[config] Add docstring

README.md

@@ -22,8 +22,10 @@ Metric to evaluate the text2text generation task, where the format is the sequen
 ## How to Use
 *Give general statement of how to use the metric*
 
-*Provide simplest possible example for using the metric*
-
+```python
+metric = evaluate.load("DarrenChensformer/eval_keyphrase")
+results = metric.compute(references=[["Hello","World"]], predictions=[["hello","world"]])
+```
 ### Inputs
 *List all input arguments in the format below*
 - **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*

eval_keyphrase.py

@@ -49,10 +49,10 @@ Examples:
     Examples should be written in doctest format, and should illustrate how
     to use the function.
 
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
+    >>> metric = evaluate.load("DarrenChensformer/eval_keyphrase")
+    >>> results = metric.compute(references=[["Hello","World"]], predictions=[["hello","world"]])
     >>> print(results)
-    {'accuracy': 1.0}
+    {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'num_pred': 2.0, 'num_gold': 2.0}
 """
 
 # TODO: Define external resources urls if needed
@@ -128,10 +128,4 @@ class eval_keyphrase(evaluate.Metric):
             macro_metrics['num_pred'].append(len(total_preds))
             macro_metrics['num_gold'].append(len(total_tgt_set))
 
-        return {
-            "precision": round(sum(macro_metrics["precision"])/len(macro_metrics["precision"]), 4),
-            "recall": round(sum(macro_metrics["recall"])/len(macro_metrics["recall"]), 4),
-            "f1": round(sum(macro_metrics["f1"])/len(macro_metrics["f1"]), 4),
-            "num_pred": round(sum(macro_metrics["num_pred"])/len(macro_metrics["num_pred"]), 4),
-            "num_gold": round(sum(macro_metrics["num_gold"])/len(macro_metrics["num_gold"]), 4),
-        }
+        return { k: round(sum(v)/len(v), 4) for k, v in macro_metrics.items()}