Update README.md

README.md

@@ -79,7 +79,7 @@ Output Example:
 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
-Example1 : only one prediction and reference, mode = strict, consider only output ALL relation score
+Example1 : only one prediction and reference, mode = strict,  only output ALL relation score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -106,7 +106,7 @@ print(evaluation_scores)
 >>> {'tp': 1, 'fp': 1, 'fn': 2, 'p': 50.0, 'r': 33.333333333333336, 'f1': 40.0, 'Macro_f1': 25.0, 'Macro_p': 25.0, 'Macro_r': 25.0}
 ```
 
-Example2 : only one prediction and reference, mode = boundaries, consider only output ALL relation score
+Example2 : only one prediction and reference, mode = boundaries, only output ALL relation score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -133,7 +133,42 @@ print(evaluation_scores)
 >>> {'tp': 2, 'fp': 0, 'fn': 1, 'p': 100.0, 'r': 66.66666666666667, 'f1': 80.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}
 ```
 
-Example with two or more prediction and reference:
+Example3 :  two or more prediction and reference, mode = boundaries, only output = False, output all relation type
+```python
+metric_path = "Ikala-allen/relation_extraction"
+module = evaluate.load(metric_path)
+# Define your predictions and references
+references = [
+  [
+    {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
+    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
+  ],
+  [
+    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
+    {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'},
+  ]
+]
+
+# Example references (ground truth)
+predictions = [
+  [
+    {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
+    {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
+  ],
+  [
+    {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'},
+    {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
+  ]
+]
+
+# Calculate evaluation scores using the loaded metric
+evaluation_scores = module.compute(predictions=predictions, references=references, mode = "boundaries", only_all = False, relation_types = [])
+
+print(evaluation_scores)
+>>> {'sell': {'tp': 3, 'fp': 1, 'fn': 0, 'p': 75.0, 'r': 100.0, 'f1': 85.71428571428571}, 'belongs_to': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0}, 'ALL': {'tp': 3, 'fp': 1, 'fn': 1, 'p': 75.0, 'r': 75.0, 'f1': 75.0, 'Macro_f1': 42.857142857142854, 'Macro_p': 37.5, 'Macro_r': 50.0}}
+```
+
+Example 4 with two or more prediction and reference:
 ```python
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)