init

README.md

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+---
+language: 
+  - fas
+  - rus
+  - zho
+
+    
+multilinguality:
+- multilingual    
+
+task_categories:
+- text-retrieval
+
+---
+
+From the NeuCLIR TREC Track 2023: https://arxiv.org/abs/2304.12367
+
+Generated from https://huggingface.co/datasets/neuclir/neuclir1
+
+```
+@misc{lawrie2024overview,
+      title={Overview of the TREC 2023 NeuCLIR Track}, 
+      author={Dawn Lawrie and Sean MacAvaney and James Mayfield and Paul McNamee and Douglas W. Oard and Luca Soldaini and Eugene Yang},
+      year={2024},
+      eprint={2404.08071},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```
+

neuclir-2023-fast.py

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+import json
+
+import datasets
+
+_CITATION = '''
+@article{lawrie2024overview,
+  title={Overview of the TREC 2023 NeuCLIR track},
+  author={Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas W and Soldaini, Luca and Yang, Eugene},
+  year={2024}
+}
+'''
+
+_LANGUAGES = [
+    'rus',
+    'fas',
+    'zho',
+]
+
+_DESCRIPTION = 'dataset load script for NeuCLIR 2023 Fast'
+
+_DATASET_URLS = {
+    lang: {
+        'test': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/test-00000-of-00001.parquet',
+    } for lang in _LANGUAGES
+}
+
+_DATASET_CORPUS_URLS = {
+    f'corpus-{lang}': {
+        'corpus': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/corpus-00000-of-00001.parquet'
+    } for lang in _LANGUAGES
+}
+
+_DATASET_QUERIES_URLS = {
+    f'queries-{lang}': {
+        'queries': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/queries-00000-of-00001.parquet'
+    } for lang in _LANGUAGES
+}
+
+
+class MLDR(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig(
+            version=datasets.Version('1.0.0'),
+            name=lang, description=f'NeuCLIR dataset in language {lang}.'
+        ) for lang in _LANGUAGES
+    ] + [
+        datasets.BuilderConfig(
+            version=datasets.Version('1.0.0'),
+            name=f'corpus-{lang}', description=f'corpus of NeuCLIR dataset in language {lang}.'
+        ) for lang in _LANGUAGES
+    ] + [ 
+        datasets.BuilderConfig(
+            version=datasets.Version('1.0.0'),
+            name=f'queries-{lang}', description=f'queries of NeuCLIR dataset in language {lang}.'
+        ) for lang in _LANGUAGES
+    ]
+
+    def _info(self):
+        name = self.config.name
+        if name.startswith('corpus-'):
+            features = datasets.Features({
+                '_id': datasets.Value('string'),
+                'text': datasets.Value('string'),
+                'title': datasets.Value('string'),
+            })
+        elif name.startswith("queries-"):
+            features = datasets.Features({
+                '_id': datasets.Value('string'),
+                'text': datasets.Value('string'),
+            })
+        else:
+            features = datasets.Features({
+                'query-id': datasets.Value('string'),
+                'corpus-id': datasets.Value('string'),
+                'score': datasets.Value('int32'),
+            })
+
+        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://arxiv.org/abs/2304.12367',
+            # License for the dataset if available
+            license=None,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        name = self.config.name
+        if name.startswith('corpus-'):
+            downloaded_files = dl_manager.download_and_extract(_DATASET_CORPUS_URLS[name])
+            splits = [
+                datasets.SplitGenerator(
+                    name='corpus',
+                    gen_kwargs={
+                        'filepath': downloaded_files['corpus'],
+                    },
+                ),
+            ]
+        elif name.startswith("queries-"):
+            downloaded_files = dl_manager.download_and_extract(_DATASET_QUERIES_URLS[name])
+            splits = [
+                datasets.SplitGenerator(
+                    name='queries',
+                    gen_kwargs={
+                        'filepath': downloaded_files['queries'],
+                    },
+                ),
+            ]
+        else:
+            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[name])
+            splits = [
+                datasets.SplitGenerator(
+                    name='test',
+                    gen_kwargs={
+                        'filepath': downloaded_files['test'],
+                    },
+                ),
+            ]
+        return splits
+
+    def _generate_examples(self, filepath):
+        import pandas as pd
+        
+        name = self.config.name
+        df = pd.read_parquet(filepath)
+        
+        if name.startswith('corpus-'):
+            for index, row in df.iterrows():
+                yield row['_id'], {
+                    '_id': row['_id'],
+                    'text': row['text'],
+                    'title': row['title']
+                }
+        elif name.startswith("queries-"):
+            for index, row in df.iterrows():
+                yield row['_id'], {
+                    '_id': row['_id'],
+                    'text': row['text']
+                }
+        else:
+            for index, row in df.iterrows():
+                yield f"{row['query-id']}-----{row['corpus-id']}", {
+                    'query-id': row['query-id'],
+                    'corpus-id': row['corpus-id'],
+                    'score': row['score']
+                }

neuclir-fas/corpus-00000-of-00001.parquet

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+size 37815775

neuclir-fas/queries-00000-of-00001.parquet

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+oid sha256:d29a2b708ccc2d2a1daa54aa2e5c8d8001ed21f6aeada342b0d931a347919bc8
+size 7410

neuclir-fas/test-00000-of-00001.parquet

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+oid sha256:4e561bc6fd740d19867d6b5b05618d4903cd8d9b2a05085f5dfe38dc0be58e6c
+size 278230

neuclir-rus/corpus-00000-of-00001.parquet

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+oid sha256:b79990dbfbe5fddbbc0caf21977279c7820022334d7674721e8198657adf8845
+size 38545671

neuclir-rus/queries-00000-of-00001.parquet

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+oid sha256:f2fb98b9add49ca727fc30395cd31c638ee306d4432f7dd27c77d5dd450b0cf7
+size 8492

neuclir-rus/test-00000-of-00001.parquet

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+oid sha256:055a7d190ffb7810247ec5467773b81f9ea5fe80f04e24b31ee3e339d42ba8c6
+size 305373

neuclir-zho/corpus-00000-of-00001.parquet

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+oid sha256:54f2386596ea886ad8ec9f10686b5c0e14a04644257fe51bf71a4362a1c9eb71
+size 37375163

neuclir-zho/queries-00000-of-00001.parquet

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+oid sha256:8054215a902481f0242cf93d0dcdc7ffe17d79b9f4eb805dc14a9a4c95a74430
+size 5936

neuclir-zho/test-00000-of-00001.parquet

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+oid sha256:701404741c9a3c23e4b44f43f8bbef2b322b404a2ecaeba3866c5ed2936ce288
+size 261971