Paper reviewComparison trialGitHub project

SAME DOCUMENT · TWO ENGINES

See where the results differ.

We ran the selected GitHub project on a published research abstract and checked the same text with the current website.

The sample

395 characters · 3 sentences

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

Read the original: Attention Is All You Need

Recorded web trial

16/09/2026, 17:26:11 UTC
SELECTED GITHUB PROJECTFree Turnitin Plagiarism Checker
Overall Plagiarism0%
Similarity Score33%
3 sentences checked0 flagged

The copied abstract was not flagged. The original source was found, but each sentence scored below the project's 50% threshold.

Sentence 137%

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder

doi.org37%arxiv.org27%doi.org27%doi.org23%
Sentence 235%

The best performing models also connect the encoder and decoder through an attention mechanism

doi.org35%arxiv.org27%
Sentence 327%

We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely

arxiv.org27%doi.org16%
CURRENT WEBSITEPaper review
91%Similarity in checked web sources
3 source texts retrieved304 matched characters

The submitted passage was located in retrieved source text. Overlapping matches are counted once.

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

These are different measurements: the GitHub project's headline counts sentences above a threshold; the current website counts verified text overlap. This single trial demonstrates behavior, not general detection accuracy.

TRY THE SCORING

Give both engines the same source.

Change the text or choose an example. This test runs the GitHub project's original scoring functions and the current matcher on the source you provide. It does not search the web.

Both engines receive exactly the same source text.
GitHub pairwise score25%Word-frequency score 2% · Five-word sequence score 25%
Current text overlap100%

Solar panels convert sunlight into electrical energy using semiconductor materials.

What this GitHub revision actually includes

Checked against the downloaded source code. Its README lists features that are absent from the current application.

CapabilityGitHub projectCurrent website
Text inputYesYes
DOCX / PDF uploadNot present in this revisionAvailable
AI writing detectionNot implementedAvailable; not part of this trial
PDF report downloadNot present in this revisionAvailable
Document coverageFirst 20 qualifying sentencesUp to 100,000 characters
Source text limitFirst 5,000 characters per pageUp to 150,000 characters per source
Match locationWhole-sentence scoreExact matching text ranges