Delete scifact-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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-
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- # Lint as: python3
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- """SciFact Dataset (Retrieval Only)"""
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-
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- import json
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-
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- import datasets
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-
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- _CITATION = """
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- @inproceedings{Wadden2020FactOF,
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- title={Fact or Fiction: Verifying Scientific Claims},
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- author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
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- booktitle={EMNLP},
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- year={2020},
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- }
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- """
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-
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- _DESCRIPTION = "dataset load script for SciFact"
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-
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- _DATASET_URLS = {
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- 'train': "https://huggingface.co/datasets/Tevatron/scifact-corpus/resolve/main/corpus.jsonl.gz",
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- }
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-
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-
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- class ScifactCorpus(datasets.GeneratorBasedBuilder):
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- VERSION = datasets.Version("0.0.1")
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-
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- BUILDER_CONFIGS = [
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- datasets.BuilderConfig(version=VERSION,
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- description="SciFact Corpus"),
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- ]
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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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-
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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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-
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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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-
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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