Perform a great comma split up tabular databases out of consumer analysis out-of a great dating software into the pursuing the articles: first name, last title, many years, town, condition, gender, sexual direction, hobbies, level of likes, amount of matches, day buyers registered brand new application, therefore the owner’s rating of one’s app anywhere between 1 and you will 5
GPT-3 failed to provide us with any line headers and you may provided you a desk with every-almost every other row having zero recommendations and only 4 rows out-of actual buyers data. In addition provided us about three articles out of appeal when we have been just looking you to, but are reasonable to help you GPT-3, we did play with an excellent plural. All that being told you, the knowledge they performed build for us is not half of bad – labels and you will sexual orientations song on best genders, the newest towns they provided all of us are within their proper says, in addition to dates fall contained in this an appropriate diversity.
We hope when we provide GPT-3 some situations it can top see what we are lookin to have. Unfortunately, on account of equipment constraints, GPT-step three can not read an entire database understand and you can generate man-made study of, so we can just only have a few analogy rows.
Its nice one GPT-step 3 deliver united states a good dataset that have particular relationship between articles and sensical studies distributions
Create a good comma split up tabular database that have column headers regarding 50 rows of customer research regarding an online dating app. Example: ID, FirstName, LastName, Decades, City, Condition, Gender, SexualOrientation, Passions, NumberofLikes, NumberofMatches, DateCustomerJoined, CustomerRating, Df78hd7, Barbara, Best, 23, Nashville, TN, Female, Lesbian, (Walking Preparing Powering), 2700, 170, , 4.0, 87hbd7h, Douglas, Trees, thirty-five, Chicago, IL, Men, Gay, (Baking Color Understanding), 3200, 150, , 3.5, asnf84n, Randy, Ownes, twenty two, il, IL, Male, Upright, (Powering Walking Knitting), five-hundred, 205, , 3.2
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