Update README.md

README.md

@@ -234,7 +234,7 @@ def extract_keyphrases(example, predictions, tokenizer, index=0):
     return np.unique([kp.strip() for kp in extracted_kps])
 
 ```
-## 📝Evaluation results
+## 📝 Evaluation results
 
 One of the traditional evaluation methods is the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
 The model achieves the following results on the Inspec test set:
@@ -243,4 +243,7 @@ The model achieves the following results on the Inspec test set:
 |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
 | Inspec Test Set   | 0.53 | 0.47 | 0.46 | 0.36 | 0.58 | 0.41  | 0.58 | 0.60 | 0.56 |
 
-For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
+For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
+
+## 🚨 Issues
+Please feel free to contact Thomas De Decker for any problems with this model.