init

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+dev filter=lfs diff=lfs merge=lfs -text
+test/ filter=lfs diff=lfs merge=lfs -text
+train/ filter=lfs diff=lfs merge=lfs -text
+train/xor-t2e-100w.jsonl filter=lfs diff=lfs merge=lfs -text

train/.gitattributes

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+xor-t2e-100w.jsonl.gz filter=lfs diff=lfs merge=lfs -text

train/xor-t2e-100w.jsonl.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aef9dc8c6bbf6fe94d5c751af2601cf48b4561a5712381d12178c0a1eba9e6e0
+size 312241526

xor-tydi.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{xorqa,
+    title   = {{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering},
+    author  = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
+    booktitle={NAACL-HLT},
+    year    = {2021}
+}
+"""
+
+_DESCRIPTION = "dataset load script for Wikipedia NQ"
+
+base = "/home/czhang/src/task-sparse/tevatron/hgf_datasets/xor-tydi"
+_DATASET_URLS = {
+    'train': f"{base}/xor-t2e-100w.jsonl",
+    'dev': f"{base}",
+    'test': f"{base}",
+}
+
+
+class XORTyDi(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(version=VERSION,
+                               description="XOR-TyDI 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."""
+        def process_train_entry(data):
+            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'] = []
+            return data['query_id'], data
+        
+        def process_dev_test_entry(data):
+            return {
+                "query_id": data["id"],
+                "query": data["question"],
+                "answers": data.get("answers", [])
+            }
+
+        for filepath in files:
+            with open(filepath, encoding="utf-8") as f:
+                for line in f:
+                    data = json.loads(line)
+
+                if "train" in filepath:
+                    yield process_train_entry(data)
+                else:
+                    yield process_dev_test_entry(data)