Add my new, shiny module.

llm_harness_mistral_arc.py

@@ -2,19 +2,6 @@ import evaluate
 import datasets
 import lm_eval
 
-# TODO: Add BibTeX citation
-_CITATION = """
-"""
-
-# TODO: Add description of the module here
-_DESCRIPTION = """
-"""
-
-
-# TODO: Add description of the arguments of the module here
-_KWARGS_DESCRIPTION = """
-"""
-
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class llm_harness_mistral_arc(evaluate.Metric):
@@ -23,19 +10,20 @@ 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=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
+            description="",
+            citation="",
+            inputs_description="",
             # This defines the format of each prediction and reference
-            features={},
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
-            # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            features=[
+                datasets.Features(
+                    {
+                        "pretrained": datasets.Value("string", id="sequence"),
+                        "tasks": datasets.Sequence(datasets.Value("string", id="sequence"), id="tasks"),
+                    }
+                )]
         )
 
-    def _compute(self, pretrained=None, tasks=[]):
+    def _compute(self, pretrained, tasks):
         outputs = lm_eval.simple_evaluate( 
               model="hf",
               model_args={"pretrained":pretrained},