metadata
language:
- de
- gmh
library_tags:
- transformers
- torch
tags:
- text-generation
- mt5
- middle-high-german
- normalization
pipeline_tag: text-generation
base_model: google/mt5-base
normaere-model
Transformer-based seq2seq normalizer for Middle High German (MHG) text, fine-tuned from google/mt5-base.
Model Details
- Base Model: google/mt5-base (580M parameters)
- Architecture: MT5ForConditionalGeneration (encoder-decoder)
- Precision: FP16
- Max Sequence Length: 512 tokens
- Vocabulary Size: 250,117 (augmented with medieval abbreviation characters)
Intended Uses
Normalizes Middle High German texts (ca. 1050–1500) according to the standards of the Referenzkorpus Mittelhochdeutsch (ReM):
- Solves common abbreviations
- Splits off and expands common pro- and enclitics
- Applies editorial post-processing
Training Data
Fine-tuned on the Referenzkorpus Mittelhochdeutsch (ReM), Version 2.1.
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("JonasHermann/normaere-model")
model = AutoModelForSeq2SeqLM.from_pretrained("JonasHermann/normaere-model")
input_text = "die stete wârheit"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
References
- mT5: Xue et al., 2021
- Training corpus: Roussel, Adam; Klein, Thomas; Dipper, Stefanie; Wegera, Klaus-Peter; Wich-Reif, Claudia (2024). Referenzkorpus Mittelhochdeutsch (1050–1350), Version 2.1, https://www.linguistics.ruhr-uni-bochum.de/rem/. ISLRN 937-948-254-174-0.