Update README.md

README.md

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 The Messis model leverages a three-tier hierarchical label structure, optimized for remote sensing tasks, to enhance its classification accuracy across different crop types. By adapting Prithvi to the specific challenges of Swiss agriculture—such as smaller field sizes and higher image resolutions by the Sentinel-2 satellites—Messis demonstrates the versatility of pretrained geospatial models in handling new downstream tasks.
 
-Additionally, Messis reduces the need for extensive labeled data by effectively utilizing Prithvi's pretrained weights. In evaluations, Messis achieved a notable F1 score of 34.8% across 48 crop classes, doubling the performance when compared to the same model initialized with random weights. 
+Additionally, Messis reduces the need for extensive labeled data by effectively utilizing Prithvi's pretrained weights. In evaluations, Messis achieved a notable F1 score of 34.8% across 48 crop classes.
 
 <img src="./assets/messis.jpeg" alt="Messis" width="600">