Update README.md

README.md

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 ### Model and Inputs
-Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for each patch. 
+Prithvi is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy with an L1 loss function. The model includes spatial attention across multiple patches and also temporal attention for each patch. 
 
 ![](GFM.png)
 
 The model expects remote sensing data in a video format (B, C, T, H, W). Note that the temporal dimension is very important here and not present in most 
-other works around remote sensing modeling. Being able to handle a time series of remote sensing images can be very helpful to a variety of downstream tasks. The model can also handle static image which can be simply fed into the model with T=1.
+other works around remote sensing modeling. Being able to handle a time series of remote sensing images can be very helpful to a variety of downstream tasks. The model can also handle static images, which can be simply fed into the model with T=1.
 
 ### Pre-training
-The model was pre-trained with NASA's HLS2 L30 product (30m granularity) from Continental United States. The bands that were used are the following: 
+The model was pre-trained with NASA's HLS2 L30 product (30m granularity) from the Continental United States. The bands that were used are the following: 
 
 1. Blue
 2.  Green