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README.md

@@ -20,7 +20,7 @@ Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoe
 
 First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
 
-Second, we considered geolocation (center latitude and longitude) and date of acquisition (year and day-of-year ranging 1-365) in pretraining. Both encoder and decoder receive time and location information for each sample and encodes them independently using 2D sin/cos encoding. They are added to the embedded tokens via a weighted sum with learned weights: one for time and one for location and separate weights for encoder and decoder. Since this metadata is often not available, we added a drop mechanism during pretraining that randomly drops the geolocation and/or the temporal data to help the model learn how to handle the absence of this information.
+Second, we considered geolocation (center latitude and longitude) and date of acquisition (year and day-of-year ranging 1-365) in the pretraining of the TL model versions. Both encoder and decoder receive time and location information for each sample and encodes them independently using 2D sin/cos encoding. They are added to the embedded tokens via a weighted sum with learned weights: one for time and one for location and separate weights for encoder and decoder. Since this metadata is often not available, we added a drop mechanism during pretraining that randomly drops the geolocation and/or the temporal data to help the model learn how to handle the absence of this information.
 
 ## Pre-trained Models