Update README.md

README.md

@@ -23,7 +23,7 @@ This metric is used for evaluating the quality of relation extraction output. By
 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:
+This metric takes 3 inputs, prediction, references(ground truth) and mode.Predictions and references are a list of list of dictionary of entity's name and entity's type. And mode define the evaluation type:
 ```python
 import evaluate
 metric_path = "Ikala-allen/relation_extraction"
@@ -48,28 +48,28 @@ evaluation_scores = module.compute(predictions=predictions, references=reference
 - **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"
 - **only_all** (`bool`): True for only output ["ALL"] relation_type score. False for output every relation_type score, default True
-- **relation_types** (`list`): define relation type that need to be evaluate, if not given, it will construct relation_types from ground truth, default []
+- **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 []
  
 ### Output Values
 
 **output** (`dictionary` of `dictionary`s) with multiple key-value pairs
-- **sell** (`dictionary`): score of type sell
+- **ALL** (`dictionary`): score of all of the relation type
   - **tp** : true positive count
   - **fp** : false positive count
   - **fn** : false negative count
   - **p** : precision
   - **r** : recall
   - **f1** : micro f1 score
-- **ALL** (`dictionary`): score of all of the type (sell and belongs to)
+  - **Macro_f1** : macro f1 score
+  - **Macro_p** : macro precision
+  - **Macro_r** : macro recall
+- **{selected relation type}** (`dictionary`): score of selected relation type
   - **tp** : true positive count
   - **fp** : false positive count
   - **fn** : false negative count
   - **p** : precision
   - **r** : recall
   - **f1** : micro f1 score
-  - **Macro_f1** : macro f1 score
-  - **Macro_p** : macro precision
-  - **Macro_r** : macro recall
  
 Output Example:
 ```python