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@@ -53,3 +53,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/dev.json filter=lfs diff=lfs merge=lfs -text
+data/id_aliases.json filter=lfs diff=lfs merge=lfs -text
+data/test.json filter=lfs diff=lfs merge=lfs -text
+data/train.json filter=lfs diff=lfs merge=lfs -text

2wikimultihopqa.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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
+"""HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering."""
+
+
+import json
+import textwrap
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{xanh2020_2wikimultihop,
+    title = "Constructing A Multi-hop {QA} Dataset for Comprehensive Evaluation of Reasoning Steps",
+    author = "Ho, Xanh  and
+      Duong Nguyen, Anh-Khoa  and
+      Sugawara, Saku  and
+      Aizawa, Akiko",
+    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
+    month = dec,
+    year = "2020",
+    address = "Barcelona, Spain (Online)",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.coling-main.580",
+    pages = "6609--6625",
+}
+"""
+
+_DESCRIPTION = """\
+"""
+
+_URL_BASE = "data"
+
+
+class TwowikimultihopQA(datasets.GeneratorBasedBuilder):
+    """2wikimultihopQA is a Dataset for Diverse, Explainable Multi-hop Question Answering."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    "supporting_facts": datasets.features.Sequence(
+                        {
+                            "title": datasets.Value("string"),
+                            "sent_id": datasets.Value("int32"),
+                        }
+                    ),
+                    "context": datasets.features.Sequence(
+                        {
+                            "title": datasets.Value("string"),
+                            "sentences": datasets.features.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                    "evidences": datasets.features.Sequence(
+                        datasets.features.Sequence(
+                            datasets.Value("string")
+                        )
+                    ),
+                    "entity_ids": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/Alab-NII/2wikimultihop",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators."""
+        paths = {
+            datasets.Split.TRAIN: f"{_URL_BASE}/train.json",
+            datasets.Split.VALIDATION: f"{_URL_BASE}/dev.json",
+            datasets.Split.TEST: f"{_URL_BASE}/test.json",
+        }
+
+        files = dl_manager.download(paths)
+
+        split_generators = []
+        for split in files:
+            split_generators.append(datasets.SplitGenerator(name=split, gen_kwargs={"data_file": files[split]}))
+
+        return split_generators
+
+    def _generate_examples(self, data_file):
+        """This function returns the examples."""
+        data = json.load(open(data_file))
+        for idx, example in enumerate(data):
+
+            # Test set has missing keys
+            for k in ["answer", "type", "level"]:
+                if k not in example.keys():
+                    example[k] = None
+
+            if "supporting_facts" not in example.keys():
+                example["supporting_facts"] = []
+
+            yield idx, {
+                "id": example["_id"],
+                "question": example["question"],
+                "answer": example["answer"],
+                "type": example["type"],
+                "supporting_facts": [{"title": f[0], "sent_id": f[1]} for f in example["supporting_facts"]],
+                "context": [{"title": f[0], "sentences": f[1]} for f in example["context"]],
+                "evidences": example["evidences"],
+                "entity_ids": example["entity_ids"]
+            }

data/dev.json

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data/id_aliases.json

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data/test.json

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data/train.json

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