Update README.md

Update the direct and out-of-scope uses

README.md

@@ -80,25 +80,26 @@ We present the ConvSwin2SR tranformer, a vision model for down-scaling (from 0.2
 
 # Uses
 
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 ## Direct Use
 
+The primary use of the ConvSwin2SR transformer is to enhance the resolution of regional reanalysis grids in the Mediterranean area. This enhancement is 
+crucial for more precise climate studies, which can aid in better decision-making for various stakeholders including policymakers, 
+researchers, and weather-dependent industries like agriculture, energy, and transportation.
 
 
 ## Out-of-Scope Use
 
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+The model is specifically designed for down-scaling regional reanalysis grids and may not perform well or provide accurate results for other types of 
+imaging tasks or geographical regions. Additionally, any use that relies on real-time or near real-time data processing may not be suitable due to the 
+computational demands of the model.
 
 
 # Bias, Risks, and Limitations
 
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-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) 
+and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes 
+across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 
 
 
@@ -110,8 +111,6 @@ Significant research has explored bias and fairness issues with language models
 
 ## Training Data
 
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 The dataset used is a composition of the ERA5 and CERRA reanalysis.
 
 The spatial coverage of the input grids (ERA5) is defined below, and corresponds to a 2D array of dimensions (60, 44):
@@ -132,8 +131,6 @@ The data samples used for training corresponds to the period from 1985 and 2013
 
 ## Training Procedure
 
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 ### Preprocessing
 
 The preprocessing of climate datasets ERA5 and CERRA, extracted from the Climate Data Store (CDS), is a critical step before their utilization in training models.