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README (2).md

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+---
+license: mit
+---

data-05.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4930e5ee853970d6b28ccde4c2b0aede9c778ad2694a57c7af33a62d9a32ac5e
+size 1064960

mmlu_560_iter_5.py

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+# coding=utf-8
+# 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.
+
+
+import csv
+import os
+
+import datasets
+import tarfile
+
+_CITATION = """\
+@article{hendryckstest2021,
+      title={Measuring Massive Multitask Language Understanding},
+      author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
+      journal={Proceedings of the International Conference on Learning Representations (ICLR)},
+      year={2021}
+    }
+"""
+
+_DESCRIPTION = """\
+This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/alonmiron/mmlu_hinted_huggingface"
+
+_URL = "https://huggingface.co/datasets/alonmiron/mmlu_560_iter_5/resolve/main/data-05.tar"
+# _URL = "https://huggingface.co/datasets/cais/mmlu/resolve/main/data.tar"
+
+_SUBJECTS = [
+    "all",
+    "abstract_algebra",
+    "anatomy",
+    "astronomy",
+    "business_ethics",
+    "clinical_knowledge",
+    "college_biology",
+    "college_chemistry",
+    "college_computer_science",
+    "college_mathematics",
+    "college_medicine",
+    "college_physics",
+    "computer_security",
+    "conceptual_physics",
+    "econometrics",
+    "electrical_engineering",
+    "elementary_mathematics",
+    "formal_logic",
+    "global_facts",
+    "high_school_biology",
+    "high_school_chemistry",
+    "high_school_computer_science",
+    "high_school_european_history",
+    "high_school_geography",
+    "high_school_government_and_politics",
+    "high_school_macroeconomics",
+    "high_school_mathematics",
+    "high_school_microeconomics",
+    "high_school_physics",
+    "high_school_psychology",
+    "high_school_statistics",
+    "high_school_us_history",
+    "high_school_world_history",
+    "human_aging",
+    "human_sexuality",
+    "international_law",
+    "jurisprudence",
+    "logical_fallacies",
+    "machine_learning",
+    "management",
+    "marketing",
+    "medical_genetics",
+    "miscellaneous",
+    "moral_disputes",
+    "moral_scenarios",
+    "nutrition",
+    "philosophy",
+    "prehistory",
+    "professional_accounting",
+    "professional_law",
+    "professional_medicine",
+    "professional_psychology",
+    "public_relations",
+    "security_studies",
+    "sociology",
+    "us_foreign_policy",
+    "virology",
+    # "world_religions",
+]
+
+
+def check_archive_contents(archive_path):
+    # Try to open the tar archive
+    try:
+        with tarfile.open(archive_path, 'r') as tar:
+            # Get the list of members
+            members = tar.getmembers()
+            if not members:
+                print("The archive is empty.")
+            else:
+                print("The archive contains files. Number of files:", len(members))
+    except tarfile.ReadError as e:
+        print("There was an error opening the tar file:", e)
+    except Exception as e:
+        print("An unexpected error occurred:", e)
+
+
+class Mmlu(datasets.GeneratorBasedBuilder):
+    """Measuring Massive Multitask Language Understanding, consisting of 57 tasks"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=sub, version=datasets.Version("1.0.0"), description=f"MMLU Subject {sub}"
+        )
+        for sub in _SUBJECTS
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "subject": datasets.Value("string"),
+                "choices": datasets.features.Sequence(datasets.Value("string")),
+                "answer": datasets.features.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        print("split_generators got called")
+        """Returns SplitGenerators."""
+        archive = dl_manager.download(_URL)
+        check_archive_contents(archive)
+        if os.path.exists(archive):
+            print("Download successful, archive is present at:", archive)
+        else:
+            print("Download failed, archive not found.")
+        return [
+            # datasets.SplitGenerator(
+            #     name=datasets.Split("auxiliary_train"),
+            #     gen_kwargs={
+            #         "iter_archive": dl_manager.iter_archive(archive),
+            #         "split": "auxiliary_train",
+            #     },
+            # ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"iter_archive": dl_manager.iter_archive(archive), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "iter_archive": dl_manager.iter_archive(archive),
+                    "split": "val",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("dev"),
+                gen_kwargs={
+                    "iter_archive": dl_manager.iter_archive(archive),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, iter_archive, split):
+        """Yields examples as (key, example) tuples."""
+        n_yielded_files = 0
+        for id_file, (path, file) in enumerate(iter_archive):
+            if f"data/{split}/" in path:
+                if split == "auxiliary_train" or f"{self.config.name}_{split}.csv" in path or self.config.name == "all":
+                    subset = path.split("/")[-1].rsplit("_",1)[0] if split != "auxiliary_train" else ""
+                    n_yielded_files += 1
+                    lines = (line.decode("utf-8") for line in file)
+                    reader = csv.reader(lines)
+                    for id_line, data in enumerate(reader):
+                        yield f"{id_file}_{id_line}", {"question": data[0], "choices": data[1:5], "answer": data[5], "subject": subset}
+                    if (n_yielded_files == 8 or split != "auxiliary_train") and self.config.name != "all":
+                        break
+