oLMpics.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 json
import os

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{talmor2020olmpics,
  title={oLMpics-on what language model pre-training captures},
  author={Talmor, Alon and Elazar, Yanai and Goldberg, Yoav and Berant, Jonathan},
  journal={Transactions of the Association for Computational Linguistics},
  volume={8},
  pages={743--758},
  year={2020},
  publisher={MIT Press}
}
"""

_DESCRIPTION = """\
This is a set a eight datasets from the paper "oLMpics - On what Language Model Pre-training Captures"
by Alon Talmor et al.
"""

_HOMEPAGE = "https://github.com/alontalmor/oLMpics"

_LICENSE = "Apache 2.0"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://huggingface.co/datasets/KevinZ/oLMpics/resolve/main/"
TASKS = ["Age_Comparison", "Always_Never", "Antonym_Negation", "Encyclopedic_Composition",
         "Multihop_Composition", "Object_Comparison", "Property_Conjunction", "Taxonomy_Conjunction"]
_URLS = {task: [f"{_URL}{task}/train.jsonl", f"{_URL}{task}/test.jsonl"] for task in TASKS}


class OLMpicsDataset(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("1.0.0"), description=f"{task} description") for task in TASKS
    ]

    def _info(self):
        features = datasets.Features(
            {
                "stem": datasets.Value("string"),
                "choices": datasets.Sequence(datasets.Value("string")),
                "answerKey": datasets.ClassLabel(names=["A", "B", "C", "D", "E"])  # oLMpics has at most 5 answer choices
            }
        )

        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):
        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[0],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[1],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                yield key, {
                    "stem": data["question"]["stem"],
                    "choices": [choice["text"] for choice in data["question"]["choices"]],
                    "answerKey": data["answerKey"],
                }