Update README.md

README.md

@@ -23,15 +23,8 @@ This metric can be used in relation extraction evaluation.
 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:
 ```
 >>> import evaluate
-
-load metric
-
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
-
-Define your predictions and references
-Example references (ground truth)
-
 >>> references = [
 ...    [
 ...       {"head": "phip igments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -39,8 +32,6 @@ Example references (ground truth)
 ...    ]
 ... ]
 
-Example predictions
-
 >>> predictions = [
 ...    [
 ...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -48,8 +39,6 @@ Example predictions
 ...    ]
 ... ]
 
-Calculate evaluation scores using the loaded metric
-
 >>> evaluation_scores = module.compute(predictions=predictions, references=references)  
 >>> print(evaluation_scores)  
 {'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}}
@@ -92,28 +81,18 @@ Example of only one prediction and reference:
 ```python
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
-
-Define your predictions and references
-Example references (ground truth)
-
 >>> references = [
 ...    [
 ...       {"head": "phip igments", "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"},
 ...    ]
 ... ]
-
-Example predictions
-
 >>> 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)
 >>> print(evaluation_scores)
 {'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}}
@@ -123,22 +102,14 @@ Example with two or more prediction and reference:
 ```python
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
-
-Define your predictions and references
-Example references (ground truth)
-
 >>> references = [
 ...     [
 ...         {"head": "phip igments", "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': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
+...           {'head': 'SABONTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}
 ...     ]
 ...   ]
-
-Example predictions
-
 >>> predictions = [
 ...    [
 ...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -148,9 +119,6 @@ Example 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)  
 >>> print(evaluation_scores)  
 {'sell': {'tp': 2, 'fp': 2, 'fn': 1, 'p': 50.0, 'r': 66.66666666666667, 'f1': 57.142857142857146}, 'ALL': {'tp': 2, 'fp': 2, 'fn': 1, 'p': 50.0, 'r': 66.66666666666667, 'f1': 57.142857142857146, 'Macro_f1': 57.142857142857146, 'Macro_p': 50.0, 'Macro_r': 66.66666666666667}}