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
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README.md
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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.
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## Training and evaluation data
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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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