psycholinguistic_eval.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 datasets
import pandas as pd


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{ettinger2020bert,
  title={What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models},
  author={Ettinger, Allyson},
  journal={Transactions of the Association for Computational Linguistics},
  volume={8},
  pages={34--48},
  year={2020},
  publisher={MIT Press}
}
"""

# You can copy an official description
_DESCRIPTION = """\
Psycholinguistic dataset from 'What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models'
by Allyson Ettinger
"""

_HOMEPAGE = "https://github.com/aetting/lm-diagnostics"

_LICENSE = """MIT License

Copyright (c) 2020 Allyson Ettinger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

# 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/psycholinguistic_eval/resolve/main/"
_URLS = {
    "CPRAG": _URL + "CPRAG/test.csv",
    "ROLE": _URL + "ROLE/test.csv",
    "NEG-NAT": _URL + "NEG-NAT/test.csv",
    "NEG-SIMP": _URL + "NEG-SIMP/test.csv",
}

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

    VERSION = datasets.Version("1.0.0")

    # 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="CPRAG", version=VERSION, description="34 questions evaluating commonsense knowledge"),
        datasets.BuilderConfig(name="ROLE", version=VERSION, description="88 questions evaluating event knowledge and semantic roles"),
        datasets.BuilderConfig(name="NEG-NAT", version=VERSION, description="[NEG-NAT description]"),
        datasets.BuilderConfig(name="NEG-SIMP", version=VERSION, description="[NEG-SIMP description]"),
    ]

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "CPRAG":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "context_s1": datasets.Value("string"),
                    "context_s2": datasets.Value("string"),
                    "expected": datasets.Value("string"),
                    "within_category": datasets.Value("string"),
                    "between_category": datasets.Value("string"),
                }
            )
        elif self.config.name == "ROLE":
            features = datasets.Features(
                {
                    "context": datasets.Value("string"),
                    "expected": datasets.Value("string"),
                }
            )
        elif self.config.name == "NEG-NAT":
            features = datasets.Features(
                {
                    "context_aff": datasets.Value("string"),
                    "context_neg": datasets.Value("string"),
                    "target_aff": datasets.Value("string"),
                    "target_neg": datasets.Value("string"),
                }
            )
        elif self.config.name == "NEG-SIMP":
            features = datasets.Features(
                {
                    "context_aff": datasets.Value("string"),
                    "context_neg": datasets.Value("string"),
                    "target_aff": datasets.Value("string"),
                    "target_neg": datasets.Value("string"),
                }
            )
        else:
            raise NotImplementedError

        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):
        # TODO: 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

        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files[self.config.name],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        df = pd.read_csv(filepath)
        for index, row in df.iterrows():
            if self.config.name == "CPRAG":
                # Yields examples as (key, example) tuples
                yield index, {
                    "context_s1": row["context_s1"],
                    "context_s2": row["context_s2"],
                    "expected": row["expected"],
                    "within_category": row["within_category"],
                    "between_category": row["between_category"],
                }
            elif self.config.name == "ROLE":
                yield index, {
                    "context": row["context"],
                    "expected": row["expected"],
                }
            elif self.config.name == "NEG-NAT":
                yield index, {
                    "context_aff": row["context_aff"],
                    "context_neg": row["context_neg"],
                    "target_aff": row["target_aff"],
                    "target_neg": row["target_neg"],
                }
            elif self.config.name == "NEG-SIMP":
                yield index, {
                    "context_aff": row["context_aff"],
                    "context_neg": row["context_neg"],
                    "target_aff": row["target_aff"],
                    "target_neg": row["target_neg"],
                }
            else:
                raise NotImplementedError