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This model accepts as input lower-cased, unpunctuated, unsegmented text in 47 languages and performs punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation). All languages are processed with the same algorithm with no need for language tags or language-specific branches in the graph. This includes continuous-script and non-continuous script languages, predicting language-specific punctuation, etc. This model is fun to play with, but the results could be better. I would recommend these newer, better models: * Better English model * Better Romance languages model * Better 47-language

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This model accepts as input lower-cased, unpunctuated English text and performs in one pass punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation). In contast to many similar models, this model can predict punctuated acronyms (e.g., "U.S.") via a special "acronym" class, as well as arbitarily-capitalized words (NATO, McDonald's, etc.) via multi-label true-casing predictions. **Widget note**: The text generation widget doesn't seem to respect line breaks. Instead, the pipeline inserts a new line token `\n` in the text where the model has predicted sentence boundaries (line breaks).

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This is an `xlm-roberta` fine-tuned to restore punctuation, true-case (capitalize), and detect sentence boundaries (full stops) in 47 languages. If you want to just play with the model, the widget on this page will suffice. To use the model offline, the following snippets show how to use the model both with a wrapper (that I wrote, available from `PyPI`) and manual usuage (using the ONNX and SentencePiece models in this repo). The easiest way to use this model is to install punctuators:

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