How does the similarity checker work?

It uses three algorithms: TF-IDF cosine similarity, Jaccard index, and overlap coefficient to measure shared vocabulary.

What do the three scores mean?

Cosine weights rare words, Jaccard is shared/total unique words, and overlap shows how much the smaller text is contained in the larger one.

Is my text data safe?

Yes. All processing is done locally in your browser.

Why are scores low for short text?

Short text has fewer unique words, so scores can be unstable. Use longer samples for accuracy.

Yes, but this tool is word-based. For line changes, use the diff checker.

✓ Sentence embeddings✓ Lexical evidence✓ On-device AI

Text Similarity Checker

Q: Can it detect plagiarism?

It can highlight similarity between two texts but does not search the internet for matches.

Compare meaning with sentence-transformer embeddings and inspect shared vocabulary.

Run AI

Semantic cosine

19%

Jaccard vocabulary

33%

Overlap coefficient

Shared (3)

patterns, data, improve

Only A (7)

machine, learning, systems, learn, predictions, through, experience

Only B (6)

algorithms, can, their, forecasts, discovering, training

⚒️

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How to use Text Similarity Checker

Three steps. Instant results.

📋

Step 1

Paste two passages.

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Step 2

Run the sentence-transformer embedding model.

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Step 3

Compare semantic cosine with lexical Jaccard and overlap evidence.

How Text Similarity Checker works

The all-MiniLM-L6-v2 sentence transformer creates normalized embedding vectors for both passages. Cosine similarity compares semantic direction, allowing paraphrases to score highly without exact wording. Jaccard and overlap scores separately expose shared content vocabulary after stop-word removal.

Why use this Text Similarity Checker?

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Semantic paraphrase detection

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Sentence-transformer embeddings

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Lexical evidence beside AI score

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Stop-word filtering

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On-device inference

Frequently asked questions

How does semantic similarity work?

An all-MiniLM-L6-v2 sentence-transformer converts each text into an embedding vector. Cosine similarity measures how closely the meanings align.

What are lexical scores?

Jaccard compares shared unique content words. Overlap measures how much of the smaller vocabulary appears in the larger text.

Can it detect paraphrases?

Semantic embeddings can detect many paraphrases that share few exact words, though subtle factual differences still require human review.

Can it detect plagiarism?

It compares the two texts you provide. It does not search external sources; use the plagiarism checker for URL-based source checks.

Is text uploaded?

No. The embedding model runs in your browser after downloading.

Why is first use slower?

The sentence-transformer model downloads and caches on first use.

Related free tools

Plagiarism Checker

Compare against pasted text and public URLs.

Diff Checker

Inspect exact edits.

Text Paraphraser

Generate alternative wording.