Convert dataset to Parquet
#1
by
ianporada
- opened
- README.md +36 -0
- davis_pdp.py +0 -129
- davis_pdp/test-00000-of-00001.parquet +3 -0
README.md
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---
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dataset_info:
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config_name: davis_pdp
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features:
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- name: text
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dtype: string
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- name: pronoun
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dtype: string
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- name: pronoun_loc
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dtype: int32
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- name: quote
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dtype: string
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- name: quote_loc
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dtype: int32
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- name: options
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sequence: string
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- name: label
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dtype:
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class_label:
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names:
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'0': '0'
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'1': '1'
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- name: source
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dtype: string
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splits:
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- name: test
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num_bytes: 17818
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num_examples: 60
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download_size: 14118
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dataset_size: 17818
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configs:
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- config_name: davis_pdp
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data_files:
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- split: test
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path: davis_pdp/test-*
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---
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davis_pdp.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors.
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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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"""The Winograd Schema Challenge Dataset"""
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import xml.etree.ElementTree as ET
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import datasets
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_DESCRIPTION = """\
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A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
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resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
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resolution. The schema takes its name from a well-known example by Terry Winograd:
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> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
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If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they''
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presumably refers to the demonstrators.
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"""
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_CITATION = """\
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@inproceedings{levesque2012winograd,
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title={The winograd schema challenge},
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author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
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booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
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year={2012},
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organization={Citeseer}
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}
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"""
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_HOMPAGE = "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html"
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_DOWNLOAD_URL = "https://cs.nyu.edu/~davise/papers/WinogradSchemas/PDPChallenge2016.xml"
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class WinogradWSCConfig(datasets.BuilderConfig):
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"""BuilderConfig for WinogradWSC."""
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def __init__(self, *args, language=None, inds=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.inds = set(inds) if inds is not None else None
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def is_in_range(self, id):
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"""Takes an index and tells you if it belongs to the configuration's subset"""
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return id in self.inds if self.inds is not None else True
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class WinogradWSC(datasets.GeneratorBasedBuilder):
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"""The Winograd Schema Challenge Dataset"""
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BUILDER_CONFIG_CLASS = WinogradWSCConfig
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BUILDER_CONFIGS = [
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WinogradWSCConfig(
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name="davis_pdp",
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description="Full set of winograd examples",
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"text": datasets.Value("string"),
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"pronoun": datasets.Value("string"),
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"pronoun_loc": datasets.Value("int32"),
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"quote": datasets.Value("string"),
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"quote_loc": datasets.Value("int32"),
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"options": datasets.Sequence(datasets.Value("string")),
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"label": datasets.ClassLabel(num_classes=2),
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"source": datasets.Value("string"),
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}
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),
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homepage=_HOMPAGE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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path = dl_manager.download_and_extract(_DOWNLOAD_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path}),
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]
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def _cleanup_whitespace(self, text):
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return " ".join(text.split())
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def _generate_examples(self, filepath):
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tree = ET.parse(filepath)
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for id, schema in enumerate(tree.getroot()):
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if not self.config.is_in_range(id):
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continue
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text_root = schema.find("text")
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quote_root = schema.find("quote")
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text_left = self._cleanup_whitespace(text_root.findtext("txt1", ""))
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text_right = self._cleanup_whitespace(text_root.findtext("txt2", ""))
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quote_left = self._cleanup_whitespace(quote_root.findtext("quote1", ""))
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quote_right = self._cleanup_whitespace(quote_root.findtext("quote2", ""))
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pronoun = self._cleanup_whitespace(text_root.findtext("pron"))
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features = {}
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features["text"] = " ".join([text_left, pronoun, text_right]).strip()
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features["quote"] = " ".join([quote_left, pronoun, quote_right]).strip()
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features["pronoun"] = pronoun
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features["options"] = [
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self._cleanup_whitespace(option.text) for option in schema.find("answers").findall("answer")
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]
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answer_txt = self._cleanup_whitespace(schema.findtext("correctAnswer"))
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features["label"] = int("B" in answer_txt) # convert " A. " or " B " strings to a 0/1 index
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features["pronoun_loc"] = len(text_left) + 1 if len(text_left) > 0 else 0
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features["quote_loc"] = features["pronoun_loc"] - (len(quote_left) + 1 if len(quote_left) > 0 else 0)
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features["source"] = self._cleanup_whitespace(schema.findtext("source"))
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yield id, features
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davis_pdp/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:de048f890a37544eaedaf3e25ac2be019ad862ada0e1f9a4fe3d1379b590725f
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size 14118
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