README.md

metadata

license: apache-2.0
base_model: google/byt5-small
language: de
model-index:
  - name: ybracke/transnormer-19c-beta-v02
    results:
      - task:
          name: Historic Text Normalization
          type: translation
        dataset:
          name: DTA EvalCorpus
          type: N/A
          split: test
        metrics:
          - name: Word Accuracy
            type: accuracy
            value: 0.98878
          - name: Word Accuracy (case insensitive)
            type: accuracy
            value: 0.99343

Transnormer 19th century (beta v01)

This model normalizes spelling variants in historical German text to the modern spelling. We fine-tuned google/byt5-small on a modified version of the DTA EvalCorpus (1780-1901).

Model description

Demo Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ybracke/transnormer-19c-beta-v02")
model = AutoModelForSeq2SeqLM.from_pretrained("ybracke/transnormer-19c-beta-v02")
sentence = "Die Königinn ſaß auf des Pallaſtes mittlerer Tribune."
inputs = tokenizer(sentence, return_tensors="pt",)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# >>> ['Die Königin saß auf des Palastes mittlerer Tribüne.']

Or use this model with the pipeline API like this:

from transformers import pipeline
transnormer = pipeline('text2text-generation', model='ybracke/transnormer-19c-beta-v02')
sentence = "Die Königinn ſaß auf des Pallaſtes mittlerer Tribune."
print(transnormer(sentence))
# >>> [{'generated_text': 'Die Königin saß auf des Palastes mittlerer Tribüne.'}]

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Framework versions

• Transformers 4.31.0
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.13.3