utilitarianism.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 json
import csv
import os
import random
import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = r"""
@article{hendrycks2020ethics,
  title={Aligning AI With Shared Human Values},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2008.02275},
  year={2020}
}
@inproceedings{sileo2021analysis,
      title={Analysis and Prediction of NLP Models Via Task Embeddings}, 
      author={Damien Sileo and Marie-Francine Moens},
      booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
      year={2022},
}
"""

# You can copy an official description
_DESCRIPTION = """"""
_HOMEPAGE = ""
_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License"

# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {"default": "https://www.dropbox.com/s/041prrjylv0tf0h/ethics.zip?dl=1"}


class Imppres(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "better_choice": datasets.Value("string"),
                "worst_choice": datasets.Value("string"),
                "comparison": datasets.Value("string"),
                "label": datasets.Value("int32"),
            })
        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,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # 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):
        """Returns SplitGenerators."""

        my_urls = _URLs["default"]
        base_config = "utilitarianism"
        data_dir = os.path.join(dl_manager.download_and_extract(my_urls), "ethics", base_config)
        return [
            datasets.SplitGenerator(
                name=split,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, f"util_{split}.csv"),
                    "split": split,
                },
            ) for split in ['train','test']
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""

        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f)
            for id_, line in enumerate(reader):
                random.seed(id_)
                label=random.randint(0,1)
                yield id_, {
                    "label":label,
                    "better_choice": line[0],
                    "worst_choice": line[1],
                    "comparison":f'"{line[1-label]}" is better than "{line[label]}"'
                }