LLaMA-Factory-Mirror/evaluation/mmlu/mmlu.py

# 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 os

import datasets
import pandas as pd


_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 = """\
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
"""

_HOMEPAGE = "https://github.com/hendrycks/test"

_LICENSE = "MIT"

_URL = "mmlu.zip"

task_list = [
    "high_school_european_history",
    "business_ethics",
    "clinical_knowledge",
    "medical_genetics",
    "high_school_us_history",
    "high_school_physics",
    "high_school_world_history",
    "virology",
    "high_school_microeconomics",
    "econometrics",
    "college_computer_science",
    "high_school_biology",
    "abstract_algebra",
    "professional_accounting",
    "philosophy",
    "professional_medicine",
    "nutrition",
    "global_facts",
    "machine_learning",
    "security_studies",
    "public_relations",
    "professional_psychology",
    "prehistory",
    "anatomy",
    "human_sexuality",
    "college_medicine",
    "high_school_government_and_politics",
    "college_chemistry",
    "logical_fallacies",
    "high_school_geography",
    "elementary_mathematics",
    "human_aging",
    "college_mathematics",
    "high_school_psychology",
    "formal_logic",
    "high_school_statistics",
    "international_law",
    "high_school_mathematics",
    "high_school_computer_science",
    "conceptual_physics",
    "miscellaneous",
    "high_school_chemistry",
    "marketing",
    "professional_law",
    "management",
    "college_physics",
    "jurisprudence",
    "world_religions",
    "sociology",
    "us_foreign_policy",
    "high_school_macroeconomics",
    "computer_security",
    "moral_scenarios",
    "moral_disputes",
    "electrical_engineering",
    "astronomy",
    "college_biology",
]


class MMLUConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)


class MMLU(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        MMLUConfig(
            name=task_name,
        )
        for task_name in task_list
    ]

    def _info(self):
        features = datasets.Features(
            {
                "question": datasets.Value("string"),
                "A": datasets.Value("string"),
                "B": datasets.Value("string"),
                "C": datasets.Value("string"),
                "D": datasets.Value("string"),
                "answer": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        task_name = self.config.name
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "data", "test", f"{task_name}_test.csv"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "data", "val", f"{task_name}_val.csv"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "data", "dev", f"{task_name}_dev.csv"),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        df = pd.read_csv(filepath)
        df.columns = ["question", "A", "B", "C", "D", "answer"]

        for i, instance in enumerate(df.to_dict(orient="records")):
            yield i, instance