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corpus.jsonl.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a790d364845af62316c7baf03a01be21c2dbf2f12771413dd5fe48eebdfa34b
+size 498194799

wikipedia-nq-corpus.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 Corpus"
+
+_DATASET_URLS = {
+    'train': "https://huggingface.co/datasets/Tevatron/wikipedia-nq-corpus/resolve/main/corpus.jsonl.gz"
+}
+
+
+class WikipediaNqCorpus(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(version=VERSION,
+                               description="Wikipedia Corpus 100-word splits"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {'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):
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
+        splits = [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                },
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                data = json.loads(line)
+                yield data['docid'], data