Update README.md

README.md

@@ -22,14 +22,16 @@ 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:
 ```
-import evaluate
+>>> import evaluate
+
+load metric
 
-#### load metric
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
 
-#### Define your predictions and references
-#### Example references (ground truth)
+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"},
@@ -37,7 +39,8 @@ import evaluate
 ...    ]
 ... ]
 
-#### Example predictions
+Example predictions
+
 >>> predictions = [
 ...    [
 ...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -45,9 +48,10 @@ import evaluate
 ...    ]
 ... ]
 
-#### Calculate evaluation scores using the loaded metric
->>> evaluation_scores = module.compute(predictions=predictions, references=references)
->>> print(evaluation_scores)
+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}}
 ```
 
@@ -89,8 +93,9 @@ Example of only one prediction and reference:
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
 
-#### Define your predictions and references
-#### Example references (ground truth)
+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"},
@@ -98,7 +103,8 @@ Example of only one prediction and reference:
 ...    ]
 ... ]
 
-#### Example predictions
+Example predictions
+
 >>> predictions = [
 ...    [
 ...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -106,7 +112,8 @@ Example of only one prediction and reference:
 ...    ]
 ... ]
 
-#### Calculate evaluation scores using the loaded metric
+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}}
@@ -117,8 +124,9 @@ Example with two or more prediction and reference:
 >>> metric_path = "Ikala-allen/relation_extraction"
 >>> module = evaluate.load(metric_path)
 
-#### Define your predictions and references
-#### Example references (ground truth)
+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"},
@@ -129,7 +137,8 @@ Example with two or more prediction and reference:
 ...     ]
 ...   ]
 
-#### Example predictions
+Example predictions
+
 >>> predictions = [
 ...    [
 ...        {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
@@ -140,9 +149,10 @@ Example with two or more prediction and reference:
 ...    ]
 ...  ]
 
-#### Calculate evaluation scores using the loaded metric
->>> evaluation_scores = module.compute(predictions=predictions, references=references)
->>> print(evaluation_scores)
+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}}
 ```