Update README.md

README.md

@@ -90,18 +90,13 @@ references = [
     {'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"},
   ]
 ]
-
-# Calculate evaluation scores using the loaded metric
 evaluation_scores = module.compute(predictions=predictions, references=references, mode = "strict", only_all=True,relation_types = [])
-
 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}
 ```
@@ -117,18 +112,13 @@ references = [
     {'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"},
   ]
 ]
-
-# Calculate evaluation scores using the loaded metric
 evaluation_scores = module.compute(predictions=predictions, references=references, mode = "strict", only_all=True,relation_types = [])
-
 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}
 ```
@@ -137,7 +127,6 @@ Example3 : two or more prediction and reference, mode = boundaries, only output
 ```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"},
@@ -148,8 +137,6 @@ references = [
     {'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"},
@@ -160,10 +147,7 @@ predictions = [
     {'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}}
 ```
@@ -182,8 +166,6 @@ references = [
     {'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"},