Update README.md

The reported results do not match what is written in the paper.
The paper reported a **micro** F1 Score.

A 70% micro F1 Score on the REBEL Dataset would also not be SOTA, since REBEL achieved 74 micro-F1 Score (Section 5 in https://aclanthology.org/2021.findings-emnlp.204.pdf)

README.md

@@ -25,9 +25,9 @@ model-index:
       name: Babelscape/rebel-dataset
       type: REBEL
     metrics:
-    - type: re+ macro f1
+    - type: re+ micro f1
       value: 70.74
-      name: RE+ Macro F1
+      name: RE+ Micro F1
 ---
 
 # KnowGL: Knowledge Generation and Linking from Text
@@ -38,7 +38,7 @@ The `knowgl-large` model is trained by combining Wikidata with an extended versi
 ```
 If there are more than one triples generated, they are separated by `$` in the output. More details in [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf).
 
-The model achieves state-of-the-art results for relation extraction on the REBEL dataset. See results in [Mihindukulasooriya et al. (ISWC 2022)](https://arxiv.org/pdf/2207.05188.pdf).
+The model achieves good results for relation extraction on the REBEL dataset. See results in [Mihindukulasooriya et al. (ISWC 2022)](https://arxiv.org/pdf/2207.05188.pdf).
 
 The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them.