fill template

README.md

@@ -13,37 +13,22 @@ pinned: false
 
 # Measurement Card for Element Count
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing measurement cards if you'd like examples.*
-
 ## Measurement Description
-*Give a brief overview of this measurement, including what task(s) it is usually used for, if any.*
+Counts the number of elements in dataset. 
 
 ## How to Use
-*Give general statement of how to use the measurement*
 
-*Provide simplest possible example for using the measurement*
+```python
+import evaluate
+measure = evaluate.load("lvwerra/element_count")
+measure.compute(["a", "b", "c")
+>>> {"element_count": 3}
+```
+
 
 ### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+- **data** *(``List``): List of strings or integers
 
 ### Output Values
 
-*Explain what this measurement outputs and provide an example of what the measurement output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
-
-*State the range of possible values that the measurement's output can take, as well as what in that range is considered good. For example: "This measurement can take on any value between 0 and 100, inclusive. Higher scores are better."*
-
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this measurement, along with the values they have reported.*
-
-### Examples
-*Give code examples of the measurement being used. Try to include examples that clear up any potential ambiguity left from the measurement 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 measurement has, with links and references if possible.*
-
-## Citation
-*Cite the source where this measurement was introduced.*
-
-## Further References
-*Add any useful further references.*
+Returns a dictionary with the element count.

app.py

@@ -1,6 +1,6 @@
-from element_count import ElementCount
+import evaluate
 from evaluate.utils import launch_gradio_widget
 
 
-module = ElementCount()
+module = evaluate.load("lvwerra/element_count")
 launch_gradio_widget(module)

element_count.py

@@ -28,29 +28,21 @@ year={2020}
 
 # TODO: Add description of the module here
 _DESCRIPTION = """\
-This new module is designed to solve this great NLP task and is crafted with a lot of care.
+A simple measurement that returns the number of elements in dataset.
 """
 
 
 # TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
+Calculates number of elements in dataset
 Args:
-    predictions: list of predictions to score. Each predictions
-        should be a string with tokens separated by spaces.
-    references: list of reference for each prediction. Each
-        reference should be a string with tokens separated by spaces.
+    data: list of elements.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    element_count: number of elements in dataset,
 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])
-    >>> print(results)
-    {'accuracy': 1.0}
+    >>> measure = evaluate.load("lvwerra/element_count")
+    >>> measure.compute(["a", "b", "c")
+    {"element_count": 3}
 """
 
 # TODO: Define external resources urls if needed
@@ -70,10 +62,10 @@ class ElementCount(evaluate.EvaluationModule):
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value('int64'),
-                'references': datasets.Value('int64'),
-            }),
+            features=[
+                datasets.Features({'data': datasets.Value('int64')}),
+                datasets.Features({'data': datasets.Value('string')})
+                ],
             # Homepage of the module for documentation
             homepage="http://module.homepage",
             # Additional links to the codebase or references
@@ -81,15 +73,8 @@ class ElementCount(evaluate.EvaluationModule):
             reference_urls=["http://path.to.reference.url/new_module"]
         )
 
-    def _download_and_prepare(self, dl_manager):
-        """Optional: download external resources useful to compute the scores"""
-        # TODO: Download external resources if needed
-        pass
-
-    def _compute(self, predictions, references):
+    def _compute(self, data):
         """Returns the scores"""
-        # TODO: Compute the different scores of the module
-        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
         return {
-            "accuracy": accuracy,
+            "element_count": len(data),
         }