Add my new, shiny module.

llm_harness_mistral_arc.py

@@ -23,17 +23,11 @@ class llm_harness_mistral_arc(evaluate.Metric):
         return evaluate.MetricInfo(
             # This is the description that will appear on the modules page.
             module_type="metric",
-            description="",
-            citation="",
-            inputs_description="",
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
-            features=[
-                datasets.Features(
-                    {
-                        "pretrained": datasets.Value("string", id="sequence"),
-                        "tasks": datasets.Sequence(datasets.Value("string", id="sequence"), id="tasks"),
-                    }
-                )],
+            features={},
             # Homepage of the module for documentation
             homepage="http://module.homepage",
             # Additional links to the codebase or references
@@ -41,7 +35,7 @@ class llm_harness_mistral_arc(evaluate.Metric):
             reference_urls=["http://path.to.reference.url/new_module"]
         )
 
-    def _compute(self, pretrained, tasks):
+    def _compute(self, pretrained=None, tasks=[]):
         outputs = lm_eval.simple_evaluate( 
               model="hf",
               model_args={"pretrained":pretrained},
@@ -50,6 +44,6 @@ class llm_harness_mistral_arc(evaluate.Metric):
           )
         results = {}
         for task in outputs['results']:
-          results[task] = {'acc':outputs['results'][task]['acc,none'], 
+           results[task] = {'acc':outputs['results'][task]['acc,none'], 
                           'acc_norm':outputs['results'][task]['acc_norm,none']}
         return results