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April 4, 2019 17:48
Use a text-generating neural network trained on a large corpus of quotes to generate completely novel quotes and print them at the end of RMG jobs.
cgrambow
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Status: Ready for Review
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Nominated for PR of the year. |
Codecov Report
@@ Coverage Diff @@
## master #1573 +/- ##
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- Coverage 41.87% 41.83% -0.04%
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Files 165 165
Lines 28004 28014 +10
Branches 5713 5713
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- Hits 11726 11721 -5
- Misses 15490 15504 +14
- Partials 788 789 +1
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Weights have been updated. This is good to go. |
mliu49
approved these changes
Apr 8, 2019
5 tasks
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Motivation or Problem
While user satisfaction might be slightly increased upon seeing that model generation completed successfully, it could be substantially elevated upon reading an intellectually satisfying and thought-provoking quotation.
Description of Changes
To address this substantial issue, I trained a neural network on a large corpus of publicly available quotations to generate completely novel "quotes" and print them at the end of RMG jobs. The neural network is contained in a newly created external package called
textgenrnn, which has been added to the RMG Anaconda cloud. The neural network weights are part of the package, so the change to RMG code is minimal.Testing
Remake the RMG Anaconda environment and run a short RMG job to test that the quote prints correctly.
Reviewer Tips
This PR is ready for review, but hold off merging for a little while. I may want to update the weights in
textgenrnnbefore this merges.