Delete loading script

generics_kb.py

@@ -1,189 +0,0 @@
-# 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],
-                    }