update wq

.gitattributes

@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wq-dev.jsonl.gz filter=lfs diff=lfs merge=lfs -text
+wq-test.jsonl.gz filter=lfs diff=lfs merge=lfs -text
+wq-train.jsonl.gz filter=lfs diff=lfs merge=lfs -text

wikipedia-wq.py

@@ -0,0 +1,112 @@
+# 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 WQ 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 WQ"
+_DATASET_URLS = {
+    'train': "https://huggingface.co/datasets/Tevatron/wikipedia-wq/resolve/main/wq-train.jsonl.gz",
+    'dev': "https://huggingface.co/datasets/Tevatron/wikipedia-wq/resolve/main/wq-dev.jsonl.gz",
+    'test': "https://huggingface.co/datasets/Tevatron/wikipedia-wq/resolve/main/wq-test.jsonl.gz",
+}
+
+
+class WikipediaWq(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(version=VERSION,
+                               description="Wikipedia WQ 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):
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
+        splits = [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name='dev',
+                gen_kwargs={
+                    "filepath": downloaded_files["dev"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name='test',
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                },
+            ),
+        ]
+        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)
+                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

wq-dev.jsonl.gz

@@ -0,0 +1,3 @@
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+oid sha256:b3fb7316e903b4a3f7fb89fc81705f8c94cd7bdacc1e86fbc234a0ed7c481b3a
+size 5997042

wq-test.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a37d5ecd5c7ea790ef8d403d52b640ebb0ce3980caf853c3395c6d43ba682b36
+size 78334

wq-train.jsonl.gz

@@ -0,0 +1,3 @@
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+oid sha256:2cc0737aa6a249a10fa5aadfd04e26c8cd826ed2b520cc43dba917564b02fdda
+size 53370240