Update README.md

README.md

@@ -12,22 +12,50 @@ app_file: app.py
 pinned: false
 ---
 
-# Metric Card for relation_extraction
+# Metric Card for relation_extraction evalutation
+This metric is for evaluating the quality of relation extraction output. By calculating the Micro and Macro F1 score of every relation extraction outputs to ensure the quality.
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
 
 ## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+This metric can be used in relation extraction evaluation. 
 
 ## How to Use
-*Give general statement of how to use the metric*
+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
 
-*Provide simplest possible example for using the metric*
+# load metric
+metric_path = "Ikala-allen/relation_extraction"
+module = evaluate.load(metric_path)
 
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+# Define your predictions and references
+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 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)
 
+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}}
+```
+
+
+### Inputs
+- **predictions** (`list` of `list`s of `dictionary`s): relation and its type of prediction
+- **references** (`list` of `list`s of `dictionary`s): references for each relation and its type.
+- 
 ### Output Values
 
 *Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*