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# 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 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"
_BASE_URL = "data/{0}"
_URLS = {
"generics_kb_best": _BASE_URL.format("GenericsKB-Best.tsv.gz"),
"generics_kb": _BASE_URL.format("GenericsKB.tsv.gz"),
"generics_kb_simplewiki": _BASE_URL.format("GenericsKB-SimpleWiki-With-Context.jsonl.gz"),
"generics_kb_waterloo": _BASE_URL.format("GenericsKB-Waterloo-With-Context.jsonl.gz"),
}
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.",
),
]
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):
filepath = dl_manager.download_and_extract(_URLS[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],
}
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