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README.md
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@@ -15,6 +15,8 @@ Multi-modal Variational Autoencoder for text embedding transformation using geom
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The current implementation is trained with only a handful of token sequences, so it's essentially front-loaded. Expect short sequences to work.
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Full-sequence pretraining will begin soon with a uniform vocabulary that takes both tokens in for a representative uniform token based on the position.
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The current implementation is trained with only a handful of token sequences, so it's essentially front-loaded. Expect short sequences to work.
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Full-sequence pretraining will begin soon with a uniform vocabulary that takes both tokens in for a representative uniform token based on the position.
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Trained specifically to encode and decode a PAIR of encodings, each slightly twisted and warped into the direction of intention from the training. This is not your usual VAE.
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