datasets:
- aehrm/dtaec-lexica
language: de
pipeline_tag: translation
model-index:
- name: aehrm/dtaec-type-normalizer
results:
- task:
name: Historic Text Normalization (type-level)
type: translation
dataset:
name: DTA EvalCorpus Lexicon
type: aehrm/dtaec-lexicon
split: dev
metrics:
- name: Word Accuracy
type: accuracy
value: 0.9546
- name: Word Accuracy OOV
type: accuracy
value: 0.9096
license: cc0-1.0
DTAEC Type Normalizer
This model is trained from scratch to normalize historic spelling of German to contemporary one. It is type-based, which means that it takes only a single token (without whitespace) as input, and generates the normalized variant. It achieves the following results on the evaluation set:
- Loss: 0.0308
- Wordacc: 0.9546
- Wordacc Oov: 0.9096
Note: This model is part of a larger system, which uses an additional GPT-based model to disambiguate different normalization forms by taking in the full context. See https://github.com/aehrm/hybrid_textnorm.
Training and evaluation data
The model has been trained on the DTA-EC Parallel Corpus Lexicon (aehrm/dtaec-lexica), which is from a parallel corpus of the Deutsche Textarchiv (German Text Archive), who aligned historic prints of documents with their moden editions in contemporary orthography.
Training was done on type-level, where, given the historic form of a type, the model must predict the corresponding normalized type that appeared most frequent in the parallel corpus.
Demo Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained('aehrm/dtaec-type-normalizer')
model = AutoModelForSeq2SeqLM.from_pretrained('aehrm/dtaec-type-normalizer')
# Note: you CANNOT normalize full sentences, only word for word!
model_in = tokenizer(['Freyheit', 'seyn', 'ſelbstthätig'], return_tensors='pt', padding=True)
model_out = model.generate(**model_in)
print(tokenizer.batch_decode(model_out, skip_special_tokens=True))
# >>> ['Freiheit', 'sein', 'selbsttätig']
Or, more compact using the huggingface pipeline
:
from transformers import pipeline
pipe = pipeline(model="aehrm/dtaec-type-normalizer")
out = pipe(['Freyheit', 'seyn', 'ſelbstthätig'])
print(out)
# >>> [{'generated_text': 'Freiheit'}, {'generated_text': 'sein'}, {'generated_text': 'selbsttätig'}]
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 64
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Training Loss | Epoch | Step | Validation Loss | Wordacc | Wordacc Oov | Gen Len |
---|---|---|---|---|---|---|
0.0912 | 1.0 | 12628 | 0.0698 | 0.8984 | 0.8421 | 12.3456 |
0.0746 | 2.0 | 25256 | 0.0570 | 0.9124 | 0.8584 | 12.3442 |
0.0622 | 3.0 | 37884 | 0.0493 | 0.9195 | 0.8717 | 12.3512 |
0.0584 | 4.0 | 50512 | 0.0465 | 0.9221 | 0.8749 | 12.3440 |
0.0497 | 5.0 | 63140 | 0.0436 | 0.9274 | 0.8821 | 12.3552 |
0.0502 | 6.0 | 75768 | 0.0411 | 0.9311 | 0.8858 | 12.3519 |
0.0428 | 7.0 | 88396 | 0.0396 | 0.9336 | 0.8878 | 12.3444 |
0.0416 | 8.0 | 101024 | 0.0372 | 0.9339 | 0.8887 | 12.3471 |
0.042 | 9.0 | 113652 | 0.0365 | 0.9396 | 0.8944 | 12.3485 |
0.0376 | 10.0 | 126280 | 0.0353 | 0.9412 | 0.8962 | 12.3485 |
0.031 | 11.0 | 138908 | 0.0339 | 0.9439 | 0.9008 | 12.3519 |
0.0298 | 12.0 | 151536 | 0.0337 | 0.9454 | 0.9013 | 12.3479 |
0.0302 | 13.0 | 164164 | 0.0322 | 0.9470 | 0.9043 | 12.3483 |
0.0277 | 14.0 | 176792 | 0.0316 | 0.9479 | 0.9040 | 12.3506 |
0.0277 | 15.0 | 189420 | 0.0323 | 0.9488 | 0.9030 | 12.3514 |
0.0245 | 16.0 | 202048 | 0.0314 | 0.9513 | 0.9072 | 12.3501 |
0.0235 | 17.0 | 214676 | 0.0313 | 0.9520 | 0.9071 | 12.3511 |
0.0206 | 18.0 | 227304 | 0.0310 | 0.9531 | 0.9084 | 12.3502 |
0.0178 | 19.0 | 239932 | 0.0307 | 0.9545 | 0.9094 | 12.3507 |
0.016 | 20.0 | 252560 | 0.0308 | 0.9546 | 0.9096 | 12.3516 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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 DTA EvalCorpus by Bryan Jurish, Henriette Ast, Marko Drotschmann, and Christian Thomas, licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License,
- historical source text by the Deutsche Textarchiv, licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License,
- contemporary target text by TextGrid, licensed under the Creative Commons Attribution 3.0 Unported License,
- contemporary target text by Project Gutenberg, licensed under the Project Gutenberg License.
Conditions on attribution and/or restrictions to commercial use may apply.