Datasets:

ctu-aic
/

csfever_v2

	# 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
	"""CsFEVERv2 dataset"""


	import csv
	import json
	import os

	import datasets


	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is aimed on Czech fact-checking task.
	"""
	#TODO
	_CITATION = ""
	# 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 = ""

	# 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 = {
	"original": {"train": "./original/train.jsonl",
	"dev" : "./original/dev.jsonl",
	"test": "./original/test.jsonl"},
	"f1": {"train": "./f1/train.jsonl",
	"dev" : "./f1/dev.jsonl",
	"test": "./f1/test.jsonl"},
	"precision": {"train": "./precision/train.jsonl",
	"dev" : "./precision/dev.jsonl",
	"test": "./precision/test.jsonl"},
	"07": {"train": "./07/train.jsonl",
	"dev" : "./07/dev.jsonl",
	"test": "./07/test.jsonl"},
	"wiki_pages": "./wiki_pages/wiki_pages.jsonl",
	"original_nli": {"train": "./original_nli/train.jsonl",
	"dev" : "./original_nli/dev.jsonl",
	"test": "./original_nli/test.jsonl"},
	"f1_nli": {"train": "./f1_nli/train.jsonl",
	"dev" : "./f1_nli/dev.jsonl",
	"test": "./f1_nli/test.jsonl"},
	"07_nli": {"train": "./07_nli/train.jsonl",
	"dev" : "./07_nli/dev.jsonl",
	"test": "./07_nli/test.jsonl"},
	"precision_nli": {"train": "./precision_nli/train.jsonl",
	"dev" : "./precision_nli/dev.jsonl",
	"test": "./precision_nli/test.jsonl"},
	}

	_ORIGINAL_DESCRIPTION = ""

	_NLI_NAMES = ["original_nli", "07_nli", "precision_nli", "f1_nli"]


	#Name of the dataset usually matches the script name with CamelCase instead of snake_case
	class CsFEVERv2(datasets.GeneratorBasedBuilder):
	"""CsFEVERv2"""

	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', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="original",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION,
	),
	datasets.BuilderConfig(
	name="f1",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION,
	),
	datasets.BuilderConfig(
	name="precision",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="07",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="wiki_pages",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="original_nli",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="07_nli",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="f1_nli",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	datasets.BuilderConfig(
	name="precision_nli",
	version=VERSION,
	description=_ORIGINAL_DESCRIPTION
	),
	]

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

	def _info(self):
	#This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if self.config.name == "original": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"label": datasets.Value("string"),
	"predicted_label": datasets.Value("string"),
	"predicted_score": datasets.Value("float"),
	"claim": datasets.Value("string"),
	"evidence": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	# These are the features of your dataset like images, labels ...
	}
	)
	elif self.config.name in _NLI_NAMES: # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"label": datasets.ClassLabel(num_classes=3, names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
	"claim": datasets.Value("string"),
	"evidence": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	)
	elif self.config.name == "wiki_pages":
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"revid": datasets.Value("int32"),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"id": datasets.Value("int32"),
	"label": datasets.Value("string"),
	"claim": datasets.Value("string"),
	"evidence": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
	# These are the features of your dataset like images, labels ...
	}
	)
	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)
	if self.config.name == "wiki_pages":
	return [datasets.SplitGenerator(
	name="wiki_pages",
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir,
	"split": "wiki_pages",
	},
	)]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev"],
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["test"],
	"split": "test"
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# 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_point = json.loads(row)
	if self.config.name == "original":
	# Yields examples as (key, example) tuples
	yield key, {
	"id": data_point["id"],
	"label": data_point["label"],
	"predicted_label": data_point["predicted_label"],
	"predicted_score": data_point["predicted_score"],
	"claim": data_point["claim"],
	"evidence": data_point["evidence"],
	}
	elif self.config.name in _NLI_NAMES:
	yield key, {
	"id": data_point["id"],
	"label": data_point["label"],
	"claim": data_point["claim"],
	"evidence": data_point["evidence"],
	}
	elif self.config.name == "wiki_pages":
	yield key, {
	"id": data_point["id"],
	"revid": data_point["revid"],
	"url": data_point["url"],
	"title": data_point["title"],
	"text": data_point["text"],
	}
	else:
	yield key, {
	"id": data_point["id"],
	"label": data_point["label"],
	"claim": data_point["claim"],
	"evidence": data_point["evidence"],
	}