import spacy
from pysbd.utils import PySBDFactory

nlp = spacy.blank('en')

# explicitly adding component to pipeline
# (recommended - makes it more readable to tell what's going on)
nlp.add_pipe(PySBDFactory(nlp))

# or you can use it implicitly with keyword
# pysbd = nlp.create_pipe('pysbd')
# nlp.add_pipe(pysbd)

doc = nlp('My name is Jonas E. Smith. Please turn to p. 55.')
print(list(doc.sents))
# [My name is Jonas E. Smith., Please turn to p. 55.]

Contributing

If you want to contribute new feature/language support or found a text that is incorrectly segmented using pySBD, then please head to CONTRIBUTING.md to know more and follow these steps.

1. Fork it ( https://github.com/nipunsadvilkar/pySBD/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Citation

If you use pysbd package in your projects or research, please cite PySBD: Pragmatic Sentence Boundary Disambiguation.

@inproceedings{sadvilkar-neumann-2020-pysbd,
    title = "{P}y{SBD}: Pragmatic Sentence Boundary Disambiguation",
    author = "Sadvilkar, Nipun  and
      Neumann, Mark",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.15",
    pages = "110--114",
    abstract = "We present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages. We aim to provide a realistic segmenter which can provide logical sentences even when the format and domain of the input text is unknown. In our work, we adapt the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter which we ported to Python with additional improvements and functionality. PySBD passes 97.92{\%} of the Golden Rule Set examplars for English, an improvement of 25{\%} over the next best open source Python tool.",
}

Credit

This project wouldn't be possible without the great work done by Pragmatic Segmenter team.