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this was one of the things i co-led at fair, then fb had ~2b users, embeddings of ~128d made it a 300b-1T parameter model depending on how you count entities (e.g. ad campaigns). at the time, this was big, now it's medium. we trained it purely on distributed cpus
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"text": "@RaiaHadsell Universal embeddings FTW 😊\n\nOne of the flagship projects at FAIR was to \"embed the world\" (i.e. represent every entity on Facebook).\nThe name was soon changed to \"Filament\", deployed internally, and eventually open-sourced as \"PyTorch-BigGraph\" \nThe techniques were more primitive than today!\n\nhttps://t.co/jcIWd3FEeC",
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