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Sentence Splitter

Split any paragraph into individual sentences instantly online. Output as plain lines, numbered list, or bullet points. Free sentence splitter tool — no signup, runs entirely in your browser.

Input Paragraph

Split Sentences

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Sentence splitting — also called sentence boundary detection or sentence segmentation — is the process of dividing a block of text into its constituent sentences. The task is straightforward in clean text but becomes nuanced with abbreviations (Dr., Inc., e.g.), decimal numbers, ellipses, and quoted speech, all of which contain periods that do not end sentences.

Sentence segmentation is a foundational step in natural language processing (NLP) pipelines. Tasks like machine translation, summarization, sentiment analysis, and named entity recognition all process text sentence by sentence. Splitting text accurately before passing it to a translation API or language model reduces context confusion and produces better results than feeding whole paragraphs.

This tool splits text into sentences in the browser using regex-based boundary detection: it splits on '. ', '! ', and '? ' followed by a capital letter, while ignoring common abbreviations. The output can be rendered as plain lines, a numbered list, or a bulleted list — formats suited to different downstream tasks like training data labeling, content auditing, or manual review.

Common Use Cases

Preparing text for machine translation APIs

Translation APIs (DeepL, Google Translate, Amazon Translate) have token limits per request and perform better on individual sentences than on long paragraphs. Splitting a document into sentences before batching API calls ensures each request stays within limits and that sentence-level translation quality is preserved, particularly for languages with different word orders.

Creating training data for NLP models

Fine-tuning language models and training text classifiers requires sentence-level labeled examples. When annotating a corpus for sentiment, intent, or entity classification, annotators work sentence by sentence. Splitting source documents into sentences as the first step produces the granular units needed for labeling tools like Prodigy, Label Studio, or custom annotation pipelines.

Auditing generated content sentence by sentence

Editorial teams reviewing AI-generated marketing copy, legal boilerplate, or product descriptions often need to evaluate each sentence independently for tone, accuracy, and compliance. Splitting the generated text into numbered sentences makes it easy to reference specific sentences in review comments and track which ones have been approved or revised.

Output Formats