Update README

README.md

@@ -4,8 +4,7 @@ tags:
 - evaluate
 - metric
 description: >-
-  a classification report is a simple tool to compute multiple metrics such as:
-  accuracy precision/recall/f1-score by class. mean/weighted average.
+  Build a text report showing the main classification metrics that are accuracy, precision, recall and F1.
 sdk: gradio
 sdk_version: 3.0.2
 app_file: app.py
@@ -15,37 +14,77 @@ license: apache-2.0
 
 # Metric Card for classification_report
 
-***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.*
+
+Build a text report showing the main classification metrics that are accuracy, precision, recall and F1.
+
 
 ## How to Use
-*Give general statement of how to use the metric*
 
-*Provide simplest possible example for using the metric*
+At minimum, this metric requires predictions and references as inputs.
 
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+```python
+>>> accuracy_metric = evaluate.load("accuracy")
+>>> results = accuracy_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}}
+```
 
-### 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}*
+### Inputs
+- **predictions** (`list` of `int`): Predicted labels.
+- **references** (`list` of `int`): Ground truth labels.
+- **labels** (`list` of `int`): Optional list of label indices to include in the report. Defaults to None.
+- **target_names** (`list` of `str`): Optional display names matching the labels (same order). Defaults to None.
+- **sample_weight** (`list` of `float`): Sample weights. Defaults to None.
+- **digits** (`int`): Number of digits for formatting output floating point values. When output_dict is True, this will be ignored and the returned values will not be rounded. Defaults to 2.
+- **zero_division** (`warn`, `0` or `1`): Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised. Defaults to `warn`.
+
 
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
+### Output Values
+- report (`str` or `dict`): Text summary of the precision, recall, F1 score for each class. Dictionary returned if output_dict is True. Dictionary has the following structure:
+        ```
+        {'label 1': {'precision':0.5,
+             'recall':1.0,
+             'f1-score':0.67,
+             'support':1},
+         'label 2': { ... },
+             ...
+        }
+        ```
+        The reported averages include macro average (averaging the unweighted mean per label), weighted average (averaging the support-weighted mean per label), and sample average (only for multilabel classification). Micro average (averaging the total true positives, false negatives and false positives) is only shown for multi-label or multi-class with a subset of classes, because it corresponds to accuracy otherwise and would be the same for all metrics. See also precision_recall_fscore_support for more details on averages.
+        Note that in binary classification, recall of the positive class is also known as “sensitivity”; recall of the negative class is “specificity”.
 
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
+Output Example(s):
+```python
+{'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}}
+```
 
 ### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
 
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
+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])
+>>> 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}}
+```
 
 ## Citation
-*Cite the source where this metric was introduced.*
+```bibtex
+@article{scikit-learn,
+  title={Scikit-learn: Machine Learning in {P}ython},
+  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+  journal={Journal of Machine Learning Research},
+  volume={12},
+  pages={2825--2830},
+  year={2011}
+}
+```
+
 
 ## Further References
-*Add any useful further references.*