Update README.md

Change results for the best performing model of BiomedBERT-large

README.md

@@ -89,7 +89,7 @@ task_categories:
 - **Homepage:** https://huggingface.co/datasets/cnachteg/DUVEL/
 - **Repository:** https://github.com/cnachteg/DUVEL
 - **Paper:** TBA
-- **Point of Contact:** TBA
+- **Point of Contact:** Charlotte Nachtegael - Charlotte.Nachtegael@ulb.be
 
 ### Dataset Summary
 This dataset was created to identity oligogenic variant combinations, i.e. relation between several genes and their mutations, causing genetic diseases in scientific articles written in english. At the moment, it contains only digenic variant combinations, i.e. relations between two genes and at least two variants. The dataset is intended for binary relation extraction where the entities are masked within the text.
@@ -98,7 +98,7 @@ This dataset was created to identity oligogenic variant combinations, i.e. relat
 
 The dataset can be used to train a model for ``text-classification`` (as the relation extraction task is here considered as a classification task). Success on this task is typically measured by achieving a high F1-score.
 
-The BioLinkBERT model (https://huggingface.co/michiyasunaga/BioLinkBERT-large) currently achieves the following score of 0.8207 F1-score, with a precision of 0.7941 and a recall of 0.8491.
+The BiomedBERT-large (https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract) currently achieves the best performance with the following F1-score of 0.8371, with a precision of 0.8506 and a recall of 0.8239.
 
 ### Languages
 
@@ -220,11 +220,11 @@ This dataset is under the Creative Commons Attribution Non Commercial Share Alik
 TBA
 
 ```bib
-@article{DUVEL_2023,
+@article{DUVEL_2024,
   author    = {},
   title     = {},
   journal   = {},
-  year      = {2023}
+  year      = {2024}
 }
 ```