Split text into sentences across 24 languages

sentencesplit

Rule-based sentence boundary detection that works out-of-the-box for 24 languages. Pure Python, zero dependencies.

Why sentencesplit

Most sentence splitters choke on abbreviations, numbered references, initials, and other ambiguous periods. sentencesplit uses a deep rule engine (derived from pySBD / Pragmatic Segmenter) to handle these correctly:

import sentencesplit

seg = sentencesplit.Segmenter(language="en")
seg.segment("My name is Jonas E. Smith. Please turn to p. 55.")
# ['My name is Jonas E. Smith. ', 'Please turn to p. 55.']

Naive split(".") or regex-based splitters would break on E., p., and 55. above. sentencesplit gets these right across English, Chinese, Japanese, Spanish, and 20+ other languages.

What it's good at:

• Abbreviations, honorifics, and initials (Dr., U.S., p. 55)
• CJK sentence-ending punctuation (。, ！, ？) with quote/bracket awareness
• Mixed-language text via the built-in en_es_zh combined profile
• Streaming/incremental input: should_wait_for_more() tells you if the last boundary might change as more text arrives
• Character-offset spans for downstream annotation, NER, or LLM token alignment
• Lists, parentheticals, ellipses, and OCR/PDF artifacts
• No model downloads, no GPU, no network calls -- just pip install and go

Install

pip install sentencesplit

Python 3.11+. No dependencies to install.

Quick start

Basic segmentation

import sentencesplit

seg = sentencesplit.Segmenter(language="en")
seg.segment("Dr. Smith called at 3 p.m. He said to see p. 55. Then he left.")
# ['Dr. Smith called at 3 p.m. ', 'He said to see p. 55. ', 'Then he left.']

Character-offset spans

seg = sentencesplit.Segmenter(language="en")
seg.segment_spans("My name is Jonas E. Smith. Please turn to p. 55.")
# [TextSpan(sent='My name is Jonas E. Smith. ', start=0, end=27),
#  TextSpan(sent='Please turn to p. 55.', start=27, end=48)]

segment_spans() always returns TextSpan objects with .sent, .start, .end regardless of the char_span constructor flag.

Streaming / lookahead

When processing streaming text (e.g. LLM output), you often can't tell if the last period is truly the end of a sentence. sentencesplit can probe for you:

seg = sentencesplit.Segmenter(language="en")

result = seg.segment_with_lookahead("The model is GPT 3.")
result.segments          # ['The model is GPT 3.']
result.should_wait_for_more  # True  -- "3." might continue as "3.5"

result = seg.segment_with_lookahead("This is the finale.")
result.should_wait_for_more  # False -- clearly a complete sentence

should_wait_for_more() works by appending tiny probe suffixes and re-running segmentation. If the final boundary changes, it returns True. This handles abbreviations, numeric decimals, and language-specific ambiguities without any special configuration.

Streaming segmentation

StreamSegmenter wraps the lookahead primitives in a stateful, feed-as-you-go API. You push text deltas (LLM tokens, ASR partials, chat chunks) and it emits completed sentences only once their boundary is stable, buffering the ambiguous tail so a downstream consumer (e.g. a TTS engine) never speaks a half-formed sentence:

from sentencesplit import StreamSegmenter

stream = StreamSegmenter(language="en")  # buffering_mode="conservative" by default

for token in ["I spoke with Dr", ".", " Smith", " yesterday", ". ", "Goodbye", "."]:
    stream.feed(token)
    for sentence in stream.get_completed_sentences():
        speak(sentence)  # 'I spoke with Dr. Smith yesterday. ' (held until "Dr." resolved)

# At end of stream, flush the buffered tail.
for sentence in stream.flush():
    speak(sentence)  # 'Goodbye.'

The cornerstone contract is streaming == non-streaming: feeding the full text and concatenating get_completed_sentences() + flush() yields exactly what Segmenter.segment() returns for that text.

stream = StreamSegmenter(language="en")
stream.feed(full_text)
assert stream.get_completed_sentences() + stream.flush() == Segmenter(language="en").segment(full_text)

StreamSegmenter accepts the same language / clean / char_span / split_mode params as Segmenter, plus a streaming-specific buffering_mode ("conservative" (default) / "balanced" / "aggressive") and an optional max_buffer_size guard against an unbounded tail.

See examples/streaming_to_tts_recipe.py for a runnable LLM-to-TTS recipe.

CJK languages

seg = sentencesplit.Segmenter(language="zh")
seg.segment("这是第一句。这是第二句！这是第三句？")
# ['这是第一句。', '这是第二句！', '这是第三句？']

Chinese (zh) and Japanese (ja) use CJKBoundaryProfile, which recognizes CJK sentence-ending punctuation and closing quotes/brackets.

Mixed-language text

Use the built-in en_es_zh profile for text that mixes English, Spanish, and Chinese:

seg = sentencesplit.Segmenter(language="en_es_zh")
seg.segment("Hola Sr. Lopez. This is Dr. Wang. 今天天气很好。")
# ['Hola Sr. Lopez. ', 'This is Dr. Wang. ', '今天天气很好。']

You can build your own combined profile by merging abbreviation lists from any languages that share the same writing system. See Multi-language segmentation below.

Split mode

A global split-bias for genuinely ambiguous boundaries - initialisms before a
capital (H.B.S. Applications), Ph.D. Smith, st., trailing-thought ellipses,
multi-sentence quotations, mid-sentence !, a.m./p.m. before a capital, and
inline ordinals vs. numbered lists. Structural rules (decimals,
period-before-comma, known abbreviations) are never affected.

# balanced (default) -- the historically tuned behavior; output is unchanged
# from earlier releases.
seg = sentencesplit.Segmenter(language="en", split_mode="balanced")

# conservative -- lean every ambiguous case toward keeping text joined
# (fewer false splits, more missed boundaries / under-split).
seg = sentencesplit.Segmenter(language="en", split_mode="conservative")

# aggressive -- lean ambiguous cases toward splitting (catches more real
# boundaries at the cost of some false splits / over-split).
seg = sentencesplit.Segmenter(language="en", split_mode="aggressive")

For example, "We discussed H.B.S. Applications are due." stays one sentence in
balanced/conservative (the surname reading) but splits in aggressive.

spaCy integration

sentencesplit registers as a spaCy pipeline component via entry points. Install with the optional spacy extra:

pip install sentencesplit[spacy]

import spacy

nlp = spacy.blank("en")
nlp.add_pipe("sentencesplit")

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.]

See examples/sentencesplit_as_spacy_component.py for more.

PDF / OCR text

seg = sentencesplit.Segmenter(language="en", clean=True, doc_type="pdf")
seg.segment(ocr_text)

clean=True normalizes HTML entities, escaped newlines, and PDF line-break artifacts before segmenting.

Supported languages

List the supported codes at runtime - cheap to call, imports no language modules:

import sentencesplit

sentencesplit.list_languages()
# ['am', 'ar', 'bg', 'da', 'de', 'el', 'en', 'en_es_zh', 'en_legal', 'es', 'fa',
#  'fr', 'hi', 'hy', 'it', 'ja', 'kk', 'mr', 'my', 'nl', 'pl', 'ru', 'sk', 'tl',
#  'ur', 'zh']

24 languages with ISO 639-1 codes, plus 2 specialized profiles:

Code	Language	Code	Language	Code	Language
am	Amharic	fa	Persian	mr	Marathi
ar	Arabic	fr	French	my	Burmese
bg	Bulgarian	el	Greek	nl	Dutch
da	Danish	hi	Hindi	pl	Polish
de	German	hy	Armenian	ru	Russian
en	English	it	Italian	sk	Slovak
es	Spanish	ja	Japanese	tl	Tagalog
kk	Kazakh	zh	Chinese	ur	Urdu

Specialized profiles: en_es_zh (combined English/Spanish/Chinese), en_legal (English legal text).

Coming from pysbd

sentencesplit is derived from pySBD and keeps the same core API, so migration is usually a rename:

# Before
import pysbd
seg = pysbd.Segmenter(language="en", clean=False)
seg.segment("My name is Jonas E. Smith. Please turn to p. 55.")

# After
import sentencesplit
seg = sentencesplit.Segmenter(language="en", clean=False)
seg.segment("My name is Jonas E. Smith. Please turn to p. 55.")

Segmenter(language=..., clean=..., char_span=...), segment(), and the TextSpan fields (.sent, .start, .end) all behave as they do in pySBD, and the English Golden Rules pass identically. What you gain on top:

• Streaming/lookahead - segment_with_lookahead() / should_wait_for_more() for incremental input, plus the higher-level StreamSegmenter feed/flush wrapper for token-by-token sources (LLM output, ASR partials).
• split_mode - a "conservative" / "balanced" / "aggressive" bias for ambiguous boundaries ("balanced" is the default and matches the historically tuned output).
• Discovery - list_languages(), and active maintenance on Python 3.11+.

