Instructions to use aehrm/dtaec-type-normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrm/dtaec-type-normalizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="aehrm/dtaec-type-normalizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aehrm/dtaec-type-normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("aehrm/dtaec-type-normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README
Browse files
README.md
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@@ -46,6 +46,7 @@ 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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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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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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