crepe_dataset.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.
"""data loading scripts for the CREPE dataset"""


import csv
import json
import pandas as pd
import os

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{
    zhang-etal-2023-causal,
    title = "Causal Reasoning About Entities and Events in Procedural Texts",
    author = "Li Zhang and Hainiu Xu and Yue Yang and Shuyan Zhou and Weiqiu You and Manni Arora and Chris Callison-Burch"
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/pdf/2301.10896.pdf"
    abstract = "Entities and events have long been regarded as the crux of machine reasoning. Procedural texts have received increasing attention due to the dynamic nature of involved entities and events. Existing work has focused either on entity state tracking (e.g., the temperature of a pan) or on counterfactual event reasoning (e.g., how likely am I to burn myself by touching the pan), while these two tasks are tightly intertwined. In this work, we propose CREPE, the first benchmark on causal reasoning about event plausibility based on entity states. We experiment with strong large language models and show that most models, including GPT3, perform close to chance at .30 F1, lagging far behind the human performance of .87 F1. Inspired by the finding that structured representations such as programming languages benefit event reasoning as a prompt to code language models such as Codex, we creatively inject the causal relations between entities and events through intermediate variables and boost the performance to .67 to .72 F1. Our proposed event representation not only allows for knowledge injection but also marks the first successful attempt of chain-of-thought reasoning with code language models."
}
"""

# You can copy an official description
_DESCRIPTION = """\
    The CREPE dataset is designed for causal reasoning on entities and events in procedural texts.
    CREPE is the first benchmark on causal reasoning about event plausibility based on entity states
"""

_HOMEPAGE = "https://huggingface.co/datasets/zharry29/CREPE"

_LICENSE = "cc-by-4.0"

# 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)
_URLS = {
    "development": "https://huggingface.co/datasets/zharry29/CREPE/blob/main/crepe_train.json",
    "testing": "https://huggingface.co/datasets/zharry29/CREPE/blob/main/crepe_test.json",
}


class CREPE(datasets.GeneratorBasedBuilder):
    """Dataset for causal reasoning about entities and events in procedural texts."""

    VERSION = datasets.Version("1.1.0")

    # 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', 'development')
    # data = datasets.load_dataset('my_dataset', 'testing')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="development", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="testing", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "development"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "goal": datasets.Value("string"),
                "steps": datasets.Value("list"),
                "event": datasets.Value("string"),
                "event_answer": datasets.Value("list"),
                "entity": datasets.Value("string"),
                "entity_answer": datasets.Value("list")
            }
        )
        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):
        # 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": os.path.join(data_dir, "crepe_train.json"),
                    "split": "development",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "crepe_test.json"),
                    "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.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = pd.read_csv(row)
                # Yields examples as (key, example) tuples
                yield key, {
                    "goal": data['goal'],
                    "steps": data['steps'],
                    "event": data["event"],
                    "event_answer": data["event_answer"],
                    "entity": data['entity'],
                    "entity_answer": data['entity_answer']
                }