
This is a vocabulary of (semiotic_class, normalized, non-normalized, freq) tuples from [Google Text Normalization Dataset](https://www.kaggle.com/datasets/richardwilliamsproat/text-normalization-for-english-russian-and-polish).

It was generated using [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py).

It can be used to perform fast text normalization, see example in this [script](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/dataset_preparation/normalize_by_gtn_vocab.py)


size_categories:1M<n<10M language:en license:cc-by-sa-4.0 text normalization inverse text normalization region:us
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1M<n<10M"], "tags": ["text normalization", "inverse text normalization"]}
TAGS
#size_categories-1M<n<10M #language-English #license-cc-by-sa-4.0 #text normalization #inverse text normalization #region-us 


This is a vocabulary of (semiotic_class, normalized, non-normalized, freq) tuples from Google Text Normalization Dataset.

It was generated using this script.

It can be used to perform fast text normalization, see example in this script


TAGS
#size_categories-1M<n<10M #language-English #license-cc-by-sa-4.0 #text normalization #inverse text normalization #region-us 

passage: TAGS
#size_categories-1M<n<10M #language-English #license-cc-by-sa-4.0 #text normalization #inverse text normalization #region-us 
