Datasets:
generics_kb

	# coding=utf-8
	# 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.
	"""Generics KB: A Knowledge Base of Generic Statements"""


	import ast
	import csv
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {GenericsKB: A Knowledge Base of Generic Statements},
	authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
	year={2020},
	publisher = {Allen Institute for AI},
	}
	"""

	_DESCRIPTION = """\
	The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
	"""

	_HOMEPAGE = "https://allenai.org/data/genericskb"

	_LICENSE = "cc-by-4.0"

	_URL = "https://drive.google.com/u/0/uc?id={0}&export=download"

	_FILEPATHS = {
	"generics_kb_best": _URL.format("12DfIzoWyHIQqssgUgDvz3VG8_ScSh6ng"),
	"generics_kb": _URL.format("1UOIEzQTid7SzKx2tbwSSPxl7g-CjpoZa"),
	"generics_kb_simplewiki": _URL.format("1SpN9Qc7XRy5xs4tIfXkcLOEAP2IVaK15"),
	"generics_kb_waterloo": "cskb-waterloo-06-21-with-bert-scores.jsonl",
	}


	class GenericsKb(datasets.GeneratorBasedBuilder):
	"""The GenericsKB is the first large-scale resource containing naturally occurring generic sentences, and is rich in high-quality, general, semantically complete statements."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="generics_kb_best",
	version=VERSION,
	description="This is the default and recommended config.Comprises of GENERICSKB generics with a score > 0.234 ",
	),
	datasets.BuilderConfig(
	name="generics_kb", version=VERSION, description="This GENERICSKB that contains 3,433,000 sentences."
	),
	datasets.BuilderConfig(
	name="generics_kb_simplewiki",
	version=VERSION,
	description="SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org)",
	),
	datasets.BuilderConfig(
	name="generics_kb_waterloo",
	version=VERSION,
	description="The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains.",
	),
	]

	@property
	def manual_download_instructions(self):
	return """\
	You need to manually download the files needed for the dataset config generics_kb_waterloo. The other configs like generics_kb_best don't need manual downloads.
	The <path/to/folder> can e.g. be `~/Downloads/GenericsKB`. Download the following required files from https://drive.google.com/drive/folders/1vqfVXhJXJWuiiXbUa4rZjOgQoJvwZUoT
	For working on "generics_kb_waterloo" data,
	1. Manually download 'GenericsKB-Waterloo-WithContext.jsonl.zip' into your <path/to/folder>.Please ensure the filename is as is.
	The Waterloo is also generics from GenericsKB.tsv, but expanded to also include their surrounding context (before/after sentences). The Waterloo generics are the majority of GenericsKB. This zip file is 1.4GB expanding to 5.5GB.
	2. Extract the GenericsKB-Waterloo-WithContext.jsonl.zip; It will create a file of 5.5 GB called cskb-waterloo-06-21-with-bert-scores.jsonl.
	Ensure you move this file into your <path/to/folder>.

	generics_kb can then be loaded using the following commands based on which data you want to work on. Data files must be present in the <path/to/folder> if using "generics_kb_waterloo" config.
	1. `datasets.load_dataset("generics_kb","generics_kb_best")`.
	2. `datasets.load_dataset("generics_kb","generics_kb")`
	3. `datasets.load_dataset("generics_kb","generics_kb_simplewiki")`
	4. `datasets.load_dataset("generics_kb","generics_kb_waterloo", data_dir="<path/to/folder>")`

	"""

	DEFAULT_CONFIG_NAME = "generics_kb_best"

	def _info(self):
	if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":

	featuredict = {
	"source_name": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"sentences_before": datasets.Sequence(datasets.Value("string")),
	"sentences_after": datasets.Sequence(datasets.Value("string")),
	"concept_name": datasets.Value("string"),
	"quantifiers": datasets.Sequence(datasets.Value("string")),
	"id": datasets.Value("string"),
	"bert_score": datasets.Value("float64"),
	}
	if self.config.name == "generics_kb_simplewiki":
	featuredict["headings"] = datasets.Sequence(datasets.Value("string"))
	featuredict["categories"] = datasets.Sequence(datasets.Value("string"))

	features = datasets.Features(featuredict)

	else:

	features = datasets.Features(
	{
	"source": datasets.Value("string"),
	"term": datasets.Value("string"),
	"quantifier_frequency": datasets.Value("string"),
	"quantifier_number": datasets.Value("string"),
	"generic_sentence": datasets.Value("string"),
	"score": datasets.Value("float64"),
	}
	)

	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,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# 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 self.config.name == "generics_kb_waterloo":
	data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
	# check if manual folder exists
	if not os.path.exists(data_dir):
	raise FileNotFoundError(
	f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('generics_kb', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
	)

	# Check if required files exist in the folder
	filepath = os.path.join(data_dir, _FILEPATHS[self.config.name])

	if not os.path.exists(filepath):
	raise FileNotFoundError(
	f"{filepath} does not exist. Make sure you required files are present in {data_dir} `. Manual download instructions: {self.manual_download_instructions})"
	)
	else:
	filepath = dl_manager.download(_FILEPATHS[self.config.name])

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepath,
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = ast.literal_eval(row)

	result = {
	"source_name": data["source"]["name"],
	"sentence": data["knowledge"]["sentence"],
	"sentences_before": data["knowledge"]["context"]["sentences_before"],
	"sentences_after": data["knowledge"]["context"]["sentences_after"],
	"concept_name": data["knowledge"]["key_concepts"][0]["concept_name"],
	"quantifiers": data["knowledge"]["key_concepts"][0]["quantifiers"],
	"id": data["id"],
	"bert_score": data["bert_score"],
	}
	if self.config.name == "generics_kb_simplewiki":
	result["headings"] = data["knowledge"]["context"]["headings"]
	result["categories"] = data["knowledge"]["context"]["categories"]

	yield id_, result
	else:

	with open(filepath, encoding="utf-8") as f:
	# Skip the header
	next(f)

	read_tsv = csv.reader(f, delimiter="\t")

	for id_, row in enumerate(read_tsv):

	quantifier = row[2]
	quantifier_frequency = ""
	quantifier_number = ""

	if quantifier != "":
	quantifier = ast.literal_eval(quantifier)
	if "frequency" in quantifier.keys():
	quantifier_frequency = quantifier["frequency"]
	if "number" in quantifier.keys():
	quantifier_number = quantifier["number"]
	yield id_, {
	"source": row[0],
	"term": row[1],
	"quantifier_frequency": quantifier_frequency,
	"quantifier_number": quantifier_number,
	"generic_sentence": row[3],
	"score": row[4],
	}