Create testcm.py

testcm.py

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+# 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.
+"""The CodeMMLU benchmark."""
+
+import os
+import json
+from glob import glob
+
+import datasets
+
+
+_CITATION = """\
+@article{nguyen2024codemmlu,
+  title={CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities},
+  author={Nguyen, Dung Manh and Phan, Thang Chau and Le, Nam Hai and Doan, Thong T. and Nguyen, Nam V. and Pham, Quang and Bui, Nghi D. Q.},
+  journal={arXiv preprint},
+  year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+CodeMMLU is a comprehensive benchmark designed to evaluate the capabilities of large language models (LLMs) in coding and software knowledge
+"""
+
+_HOMEPAGE = "https://fsoft-ai4code.github.io/codemmlu/"
+
+_URL = "./data/test"
+
+_SUBJECTS = [
+    "programming_syntax", "api_frameworks",
+    "software_principles", "dbms_sql", "others",
+    "code_completion", "fill_in_the_middle", "code_repair", "defect_detection"
+]
+
+
+class CodeMMLU(datasets.GeneratorBasedBuilder):
+    """CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities"""
+    # Version history:
+    # 0.0.1: Initial release.
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=sub, version=datasets.Version("0.0.1"),
+            description="CodeMMLU test subject {}".format(sub)
+        ) for sub in _SUBJECTS
+    ]
+
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "task_id": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "choices": datasets.features.Sequence(datasets.Value("string")),
+            }
+        )
+        
+        if self.config.name == "fill_in_the_middle":
+            features["problem_description"] = datasets.Value("string")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path = os.path.join(_URL, self.config.name + ".jsonl")
+        dl_dir = dl_manager.download(path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_path": dl_dir},
+            ),
+        ]
+
+    def _generate_examples(self, data_path):
+        """This function returns the examples in the raw (text) form."""
+        if data_path.endswith(".jsonl"):
+            lines = open(data_path, "r", encoding="utf-8").readlines()
+            reader = [json.loads(line) for line in lines]
+            for idx, data in enumerate(reader):
+                return_dict = {
+                    "task_id": data['task_id'],
+                    "question": data['question'], 
+                    "choices": data['choices'],
+                }
+
+                if "fill_in_the_middle" in data_path:
+                    return_dict['problem_description'] = data['problem_description']
+
+                yield idx, return_dict