moderngbert-redewiedergabe

ModernGBERT Redewiedergabe Tagger

This model is a token classifier that recognizes German speech, thought and writing representation (STWR), that is being used in 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).

This model is a fine-tuned version of LSX-UniWue/ModernGBERT_1B on the REDEWIEDERGABE corpus (Annotation guidelines).

Training Script.

Performance

We report simplified F1 scores on a binarized variant (O vs B/I) for each speech type resp. medium.

Demo Usage

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.

License

The model weights are marked with CC0 1.0 Universal.

NOTE: This model and its inferences or derivative works may be considered an Adaptation of the Corpus REDEWIEDERGABE, licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Conditions on attribution and/or restrictions to commercial use may apply.

Framework versions

• PEFT 0.17.0
• Transformers 4.55.2
• Pytorch 2.8.0+cu128
• Datasets 2.21.0
• Tokenizers 0.21.4