Instructions to use saarus72/russian_text_normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saarus72/russian_text_normalizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("saarus72/russian_text_normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("saarus72/russian_text_normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Reference correct package
Browse files
README.md
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@@ -10,7 +10,7 @@ Finetuned version of [FRED-T5 large 820M](https://huggingface.co/ai-forever/FRED
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Code [repo](https://github.com/saarus72/text_normalization/tree/dev).
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Trained on [ficbook](https://huggingface.co/datasets/IlyaGusev/ficbook), [librusec](https://huggingface.co/datasets/IlyaGusev/librusec) and [pikabu](https://huggingface.co/datasets/IlyaGusev/pikabu) sentences, inverse text normalized using modified version of [
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## Usage
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Code [repo](https://github.com/saarus72/text_normalization/tree/dev).
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Trained on [ficbook](https://huggingface.co/datasets/IlyaGusev/ficbook), [librusec](https://huggingface.co/datasets/IlyaGusev/librusec) and [pikabu](https://huggingface.co/datasets/IlyaGusev/pikabu) sentences, inverse text normalized using modified version of [word_to_number_ru](https://github.com/flockentanz/word_to_number_ru) package. Haven't trained anything yet but number normalization.
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## Usage
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