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--- |
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datasets: |
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- aehrm/dtaec-lexica |
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language: de |
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pipeline_tag: translation |
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model-index: |
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- name: aehrm/dtaec-type-normalizer |
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results: |
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- task: |
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name: Historic Text Normalization (type-level) |
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type: translation |
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dataset: |
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name: DTA EvalCorpus Lexicon |
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type: aehrm/dtaec-lexicon |
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split: dev |
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metrics: |
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- name: Word Accuracy |
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type: accuracy |
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value: 0.9546 |
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- name: Word Accuracy OOV |
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type: accuracy |
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value: 0.9096 |
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license: cc0-1.0 |
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--- |
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# DTAEC Type Normalizer |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0308 |
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- Wordacc: 0.9546 |
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- Wordacc Oov: 0.9096 |
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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>. |
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## Training and evaluation data |
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The model has been trained on the DTA-EC Parallel Corpus Lexicon ([aehrm/dtaec-lexica](https://huggingface.co/datasets/aehrm/dtaec-lexicon)), which is from a [parallel corpus](https://kaskade.dwds.de/~moocow/software/dtaec/) of the Deutsche Textarchiv (German Text Archive), who aligned historic prints of documents with their moden editions in contemporary orthography. |
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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*. |
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## Demo Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained('aehrm/dtaec-type-normalizer') |
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model = AutoModelForSeq2SeqLM.from_pretrained('aehrm/dtaec-type-normalizer') |
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# Note: you CANNOT normalize full sentences, only word for word! |
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model_in = tokenizer(['Freyheit', 'seyn', 'ſelbstthätig'], return_tensors='pt', padding=True) |
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model_out = model.generate(**model_in) |
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print(tokenizer.batch_decode(model_out, skip_special_tokens=True)) |
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# >>> ['Freiheit', 'sein', 'selbsttätig'] |
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``` |
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Or, more compact using the huggingface `pipeline`: |
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```python |
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from transformers import pipeline |
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pipe = pipeline(model="aehrm/dtaec-type-normalizer") |
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out = pipe(['Freyheit', 'seyn', 'ſelbstthätig']) |
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print(out) |
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# >>> [{'generated_text': 'Freiheit'}, {'generated_text': 'sein'}, {'generated_text': 'selbsttätig'}] |
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``` |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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- seed: 12345 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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## Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wordacc | Wordacc Oov | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-----------:|:-------:| |
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| 0.0912 | 1.0 | 12628 | 0.0698 | 0.8984 | 0.8421 | 12.3456 | |
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| 0.0746 | 2.0 | 25256 | 0.0570 | 0.9124 | 0.8584 | 12.3442 | |
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| 0.0622 | 3.0 | 37884 | 0.0493 | 0.9195 | 0.8717 | 12.3512 | |
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| 0.0584 | 4.0 | 50512 | 0.0465 | 0.9221 | 0.8749 | 12.3440 | |
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| 0.0497 | 5.0 | 63140 | 0.0436 | 0.9274 | 0.8821 | 12.3552 | |
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| 0.0502 | 6.0 | 75768 | 0.0411 | 0.9311 | 0.8858 | 12.3519 | |
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| 0.0428 | 7.0 | 88396 | 0.0396 | 0.9336 | 0.8878 | 12.3444 | |
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| 0.0416 | 8.0 | 101024 | 0.0372 | 0.9339 | 0.8887 | 12.3471 | |
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| 0.042 | 9.0 | 113652 | 0.0365 | 0.9396 | 0.8944 | 12.3485 | |
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| 0.0376 | 10.0 | 126280 | 0.0353 | 0.9412 | 0.8962 | 12.3485 | |
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| 0.031 | 11.0 | 138908 | 0.0339 | 0.9439 | 0.9008 | 12.3519 | |
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| 0.0298 | 12.0 | 151536 | 0.0337 | 0.9454 | 0.9013 | 12.3479 | |
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| 0.0302 | 13.0 | 164164 | 0.0322 | 0.9470 | 0.9043 | 12.3483 | |
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| 0.0277 | 14.0 | 176792 | 0.0316 | 0.9479 | 0.9040 | 12.3506 | |
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| 0.0277 | 15.0 | 189420 | 0.0323 | 0.9488 | 0.9030 | 12.3514 | |
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| 0.0245 | 16.0 | 202048 | 0.0314 | 0.9513 | 0.9072 | 12.3501 | |
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| 0.0235 | 17.0 | 214676 | 0.0313 | 0.9520 | 0.9071 | 12.3511 | |
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| 0.0206 | 18.0 | 227304 | 0.0310 | 0.9531 | 0.9084 | 12.3502 | |
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| 0.0178 | 19.0 | 239932 | 0.0307 | 0.9545 | 0.9094 | 12.3507 | |
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| 0.016 | 20.0 | 252560 | 0.0308 | 0.9546 | 0.9096 | 12.3516 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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### License |
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The model weights are marked with [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1). |
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**NOTE:** This model and its inferences or derivative works *may* be considered an Adaptation of |
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- the DTA EvalCorpus by Bryan Jurish, Henriette Ast, Marko Drotschmann, and Christian Thomas, licensed under the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/), |
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- historical source text by the Deutsche Textarchiv, licensed under the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/), |
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- contemporary target text by TextGrid, licensed under the [Creative Commons Attribution 3.0 Unported License](http://creativecommons.org/licenses/by/3.0/), |
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- contemporary target text by Project Gutenberg, licensed under the [Project Gutenberg License](https://www.gutenberg.org/policy/license.html). |
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Conditions on attribution and/or restrictions to commercial use may apply. |
