init

.gitattributes

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mr-tydi.py

@@ -0,0 +1,142 @@
+# 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
+
+import json
+
+import datasets
+from dataclasses import dataclass
+
+_CITATION = '''
+@article{mrtydi,
+      title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+      author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+      year={2021},
+      journal={arXiv:2108.08787},
+}
+'''
+
+languages = [
+    'arabic',
+    'bengali',
+    'english',
+    'indonesian',
+    'finnish',
+    'korean',
+    'russian',
+    'swahili',
+    'telugu',
+    'thai',
+    'japanese',
+    'combined',
+]
+
+_DESCRIPTION = 'dataset load script for Mr. TyDi'
+
+_DATASET_URLS = {
+    lang: {
+        'train': f'https://huggingface.co/datasets/castorini/mr-tydi/resolve/main/mrtydi-v1.1-{lang}/train.jsonl.gz',
+        'dev': f'https://huggingface.co/datasets/castorini/mr-tydi/resolve/main/mrtydi-v1.1-{lang}/dev.jsonl.gz',
+        'test': f'https://huggingface.co/datasets/castorini/mr-tydi/resolve/main/mrtydi-v1.1-{lang}/test.jsonl.gz',
+    } for lang in languages
+}
+
+_DATASET_URLS['combined'] = {
+    'train': "https://huggingface.co/datasets/castorini/mr-tydi/resolve/main/mrtydi-v1.1-combined/train.jsonl.gz"
+}
+
+
+class MrTyDi(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig(
+            version=datasets.Version('1.1.0'),
+            name=lang, description=f'Mr TyDi dataset in language {lang}.'
+        ) for lang in languages
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            'query_id': datasets.Value('string'),
+            'query': 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='https://github.com/castorini/mr.tydi',
+            # License for the dataset if available
+            license='',
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        lang = self.config.name
+        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[lang])
+
+        if lang != "combined":
+            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'],
+                    },
+                ),
+            ]
+        else:
+            splits = [
+                datasets.SplitGenerator(
+                    name='train',
+                    gen_kwargs={
+                        'filepath': downloaded_files['train'],
+                    },
+                ),
+            ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        with open(filepath) as f:
+            for i, line in enumerate(f):
+                data = json.loads(line)
+                for feature in ['negative_passages', 'positive_passages']:
+                    if data.get(feature) is None:
+                        data[feature] = []
+
+                yield data['query_id'], data

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