Xueguang Ma
commited on
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Parent(s):
9c44247
update
Browse files- wikipedia-wq-corpus.py +83 -0
wikipedia-wq-corpus.py
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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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# Lint as: python3
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"""Wikipedia WQ dataset."""
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import json
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import datasets
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_CITATION = """
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@inproceedings{karpukhin-etal-2020-dense,
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title = "Dense Passage Retrieval for Open-Domain Question Answering",
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author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
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month = nov,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
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doi = "10.18653/v1/2020.emnlp-main.550",
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pages = "6769--6781",
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}
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"""
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_DESCRIPTION = "dataset load script for Wikipedia WQ Corpus"
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_DATASET_URLS = {
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'train': "https://huggingface.co/datasets/Tevatron/wikipedia-nq-corpus/resolve/main/corpus.jsonl.gz",
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}
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class WikipediaWqCorpus(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.0.1")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(version=VERSION,
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description="Wikipedia WQ corpus"),
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]
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def _info(self):
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features = datasets.Features(
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{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
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'title': datasets.Value('string')},
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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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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="",
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# License for the dataset if available
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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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downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
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splits = [
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datasets.SplitGenerator(
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name="train",
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gen_kwargs={
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"filepath": downloaded_files["train"],
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},
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),
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]
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return splits
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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for line in f:
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data = json.loads(line)
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yield data['docid'], data
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