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wikipedia-nq.py

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-# 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.
-
-# Lint as: python3
-"""Wikipedia NQ dataset."""
-
-import json
-
-import datasets
-
-_CITATION = """
-@inproceedings{karpukhin-etal-2020-dense,
-    title = "Dense Passage Retrieval for Open-Domain Question Answering",
-    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",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
-    doi = "10.18653/v1/2020.emnlp-main.550",
-    pages = "6769--6781",
-}
-"""
-
-_DESCRIPTION = "dataset load script for Wikipedia NQ"
-
-_DATASET_URLS = {
-    'train': "https://huggingface.co/datasets/Tevatron/wikipedia-nq/resolve/main/nq-train.jsonl.gz",
-    'dev': "https://huggingface.co/datasets/Tevatron/wikipedia-nq/resolve/main/nq-dev.jsonl.gz",
-    'test': "https://huggingface.co/datasets/Tevatron/wikipedia-nq/resolve/main/nq-test.jsonl.gz",
-}
-
-
-class WikipediaNq(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(version=VERSION,
-                               description="Wikipedia NQ train/dev/test datasets"),
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'query_id': datasets.Value('string'),
-            'query': datasets.Value('string'),
-            'answers': [datasets.Value('string')],
-            'positive_passages': [
-                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
-                 'title': datasets.Value('string')}
-            ],
-            'negative_passages': [
-                {'docid': datasets.Value('string'), 'text': datasets.Value('string'),
-                 'title': datasets.Value('string')}
-            ],
-        })
-        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
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="",
-            # License for the dataset if available
-            license="",
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.data_files:
-            downloaded_files = self.config.data_files
-        else:
-            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
-        splits = [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={
-                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
-                },
-            ) for split in downloaded_files
-        ]
-        return splits
-
-    def _generate_examples(self, files):
-        """Yields examples."""
-        for filepath in files:
-            with open(filepath, encoding="utf-8") as f:
-                for line in f:
-                    data = json.loads(line)
-                    if data.get('negative_passages') is None:
-                        data['negative_passages'] = []
-                    if data.get('positive_passages') is None:
-                        data['positive_passages'] = []
-                    if data.get('answers') is None:
-                        data['answers'] = []
-                    yield data['query_id'], data