Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
# 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], | |
} | |