generics_kb / generics_kb.py
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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 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],
}