Commit
•
aba8bf0
1
Parent(s):
32a04d2
Convert dataset to Parquet (#4)
Browse files- Convert dataset to Parquet (cff62b481367aaecb1d46e4b7c7bc78be688a9d9)
- Add 'generics_kb' config data files (eb76b794ef7c649b32c7cb0924e24aaa8326f70e)
- Add 'generics_kb_simplewiki' config data files (830f2d183da530dbf1a23e2166b5ca238311302c)
- Add 'generics_kb_waterloo' config data files (66cf90ad20f3195fb5eb5f271b5ed07d1ec501b6)
- Delete data file (7d7eb9adcb591f3435fca96d940e6b4c1fcaeee8)
- Delete data file (8a14755f448977b3af212dadeb70fa99815603a9)
- Delete loading script (a18eee6fc5c1166bfd5de1016227447306755070)
- Delete data file (74fd13f7277e34986fbf37adcc63d48629f73afb)
- Delete data file (0a7b49bedb4438667effbfcce0eb3fb832408e1f)
- README.md +39 -21
- generics_kb.py +0 -189
- data/GenericsKB.tsv.gz → generics_kb/train-00000-of-00001.parquet +2 -2
- data/GenericsKB-Best.tsv.gz → generics_kb_best/train-00000-of-00001.parquet +2 -2
- data/GenericsKB-SimpleWiki-With-Context.jsonl.gz → generics_kb_simplewiki/train-00000-of-00001.parquet +2 -2
- data/GenericsKB-Waterloo-With-Context.jsonl.gz → generics_kb_waterloo/train-00000-of-00009.parquet +2 -2
- generics_kb_waterloo/train-00001-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00002-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00003-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00004-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00005-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00006-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00007-of-00009.parquet +3 -0
- generics_kb_waterloo/train-00008-of-00009.parquet +3 -0
README.md
CHANGED
@@ -19,10 +19,15 @@ task_categories:
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task_ids: []
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paperswithcode_id: genericskb
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pretty_name: GenericsKB
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tags:
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- knowledge-base
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dataset_info:
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-
- config_name:
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features:
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- name: source
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dtype: string
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@@ -38,11 +43,11 @@ dataset_info:
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dtype: float64
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splits:
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- name: train
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-
num_bytes:
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-
num_examples:
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-
download_size:
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-
dataset_size:
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-
- config_name:
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features:
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- name: source
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dtype: string
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@@ -58,10 +63,10 @@ dataset_info:
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dtype: float64
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splits:
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- name: train
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-
num_bytes:
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-
num_examples:
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-
download_size:
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-
dataset_size:
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- config_name: generics_kb_simplewiki
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features:
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- name: source_name
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@@ -86,10 +91,10 @@ dataset_info:
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sequence: string
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splits:
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- name: train
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-
num_bytes:
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num_examples: 12765
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-
download_size:
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-
dataset_size:
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- config_name: generics_kb_waterloo
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features:
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- name: source_name
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@@ -110,15 +115,28 @@ dataset_info:
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dtype: float64
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splits:
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- name: train
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-
num_bytes:
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num_examples: 3666725
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-
download_size:
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-
dataset_size:
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-
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-
- generics_kb
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---
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# Dataset Card for Generics KB
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task_ids: []
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paperswithcode_id: genericskb
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pretty_name: GenericsKB
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+
config_names:
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+
- generics_kb
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+
- generics_kb_best
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+
- generics_kb_simplewiki
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+
- generics_kb_waterloo
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tags:
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- knowledge-base
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dataset_info:
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+
- config_name: generics_kb
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features:
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- name: source
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dtype: string
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dtype: float64
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splits:
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- name: train
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+
num_bytes: 348152086
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+
num_examples: 3433000
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+
download_size: 140633166
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+
dataset_size: 348152086
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+
- config_name: generics_kb_best
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features:
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- name: source
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dtype: string
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dtype: float64
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splits:
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- name: train
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+
num_bytes: 99895659
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+
num_examples: 1020868
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+
download_size: 39007320
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+
dataset_size: 99895659
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- config_name: generics_kb_simplewiki
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features:
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- name: source_name
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sequence: string
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splits:
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- name: train
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+
num_bytes: 10039243
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num_examples: 12765
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+
download_size: 3895754
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+
dataset_size: 10039243
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- config_name: generics_kb_waterloo
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features:
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- name: source_name
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dtype: float64
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splits:
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- name: train
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+
num_bytes: 4277200021
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num_examples: 3666725
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+
download_size: 2341097052
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+
dataset_size: 4277200021
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+
configs:
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+
- config_name: generics_kb
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+
data_files:
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+
- split: train
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path: generics_kb/train-*
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+
- config_name: generics_kb_best
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data_files:
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+
- split: train
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path: generics_kb_best/train-*
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default: true
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+
- config_name: generics_kb_simplewiki
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+
data_files:
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+
- split: train
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+
path: generics_kb_simplewiki/train-*
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+
- config_name: generics_kb_waterloo
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data_files:
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+
- split: train
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+
path: generics_kb_waterloo/train-*
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---
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# Dataset Card for Generics KB
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generics_kb.py
DELETED
@@ -1,189 +0,0 @@
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-
# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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-
"""Generics KB: A Knowledge Base of Generic Statements"""
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import ast
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import csv
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import datasets
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {GenericsKB: A Knowledge Base of Generic Statements},
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authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
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year={2020},
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publisher = {Allen Institute for AI},
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}
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"""
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_DESCRIPTION = """\
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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.
