Update README.md

README.md

@@ -25,8 +25,8 @@ Build a text report showing the main classification metrics that are accuracy, p
 At minimum, this metric requires predictions and references as inputs.
 
 ```python
->>> accuracy_metric = evaluate.load("accuracy")
->>> results = accuracy_metric.compute(references=[0, 1], predictions=[0, 1])
+>>> classification_report_metric = evaluate.load("bstrai/classification_report")
+>>> results = classification_report_metric.compute(references=[0, 1], predictions=[0, 1])
 >>> print(results)
 {'0': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, '1': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}, 'accuracy': 1.0, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}}
 ```
@@ -65,8 +65,8 @@ Output Example(s):
 
 Simple Example:
 ```python
->>> accuracy_metric = evaluate.load("bstrai/classification_report")
->>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+>>> classification_report_metric = evaluate.load("bstrai/classification_report")
+>>> results = classification_report_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
 >>> print(results)
 {'0': {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.5, 'support': 2}, '1': {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}, '2': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 2}, 'accuracy': 0.5, 'macro avg': {'precision': 0.38888888888888884, 'recall': 0.5, 'f1-score': 0.43333333333333335, 'support': 6}, 'weighted avg': {'precision': 0.38888888888888884, 'recall': 0.5, 'f1-score': 0.43333333333333335, 'support': 6}}
 ```