred, speech_label if pred != 'O' else '')
```
### Training results
F1 Score refers to the micro average over all 36 classification outputs.
| Training Loss | Epoch | Step | Validation Loss | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1951 | 1.0 | 193 | 0.1332 | 0.1180 |
| 0.0885 | 2.0 | 386 | 0.2474 | 0.2724 |
| 0.0417 | 3.0 | 579 | 0.1455 | 0.4604 |
| 0.0753 | 4.0 | 772 | 0.1399 | 0.5522 |
| 0.0277 | 5.0 | 965 | 0.1447 | 0.6170 |
| 0.0238 | 6.0 | 1158 | 0.1770 | 0.6200 |
| 0.0153 | 7.0 | 1351 | 0.2257 | 0.6930 |
| 0.009 | 8.0 | 1544 | 0.6031 | 0.7336 |
| 0.0108 | 9.0 | 1737 | 0.4965 | 0.7428 |
| 0.0066 | 10.0 | 1930 | 0.4575 | 0.7492 |
| 0.0058 | 11.0 | 2123 | 0.7781 | 0.7983 |
| 0.006 | 12.0 | 2316 | 0.8648 | 0.8062 |
| 0.0043 | 13.0 | 2509 | 1.0377 | 0.8148 |
| 0.0033 | 14.0 | 2702 | 1.3040 | 0.8217 |
| 0.0025 | 15.0 | 2895 | 1.2637 | 0.8359 |
| 0.003 | 16.0 | 3088 | 1.3230 | 0.8477 |
| 0.0019 | 17.0 | 3281 | 1.9811 | 0.8439 |
| 0.0014 | 18.0 | 3474 | 2.1191 | 0.8482 |
| 0.0011 | 19.0 | 3667 | 2.3599 | 0.8510 |
| 0.0009 | 20.0 | 3860 | 2.4453 | 0.8528 |
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 2.21.0
- Tokenizers 0.21.4
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