Updating Readme.md

README.md

@@ -17,34 +17,48 @@ pinned: false
 ***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.*
+Coordinates Accuracy aims to evaluate model performance in coordinates prediction tasks where the model has to predict a coordinate of a geographic entity in the form **(__lat__, __long__)**. It checks if the coordinates generated are inside a circle of radius __d__ and centered at gold coordinates.
 
 ## How to Use
-*Give general statement of how to use the metric*
+This metric takes 2 mandatory arguments : `generations` (a list of string of generated coordinates), `golds` (a list of list of floats of gold coordinates)
 
-*Provide simplest possible example for using the metric*
+```python
+import evaluate
+coord_acc = evaluate.load("rfr2003/coord_eval")
+results = coord_acc.compute(generations=["(12.7, 67.8)", "(16.7, 89.6)"], golds=[[12.7, 67.8], [10.9, 80.6]], d_range=20)
+print(results)
+{'coord_accuracy': 0.5}
+```
+
+This metric also accepts an optional argument: 
+
+
+`d` (int): Radius of the circle. The default value is `20`.
 
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
 
 ### 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}*
+This metric outputs a dictionary with the following values:
 
-*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."*
+`coord_accuracy`: The coordinates accuracy between `generations` and `golds`, which ranges from 0.0 to 1.0. 
 
 #### 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.*
+
 
 ### 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.*
+
+```python
+import evaluate
+coord_acc = evaluate.load("rfr2003/coord_eval")
+results = coord_acc.compute(generations=["(12.7, 67.8)", "(16.7, 89.6)"], golds=[[12.7, 67.8], [10.9, 80.6]], d_range=20)
+print(results)
+{'coord_accuracy': 0.5}
+```
 
 ## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
+
 
 ## Citation
-*Cite the source where this metric was introduced.*
+
 
 ## Further References
-*Add any useful further references.*