Update README.md

README.md

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 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a finetuning of the ESGBERT/EnvRoBERTa-base language model, trained to classify texts on climate adaptation in the ESG/climate domain.
 
+Using the EnvironmentalBERT-base model as a starting point, the AdaptationBERT Language Model is additionally fine-trained on a 2k adaptation dataset to detect climate adaptation and resilience text samples.
 
 
 ## Model Details
 
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
@@ -69,7 +71,11 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+See these tutorials from Tobias Schimanski on Medium for a guide on <a href='https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2'>model usage</a>, <a href="https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2">large-scale analysis</a>, and <a href="https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-3-fine-tune-your-own-models-e3692fc0b3c0">fine-tuning</a>.
+
+It is highly recommended to first classify a sentence to be "environmental" or not with the EnvironmentalBERT-environmental model before classifying whether it is "forest" or not.
+
+You can use the model with a pipeline for text classification:
 
 [More Information Needed]