Multi-language segmentation

Languages with similar writing systems can be combined into a single segmenter by merging their abbreviation lists. This avoids needing to detect the language of each sentence before segmenting.

import sentencesplit
from sentencesplit.abbreviation_replacer import AbbreviationReplacer
from sentencesplit.lang.common import Common, Standard
from sentencesplit.lang.english import English
from sentencesplit.lang.spanish import Spanish
from sentencesplit.lang.french import French
from sentencesplit.languages import LANGUAGE_CODES

class MultiLang(Common, Standard):
    iso_code = 'multi'

    class Abbreviation(Standard.Abbreviation):
        ABBREVIATIONS = sorted(set(
            Standard.Abbreviation.ABBREVIATIONS +
            Spanish.Abbreviation.ABBREVIATIONS +
            French.Abbreviation.ABBREVIATIONS
        ))
        PREPOSITIVE_ABBREVIATIONS = sorted(set(
            Standard.Abbreviation.PREPOSITIVE_ABBREVIATIONS +
            Spanish.Abbreviation.PREPOSITIVE_ABBREVIATIONS +
            French.Abbreviation.PREPOSITIVE_ABBREVIATIONS
        ))
        NUMBER_ABBREVIATIONS = sorted(set(
            Standard.Abbreviation.NUMBER_ABBREVIATIONS +
            Spanish.Abbreviation.NUMBER_ABBREVIATIONS +
            French.Abbreviation.NUMBER_ABBREVIATIONS
        ))

    class AbbreviationReplacer(AbbreviationReplacer):
        SENTENCE_STARTERS = English.AbbreviationReplacer.SENTENCE_STARTERS

from sentencesplit.languages import register_language

register_language("multi", MultiLang)  # or: LANGUAGE_CODES["multi"] = MultiLang

seg = sentencesplit.Segmenter(language="multi", clean=False)
print(seg.segment("Hola Srta. Ledesma. How are you?"))
# ['Hola Srta. Ledesma. ', 'How are you?']

register_language() (and unregister_language()) mutate a process-global, non-thread-safe registry shared by every Segmenter. Register custom languages once at import time, before any concurrent segmentation, rather than from worker threads.

This works well for languages that share the Common and Standard base classes and use the same sentence-ending punctuation (., !, ?). The same pattern can be extended to other similar languages like Italian, Dutch, or Danish. Languages with different writing systems or punctuation (e.g. Japanese, Arabic) would need a different approach.

Custom processor hooks

If you need to customize segmentation beyond regex tables and abbreviation lists, override Processor hooks on your language class.

The processor treats most hooks as pure transformations:

• replace_abbreviations(text: str) -> str
• replace_numbers(text: str) -> str
• replace_continuous_punctuation(text: str) -> str
• replace_periods_before_numeric_references(text: str) -> str
• between_punctuation(text: str) -> str
• split_into_segments(text: str | None = None) -> list[str]
• _resplit_segments(sentences: list[str]) -> list[str]
• _merge_orphan_fragments(sentences: list[str]) -> list[str]

For most languages, overriding one or two of these hooks is enough. Prefer calling super() and transforming the returned text instead of mutating self.text directly.

from sentencesplit.lang.common import Common, Standard
from sentencesplit.languages import LANGUAGE_CODES
from sentencesplit.processor import Processor


class Demo(Common, Standard):
    iso_code = "demo"

    class Processor(Processor):
        def replace_numbers(self, text: str) -> str:
            text = super().replace_numbers(text)
            # Example: protect section markers like "§. 5"
            return text.replace("§.", "§∯")

        def _resplit_segments(self, sentences: list[str]) -> list[str]:
            # Reuse the default resplit logic, then add project-specific tweaks.
            return super()._resplit_segments(sentences)


LANGUAGE_CODES["demo"] = Demo

sentencesplit.language_profile.LanguageProfile is the internal adapter that resolves these hooks and compiled regexes for the processor. It is useful for contributors working on the engine, but it is not intended as a stable public extension API.

Releasing

Releases are published manually from GitHub Actions.

One-time setup:

1. In GitHub, create an environment named pypi.
2. In PyPI, add a Trusted Publisher for repo yisding/sentencesplit, workflow .github/workflows/publish.yml, and environment pypi.

Release steps:

1. Merge the code you want to publish into main.
2. Open GitHub Actions and run the Release workflow on main.
3. Choose the version bump: patch, minor, major, or prerelease.
4. Set dry_run=true to preview the release, then run it again with dry_run=false for the real release.
5. The Release workflow creates the version commit, tag, changelog update, and GitHub Release. It does not publish to PyPI - publishing is a separate, manual step.
6. To publish, run the Publish to PyPI workflow manually (workflow_dispatch) and enter the tag to publish, for example v0.0.1; it checks out that tag and uploads the built distributions using Trusted Publishing.

python-semantic-release uses Conventional Commits to generate changelog entries, so commit messages like fix: ..., feat: ..., and feat!: ... are recommended.

Citation

This project is derived from pySBD. If you use it in your projects or research, please cite the original PySBD: Pragmatic Sentence Boundary Disambiguation paper.

@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 is derived from pySBD by Nipun Sadvilkar, which itself wouldn't be possible without the great work done by the Pragmatic Segmenter team.