Add my new, shiny module.

llm_harness_mistral_arc.py

@@ -1,66 +1,23 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""TODO: Add a description here."""
-
 import evaluate
 import datasets
-
+import lm_eval
 
 # TODO: Add BibTeX citation
-_CITATION = """\
-@InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
-}
+_CITATION = """
 """
 
 # TODO: Add description of the module here
-_DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+_DESCRIPTION = """
 """
 
 
 # TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
-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.
-Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
-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}
 """
 
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class llm_harness_mistral_arc(evaluate.Metric):
-    """TODO: Short description of my evaluation module."""
-
     def _info(self):
         # TODO: Specifies the evaluate.EvaluationModuleInfo object
         return evaluate.MetricInfo(
@@ -70,10 +27,14 @@ class llm_harness_mistral_arc(evaluate.Metric):
             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(
+                    {
+                        "pretrained": datasets.Value("string", id="sequence"),
+                        "tasks": datasets.Value(datasets.Value("string", id="sequence"), id="tasks"),
+                    }
+                )
+            ],
             # Homepage of the module for documentation
             homepage="http://module.homepage",
             # Additional links to the codebase or references
@@ -81,15 +42,15 @@ class llm_harness_mistral_arc(evaluate.Metric):
             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):
-        """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,
-        }
+    def _compute(self, pretrained, tasks):
+        outputs = lm_eval.simple_evaluate( 
+              model="hf",
+              model_args={"pretrained":pretrained},
+              tasks=tasks,
+              num_fewshot=0,
+          )
+        results = {}
+        for task in outputs['results']:
+        results[task] = {'acc':outputs['results'][task]['acc,none'], 
+                          'acc_norm':outputs['results'][task]['acc_norm,none']}
+        return results

requirements.txt

@@ -1 +1,2 @@
-git+https://github.com/huggingface/evaluate@main
+git+https://github.com/huggingface/evaluate@main
+git+https://github.com/EleutherAI/lm-evaluation-harness@main