Update README.md

README.md

@@ -28,5 +28,12 @@ It was created as a part of the research project "Run Away: Coercion and Autonom
 Starting with the ENO dataset, we created a binary classifier model to isolate the labour advertisements from the rest of the material. This model found 241.000
 advertisements, but the original text segmentation from the ENO dataset was geared more toward "text between rubrices" rather than individual advertisements. Therefore, we
 created a bespoke RegEx-based tokenizer to segment the positively identified texts into individual labour advertisements. This process resulted in around 344.000 advertisements.
-We then created two other classification models to identify the intended sex of the employee and whether an advertisement belonged to the supply-side or the demand-side.
+We then created two other classification models to identify the intended sex of the employee and whether an advertisement belonged to the supply-side or the demand-side,
+enriching the dataset with two analytical categories.
+
+## Models used for identification and categorization
+
+1. Labour advertisement identifier: https://huggingface.co/JohanHeinsen/Labour_advertisement_identifier
+2. Gender model: https://huggingface.co/JohanHeinsen/Labour_ads_gender
+3. Demand model: https://huggingface.co/JohanHeinsen/Labour_ads_demand