Update README.md

README.md

@@ -21,7 +21,7 @@ This metric can be used in relation extraction evaluation.
 
 ## How to Use
 This metric takes 2 inputs, prediction and references(ground truth). Both of them are a list of list of dictionary of entity's name and entity's type:
-```
+```python
 >>> import evaluate
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
@@ -74,7 +74,8 @@ Output Example:
 ```python
 {'sell': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0}, 'ALL': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}}
 ```
-Macro_f1、Macro_p、Macro_r、p、r、f1 are always a number between 0 and 1. And tp、fp、fn depend on how many data inputs.
+
+Remind : Macro_f1、Macro_p、Macro_r、p、r、f1 are always a number between 0 and 1. And tp、fp、fn depend on how many data inputs.
 
 ### Examples
 Example of only one prediction and reference: