Update README.md

README.md

@@ -51,8 +51,8 @@ Each entry in HiFi-KPI includes the following fields:
 | Test   | 179K       | 491K       |
 
 ## Data Splits
-- **HiFi-KPI (full dataset)**: Includes all extracted entities see github for an example in how to get granular labels [GitHub Repository](https://github.com/rasmus393/HiFi-KPI)
-In the dataset there is also calculationMasterTaxonomy.json and presentationMasterTaxonomy.json that describes the master hiearchy for the presentation and calculation layer 
+- **HiFi-KPI (full dataset)**: Contains all extracted entities. See the [GitHub Repository](https://github.com/rasmus393/HiFi-KPI) for an example of how to obtain granular labels.  
+- The dataset includes **calculationMasterTaxonomy.json** and **presentationMasterTaxonomy.json**, which define the master hierarchical structure for the calculation and presentation layers.
 
 ## Baselines and Benchmarks
 We establish baselines using:
@@ -66,11 +66,11 @@ We establish baselines using:
 | 10              | 1266         | 0.6321             | 0.0249             | 0.5984       | 0.0194       |
 
 ### Macro F1 Performance on 1000 Most Common Labels
-| Model                                       | Macro F1 |
-|---------------------------------------------|----------|
-| 1000 Most Common (n=0)                      | 0.7182   |
-| Calculation (1000 Most Common) (SL)         | 0.7121   |
-| Presentation (1000 Most Common) (SL)        | 0.7124   |
+| Model | Macro F1 |
+|-------|----------|
+| [1000 Most Common (n=0)](https://huggingface.co/AAU-NLP/BERT-SL1000) | 0.7182 |
+| [Calculation (1000 Most Common) (SL)](https://huggingface.co/AAU-NLP/Cal-BERT-SL1000) | 0.7121 |
+| [Presentation (1000 Most Common) (SL)](https://huggingface.co/AAU-NLP/Pre-BERT-SL1000) | 0.7124 |
 
 ## Uses and Applications
 HiFi-KPI can be used for: