Similarity

Updated December 02, 2024 13:46 These instructions are for users of the Turnitin website. If you are accessing Turnitin via a LMS, please see our LMS guidance.In this guide:Accessing the Similarity ReportWhy is there no Similarity Score?Accessing the Similarity ReportTo access a Similarity Report, log in to Turnitin. If you are already logged in, access the main homepage by selecting the All Classes tab from the top of any page.From the main homepage, locate the class name and select the Open button to view the assignment dashboard.Find the assignment you submitted to. Under the Similarity column, you'll find your similarity score. This is a numerical and visual indicator of the amount of similarity we found in your work.Selecting a similarity score percentage will open the Similarity Report in a new window so you can view details about any matches found.If a new window does not appear, you may need to add turnitin.com to the list of sites allowed to create pop-up windows on the web browser that you are using.Why is there no Similarity Score?There could be two reasons a similarity score does not appear under the Similarity column:If your instructor turned off student access to the Similarity Report, you will see a paper icon with a line cut through it .A dash ( — ) under the Similarity column means that the Similarity Report is still being generated and will be updated once it has finished processing.Turnitin compares submissions against an ever-expanding database of active and archived Internet pages, a subscription repository of periodicals, journals, and publications, as well as a repository of previously submitted papers.The similarity score is the overall percentage of the paper's text that matches to other sources; it is not an assessment of whether the paper includes plagiarized material. The Similarity Report is a tool for instructors and students to easily find text similarity within submitted work.

Campaign Bringing transparency to the writing process cancel Unicheck service ended on January 1, 2025 Explore more opportunities with Turnitin Thank you for your interest in Unicheck. We appreciate your support and encourage you to explore Turnitin Similarity as a trusted alternative to help advance academic integrity at your institution. Why consider Turnitin Similarity?Turnitin Similarity offers many of the same features as Unicheck, including comprehensive Similarity Reports with percentage matches and source lists, seamless LMS integration, and full technical and training support—ideal for maintaining academic integrity and simplifying workflows. Benefit from an integrated workflow Like Unicheck, Turnitin Similarity integrates with all major LMSs, including Canvas, Moodle, Blackboard, and Schoology, allowing you to work within a familiar environment while gaining valuable insights into student writing. Take a sneak peek at the Similarity Report With the launch of Turnitin’s enhanced Similarity Report, all Turnitin Similarity customers can benefit from its intuitive, insightful design. Take a demo tour to explore its features—no sign-up required! Address emerging trends in academic misconduct With additional licensing, Turnitin Similarity can support your institution in identifying when AI writing tools, such as ChatGPT or AI paraphrasing tools (text spinners), may have been used in submitted work. Learn more about AI writing detection. Take the next step Discover how Turnitin Similarity can seamlessly align with your existing workflows, integrate with your institution’s LMS, and learn about additional features available through optional licensing.

'dog':", similarity_dog)print("Similarity with 'cat':", similarity_cat)And the output is as follows:Similarity with 'hello this world': [[0.95750431]]Similarity with 'not in this world': [[0.74482829]]Similarity with 'dog': [[0.56089179]]Similarity with 'cat': [[0.52223297]]The code above does a cosine similarity comparison of “hello world” with other given text. When cosine similarity is used, values closer to 1 in the range of -1 to 1 indicates similar meaning. As seen in the output, the value of “hello this world” is the most closer to “hello world” indicating close semantic meaning compared to the words such “not in this world”, “dog” & “cat”.Pgvector is an open source PostgreSQL extension which helps users store, index, and search over embeddings.This extension offers a new datatype named ‘vector’ which can then be used to store the embeddings and perform similarity comparisons. Here is an example where you can use the SQL semantics in combination with pgvector datatype and operations to calculate similarity of contents stored in a PostgreSQL database:Store the embedding of “hello world” and other strings from the example above, into the database and run the cosine similarity search:simplepgvectortest=> CREATE TABLE testembeddings (id serial, embedding vector(6));CREATE TABLEsimplepgvectortest=> INSERT INTO testembeddings (embedding) VALUES ('[0.0, 0.0, 0.0, 0.1, 0.3, 0.5]'), ('[0.1, 0.3, 0.5, 0.3, 0.1, 0.7]'), ('[0.6, 0.2, 0.4, 0.7, 0.5, 0.3]'), ('[0.0, 0.0, 0.0, 0.8, 0.6, 0.1]'), ('[0.0, 0.0, 0.0, 0.9, 0.3, 0.2]');INSERT 0 5simplepgvectortest=> SELECT * FROM testembeddings WHERE id!=1 ORDER BY embedding (SELECT embedding FROM testembeddings WHERE id = 1) LIMIT 5; id | embedding----+--------------------------- 2 | [0.1,0.3,0.5,0.3,0.1,0.7] 3 | [0.6,0.2,0.4,0.7,0.5,0.3] 4 | [0,0,0,0.8,0.6,0.1] 5 | [0,0,0,0.9,0.3,0.2](4 rows)The “hello this world” shows up as the one closer to the "hello world", followed by the “not this world” and then “dog” and “cat”.The pgvector open source extension is supported now in both:Azure Cosmos DB for PostgreSQLAzure Database for PostgreSQL - Flexible