3 ms·
Relevant discussion at end of this thread: https://encode.ru/threads/3059-How-much-further-can-the-best-compression-go https://encode.ru/threads/3059-How-much-f
by Klasiaster 8y ago
Relevant discussion at end of this thread: https://encode.ru/threads/3059-How-much-further-can-the-best-compression-go https://encode.ru/threads/3059-How-much-further-can-the-best...
tldr: the measurements here are not meaningful for a comparison, read https://encode.ru/threads/3059-How-much-further-can-the-best-compression-go?p=59178&viewfull=1#post59178 https://encode.ru/threads/3059-How-much-further-can-the-best...
- yorwba 8y agoThat comment complains that the model is not useful for compression, because it stores all that linguistic knowledge in the 3GB of model weights. But for NLP applications, you want the model to store as much information as possible, and it only matters whether it generalizes to unseen data or not. The compression measurement is just to show that, not to imply that the model would be useful as a general-purpose compression algorithm.