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.gitattributes

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test/xor_test_retrieve_eng_span_q_only_v1_1.jsonl → eng_span/xor-tydi-dev.parquet

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dev/xor_dev_retrieve_eng_span_v1_1.jsonl → full/xor-tydi-dev.parquet

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test/xor_test_full_q_only_v1_1.jsonl → full/xor-tydi-test.parquet

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full/xor-tydi-train-00000-of-00002.parquet

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train/.gitattributes

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

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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 = {
-    'eng_span': {
-        'train': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
-        'dev': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/dev/xor_dev_retrieve_eng_span_v1_1.jsonl',
-        'test': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/test/xor_test_retrieve_eng_span_q_only_v1_1.jsonl',
-    },
-    'full': {
-        'train': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
-        'dev': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/dev/xor_dev_full_v1_1.jsonl',
-        'test': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/test/xor_test_full_q_only_v1_1.jsonl',
-    }
-    # 'test': f"{base}",
-}
-
-
-class XORTyDi(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            version=VERSION,
-            name="eng_span",
-            description="XOR-TyDI train/dev/test datasets of English Span Task"),
-        datasets.BuilderConfig(
-            version=VERSION,
-            name="full",
-            description="XOR-TyDI train/dev/test datasets of Full Task"), 
-    ]
-
-    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):
-        group = self.config.name
-        if self.config.data_files:
-            downloaded_files = self.config.data_files
-        else:
-            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[group])
-        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 data["id"], {
-                "query_id": data["id"],
-                "query": data["question"],
-                "answers": data.get("answers", []),
-                "positive_passages": [],
-                "negative_passages": [],
-            }
-
-        for filepath in files:
-            with open(filepath, encoding="utf-8") as f:
-                for line in f:
-                    data = json.loads(line)
-
-                    if "id" in data and "query_id" not in data:
-                        yield process_dev_test_entry(data)
-                    else:
-                        yield process_train_entry(data)
-