# 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"], }