update readme

README.md

@@ -14,7 +14,9 @@ This Named Entity Recognition (NER) model is designed to extract book titles fro
 
 ## Model Details
 
-The model has been fine-tuned on a Dutch dataset consisting of 12,535 book reviews from the Leeuwarder Courant, identifying 23,529 book titles. The dataset utilizes the IO Tagging Schema. Training involved the Majority or Minority loss function, achieving an F1 score of 84.3%, Precision of 83.4%, and Recall of 85.2% on the test split.
+The model has been fine-tuned and evaluated (15%) on a Dutch dataset consisting of 12,535 book reviews from the Leeuwarder Courant, identifying 23,529 book titles. The dataset utilizes the IO Tagging Schema. Training involved the Majority or Minority loss function, achieving an F1 score of 84.3%, Precision of 83.4%, and Recall of 85.2% on the test split.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/661fcac6ccc447675983951b/Ap95lefSlrwJGDg6eupVF.png)
 
 ### Model Description
 
@@ -40,4 +42,6 @@ nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 text = "Gisteren heb ik het boek Nijntje in de dierentuin gelezen. Ik kan niet anders zeggen dat dit boek fantastisch was!"
 entities = nlp(text)
 print(entities)
-```
+```
+
+