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 - Telegram channel t.me/s/oflameron
Linguistic memes (pictures with text) suddenly turned out to be in demand for training AGI - high-complexity language models. These are subtle linguistic structures for which there is not enough Data Sets to train AGI.
Correct classification (in fact, understanding) generation of such memes (analogous to natural humor) immediately shows the level of AGI.
The number of linguistic memes (created by the author Valery Shmelev) exceeds 2000. But for a full-fledged Data Set, this is very little. However, there are technical solutions for quickly scaling this Data Set to 100,000 - 500,000 elements.
JOB in Europe >>
AGI Training
t.me/s/oflameron - Data sets WEB channel
All images are created by the program. Any apparent resemblance to real persons is subjective.
     
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A neural network is used to search for images for comments. This is an experimental program in Python. Then all the compositions (memes) are reviewed by a specialist and a Data Set is compiled for precise training of the AGI. Several hundred images are added to ZIP archives.
Google Drive. Data Sets in the archives;
Source Data Set in ZIP >>
Expanded Source Data Set in ZIP >>
Pictures num.330 in ZIP >>
HOLLYwindow num.340 in ZIP >>
Pictures num.320 in ZIP >>
Pictures num.300 in ZIP >>
Pictures num.290 in ZIP >>
Pictures num.270 in ZIP >>
Pictures num.250 in ZIP >>
Pictures num.240 in ZIP >>
Pictures num.230 in ZIP >>
Pictures num.210 in ZIP >>
Pictures num.190 in ZIP >>
Pictures num.170 in ZIP >>
Pictures num.160 in ZIP >>
Data Set EXAMPLE >>
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