Update README.md

README.md

@@ -44,17 +44,28 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model implements a novel approach to classifying climate change skepticism arguments 
+    by utilizing Large Language Models (LLMs) for synthetic data. The base architecture uses ModernBERT with 
+    only synthetic data used for training. The model achieves exceptional performance with an accuracy of 99.45% on the validation dataset which is therefore the [QuotaClimat](QuotaClimat/frugalaichallenge-text-train) dataset.
+
+    The model categorizes text into the following skepticism types:
+
+- `label`: Following categories:
+  - `0_not_relevant`: No relevant claim detected or claims that don't fit other categories
+  - `1_not_happening`: Claims denying the occurrence of global warming and its effects - Global warming is not happening. Climate change is NOT leading to melting ice (such as glaciers, sea ice, and permafrost), increased extreme weather, or rising sea levels. Cold weather also shows that climate change is not happening
+  - `2_not_human`: Claims denying human responsibility in climate change - Greenhouse gases from humans are not the causing climate change.
+  - `3_not_bad`: Claims minimizing or denying negative impacts of climate change - The impacts of climate change will not be bad and might even be beneficial.
+  - `4_solutions_harmful_unnecessary`: Claims against climate solutions - Climate solutions are harmful or unnecessary
+  - `5_science_is_unreliable`: Claims questioning climate science validity - Climate science is uncertain, unsound, unreliable, or biased.
+  - `6_proponents_biased`: Claims attacking climate scientists and activists - Climate scientists and proponents of climate action are alarmist, biased, wrong, hypocritical, corrupt, and/or politically motivated.
+  - `7_fossil_fuels_needed`: Claims promoting fossil fuel necessity - We need fossil fuels for economic growth, prosperity, and to maintain our standard of living.    
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used for multi-class text classification tasks where the input text needs to be categorized into one of the eight predefined classes. It is particularly suited for datasets with class imbalance, thanks to its weighted loss function.
 
 ## Training and evaluation data
 
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters