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Create similar addresses: | |
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You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 variations of the provided address as a regular person may enter it into some system. All outputs should be a list of 10 sample. | |
Create similar but different addresses: | |
You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 variations of the provided address that are actually different addresses. Address formats should be as if a regular person may enter it into some system. | |
Generate completely different addresses. | |
You are tasked with helping to generate test data for machine learning dataset. Do no prefix numbers before each line of output. User is expected to prompt with a sample US address but without the City, State, Zipcode part. As a model your task is to generate 10 random addresses that are inspired by the structure of the address user provided. Address formats should be as if a regular person may enter it into some system. | |
Your task is to generate training samples based on an evaluation set provided for a neural network training problem. | |
The format of the evaluation set as follows. | |
1. A let of rows separated by newlines. | |
2. Each row has the following structure: | |
US address 1|US address 2|1 | |
or | |
US address 1|US address 2|0 | |
The first and second address are compared to see if they are the same address as may have been typed | |
by a user into some system. Users cannot alter the format or position of two letter state code or the five digit zipcode. | |
All the letters are always capitalized so user inputs are always uppercased for purposes of training data. | |
Given the following evaluation set generate 50 rows of training data that would produce a network with high accuracy but | |
not so similar that they would cause overfitting to the evaluation set. | |
Evaluation set: | |
1061 SCHMIDT LN, NORTH BRUNSWICK TOWNSHIP, NJ 08902|1061 SCHMIDT LANE, NORTH BRUNSWICK TOWNSHIP, NJ 08902|1 | |
1061 SCHMIDT LN, NORTH BRUNSWICK TOWNSHIP, NJ 08902|934 SCHMIDT LN, NORTH BRUNSWICK TOWNSHIP, NJ 08902|0 | |
115 DR JIMMY CARR ST, STE 3F, SEARCY, AR 72143|115 DR JIMMY CARR ST, SEARCY, AR 72143|0 | |
14143 WINECUP LN, HOUSTON, TX 77047|14121 WINECUP LANE, HOUSTON, TX 77047|0 | |
1555 RUTH RD STE 5, NORTH BRUNSWICK TOWNSHIP, NJ 08902|1555 RUTH ROAD SUIT 5, NORTH BRUNSWICK TOWNSHIP, NJ 08902|1 | |
1555 RUTH RD STE 5, NORTH BRUNSWICK TOWNSHIP, NJ 08902|1558 RUTH ROAD STE 5, NORTH BRUNSWICK TOWNSHIP, NJ 08902|0 | |
17752 MAIN ST, HANOVER, PA 23393|177 52 MAIN ST, HANOVER, PA 23393|1 | |
17752 MAIN ST, HANOVER, PA 23393|177 52 MAIN STREET, HANOVER, PA 23393|1 | |
217-12 LOUDON RD, CONCORD, NH 03301|217-22 LOUDON RD, CONCORD, NH 03301|0 | |
2575 US HWY 43, ST 3-A, WINFIELD, AL 35594|25-75 US HWY 43, STREET 3A, WINFIELD, AL 35594|1 | |
2575 US HWY 43, ST 3-A, WINFIELD, AL 35594|2575 US HWY 43, ST 3B, WINFIELD, AL 35594|0 | |
440 TECHNOLOGY CENTER DRIVE, BOSTON, MA 10034|200 TECHNOLOGY CENTER DRIVE, BOSTON, MA 10034|0 | |
440 TECHNOLOGY CENTER DRIVE, BOSTON, MA 10034|440 TECHNOLOGY CENTER DR., BOSTON, MA 10034|1 | |
440 TECHNOLOGY CENTER DRIVE, BOSTON, MA 10034|87 TECHNOLOGY CENTER DRIVE, BOSTON, MA 10034|0 | |
545 16TH ST, STE 3, GULFPORT, MS 39507|545 16TH ST, FLOOR 3, GULFPORT, MS 39507|0 | |
5844 N ORANGE BLOSSOM TRAIL, ORLANDO, FL 32810|5844 NORTH ORANGE BLOSSOM TRAIL, ORLANDO, FL 32810|1 | |
65 MOUNTAIN BLVD EXT, WARREN, NJ 07059|112 MOUNTAIN BLVD EXT, WARREN, NJ 07059|0 | |
65 MOUNTAIN BLVD EXT, WARREN, NJ 07059|5078 S MARYLAND PKWY, LAS VEGAS, NV 89119|0 | |
65 MOUNTAIN BLVD EXT, WARREN, NJ 07059|65 MOUNTAIN BOULEVARD EXT, WARREN, NJ 07059|1 | |
6701 FANNIN ST #1400, HOUSTON, TX 77030|6701 FANNIN STE #1400, HOUSTON, TX 77030|1 | |
87 24 ROUTE 13, CORTLANDVILLE, NY 13045|87-24 ROUTE 13, CORTLANDVILLE, NY 13045|1 | |
87-43 ROUTE 13, CORTLANDVILLE, NY 13045|8724 ROUTE 13, CORTLANDVILLE, NY 13045|0 | |
87-44 ROUTE 13, CORTLANDVILLE, NY 13045|87 24 ROUTE 13, CORTLANDVILLE, NY 13045|0 | |
872 ROUTE 13, CORTLANDVILLE, NY 13045|87-2 ROUTE 13, CORTLANDVILLE ,NY 13045|1 | |
8724 ROUTE 13, CORTLANDVILLE, NY 13045|87-24 ROUTE 13, CORTLANDVILLE, NY 13045|1 | |
HEART HEALTH, 90 N COLUMBUS AVE, LOUISVILLE, MS 39339|90 N COLUMBUS AVE, LOUISVILLE, MS 39339|1 | |
115 34 SHOREWAY DR, QUEENSTOWN, MD 21658|115-43 SHOREWAY DR, QUEENSTOWN, MD 21658|0 | |
112 24 SHOREWAY DR, QUEENSTOWN, MD 21658|112-24 SHOREWAY DR, QUEENSTOWN, MD 21658|1 | |
3619 S 22ND DR, YUMA, AZ 85364|3636 S 22ND DR, YUMA, AZ 85364|0 | |
7325 FRANKLIN BLVD, SACRAMENTO, CA 95823|73235 FRANKLIN BLVD, SACRAMENTO, CA 95823|0 | |
3660 MAIN ST, TUCSON, AZ 85721|3701 MAIN ST, TUCSON, AZ 85721|0 | |
3910 MAGNET RD, MALVERN, AR 72104|3910 MAGNET RD, STE 206 MALVERN, AR 72104|0 | |
15702 OBERLIN RD, RALEIGH, NC 27605|15702 OBERLIN RD FL 1, RALEIGH, NC 27605|1 | |
14425 ROOSOVELT AVE APT 322, LA JOLLA, CA 92092|14325 ROOSOVELT AVE, LA JOLLA, CA 92092|0 | |
14425 ROOSOVELT AVE APT 322, LA JOLLA, CA 92092|144-25 ROOSOVELT AVE APT 322, LA JOLLA, CA 92092|1 | |
14425 ROOSOVELT AVE, LA JOLLA, CA 92092|144-25A ROOSOVELT AVENUE, LA JOLLA, CA 92092|0 | |
Training samples: | |
good but now instead of varying one part of the address like the building number vary two or more | |
parts of the address where some parts will have differences like the building number may have one | |
different character and other variations may be because STREET is spelled STR or include other common | |
variations that don't actually change the meaning of the address. Remember to generate both | |
positive and negative pairs that will fit the evaluation set. Generate 50 samples | |