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"""
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_HOMEPAGE = "https://allenai.org/data/genericskb"
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_LICENSE = "cc-by-4.0"
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_BASE_URL = "data/{0}"
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_URLS = {
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"generics_kb_best": _BASE_URL.format("GenericsKB-Best.tsv.gz"),
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"generics_kb": _BASE_URL.format("GenericsKB.tsv.gz"),
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"generics_kb_simplewiki": _BASE_URL.format("GenericsKB-SimpleWiki-With-Context.jsonl.gz"),
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"generics_kb_waterloo": _BASE_URL.format("GenericsKB-Waterloo-With-Context.jsonl.gz"),
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}
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class GenericsKb(datasets.GeneratorBasedBuilder):
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"""The GenericsKB is the first large-scale resource containing naturally occurring generic sentences, and is rich in high-quality, general, semantically complete statements."""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="generics_kb_best",
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version=VERSION,
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description="This is the default and recommended config. Comprises of GENERICSKB generics with a score > 0.234 ",
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),
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datasets.BuilderConfig(
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name="generics_kb", version=VERSION, description="This GENERICSKB that contains 3,433,000 sentences."
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),
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datasets.BuilderConfig(
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name="generics_kb_simplewiki",
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version=VERSION,
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description="SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org)",
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),
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datasets.BuilderConfig(
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name="generics_kb_waterloo",
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version=VERSION,
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description="The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains.",
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),
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]
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DEFAULT_CONFIG_NAME = "generics_kb_best"
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def _info(self):
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if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
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featuredict = {
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"source_name": datasets.Value("string"),
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"sentence": datasets.Value("string"),
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"sentences_before": datasets.Sequence(datasets.Value("string")),
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"sentences_after": datasets.Sequence(datasets.Value("string")),
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"concept_name": datasets.Value("string"),
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"quantifiers": datasets.Sequence(datasets.Value("string")),
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"id": datasets.Value("string"),
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"bert_score": datasets.Value("float64"),
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}
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if self.config.name == "generics_kb_simplewiki":
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featuredict["headings"] = datasets.Sequence(datasets.Value("string"))
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featuredict["categories"] = datasets.Sequence(datasets.Value("string"))
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features = datasets.Features(featuredict)
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else:
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features = datasets.Features(
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{
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"source": datasets.Value("string"),
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"term": datasets.Value("string"),
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"quantifier_frequency": datasets.Value("string"),
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"quantifier_number": datasets.Value("string"),
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"generic_sentence": datasets.Value("string"),
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"score": datasets.Value("float64"),
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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filepath = dl_manager.download_and_extract(_URLS[self.config.name])
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": filepath,
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},
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),
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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if self.config.name == "generics_kb_waterloo" or self.config.name == "generics_kb_simplewiki":
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with open(filepath, encoding="utf-8") as f:
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for id_, row in enumerate(f):
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data = ast.literal_eval(row)
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result = {
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"source_name": data["source"]["name"],
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"sentence": data["knowledge"]["sentence"],
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"sentences_before": data["knowledge"]["context"]["sentences_before"],
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"sentences_after": data["knowledge"]["context"]["sentences_after"],
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"concept_name": data["knowledge"]["key_concepts"][0]["concept_name"],
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"quantifiers": data["knowledge"]["key_concepts"][0]["quantifiers"],
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"id": data["id"],
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"bert_score": data["bert_score"],
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}
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if self.config.name == "generics_kb_simplewiki":
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result["headings"] = data["knowledge"]["context"]["headings"]
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result["categories"] = data["knowledge"]["context"]["categories"]
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yield id_, result
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else:
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with open(filepath, encoding="utf-8") as f:
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# Skip the header
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next(f)
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read_tsv = csv.reader(f, delimiter="\t")
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for id_, row in enumerate(read_tsv):
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quantifier = row[2]
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quantifier_frequency = ""
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quantifier_number = ""
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if quantifier != "":
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quantifier = ast.literal_eval(quantifier)
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if "frequency" in quantifier.keys():
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quantifier_frequency = quantifier["frequency"]
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if "number" in quantifier.keys():
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quantifier_number = quantifier["number"]
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yield id_, {
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"source": row[0],
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"term": row[1],
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"quantifier_frequency": quantifier_frequency,
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"quantifier_number": quantifier_number,
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"generic_sentence": row[3],
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"score": row[4],
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}
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