mmmlu.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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import os

import datasets

from pathlib import Path


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "MIT License"

ROOT = Path("data")
_URLS = {
    "validation": list((ROOT / "val").glob("*.csv")),
    "dev": list((ROOT / "dev").glob("*.csv")),
    "test": list((ROOT / "test").glob("*.csv")),
}
_URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
CONFIG_NAMES = [
    "abstract_algebra",
    "high_school_mathematics",
    "nutrition",
    "high_school_macroeconomics",
    "world_religions",
    "high_school_statistics",
    "clinical_knowledge",
    "medical_genetics",
    "college_physics",
    "professional_law",
    "virology",
    "astronomy",
    "moral_disputes",
    "electrical_engineering",
    "high_school_psychology",
    "public_relations",
    "college_biology",
    "college_mathematics",
    "econometrics",
    "anatomy",
    "miscellaneous",
    "international_law",
    "management",
    "prehistory",
    "formal_logic",
    "high_school_world_history",
    "conceptual_physics",
    "high_school_microeconomics",
    "high_school_computer_science",
    "elementary_mathematics",
    "human_aging",
    "logical_fallacies",
    "sociology",
    "us_foreign_policy",
    "moral_scenarios",
    "human_sexuality",
    "high_school_us_history",
    "computer_security",
    "marketing",
    "high_school_european_history",
    "security_studies",
    "college_computer_science",
    "jurisprudence",
    "high_school_geography",
    "high_school_physics",
    "philosophy",
    "machine_learning",
    "high_school_chemistry",
    "high_school_biology",
    "professional_accounting",
    "business_ethics",
    "professional_psychology",
    "high_school_government_and_politics",
    "college_medicine",
    "professional_medicine",
    "college_chemistry",
    "global_facts"
]

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=task,
            version=datasets.Version("1.1.0"),
            description=f"Task {task}"
        )
        for task in CONFIG_NAMES
    ]

    DEFAULT_CONFIG_NAME = CONFIG_NAMES[0]

    def _info(self):
        features = datasets.Features(
            {
                "question": datasets.Value("string"),
                "option1": datasets.Value("string"),
                "option2": datasets.Value("string"),
                "option3": datasets.Value("string"),
                "option4": datasets.Value("string"),
                "answer": datasets.Value("string")
                # These are the features of your dataset like images, labels ...
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

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

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filename):
        # read in the csv file
        with open(filename, encoding="utf-8") as f:
            csv_reader = csv.reader(f, delimiter=",")
            for id_, row in enumerate(csv_reader):
                # row format: question, option1, option2, option3, option4, answer
                yield id_, {
                    "question": str(row[0]),
                    "option1": str(row[1]),
                    "option2": str(row[2]),
                    "option3": str(row[3]),
                    "option4": str(row[4]),
                    "answer": str(row[5]),
                }