updated model card

README.md

@@ -1,14 +1,22 @@
 ---
-license: cc-by-nc-4.0
+license: mit
+language:
+- en
+library_name: LTSM
+inference: false
+datasets:
+- databloom/smartmeterdata
 ---
-### AI Energy Forecast using LTSM 
 
-It basically takes some smartmeter data (5 cols, > 12mil. instances, cols: id, device_name, property, value, timestamp) and creates a custom forecast based on selected window. 
-The file is available in .py and .ipynb format, so you can choose according to your preferences.
+**Owner:** DataBloom AI, Inc.
+
+### Model Overview ###
+LSTEnergy [last energy] is a Long-Term-Short-Memory model to predict energy consumption forecasts based on historical data. It basically takes 
+some smartmeter data (5 cols, > 12mil. instances, cols: id, device_name, property, value, timestamp) and creates a custom forecast based on selected window. 
 
 Please notice that once you load up the smartmeter data, there are inputs created on the timestamp col like wd_input (the weekday of the timestamp), as well as a cos(inus) and sin(us)
-time inputs, giving the model the ability to keep track of the daytime of each instance. Finally, the inputs are merged to an input df, standardized and differenced.
-After that, some functions are used to give the user the ability to use time windows from the data. Based on these, the model generates forecasts.   
+time inputs, giving the model the ability to keep track of the daytime of each instance. Finally, the inputs are merged into an input df, standardized, and differenced.
+After that, some functions are used to give the user the ability to use time windows from the data. Based on these, the model generates forecasts.
    
 ![Model](https://github.com/databloom-ai/LLM-LTSM/blob/main/energy-forcast/model.png?raw=true)