modified: README.md

README.md

@@ -21,7 +21,7 @@ results = metric.compute(references=references, prediction_scores=prediction_sco
 ```
 
 ### Inputs
-<!-- - **y_true** (`ndarray` of shape (n_samples,) or (n_samples, n_classes)): True binary labels or binary label indicators.
+- **y_true** (`ndarray` of shape (n_samples,) or (n_samples, n_classes)): True binary labels or binary label indicators.
 - **y_score** (`ndarray` of shape (n_samples,) or (n_samples, n_classes)):
 Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by :term:`decision_function` on some classifiers).
 - **average**: {'micro', 'samples', 'weighted', 'macro'} or None, default='macro`
@@ -41,7 +41,7 @@ Target scores, can either be probability estimates of the positive class, confid
         Calculate metrics for each label, and find their average
     Will be ignored when ``y_true`` is binary.
 - **pos_label** (`int` or `str`, default=1): The label of the positive class. Only applied to binary ``y_true``. For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
-- **sample_weight** (`array-like` of shape (n_samples,), default=None): Sample weights. -->
+- **sample_weight** (`array-like` of shape (n_samples,), default=None): Sample weights.
 
 ### Output Values