Update README.md

README.md

@@ -44,16 +44,16 @@ evaluation_scores = module.compute(predictions=predictions, references=reference
 ```
 
 ### Inputs
-- **predictions** (`list` of `list` of `dictionary`): a list of list of dictionary with every element's relation_type and their entity name
-- **references** (`list` of `list` of `dictionary`): a list of list of dictionary with every element's relation_type and their entity name
-- **mode** (`str`): define strict or boundaries mode for evaluation, strict mode consider "head_type" and "tail_type", boundaries mode doesn't consider "head_type" and "tail_type"
-- **show** (`bool`): False for only output all relation_type score. True for output every relation_type score, default False
-- **relation_types** (`list`): define which relation type that need to be evaluate and show, if not given, it will construct relation_types from ground truth, default []
+- **predictions** (`list` of `list` of `dictionary`): A list of predicted relations from the model.
+- **references** (`list` of `list` of `dictionary`): A list of ground-truth or reference relations to compare the predictions against.
+- **mode** (`str`): Evaluation mode - 'strict' or 'boundaries'. 'strict' mode takes into account both entities type and their relationships, while 'boundaries' mode only considers the entity spans of the relationships.
+- **detailed_scores** (`bool`): If True it returns scores for each relation type specifically, if False it returns the overall scores.
+- **relation_types** (`list`): A list of relation types to consider while evaluating. If not provided, relation types will be constructed from the ground truth or reference data.
  
 ### Output Values
 
-**output** (`dictionary` of `dictionary`s) with multiple key-value pairs
-- **ALL** (`dictionary`): score of all of the relation type
+**output** (`dictionary` of `dictionary`s) A dictionary mapping each entity type to its respective scoring metrics such as Precision, Recall, F1 Score.
+- **ALL** (`dictionary`): score of total relation type 
   - **tp** : true positive count
   - **fp** : false positive count
   - **fn** : false negative count
@@ -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, show=False, only output total relation score
+Example1 : only one prediction and reference, mode=strict, detailed_scores=False, only output total relation score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -96,12 +96,12 @@ predictions = [
     {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
   ]
 ]
-evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", show=False, relation_types=[])
+evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", detailed_scores=False, 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}
 ```
 
-Example2 : only one prediction and reference, mode=boundaries, show=False, only output total relation score
+Example2 : only one prediction and reference, mode=boundaries, detailed_scores=False, only output total relation score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -118,12 +118,12 @@ predictions = [
     {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"},
   ]
 ]
-evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", show=False, relation_types=[])
+evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", detailed_scores=False, 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}
 ```
 
-Example3 : two or more prediction and reference, mode=boundaries, show=True, output all relation type score
+Example3 : two or more prediction and reference, mode=boundaries, detailed_scores=True, output all relation type score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -147,12 +147,12 @@ predictions = [
     {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
   ]
 ]
-evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", show=True, relation_types=[])
+evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", detailed_scores=True, 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 : two or more prediction and reference, mode=boundaries, show=True, output all relation type score, relation_types  = ["belongs_to"], only consider belongs_to type score
+Example 4 : two or more prediction and reference, mode=boundaries, detailed_scores=True, output all relation type score, relation_types  = ["belongs_to"], only consider belongs_to type score
 ```python
 metric_path = "Ikala-allen/relation_extraction"
 module = evaluate.load(metric_path)
@@ -176,7 +176,7 @@ predictions = [
     {'head': 'SNTAIWAN', 'tail': '大馬士革玫瑰有機光燦系列', 'head_type': 'brand', 'tail_type': 'product', 'type': 'sell'}  
   ]
 ]
-evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", show=True, relation_types=["belongs_to"])
+evaluation_scores = module.compute(predictions=predictions, references=references, mode="boundaries", detailed_scores=True, relation_types=["belongs_to"])
 print(evaluation_scores)  
 >>> {'belongs_to': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0}, 'ALL': {'tp': 0, 'fp': 0, 'fn': 1, 'p': 0, 'r': 0, 'f1': 0, 'Macro_f1': 0.0, 'Macro_p': 0.0, 'Macro_r': 0.0}}
 ```