climate-fever-nli-stsb.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
"""Loading script for climate-fever-nli-stsb dataset"""


import csv
import json
import os

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {climate-fever-nli-stsb},
author={Steve Henty, Omdena, "Cologne, Germany Chapter - Detecting Bias in Climate Reporting in English and German Language News Media"},
year={2023}
}
"""

# You can copy an official description
_DESCRIPTION = """\
A modified CLIMATE-FEVER dataset that includes NLI-style features and STSb-features suitable for SentenceBERT training scripts.
"""

# 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 = ""

# 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)
_URLS = {
}

_DATAFILES = {
    'cf-nli': {
        'url': 'https://huggingface.co/datasets/slhenty/climate-fever-nli-stsb/resolve/main/cf-nli.zip',
        'zip_path': './cf-nli.zip',
        'filename': {
            'train': 'train.tsv',
            'dev': 'dev.tsv',
            'test': 'test.tsv'
        }
    },
    'cf-nli-nei': {
        'url': 'https://huggingface.co/datasets/slhenty/climate-fever-nli-stsb/resolve/main/cf-nli-nei.zip',
        'zip_path': './cf-nli-nei.zip',
        'filename': {
            'train': 'train.tsv',
            'dev': 'dev.tsv',
            'test': 'test.tsv'
        }
    },
    'cf-stsb': {
        'url': 'https://huggingface.co/datasets/slhenty/climate-fever-nli-stsb/resolve/main/cf-stsb.zip',
        'zip_path': './cf-stsb.zip',
        'filename': {
            'train': 'train.tsv',
            'dev': 'dev.tsv',
            'test': 'test.tsv'
        }
    }
}

# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class ClimateFeverNliStsb(datasets.GeneratorBasedBuilder):
    """A modified CLIMATE-FEVER dataset that includes NLI-style features and STSb-features suitable for SentenceBERT training scripts."""

    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('climate-fever-nli-stsb', 'cf-nli')
    # data = datasets.load_dataset('climate-fever-nli-stsb', 'cf-nli-nei')
    # data = datasets.load_dataset('climate-fever-nli-stsb', 'cf-stsb')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="cf-nli", version=VERSION, description="Dataset with NLI-style features derived from SUPPORTS and REFUTES evidence only"),
        datasets.BuilderConfig(name="cf-nli-nei", version=VERSION, description="Dataset with NLI-style features derived from SUPPORTS, REFUTES, and NOT_ENOUGH_INFO (NEI) evidence"),
        datasets.BuilderConfig(name="cf-stsb", version=VERSION, description="Dataset with STSb-style features including similarity scores derived from evidence_label, votes, and entropy"),
    ]

    def _info(self):
        if self.config.name in ("cf-nli", "cf-nli-nei"):
            features = datasets.Features(
                {
                    "split": datasets.Value("string"),
                    "sentence1": datasets.Value("string"),
                    "sentence2": datasets.Value("string"),
                    "label": datasets.Value("string")
                }
            )
        else: # "cf-stsb"
            features = datasets.Features(
                {
                    "split": datasets.Value("string"),
                    "sentence1": datasets.Value("string"),
                    "sentence2": datasets.Value("string"),
                    "score": datasets.Value("float")
                }
            )

        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
        datapath = _DATAFILES[self.config.name]
        url = datapath['url']
        zipfile = datapath['zip_path']
        filename = datapath['filename']
        data_dir = dl_manager.download_and_extract(url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, filename['train']),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.NamedSplit('dev'),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, filename['dev']),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, filename['test']),
                    "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:
            reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
            for key, row in enumerate(reader):
                if self.config.name in ("cf-nli", "cf-nli-nei"):
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "split": row["split"],
                        "sentence1": row["sentence1"],
                        "sentence2": row["sentence2"],
                        "label": row["label"],
                    }
                else: # cf-stsb
                    yield key, {
                        "split": row["split"],
                        "sentence1": row["sentence1"],
                        "sentence2": row["sentence2"],
                        "score": row["score"],
                    }